Bacteriophages and Bacterial Plant Diseases

PubMed Central

Buttimer, Colin; McAuliffe, Olivia; Ross, R. P.; Hill, Colin; O’Mahony, Jim; Coffey, Aidan

2017-01-01

Losses in crop yields due to disease need to be reduced in order to meet increasing global food demands associated with growth in the human population. There is a well-recognized need to develop new environmentally friendly control strategies to combat bacterial crop disease. Current control measures involving the use of traditional chemicals or antibiotics are losing their efficacy due to the natural development of bacterial resistance to these agents. In addition, there is an increasing awareness that their use is environmentally unfriendly. Bacteriophages, the viruses of bacteria, have received increased research interest in recent years as a realistic environmentally friendly means of controlling bacterial diseases. Their use presents a viable control measure for a number of destructive bacterial crop diseases, with some phage-based products already becoming available on the market. Phage biocontrol possesses advantages over chemical controls in that tailor-made phage cocktails can be adapted to target specific disease-causing bacteria. Unlike chemical control measures, phage mixtures can be easily adapted for bacterial resistance which may develop over time. In this review, we will examine the progress and challenges for phage-based disease biocontrol in food crops. PMID:28163700

Enhancing agricultural productivity and rural incomes through sustainable use of natural resources in the semi arid tropics.

PubMed

Wani, Suhas P; Dixin, Yin; Li, Zhong; Dar, William D; Chander, Girish

2012-03-30

A participatory watershed management approach is one of the tested, sustainable and eco-friendly options to upgrade rain-fed agriculture to meet growing food demand along with additional multiple benefits in terms of improving livelihoods, addressing equity issues and biodiversity concerns. Watershed interventions at study sites in Thailand (Tad Fa and Wang Chai) and India (Kothapally) effectively reduced runoff and the associated soil loss. Such interventions at Xiaoxincun (China) and Wang Chai improved groundwater recharging and availability. Enhanced productive transpiration increased rainwater use efficiency for crop production by 13-29% at Xiaoxincun; 13-160% at Lucheba (China), 32-37% at Tad Fa and 23-46% at Wang Chai and by two to five times at Kothapally. Watershed interventions increased significantly the additional net returns from crop production as compared with the pre-watershed intervention period. Increased water availability opened up options for crop diversification with high-value crops, including increased forage production and boosted livestock-based livelihoods. In dryland tropics, integrated watershed management approach enabled farmers to diversify the systems along with increasing agricultural productivity through increased water availability, while conserving the natural resource base. Household incomes increased substantially, leading to improved living and building the resilience of the community and natural resources. Copyright © 2011 Society of Chemical Industry.

A scalable satellite-based crop yield mapper: Integrating satellites and crop models for field-scale estimation in India

NASA Astrophysics Data System (ADS)

Jain, M.; Singh, B.; Srivastava, A.; Lobell, D. B.

2015-12-01

Food security will be challenged over the upcoming decades due to increased food demand, natural resource degradation, and climate change. In order to identify potential solutions to increase food security in the face of these changes, tools that can rapidly and accurately assess farm productivity are needed. With this aim, we have developed generalizable methods to map crop yields at the field scale using a combination of satellite imagery and crop models, and implement this approach within Google Earth Engine. We use these methods to examine wheat yield trends in Northern India, which provides over 15% of the global wheat supply and where over 80% of farmers rely on wheat as a staple food source. In addition, we identify the extent to which farmers are shifting sow date in response to heat stress, and how well shifting sow date reduces the negative impacts of heat stress on yield. To identify local-level decision-making, we map wheat sow date and yield at a high spatial resolution (30 m) using Landsat satellite imagery from 1980 to the present. This unique dataset allows us to examine sow date decisions at the field scale over 30 years, and by relating these decisions to weather experienced over the same time period, we can identify how farmers learn and adapt cropping decisions based on weather through time.

Effects of climate change on water requirements and phenological period of major crops in Heihe River basin, China - Based on the accumulated temperature threshold method

NASA Astrophysics Data System (ADS)

Han, Dongmei; Xu, Xinyi; Yan, Denghua

2016-04-01

In recent years, global climate change has significantly caused a serious crisis of water resources throughout the world. However, mainly through variations in temperature, climate change will affect water requirements of crop. It is obvious that the rise of temperature affects growing period and phenological period of crop directly, then changes the water demand quota of crop. Methods including accumulated temperature threshold and climatic tendency rate were adopted, which made up for the weakness of phenological observations, to reveal the response of crop phenological change during the growing period. Then using Penman-Menteith model and crop coefficients from the United Nations Food& Agriculture Organization (FAO), the paper firstly explored crop water requirements in different growth periods, and further forecasted quantitatively crop water requirements in Heihe River Basin, China under different climate change scenarios. Results indicate that: (i) The results of crop phenological change established in the method of accumulated temperature threshold were in agreement with measured results, and (ii) there were many differences in impacts of climate warming on water requirement of different crops. The growth periods of wheat and corn had tendency of shortening as well as the length of growth periods. (ii)Results of crop water requirements under different climate change scenarios showed: when temperature increased by 1°C, the start time of wheat growth period changed, 2 days earlier than before, and the length of total growth period shortened 2 days. Wheat water requirements increased by 1.4mm. However, corn water requirements decreased by almost 0.9mm due to the increasing temperature of 1°C. And the start time of corn growth period become 3 days ahead, and the length of total growth period shortened 4 days. Therefore, the contradiction between water supply and water demands are more obvious under the future climate warming in Heihe River Basin, China.

Possible effects of elk harvest on fall distribution of grizzly bears in the Greater Yellowstone Ecosystem

USGS Publications Warehouse

Haroldson, M.A.; Schwartz, C.C.; Cherry, S.; Moody, D.

2004-01-01

 The tradition of early elk (Cervus elaphus) hunting seasons adjacent to Yellowstone National Park (YNP), USA, provides grizzly bears (Ursus arctos horribilis) with ungulate remains left by hunters. We investigated the fall (Aug–Oct) distribution of grizzly bears relative to the boundaries of YNP and the opening of September elk hunting seasons. Based on results from exact tests of conditional independence, we estimated the odds of radiomarked bears being outside YNP during the elk hunt versus before the hunt. Along the northern boundary, bears were 2.40 times more likely to be outside YNP during the hunt in good whitebark pine (Pinus albicaulis) seed-crop years and 2.72 times more likely in poor seed-crop years. The level of confidence associated with 1-sided confidence intervals with a lower endpoint of 1 was approximately 94% in good seed-crop years and 61% in poor years. Along the southern boundary of YNP, radiomarked bears were 2.32 times more likely to be outside the park during the hunt in good whitebark pine seed-crop years and 4.35 times more likely in poor seed-crop years. The level of confidence associated with 1-sided confidence intervals with a lower endpoint of 1 was approximately 93% in both cases. Increased seasonal bear densities and human presence in early hunt units increases potential for conflicts between bears and hunters. Numbers of reported hunting-related grizzly bear mortalities have increased in the Greater Yellowstone Ecosystem (GYE) during the last decade, and nearly half of this increase is due to bear deaths occurring in early hunt units during September. Human-caused grizzly bear mortality thresholds established by the U.S. Fish and Wildlife Service (USFWS) have not been exceeded in recent years. This is because agency actions have reduced other sources of human-caused mortalities, and because population parameters that mortality thresholds are based on have increased. Agencies must continue to monitor and manage hunter-caused grizzly bear mortality at sustainable levels to ensure the long-term health of the GYE population.

Multi-temporal UAV based data for mapping crop type and structure in smallholder dominated Tanzanian agricultural landscape

NASA Astrophysics Data System (ADS)

Nagol, J. R.; Chung, C.; Dempewolf, J.; Maurice, S.; Mbungu, W.; Tumbo, S.

2015-12-01

Timely mapping and monitoring of crops like Maize, an important food security crop in Tanzania, can facilitate timely response by government and non-government organizations to food shortage or surplus conditions. Small UAVs can play an important role in linking the spaceborne remote sensing data and ground based measurement to improve the calibration and validation of satellite based estimates of in-season crop metrics. In Tanzania most of the growing season is often obscured by clouds. UAV data, if collected within a stratified statistical sampling framework, can also be used to directly in lieu of spaceborne data to infer mid-season yield estimates at regional scales.Here we present an object based approach to estimate crop metrics like crop type, area, and height using multi-temporal UAV based imagery. The methods were tested at three 1km2 plots in Kilosa, Njombe, and Same districts in Tanzania. At these sites both ground based and UAV based data were collected on a monthly time-step during the year 2015 growing season. SenseFly eBee drone with RGB and NIR-R-G camera was used to collect data. Crop type classification accuracies of above 85% were easily achieved.

Integrating remote sensing, geographic information system and modeling for estimating crop yield

NASA Astrophysics Data System (ADS)

Salazar, Luis Alonso

This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

NASA Technical Reports Server (NTRS)

Muller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven;

Integrated modelling of crop production and nitrate leaching with the Daisy model.

PubMed

Manevski, Kiril; Børgesen, Christen D; Li, Xiaoxin; Andersen, Mathias N; Abrahamsen, Per; Hu, Chunsheng; Hansen, Søren

2016-01-01

An integrated modelling strategy was designed and applied to the Soil-Vegetation-Atmosphere Transfer model Daisy for simulation of crop production and nitrate leaching under pedo-climatic and agronomic environment different than that of model original parameterisation. The points of significance and caution in the strategy are: •Model preparation should include field data in detail due to the high complexity of the soil and the crop processes simulated with process-based model, and should reflect the study objectives. Inclusion of interactions between parameters in a sensitivity analysis results in better account for impacts on outputs of measured variables.•Model evaluation on several independent data sets increases robustness, at least on coarser time scales such as month or year. It produces a valuable platform for adaptation of the model to new crops or for the improvement of the existing parameters set. On daily time scale, validation for highly dynamic variables such as soil water transport remains challenging. •Model application is demonstrated with relevance for scientists and regional managers. The integrated modelling strategy is applicable for other process-based models similar to Daisy. It is envisaged that the strategy establishes model capability as a useful research/decision-making, and it increases knowledge transferability, reproducibility and traceability.

Weather-based forecasts of California crop yields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lobell, D B; Cahill, K N; Field, C B

2005-09-26

Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over themore » 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.« less

Efficient crop type mapping based on remote sensing in the Central Valley, California

NASA Astrophysics Data System (ADS)

Zhong, Liheng

Most agricultural systems in California's Central Valley are purposely flexible and intentionally designed to meet the demands of dynamic markets. Agricultural land use is also impacted by climate change and urban development. As a result, crops change annually and semiannually, which makes estimating agricultural water use difficult, especially given the existing method by which agricultural land use is identified and mapped. A minor portion of agricultural land is surveyed annually for land-use type, and every 5 to 8 years the entire valley is completely evaluated. So far no effort has been made to effectively and efficiently identify specific crop types on an annual basis in this area. The potential of satellite imagery to map agricultural land cover and estimate water usage in the Central Valley is explored. Efforts are made to minimize the cost and reduce the time of production during the mapping process. The land use change analysis shows that a remote sensing based mapping method is the only means to map the frequent change of major crop types. The traditional maximum likelihood classification approach is first utilized to map crop types to test the classification capacity of existing algorithms. High accuracy is achieved with sufficient ground truth data for training, and crop maps of moderate quality can be timely produced to facilitate a near-real-time water use estimate. However, the large set of ground truth data required by this method results in high costs in data collection. It is difficult to reduce the cost because a trained classification algorithm is not transferable between different years or different regions. A phenology based classification (PBC) approach is developed which extracts phenological metrics from annual vegetation index profiles and identifies crop types based on these metrics using decision trees. According to the comparison with traditional maximum likelihood classification, this phenology-based approach shows great advantages when the size of the training set is limited by ground truth availability. Once developed, the classifier is able to be applied to different years and a vast area with only a few adjustments according to local agricultural and annual weather conditions. 250 m MODIS imagery is utilized as the main input to the PBC algorithm and displays promising capacity in crop identification in several counties in the Central Valley. A time series of Landsat TM/ETM+ images at a 30 m resolution is necessary in the crop mapping of counties with smaller land parcels, although the processing time is longer. Spectral characteristics are also employed to identify crops in PBC. Spectral signatures are associated with phenological stages instead of imaging dates, which highly increases the stability of the classifier performance and overcomes the problem of over-fitting. Moderate accuracies are achieved by PBC, with confusions mostly within the same crop categories. Based on a quantitative analysis, misclassification in PBC has very trivial impacts on the accuracy of agricultural water use estimate. The cost of the entire PBC procedure is controlled to a very low level, which will enable its usage in routine annual crop mapping in the Central Valley.

Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops.

PubMed

Qian, Lunwen; Hickey, Lee T; Stahl, Andreas; Werner, Christian R; Hayes, Ben; Snowdon, Rod J; Voss-Fels, Kai P

2017-01-01

In order to meet future food, feed, fiber, and bioenergy demands, global yields of all major crops need to be increased significantly. At the same time, the increasing frequency of extreme weather events such as heat and drought necessitates improvements in the environmental resilience of modern crop cultivars. Achieving sustainably increase yields implies rapid improvement of quantitative traits with a very complex genetic architecture and strong environmental interaction. Latest advances in genome analysis technologies today provide molecular information at an ultrahigh resolution, revolutionizing crop genomic research, and paving the way for advanced quantitative genetic approaches. These include highly detailed assessment of population structure and genotypic diversity, facilitating the identification of selective sweeps and signatures of directional selection, dissection of genetic variants that underlie important agronomic traits, and genomic selection (GS) strategies that not only consider major-effect genes. Single-nucleotide polymorphism (SNP) markers today represent the genotyping system of choice for crop genetic studies because they occur abundantly in plant genomes and are easy to detect. SNPs are typically biallelic, however, hence their information content compared to multiallelic markers is low, limiting the resolution at which SNP-trait relationships can be delineated. An efficient way to overcome this limitation is to construct haplotypes based on linkage disequilibrium, one of the most important features influencing genetic analyses of crop genomes. Here, we give an overview of the latest advances in genomics-based haplotype analyses in crops, highlighting their importance in the context of polyploidy and genome evolution, linkage drag, and co-selection. We provide examples of how haplotype analyses can complement well-established quantitative genetics frameworks, such as quantitative trait analysis and GS, ultimately providing an effective tool to equip modern crops with environment-tailored characteristics.

The state of genetically modified crop regulation in Canada

PubMed Central

Smyth, Stuart J

2014-01-01

Genetically modified (GM) crops were first commercialized in Canada in 1995 and the 2014 crop represents the 20th year of successful production. Prior to the first commercialization of GM crops, Canada reviewed its existing science-based regulatory framework and adapted the existing framework to allow for risk assessments on the new technology to be undertaken in a timely and efficient manner. The result has been the rapid and widespread adoption of GM varieties of canola, corn and soybeans. The first decade of GM crop production precipitated 2 landmark legal cases relating to patent infringement and economic liability, while the second decade witnessed increased political efforts to have GM crops labeled in Canada as well as significant challenges from the low level comingling of GM crops with non-GM commodities. This article reviews the 20 y of GM crop production in Canada from a social science perspective that includes intellectual property, consumer acceptance and low level presence. PMID:25437238

Planetary opportunities in crop water management: Potential to outweigh cropland expansion

NASA Astrophysics Data System (ADS)

Jägermeyr, Jonas; Gerten, Dieter; Lucht, Wolfgang; Heinke, Jens

2014-05-01

Global available land and water resources probably cannot feed projected future human populations under current productivity levels. Moreover, the planetary boundaries of both land use change and water consumption are being approached rapidly, and at the same time competition between food production, bioenergy plantations and biodiversity conservation is increasing. Global cropland is expected to expand to meet future demands, while considerable yield gaps remain in many world regions. Yield increases in Sub-Saharan Africa, for example, are currently mainly based on expansion of arable land into currently non-agricultural areas - while small-scale irrigation and water conservancy methods are considered very promising to boost yields there. In the here presented modeling study we investigate, at global scale, to what degree different on-farm options to better manage green and blue water might contribute to a global crop yield increase under conditions of current climate and projected future climate change. We consider methods aiming for a maximization of crops' water use efficiency and an optimal use of available on-farm water (precipitation): reducing unproductive soil evaporation (vapor shift, VS), collecting surface runoff after rain events to mitigate subsequent dry-spells (rain-water harvesting, RWH), increasing irrigation efficiency, and expanding irrigated area into rain-fed cropland (based on water savings from higher efficiencies). Global yield simulations based on hypothetical scenarios of these management opportunities are performed with the LPJmL ecohydrological modeling framework driven by reanalysis data and GCM ensemble simulations. We consider a range of about 20 climate change projections to cover respective uncertainties, and we analyze the effects of increasing CO2 concentration on the crops and their water demand. Crops are represented in a process-based and dynamic way by 12 crop functional types, each for rain-fed and irrigated areas, with prescribed annual fractions of cropland per 0.5° x 0.5° grid cell. We recalculate from the yield increase how much cropland expansion can be avoided in 30-yr averages. Our results show that the studied affordable low-tech solutions for small-scale farmers on water-limited croplands can have a considerable effect on yields at the global scale. A simulated global ~15% yield increase from a low-intensity water management scenario (25% of runoff used for RWH, 25% of soil evaporation avoided to achieve VS, slight irrigation efficiency improvement) could outweigh, i.e. possibly avoid, an estimated 120 Mha of cropland expansion under current climatic conditions. A (rather theoretical) maximum-intensity water management scenario (85% VS, 85% RWH, surface irrigation replaced by sprinkler systems) shows the potential to increase global yields by more than 35% without expansion or withdrawing additional irrigation water. Climate change will have adverse effects on crop yields in many regions, but as we sow such adaptation opportunities have the potential to mitigate or compensate these impacts in many countries. Overall, proper water management (sustainably maximizing on-farm water use efficiency) can substantially increase global crop yields and at the same time relax rates of land cover conversion.

Shifts in comparative advantages for maize, oat and wheat cropping under climate change in Europe.

PubMed

Elsgaard, L; Børgesen, C D; Olesen, J E; Siebert, S; Ewert, F; Peltonen-Sainio, P; Rötter, R P; Skjelvåg, A O

2012-01-01

Climate change is anticipated to affect European agriculture, including the risk of emerging or re-emerging feed and food hazards. Indirectly, climate change may influence such hazards (e.g. the occurrence of mycotoxins) due to geographic shifts in the distribution of major cereal cropping systems and the consequences this may have for crop rotations. This paper analyses the impact of climate on cropping shares of maize, oat and wheat on a 50-km square grid across Europe (45-65°N) and provides model-based estimates of the changes in cropping shares in response to changes in temperature and precipitation as projected for the time period around 2040 by two regional climate models (RCM) with a moderate and a strong climate change signal, respectively. The projected cropping shares are based on the output from the two RCMs and on algorithms derived for the relation between meteorological data and observed cropping shares of maize, oat and wheat. The observed cropping shares show a south-to-north gradient, where maize had its maximum at 45-55°N, oat had its maximum at 55-65°N, and wheat was more evenly distributed along the latitudes in Europe. Under the projected climate changes, there was a general increase in maize cropping shares, whereas for oat no areas showed distinct increases. For wheat, the projected changes indicated a tendency towards higher cropping shares in the northern parts and lower cropping shares in the southern parts of the study area. The present modelling approach represents a simplification of factors determining the distribution of cereal crops, and also some uncertainties in the data basis were apparent. A promising way of future model improvement could be through a systematic analysis and inclusion of other variables, such as key soil properties and socio-economic conditions, influencing the comparative advantages of specific crops.

Atmospheric inversion of surface carbon flux with consideration of the spatial distribution of US crop production and consumption

DOE PAGES

Chen, J. M.; Fung, J. W.; Mo, G.; ...

2015-01-19

In order to improve quantification of the spatial distribution of carbon sinks and sources in the conterminous US, we conduct a nested global atmospheric inversion with detailed spatial information on crop production and consumption. County-level cropland net primary productivity, harvested biomass, soil carbon change, and human and livestock consumption data over the conterminous US are used for this purpose. Time-dependent Bayesian synthesis inversions are conducted based on CO₂ observations at 210 stations to infer CO₂ fluxes globally at monthly time steps with a nested focus on 30 regions in North America. Prior land surface carbon fluxes are first generated usingmore » a biospheric model, and the inversions are constrained using prior fluxes with and without adjustments for crop production and consumption over the 2002–2007 period. After these adjustments, the inverted regional carbon sink in the US Midwest increases from 0.25 ± 0.03 to 0.42 ± 0.13 Pg C yr⁻¹, whereas the large sink in the US southeast forest region is weakened from 0.41 ± 0.12 to 0.29 ± 0.12 Pg C yr⁻¹. These adjustments also reduce the inverted sink in the west region from 0.066 ± 0.04 to 0.040 ± 0.02 Pg C yr⁻¹ because of high crop consumption and respiration by humans and livestock. The general pattern of sink increases in crop production areas and sink decreases (or source increases) in crop consumption areas highlights the importance of considering the lateral carbon transfer in crop products in atmospheric inverse modeling, which provides a reliable atmospheric perspective of the overall carbon balance at the continental scale but is unreliable for separating fluxes from different ecosystems.« less

Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe

NASA Astrophysics Data System (ADS)

Donatelli, Marcello; Srivastava, Amit Kumar; Duveiller, Gregory; Niemeyer, Stefan; Fumagalli, Davide

2015-07-01

This study presents an estimate of the effects of climate variables and CO2 on three major crops, namely wheat, rapeseed and sunflower, in EU27 Member States. We also investigated some technical adaptation options which could offset climate change impacts. The time-slices 2000, 2020 and 2030 were chosen to represent the baseline and future climate, respectively. Furthermore, two realizations within the A1B emission scenario proposed by the Special Report on Emissions Scenarios (SRES), from the ECHAM5 and HadCM3 GCM, were selected. A time series of 30 years for each GCM and time slice were used as input weather data for simulation. The time series were generated with a stochastic weather generator trained over GCM-RCM time series (downscaled simulations from the ENSEMBLES project which were statistically bias-corrected prior to the use of the weather generator). GCM-RCM simulations differed primarily for rainfall patterns across Europe, whereas the temperature increase was similar in the time horizons considered. Simulations based on the model CropSyst v. 3 were used to estimate crop responses; CropSyst was re-implemented in the modelling framework BioMA. The results presented in this paper refer to abstraction of crop growth with respect to its production system, and consider growth as limited by weather and soil water. How crop growth responds to CO2 concentrations; pests, diseases, and nutrients limitations were not accounted for in simulations. The results show primarily that different realization of the emission scenario lead to noticeably different crop performance projections in the same time slice. Simple adaptation techniques such as changing sowing dates and the use of different varieties, the latter in terms of duration of the crop cycle, may be effective in alleviating the adverse effects of climate change in most areas, although response to best adaptation (within the techniques tested) differed across crops. Although a negative impact of climate scenarios is evident in most areas, the combination of rainfall patterns and increased photosynthesis efficiency due to CO2 concentrations showed possible improvements of production patterns in some areas, including Southern Europe. The uncertainty deriving from GCM realizations with respect to rainfall suggests that articulated and detailed testing of adaptation techniques would be redundant. Using ensemble simulations would allow for the identification of areas where adaptation, like those simulated, may be run autonomously by farmers, hence not requiring specific intervention in terms of support policies.

Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

NASA Astrophysics Data System (ADS)

Yeo, I.-Y.; Lee, S.; Sadeghi, A. M.; Beeson, P. C.; Hively, W. D.; McCarty, G. W.; Lang, M. W.

2013-11-01

Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay Watershed (CBW), which is located in the Mid-Atlantic US, winter cover crop use has been emphasized and federal and state cost-share programs are available to farmers to subsidize the cost of winter cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops at the watershed scale and to identify critical source areas of high nitrate export. A physically-based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data and satellite-based estimates of winter cover crop species performance to simulate hydrological processes and nutrient cycling over the period of 1991-2000. Multiple scenarios were developed to obtain baseline information on nitrate loading without winter cover crops planted and to investigate how nitrate loading could change with different winter cover crop planting scenarios, including different species, planting times, and implementation areas. The results indicate that winter cover crops had a negligible impact on water budget, but significantly reduced nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading was approximately 14 kg ha-1, but it decreased to 4.6-10.1 kg ha-1 with winter cover crops resulting in a reduction rate of 27-67% at the watershed scale. Rye was most effective, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of winter cover crops (~30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~2 kg ha-1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of winter cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implement of winter cover crop programs, in part by helping to target critical pollution source areas for winter cover crop implementation.

Ozone risk for crops and pastures in present and future climates

NASA Astrophysics Data System (ADS)

Fuhrer, Jürg

2009-02-01

Ozone is the most important regional-scale air pollutant causing risks for vegetation and human health in many parts of the world. Ozone impacts on yield and quality of crops and pastures depend on precursor emissions, atmospheric transport and leaf uptake and on the plant’s biochemical defence capacity, all of which are influenced by changing climatic conditions, increasing atmospheric CO2 and altered emission patterns. In this article, recent findings about ozone effects under current conditions and trends in regional ozone levels and in climatic factors affecting the plant’s sensitivity to ozone are reviewed in order to assess implications of these developments for future regional ozone risks. Based on pessimistic IPCC emission scenarios for many cropland regions elevated mean ozone levels in surface air are projected for 2050 and beyond as a result of both increasing emissions and positive effects of climate change on ozone formation and higher cumulative ozone exposure during an extended growing season resulting from increasing length and frequency of ozone episodes. At the same time, crop sensitivity may decline in areas where warming is accompanied by drying, such as southern and central Europe, in contrast to areas at higher latitudes where rapid warming is projected to occur in the absence of declining air and soil moisture. In regions with rapid industrialisation and population growth and with little regulatory action, ozone risks are projected to increase most dramatically, thus causing negative impacts major staple crops such as rice and wheat and, consequently, on food security. Crop improvement may be a way to increase crop cross-tolerance to co-occurring stresses from heat, drought and ozone. However, the review reveals that besides uncertainties in climate projections, parameters in models for ozone risk assessment are also uncertain and model improvements are necessary to better define specific targets for crop improvements, to identify regions most at risk from ozone in a future climate and to set robust effect-based ozone standards.

Field based measurements of albedo for two candidate perennial cellulosic feedstocks and row crops in Central Illinois

NASA Astrophysics Data System (ADS)

Miller, J. N.; VanLoocke, A.; Bernacchi, C. J.

2012-12-01

The production of perennial cellulosic feedstocks for bioenergy present the potential to diversify regional economies and the national energy supply, while also serving as a climate 'regulators' due to a number of biogeochemical and biophysical differences relative to row crops. Numerous observationally and modeling based approaches, including life cycle analyses have investigated biogeochemical tradeoffs, such as increased carbon sequestration and biophysical increased water use, associated with growing cellulosic feedstocks. A less understood aspect is the biophysical changes associated with the difference in albedo, which will alter the local energy balance and could cause a local to regional cooling several times larger than that associated with offsetting carbon. To address this factor an experiment consisting of paired fields of Miscanthus and Switchgrass, two of the leading perennial cellulosic feedstock candidates, and traditional row crops was established in central Illinois. Data from the first two growing seasons indicate that this effect is most pronounced during the spring and fall as perennial biofuel crops green up earlier and senesce later than common annual row crops. The albedo of the perennials converges to that of the row crops during the growing season as the canopies develop. During the early winter, before the perennial crops are harvested, the albedo over fallow soybean and maize fields can vary greatly depending on snowfall and, to a lesser extent, soil moisture, whereas perennials show less variation. Thus, perennial biofuel crops also have the potential to buffer the local environment against short-term variations in climate. These factors should be considered when evaluating the tradeoffs and climate-regulation services associated with large-scale planting of bioenergy crops.

The iPot Project: improved potato monitoring in Belgium using remote sensing and crop growth modelling

NASA Astrophysics Data System (ADS)

Piccard, Isabelle; Nackaerts, Kris; Gobin, Anne; Goffart, Jean-Pierre; Planchon, Viviane; Curnel, Yannick; Tychon, Bernard; Wellens, Joost; Cools, Romain; Cattoor, Nele

2015-04-01

Belgian potato processors, traders and packers are increasingly working with potato contracts. The close follow up of contracted parcels on the land as well as from above is becoming an important tool to improve the quantity and quality of the potato crop and reduce risks in order to plan the storage, packaging or processing and as such to strengthen the competitiveness of the Belgian potato chain in a global market. At the same time, precision agriculture continues to gain importance and progress. Farmers are obligated to invest in new technologies. Between mid-May and the end of June 2014 potato fields in Gembloux were monitored from emergence till canopy closure. UAV images (RGB) and digital (hemispherical) photographs were taken at ten-daily intervals. Crop emergence maps show the time (date) and degree of crop emergence and crop closure (in terms of % cover). For three UAV flights during the growing season RGB images at 3 cm resolution were processed using a K-means clustering algorithm to classify the crop according to its greenness. Based on the greenness %cover and daily cover growth were derived for 5x5m pixels and 25x25m pixels. The latter resolution allowed for comparison with high resolution satellite imagery. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) from high resolution satellite images (DMC/Deimos, 22m pixel size). DMC based cover maps showed similar patterns as compared with the UAV-based cover maps, and allows for further applications of the data in crop management. Today the use of geo-information by the (private) agricultural sector in Belgium is rather limited, notwithstanding the great benefits this type of information may offer, as recognized by the sector. The iPot project, financed by the Belgian Science Policy Office (BELSPO), aims to provide the Belgian potato sector, represented by Belgapom, with near real time information on field condition (weather-soil) and crop development and with early yield estimates, derived from a combination of satellite images and crop growth models. An intuitive web based geo-information platform is being developed to allow both the Belgian potato industry and the potato research centres to access, analyse and combine the data with their own field observations in close collaboration with the farmers, for improved decision-making.

Evaluating the temporal stability of synthetically generated time-series for crop types in central Germany

USDA-ARS?s Scientific Manuscript database

Synthetically generated Landsat time-series based on the STARFM algorithm are increasingly used for applications in forestry or agriculture. Although successes in classification and derivation of phenological orbiomass parameters are evident, a thorough evaluation of the limits of the method is stil...

Big agronomic data validates an oxymoron: Sustainable intensification under climate change

USDA-ARS?s Scientific Manuscript database

Crop science is increasingly embracing big data to reconcile the apparent rift between intensification of food production and sustainability of a steadily stressed production base. A strategy based on long-term agroecosystem research and modeling simulation of crops, crop rotations and cropping sys...

A triangular climate-based decision model to forecast crop anomalies in Kenya

NASA Astrophysics Data System (ADS)

Guimarães Nobre, G.; Davenport, F.; Veldkamp, T.; Jongman, B.; Funk, C. C.; Husak, G. J.; Ward, P.; Aerts, J.

2017-12-01

By the end of 2017, the world is expected to experience unprecedented demands for food assistance where, across 45 countries, some 81 million people will face a food security crisis. Prolonged droughts in Eastern Africa are playing a major role in these crises. To mitigate famine risk and save lives, government bodies and international donor organisations are increasingly building up efforts to resolve conflicts and secure humanitarian relief. Disaster-relief and financing organizations traditionally focus on emergency response, providing aid after an extreme drought event, instead of taking actions in advance based on early warning. One of the reasons for this approach is that the seasonal risk information provided by early warning systems is often considered highly uncertain. Overcoming the reluctance to act based on early warnings greatly relies on understanding the risk of acting in vain, and assessing the cost-effectiveness of early actions. This research develops a triangular climate-based decision model for multiple seasonal time-scales to forecast strong anomalies in crop yield shortages in Kenya using Casual Discovery Algorithms and Fast and Frugal Decision Trees. This Triangular decision model (1) estimates the causality and strength of the relationship between crop yields and hydro climatological predictors (extracted from the Famine Early Warning Systems Network's data archive) during the crop growing season; (2) provides probabilistic forecasts of crop yield shortages in multiple time scales before the harvesting season; and (3) evaluates the cost-effectiveness of different financial mechanisms to respond to early warning indicators of crop yield shortages obtained from the model. Furthermore, we reflect on how such a model complements and advances the current state-of-art FEWS Net system, and examine its potential application to improve the management of agricultural risks in Kenya.

Understanding the relationship between the variability in agrometeorological indices and adaptation practices across the Canadian Prairies

NASA Astrophysics Data System (ADS)

Chipanshi, A.; Qi, D.; Zhang, Y.; Cherneski, P.

2017-12-01

In an attempt to understand how agriculture will adapt to the changing and variable climate, crop based agrometeorological indices including the Effective Growing Degree Days (EGDDs), Growing Season Length (GSL), Heat waves, Water Demand (Precipitation - Evapotranspiration) and the Standardized Precipitation Evapotranspiration Index (SPEI) were analyzed in terms of frequency, duration and trend over a 63-year timeframe (1950 to 2012) from the Canadian Prairies and related to crop production. The heat based indices (EGDD, GSL and Heat waves) increased over the analysis period due to an upward increase in the observed mean temperature. The change was most noticeable in the northern portion of the study area where agriculture is limited by insufficient heat units under the present climate. Heat waves became more frequent in the southern parts of the study area (there were more days above the 30oC threshold). Water availability as assessed from water demand (P-PE) and SPEI trended downward especially in Alberta and Saskatchewan. In spite of the increased severity and frequency in water deficits, there was a noticeable reduction in the variability of crop yield over time. This was attributed to the increased adaptive capacity that has been gained through the use of improved seed hybrids, fertilizer, the use of fungicides and adoption of best management practices such as zero till and direct seeding. After crop yields were de-trended to remove effects of technology, the cumulative precipitation during the growing season explained the majority of the variance in crop yield. This initial analysis has set the stage for analyzing the characteristics of agrometeorological indices under climate change scenarios and how accumulated precipitation during the growing season will affect crop yield and production.

Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

USGS Publications Warehouse

Funk, Chris; Budde, Michael E.

2009-01-01

For thirty years, simple crop water balance models have been used by the early warning community to monitor agricultural drought. These models estimate and accumulate actual crop evapotranspiration, evaluating environmental conditions based on crop water requirements. Unlike seasonal rainfall totals, these models take into account the phenology of the crop, emphasizing conditions during the peak grain filling phase of crop growth. In this paper we describe an analogous metric of crop performance based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. A special temporal filter is used to screen for cloud contamination. Regional NDVI time series are then composited for cultivated areas, and adjusted temporally according to the timing of the rainy season. This adjustment standardizes the NDVI response vis-??-vis the expected phenological response of maize. A national time series index is then created by taking the cropped-area weighted average of the regional series. This national time series provides an effective summary of vegetation response in agricultural areas, and allows for the identification of NDVI green-up during grain filling. Onset-adjusted NDVI values following the grain filling period are well correlated with U.S. Department of Agriculture production figures, possess desirable linear characteristics, and perform better than more common indices such as maximum seasonal NDVI or seasonally averaged NDVI. Thus, just as appropriately calibrated crop water balance models can provide more information than seasonal rainfall totals, the appropriate agro-phenological filtering of NDVI can improve the utility and accuracy of space-based agricultural monitoring.

The uncertainty of crop yield projections is reduced by improved temperature response functions

USDA-ARS?s Scientific Manuscript database

Increasing the accuracy of crop productivity estimates is a key Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on cr...

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

PubMed

Jayaraman, Prem Prakash; Yavari, Ali; Georgakopoulos, Dimitrios; Morshed, Ahsan; Zaslavsky, Arkady

2016-11-09

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

Quantifying the effect of crop spatial arrangement on weed suppression using functional-structural plant modelling.

PubMed

Evers, Jochem B; Bastiaans, Lammert

2016-05-01

Suppression of weed growth in a crop canopy can be enhanced by improving crop competitiveness. One way to achieve this is by modifying the crop planting pattern. In this study, we addressed the question to what extent a uniform planting pattern increases the ability of a crop to compete with weed plants for light compared to a random and a row planting pattern, and how this ability relates to crop and weed plant density as well as the relative time of emergence of the weed. To this end, we adopted the functional-structural plant modelling approach which allowed us to explicitly include the 3D spatial configuration of the crop-weed canopy and to simulate intra- and interspecific competition between individual plants for light. Based on results of simulated leaf area development, canopy photosynthesis and biomass growth of the crop, we conclude that differences between planting pattern were small, particularly if compared to the effects of relative time of emergence of the weed, weed density and crop density. Nevertheless, analysis of simulated weed biomass demonstrated that a uniform planting of the crop improved the weed-suppression ability of the crop canopy. Differences in weed suppressiveness between planting patterns were largest with weed emergence before crop emergence, when the suppressive effect of the crop was only marginal. With simultaneous emergence a uniform planting pattern was 8 and 15 % more competitive than a row and a random planting pattern, respectively. When weed emergence occurred after crop emergence, differences between crop planting patterns further decreased as crop canopy closure was reached early on regardless of planting pattern. We furthermore conclude that our modelling approach provides promising avenues to further explore crop-weed interactions and aid in the design of crop management strategies that aim at improving crop competitiveness with weeds.

Comparing estimates of climate change impacts from process-based and statistical crop models

NASA Astrophysics Data System (ADS)

Lobell, David B.; Asseng, Senthold

2017-01-01

The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches’ treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally requiring fewer resources to produce robust estimates, especially when applied to crops beyond the major grains.

Beyond conservation agriculture.

PubMed

Giller, Ken E; Andersson, Jens A; Corbeels, Marc; Kirkegaard, John; Mortensen, David; Erenstein, Olaf; Vanlauwe, Bernard

2015-01-01

Global support for Conservation Agriculture (CA) as a pathway to Sustainable Intensification is strong. CA revolves around three principles: no-till (or minimal soil disturbance), soil cover, and crop rotation. The benefits arising from the ease of crop management, energy/cost/time savings, and soil and water conservation led to widespread adoption of CA, particularly on large farms in the Americas and Australia, where farmers harness the tools of modern science: highly-sophisticated machines, potent agrochemicals, and biotechnology. Over the past 10 years CA has been promoted among smallholder farmers in the (sub-) tropics, often with disappointing results. Growing evidence challenges the claims that CA increases crop yields and builds-up soil carbon although increased stability of crop yields in dry climates is evident. Our analyses suggest pragmatic adoption on larger mechanized farms, and limited uptake of CA by smallholder farmers in developing countries. We propose a rigorous, context-sensitive approach based on Systems Agronomy to analyze and explore sustainable intensification options, including the potential of CA. There is an urgent need to move beyond dogma and prescriptive approaches to provide soil and crop management options for farmers to enable the Sustainable Intensification of agriculture.

GM as a route for delivery of sustainable crop protection.

PubMed

Bruce, Toby J A

2012-01-01

Modern agriculture, with its vast monocultures of lush fertilized crops, provides an ideal environment for adapted pests, weeds, and diseases. This vulnerability has implications for food security: when new pesticide-resistant pest biotypes evolve they can devastate crops. Even with existing crop protection measures, approximately one-third yield losses occur globally. Given the projected increase in demand for food (70% by 2050 according to the UN), sustainable ways of preventing these losses are needed. Development of resistant crop cultivars can make an important contribution. However, traditional crop breeding programmes are limited by the time taken to move resistance traits into elite crop genetic backgrounds and the limited gene pools in which to search for novel resistance. Furthermore, resistance based on single genes does not protect against the full spectrum of pests, weeds, and diseases, and is more likely to break down as pests evolve counter-resistance. Although not necessarily a panacea, GM (genetic modification) techniques greatly facilitate transfer of genes and thus provide a route to overcome these constraints. Effective resistance traits can be precisely and conveniently moved into mainstream crop cultivars. Resistance genes can be stacked to make it harder for pests to evolve counter-resistance and to provide multiple resistances to different attackers. GM-based crop protection could substantially reduce the need for farmers to apply pesticides to their crops and would make agricultural production more efficient in terms of resources used (land, energy, water). These benefits merit consideration by environmentalists willing to keep an open mind on the GM debate.

Examining the impact of climate change and variability on sweet potatoes in East Africa

NASA Astrophysics Data System (ADS)

Ddumba, S. D.; Andresen, J.; Moore, N. J.; Olson, J.; Snapp, S.; Winkler, J. A.

2013-12-01

Climate change is one of the biggest challenges to food security for the rapidly increasing population of East Africa. Rainfall is becoming more variable and temperatures are rising, consequently leading to increased occurrence of droughts and floods, and, changes in the timing and length of growing seasons. These changes have serious implications on crop production with the greatest impact likely to be on C4 crops such as cereals compared to C3 crops such as root tubers. Sweet potatoes is one the four most important food crops in East Africa owing to its high nutrition and calorie content, and, high tolerance to heat and drought, but little is known about how the crop will be affected by climate change. This study identifies the major climatic constraints to sweet potato production and examines the impact of projected future climates on sweet potato production in East Africa during the next 10 to 30 years. A process-based Sweet POTato COMputer Simulation (SPOTCOMS) model is used to assess four sweet potato cultivars; Naspot 1, Naspot 10, Naspot 11 and SPK 004-Ejumula. This is work in progress but preliminary results from the crop modeling experiments and the strength and weakness of the crop model will be presented.

More Yield with Less Water: Increasing Water Use Efficiency by Capitalizing on the Adaptation of Native Shrubs in the Sudano-Sahel

NASA Astrophysics Data System (ADS)

Bogie, Nathaniel; Bayala, Roger; Diedhiou, Ibrahima; Dick, Richard; Ghezzehei, Teamrat

2016-04-01

A changing climate along with human and animal population pressure can have a devastating effect on crop yields and food security in the Sudano-Sahel. Agricultural solutions to address soil degradation and crop water stress are needed to combat this increasingly difficult situation. Significant differences in crop success have been observed in peanut and millet grown in association with two native evergreen shrubs Piliostigma reticulatum, and Guiera senegalensis at the sites of Nioro du Rip and Keur Matar, respectively. We investigate how farmers can increase crop productivity by capitalizing on the evolutionary adaptation of native shrubs to the harsh Sudano-Sahelian environment as well as the physical mechanisms at work in the system that can lead to more robust yields. Research plots at Keur Matar Arame with no fertilizer added were monitored in 2013 using two soil moisture sensor networks at depths of 10, 20, 40, 60, 100, 200, and 300cm. Cropping season water use total calculated based on beginning and end of season soil moisture and seasonal precipitation data revealed that crop-only plot used 411±32 mm of water, and the crop and shrub plot used 439±42 mm of water. Taking into account the quantity of crop biomass produced and neglecting the shrub biomass produced, the crop and shrub plot had a water use efficiency of 1.60 kg ha-1 mm-1 and the crop only plot had 0.269 kg ha-1 mm-1. Water status was measured three times throughout the season on millet leaves and revealed no significant trends. Handheld NDVI readings revealed significantly higher NDVI values in crop and shrub plots at all measurement dates. These findings build on work that was completed in 2004 at the site, but further increases in crop yields have been shown. Increasing water use efficiency by over 500% can be a great advantage in years of limited water availability such as 2013. Using even the limited resources that farmers possess, this agroforestry technique can be expanded over wide swaths of the Sahel.

Replacing fallow with continuous cropping reduces crop water productivity of semiarid wheat

USDA-ARS?s Scientific Manuscript database

Water supply frequently limits crop yield in semiarid cropping systems; water deficits can restrict yields in drought-affected subhumid regions. In semiarid wheat (Triticum aestivumL.)-based cropping systems, replacing an uncropped fallow period with a crop can increase precipitation use efficiency ...

Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing.

PubMed

Willems, Sander; Fraiture, Marie-Alice; Deforce, Dieter; De Keersmaecker, Sigrid C J; De Loose, Marc; Ruttink, Tom; Herman, Philippe; Van Nieuwerburgh, Filip; Roosens, Nancy

2016-02-01

Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgene event in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of Seasonal Climate-induced Variations in Global Food Production

NASA Technical Reports Server (NTRS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-01-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

Land Cover Monitoring for Water Resources Management in Angola

NASA Astrophysics Data System (ADS)

Miguel, Irina; Navarro, Ana; Rolim, Joao; Catalao, Joao; Silva, Joel; Painho, Marco; Vekerdy, Zoltan

2016-08-01

The aim of this paper is to assess the impact of improved temporal resolution and multi-source satellite data (SAR and optical) on land cover mapping and monitoring for efficient water resources management. For that purpose, we developed an integrated approach based on image classification and on NDVI and SAR backscattering (VV and VH) time series for land cover mapping and crop's irrigation requirements computation. We analysed 28 SPOT-5 Take-5 images with high temporal revisiting time (5 days), 9 Sentinel-1 dual polarization GRD images and in-situ data acquired during the crop growing season. Results show that the combination of images from different sources provides the best information to map agricultural areas. The increase of the images temporal resolution allows the improvement of the estimation of the crop parameters, and then, to calculate of the crop's irrigation requirements. However, this aspect was not fully exploited due to the lack of EO data for the complete growing season.

Developing Global Leaders for Research, Regulation, and Stewardship of Crop Protection Chemistry in the 21st Century

EPA Science Inventory

To provide sufficient food and fiber to the increasing global population, the technologies associated with crop protection are growing ever more sophisticated but, at the same time, societal expectations for the safe use of crop protection chemistry tools are also increasing. The...

Building the crops of tomorrow: advantages of symbiont-based approaches to improving abiotic stress tolerance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coleman-Derr, Devin; Tringe, Susannah G.

The exponential growth in world population is feeding a steadily increasing global need for arable farmland, a resource that is already in high demand. This trend has led to increased farming on subprime arid and semi-arid lands, where limited availability of water and a host of environmental stresses often severely reduce crop productivity. The conventional approach to mitigating the abiotic stresses associated with arid climes is to breed for stress-tolerant cultivars, a time and labor intensive venture that often neglects the complex ecological context of the soil environment in which the crop is grown. In recent years, studies have attemptedmore » to identify microbial symbionts capable of conferring the same stress-tolerance to their plant hosts, and new developments in genomic technologies have greatly facilitated such research. Here in this paper, we highlight many of the advantages of these symbiont-based approaches and argue in favor of the broader recognition of crop species as ecological niches for a diverse community of microorganisms that function in concert with their plant hosts and each other to thrive under fluctuating environmental conditions« less

Food for Thought: Crop Yields in the Columbia River Basin in an Altered Future

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K.; Nelson, R.; Stockle, C.; Kruger, C.; Brady, M.; Adam, J. C.

2013-12-01

Growth of global population and food consumption in the next several decades is expected to result in a food security challenge. Strategies to address this challenge, such as enhancing agricultural productivity and resiliency, need to be considered within the context of a full range of plausible consequences so as to identify investments that create win-win-win scenarios for the environment, economy, and society. Regional earth systems models can provide the necessary scale-appropriate framework to inform the decision making context for adaptation strategies, especially in the context of global change. In an altered future, changes to climate, technology and socioeconomics affect regional agriculture both directly and indirectly. These effects are not independent and an integrated process-based model may better capture unanticipated non-linear and non-monotonic responses and feedbacks over time . BioEarth is a research initiative designed to explore the coupling of multiple stand-alone earth systems models to generate usable information for agricultural and natural resource decision making at the regional scale at decadal time-steps. This project focuses on the U.S. Pacific Northwest (PNW) region and is a framework that integrates atmospheric, terrestrial, aquatic, and economic models. We apply component models of BioEarth to the Columbia River basin in the PNW to study the direct and indirect impacts of climate change on regional irrigated and dryland crop yields for a variety of annual and perennial crops. Results indicate that the net effect of climate change on crop yields is dependent on the crop type. There is a negative effect of temperature on yields for most crops. Dryland winter wheat is a notable exception. With warming, although the available growing season increases, faster thermal accumulation results in a shorter time to maturity. Precipitation changes in the region have a positive impact on dryland agriculture. Carbon dioxide (CO2) fertilization has a positive impact on crop yields for most crops. This positive impact is minimal for corn which is a C4 crop that is already CO2 efficient. The net response is an increase in yields for dryland agriculture and depends on the crop type for irrigated agriculture. Although, climate change results in increased water shortages and water rights curtailment in the region, this does not translate into an increased negative effect on yields. This could be attributed to higher water use efficiency under elevated CO2 levels as well crops getting through growth stages earlier in the season with wetter spring conditions. The non linear and non monotonic nature of the response of climate change on crop yields is discussed. In accounting for biophysical effects of climate change on crop yields, socio-economic effects cannot be ignored because biophysical effects are nested with the framework of human decision making. We also discuss our results in the context of socioeconomic factors . Current results assume no adaptation strategies and incorporating this is our next step.

A generic model for estimating biomass accumulation and greenhouse gas emissions from perennial crops

NASA Astrophysics Data System (ADS)

Ledo, Alicia; Heathcote, Richard; Hastings, Astley; Smith, Pete; Hillier, Jonathan

2017-04-01

Agriculture is essential to maintain humankind but is, at the same time, a substantial emitter of greenhouse gas (GHG) emissions. With a rising global population, the need for agriculture to provide secure food and energy supply is one of the main human challenges. At the same time, it is the only sector which has significant potential for negative emissions through the sequestration of carbon and offsetting via supply of feedstock for energy production. Perennial crops accumulate carbon during their lifetime and enhance organic soil carbon increase via root senescence and decomposition. However, inconsistency in accounting for this stored biomass undermines efforts to assess the benefits of such cropping systems when applied at scale. A consequence of this exclusion is that efforts to manage this important carbon stock are neglected. Detailed information on carbon balance is crucial to identify the main processes responsible for greenhouse gas emissions in order to develop strategic mitigation programs. Perennial crops systems represent 30% in area of total global crop systems, a considerable amount to be ignored. Furthermore, they have a major standing both in the bioenergy and global food industries. In this study, we first present a generic model to calculate the carbon balance and GHGs emissions from perennial crops, covering both food and bioenergy crops. The model is composed of two simple process-based sub-models, to cover perennial grasses and other perennial woody plants. The first is a generic individual based sub-model (IBM) covering crops in which the yield is the fruit and the plant biomass is an unharvested residue. Trees, shrubs and climbers fall into this category. The second model is a generic area based sub-model (ABM) covering perennial grasses, in which the harvested part includes some of the plant parts in which the carbon storage is accounted. Most second generation perennial bioenergy crops fall into this category. Both generic sub-models presented in this paper can be parametrized for different crops. Quantifying CO2 capture by plants and biomass accumulation and changes in soil carbon, are key in evaluating the impacts of perennial crops in life cycle analysis. We then use this model to illustrate the importance of biomass in the overall GHG estimation from four important perennial crops - sugarcane, Miscanthus, coffee, and apples - which were chosen to cover tropical and temperate regions, trees and grasses, and energy and food supply.

Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

NASA Technical Reports Server (NTRS)

Teng, William; Shannon, Harlan

2011-01-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attach s, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Historically, these analog years are visually identified; however, the qualitative nature of this method sometimes precludes the definitive identification of the best analog year. Thus, one goal of this study is to derive a more rigorous, statistical approach for identifying analog years, based on a modified coefficient of determination, termed the analog index (AI). A second goal is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data).

Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

USGS Publications Warehouse

Yeo, In-Young; Lee, Sangchui; Sadeghi, Ali M.; Beeson, Peter C.; Hively, W. Dean; McCarty, Greg W.; Lang, Megan W.

2013-01-01

Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay Watershed (CBW), which is located in the Mid-Atlantic US, winter cover crop use has been emphasized and federal and state cost-share programs are available to farmers to subsidize the cost of winter cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops at the watershed scale and to identify critical source areas of high nitrate export. A physically-based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data and satellite-based estimates of winter cover crop species performance to simulate hydrological processes and nutrient cycling over the period of 1991–2000. Multiple scenarios were developed to obtain baseline information on nitrate loading without winter cover crops planted and to investigate how nitrate loading could change with different winter cover crop planting scenarios, including different species, planting times, and implementation areas. The results indicate that winter cover crops had a negligible impact on water budget, but significantly reduced nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading was approximately 14 kg ha−1, but it decreased to 4.6–10.1 kg ha−1 with winter cover crops resulting in a reduction rate of 27–67% at the watershed scale. Rye was most effective, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of winter cover crops (~30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~2 kg ha−1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of winter cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implement of winter cover crop programs, in part by helping to target critical pollution source areas for winter cover crop implementation.

Rice crop mapping and change prediction using multi-temporal satellite images in the Mekong Delta, Vietnam

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.

2014-12-01

The rice cropping systems in the Vietnamese Mekong Delta (VMD) has been undergoing major changes to cope with developing agro-economics, increasing population and changing climate. Information on rice cropping practices and changes in cropping systems is critical for policymakers to devise successful strategies to ensure food security and rice grain exports for the country. The primary objective of this research is to map rice cropping systems and predict future dynamics of rice cropping systems using the MODIS time-series data of 2002, 2006, and 2010. First, a phenology-based classification approach was applied for the classification and assessment of rice cropping systems in study region. Second, the Cellular Automata-Markov (CA-Markov) models was used to simulate the rice-cropping system map of VMD for 2010. The comparisons between the classification maps and the ground reference data indicated satisfactory results with overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2010. The simulated map of rice cropping system for 2010 was extrapolated by CA-Markov model based on the trend of rice cropping systems during 2002~2006. The comparison between predicted scenario and classification map for 2010 presents a reasonably closer agreement. In conclusion, the CA-Markov model performs a powerful tool for the dynamic modeling of changes in rice cropping systems, and the results obtained demonstrate that the approach produces satisfactory results in terms of accuracy, quantitative forecast and spatial pattern changes. Meanwhile, the projections of the future changes would provide useful inputs to the agricultural policy for effective management of the rice cropping practices in VMD.

Cover crops do not increase C sequestration in production crops: evidence from 12 years of continuous measurements

NASA Astrophysics Data System (ADS)

Buysse, Pauline; Bodson, Bernard; Debacq, Alain; De Ligne, Anne; Heinesch, Bernard; Manise, Tanguy; Moureaux, Christine; Aubinet, Marc

2017-04-01

The numerous reports on carbon (C) loss from cropland soils have recently raised awareness on the climate change mitigation potential of these ecosystems, and on the necessity to improve C sequestration in these soils. Among the multiple solutions that are proposed, several field measurement and modelling studies reported that growing cover crops over fall and winter time could appear as an efficient solution. However, while the large majority of these studies are based on SOC stock inventories and very few information exists from the CO2 flux dynamics perspective. In the present work, we use the results from long-term (12 years) eddy-covariance measurements performed at the Lonzée Terrestrial Observatory (LTO, candidate ICOS site, Belgium) and focus on six intercrop periods managed with (3) and without (3) cover crops after winter wheat main crops, in order to compare their response to environmental factors and to investigate the impact of cover crops on Net Ecosystem Exchange (NEE). Our results showed that cumulated NEE was not significantly affected by the presence of cover crops. Indeed, while larger CO2 assimilation occurred during cover crop growth, this carbon gain was later lost by larger respiration rates due to larger crop residue amounts brought to the soil. As modelled by a Q10-like relationship, significantly larger R10 values were indeed observed during the three intercrop periods cultivated with cover crops. These CO2 flux-based results therefore tend to moderate the generally acknowledged positive impact of cover crops on net C sequestration by croplands. Our results indicate that the effect of growing cover crops on C sequestration could be less important than announced, at least at certain sites.

What is the potential of cropland albedo management in the fight against global warming? A case study based on the use of cover crops

NASA Astrophysics Data System (ADS)

Carrer, Dominique; Pique, Gaétan; Ferlicoq, Morgan; Ceamanos, Xavier; Ceschia, Eric

2018-04-01

Land cover management in agricultural areas is a powerful tool that could play a role in the mitigation of climate change and the counterbalance of global warming. First, we attempted to quantify the radiative forcing that would increase the surface albedo of croplands in Europe following the inclusion of cover crops during the fallow period. This is possible since the albedo of bare soil in many areas of Europe is lower than the albedo of vegetation. By using satellite data, we demonstrated that the introduction of cover crops into the crop rotation during the fallow period would increase the albedo over 4.17% of Europe’s surface. According to our study, the effect resulting from this increase in the albedo of the croplands would be equivalent to a mitigation of 3.16 MtCO2-eq.year‑1 over a 100 year time horizon. This is equivalent to a mitigation potential per surface unit (m2) of introduced cover crop over Europe of 15.91 gCO2-eq.year‑1.m‑2. This value, obtained at the European scale, is consistent with previous estimates. We show that this mitigation potential could be increased by 27% if the cover crop is maintained for a longer period than 3 months and reduced by 28% in the case of no irrigation. In the second part of this work, based on recent studies estimating the impact of cover crops on soil carbon sequestration and the use of fertilizer, we added the albedo effect to those estimates, and we argued that, by considering areas favourable to their introduction, cover crops in Europe could mitigate human-induced agricultural greenhouse gas emissions by up to 7% per year, using 2011 as a reference. The impact of the albedo change per year would be between 10% and 13% of this total impact. The countries showing the greatest mitigation potentials are France, Bulgaria, Romania, and Germany.

Deriving crop calendar using NDVI time-series

NASA Astrophysics Data System (ADS)

Patel, J. H.; Oza, M. P.

2014-11-01

Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting - peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.

Regulation of Population Densities of Heterodera cajani and Other Plant-Parasitic Nematodes by Crop Rotations on Vertisols, in Semi-Arid Tropical Production Systems in India

PubMed Central

Sharma, S. B.; Rego, T. J.; Mohiuddin, M.; Rao, V. N.

1996-01-01

The significance of double crop (intercrop and sequential crop), single crop (rainy season crop fallow from June to September), and rotations on densities of Heterodera cajani, Helicotylenchus retusus, and Rotylenchulus reniformis was studied on Vertisol (Typic Pellusterts) between 1987 and 1993. Cowpea (Vigna sinensis), mungbean (Phaseolus aureus), and pigeonpea (Cajanus cajan) greatly increased the population densities of H. cajani and suppressed the population densities of other plant-parasitic nematodes. Mean population densities of H. cajani were about 8 times lower in single crop systems than in double crop systems, with pigeonpea as a component intercrop. Plots planted to sorghum, safflower, and chickpea in the preceding year contained fewer H. cajani eggs and juveniles than did plots previously planted to pigeonpea, cowpea, or mungbean. Continuous cropping of sorghum in the rainy season and safflower in the post-rainy season markedly reduced the population density of H. cajani. Sorghum, safflower, and chickpea favored increased population densities of H. retusus. Adding cowpea to the system resulted in a significant increase in the densities of R. reniformis. Mean densities of total plant-parasitic nematodes were three times greater in double crop systems, with pigeonpea as a component intercrop than in single crop systems with rainy season fallow component. Cropping systems had a regulatory effect on the nematode populations and could be an effective nematode management tactic. Intercropping of sorghum with H. cajani tolerant pigeonpea could be effective in increasing the productivity of traditional production systems in H. cajani infested regions. PMID:19277141

Applications of UAVs in row-crop agriculture: advantages and limitations

NASA Astrophysics Data System (ADS)

Basso, B.; Putnam, G.; Price, R.; Zhang, J.

2016-12-01

The application of Unmanned Aerial Vehicles (UAV) to monitor agricultural fields has increased over the last few years due to advances in the technology, sensors, post-processing software for image analysis, along with more favorable regulations that allowed UAVs to be flown for commercial use. UAV have several capabilities depending on the type of sensors that are mounted onboard. The most widely used application remains crop scouting to identify areas within fields where the crops underperform for various reasons (nutritional status and water stress, presence of weeds, poor stands etc). In this talk, we present the preliminary results of UAVs field based research to better understand spatial and temporal variability of crop yield. Their advantage in providing timely information is critical, but adaptive management requires a system approach to account for the interactions occurring between genetics, environment and management.

Effects of crop rotation on properties of a Vietnam clay soil under rice-based cropping systems in small-scale farmers' fields

USDA-ARS?s Scientific Manuscript database

In tropical deltas, intensive monocultures with three rice crops per year have been the standard for decades. In recent years, though, rice-based rotations with one or more upland crops per year are being adopted by several farmers. Their trends of increasing grain yields raise the question whether ...

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

PubMed Central

Jayaraman, Prem Prakash; Yavari, Ali; Georgakopoulos, Dimitrios; Morshed, Ahsan; Zaslavsky, Arkady

2016-01-01

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations. PMID:27834862

Embodied crop calories in animal products

NASA Astrophysics Data System (ADS)

Pradhan, Prajal; Lüdeke, Matthias K. B.; Reusser, Dominik E.; Kropp, Jürgen P.

2013-12-01

Increases in animal products consumption and the associated environmental consequences have been a matter of scientific debate for decades. Consequences of such increases include rises in greenhouse gas emissions, growth of consumptive water use, and perturbation of global nutrients cycles. These consequences vary spatially depending on livestock types, their densities and their production system. In this letter, we investigate the spatial distribution of embodied crop calories in animal products. On a global scale, about 40% of the global crop calories are used as livestock feed (we refer to this ratio as crop balance for livestock) and about 4 kcal of crop products are used to generate 1 kcal of animal products (embodied crop calories of around 4). However, these values vary greatly around the world. In some regions, more than 100% of the crops produced is required to feed livestock requiring national or international trade to meet the deficit in livestock feed. Embodied crop calories vary between less than 1 for 20% of the livestock raising areas worldwide and greater than 10 for another 20% of the regions. Low values of embodied crop calories are related to production systems for ruminants based on fodder and forage, while large values are usually associated with production systems for non-ruminants fed on crop products. Additionally, we project the future feed demand considering three scenarios: (a) population growth, (b) population growth and changes in human dietary patterns and (c) changes in population, dietary patterns and feed conversion efficiency. When considering dietary changes, we project the global feed demand to be almost doubled (1.8-2.3 times) by 2050 compared to 2000, which would force us to produce almost equal or even more crops to raise our livestock than to directly nourish ourselves in the future. Feed demand is expected to increase over proportionally in Africa, South-Eastern Asia and Southern Asia, putting additional stress on these regions.

Atmospheric inversion of the surface carbon flux with consideration of the spatial distributions of US crop production and consumption

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, J. M.; Fung, J. W.; Mo, G.

2015-01-01

In order to improve quantification of the spatial distribution of carbon sinks and sources in the conterminous USA, we conduct a nested global atmospheric inversion with consideration of the spatial information of crop production and consumption. Spatially distributed 5 county-level cropland net primary productivity, harvested biomass, soil carbon change, and human and livestock consumption data over the conterminous USA are used for this purpose. Time-dependent Bayesian synthesis inversions are conducted based on CO₂ observations at 210 stations to infer CO₂ fluxes globally at monthly time steps with a nested focus on 30 regions in North America. Prior land surface carbonmore » 10 fluxes are first generated using a biospheric model, and the inversions are constrained using prior fluxes with and without adjustments for crop production and consumption over the 2002–2007 period. After these adjustments, the inverted regional carbon sink in the US Midwest increases from 0.25 ± 0.03 Pg C yr⁻¹ to 0.42 ± 0.13 Pg C yr⁻¹, whereas the large sink in the US Southeast forest region is weakened from 0.41±0.12 Pg C yr⁻¹ 15 to 0.29 ±0.12 Pg C yr⁻¹. These adjustments also reduce the inverted sink in the West region from 0.066 ± 0.04 Pg C yr⁻¹ to 0.040 ± 0.02 Pg C yr⁻1 because of high crop consumption and respiration by humans and livestock. The general pattern of sink increase in crop production areas and sink decreases (or source increases) in crop consumption areas highlights the importance of considering the lateral carbon transfer in crop 20 products in atmospheric inverse modeling, which provides an atmospheric perspective of the overall carbon balance of a region.« less

Weather based risks and insurances for crop production in Belgium

NASA Astrophysics Data System (ADS)

Gobin, Anne

2014-05-01

Extreme weather events such as late frosts, droughts, heat waves and rain storms can have devastating effects on cropping systems. Damages due to extreme events are strongly dependent on crop type, crop stage, soil type and soil conditions. The perspective of rising risk-exposure is exacerbated further by limited aid received for agricultural damage, an overall reduction of direct income support to farmers and projected intensification of weather extremes with climate change. According to both the agriculture and finance sectors, a risk assessment of extreme weather events and their impact on cropping systems is needed. The impact of extreme weather events particularly during the sensitive periods of the farming calendar requires a modelling approach to capture the mixture of non-linear interactions between the crop, its environment and the occurrence of the meteorological event. The risk of soil moisture deficit increases towards harvesting, such that drought stress occurs in spring and summer. Conversely, waterlogging occurs mostly during early spring and autumn. Risks of temperature stress appear during winter and spring for chilling and during summer for heat. Since crop development is driven by thermal time and photoperiod, the regional crop model REGCROP (Gobin, 2010) enabled to examine the likely frequency, magnitude and impacts of frost, drought, heat stress and waterlogging in relation to the cropping season and crop sensitive stages. The risk profiles were subsequently confronted with yields, yield losses and insurance claims for different crops. Physically based crop models such as REGCROP assist in understanding the links between different factors causing crop damage as demonstrated for cropping systems in Belgium. Extreme weather events have already precipitated contraction of insurance coverage in some markets (e.g. hail insurance), and the process can be expected to continue if the losses or damages from such events increase in the future. Climate change will stress this further and impacts on crop growth are expected to be twofold, owing to the sensitive stages occurring earlier during the growing season and to the changes in return period of extreme weather events. Though average yields have risen continuously due to technological advances, there is no evidence that relative tolerance to adverse weather events has improved. The research is funded by the Belgian Science Policy Organisation (Belspo) under contract nr SD/RI/03A.

The CSAICLAWPS project: a multi-scalar, multi-data source approach to providing climate services for both modelling of climate change impacts on crop yields and development of community-level adaptive capacity for sustainable food security

NASA Astrophysics Data System (ADS)

Forsythe, N. D.; Fowler, H. J.

2017-12-01

The "Climate-smart agriculture implementation through community-focused pursuit of land and water productivity in South Asia" (CSAICLAWPS) project is a research initiative funded by the (UK) Royal Society through its Challenge Grants programme which is part of the broader UK Global Challenges Research Fund (GCRF). CSAICLAWPS has three objectives: a) development of "added-value" - bias assessed, statistically down-scaled - climate projections for selected case study sites across South Asia; b) investigation of crop failure modes under both present (observed) and future (projected) conditions; and c) facilitation of developing local adaptive capacity and resilience through stakeholder engagement. At AGU we will be presenting both next steps and progress to date toward these three objectives: [A] We have carried out bias assessments of a substantial multi-model RCM ensemble (MME) from the CORDEX South Asia (CORDEXdomain for case studies in three countries - Pakistan, India and Sri Lanka - and (stochastically) produced synthetic time-series for these sites from local observations using a Python-based implementation of the principles underlying the Climate Research Unit Weather Generator (CRU-WG) in order to enable probabilistic simulation of current crop yields. [B] We have characterised present response of local crop yields to climate variability in key case study sites using AquaCrop simulations parameterised based on input (agronomic practices, soil conditions, etc) from smallholder farmers. [C] We have implemented community-based hydro-climatological monitoring in several case study "revenue villages" (panchayats) in the Nainital District of Uttarakhand. The purpose of this is not only to increase availability of meteorological data, but also has the aspiration of, over time, leading to enhanced quantitative awareness of present climate variability and potential future conditions (as projected by RCMs). Next steps in our work will include: 1) future crop yield simulations driven by "perturbation" of synthetic time-series using "change factors from the CORDEX-SA MME; 2) stakeholder dialogues critically evaluating potential strategies at the grassroots (implementation) level to mitigate impacts of climate variability and change on crop yields.

Rice crop risk map in Babahoyo canton (Ecuador)

NASA Astrophysics Data System (ADS)

Valverde Arias, Omar; Tarquis, Ana; Garrido, Alberto

2016-04-01

It is widely known that extreme climatic phenomena occur with more intensity and frequency. This fact has put more pressure over farming, making agricultural and livestock production riskier. In order to reduce hazards and economic loses that could jeopardize farmer's incomes and even its business continuity, it is very important to implement agriculture risk management plans by governments and institutions. One of the main strategies is transfer risk by agriculture insurance. Agriculture insurance based in indexes has a significant growth in the last decade. And consist in a comparison between measured index values with a defined threshold that triggers damage losses. However, based index insurance could not be based on an isolated measurement. It is necessary to be integrated in a complete monitoring system that uses many sources of information and tools. For example, index influence areas, crop production risk maps, crop yields, claim statistics, and so on. Crop production risk is related with yield variation of crops and livestock, due to weather, pests, diseases, and other factors that affect both the quantity and quality of commodities produced. This is the risk which farmers invest more time managing, and it is completely under their control. The aim of this study is generate a crop risk map of rice that can provide risk manager important information about the status of crop facing production risks. Then, based on this information, it will be possible to make best decisions to deal with production risk. The rice crop risk map was generated qualifying a 1:25000 scale soil and climatic map of Babahoyo canton, which is located in coast region of Ecuador, where rice is one of the main crops. The methodology to obtain crop risk map starts by establishing rice crop requirements and indentifying the risks associated with this crop. A second step is to evaluate soil and climatic conditions of the study area related to optimal crop requirements. Based on it, we can determinate which level of rice crop requirement is met. Finally we have established rice crop zones classified as: suitable, moderate suitable, marginal suitable and unsuitable. Several methods have been used to estimate the degree with which crop requirements are satisfied, pondering weights of limiting factors to adequate crop conditions. Better conditions for cropping in a specific area imply less risk in production. In this case, crop will be less affected by pests and disease, although this closely depends on crop management. Farmers have to invest less money to produce and could increase their benefit. Results are showed and discussed with the aim to study the efficiency and potential of this risk map.

Near-Real-Time Monitoring and Reporting of Crop Growth Condition and Harvest Status Using an Integrated Optical and Radar Approach at the National-Scale in Canada

NASA Astrophysics Data System (ADS)

Shang, J.

2015-12-01

There has been an increasing need to have accurate and spatially detailed information on crop growth condition and harvest status over Canada's agricultural land so that the impacts of environmental conditions, market supply and demand, and transportation network limitations on crop production can be understood fully and acted upon in a timely manner. Presently, Canada doesn't have a national dataset that can provide near-real-time geospatial information on crop growth stage and harvest systematically so that reporting on risk events can be linked directly to the grain supply chain and crop production fluctuations. The intent of this study is to develop an integrated approach using Earth observation (EO) technology to provide a consistent, comprehensive picture of crop growth cycles (growth conditions and stages) and agricultural management activities (field preparation for seeding, harvest, and residue management). Integration of the optical and microwave satellite remote sensing technologies is imperative for robust methodology development and eventually for operational implementation. Particularly, the current synthetic aperture radar (SAR) system Radarsat-2 and to be launched Radarsat Constellation Mission (RCM) are unique EO resources to Canada. Incorporating these Canadian SAR resources with international SAR missions such as the Cosmesky-Med and TerraSAR, could be of great potential for developing change detection technologies particularly useful for monitoring harvest as well as other types of agricultural management events. The study revealed that radar and multi-scale (30m and 250m) optical satellite data can directly detect or infer 1) seeding date, 2) crop growth stages and gross primary productivity (GPP), and 3) harvest progress. Operational prototypes for providing growing-season information at the crop-specific level will be developed across the Canadian agricultural land base.

Application of water footprint in a fertirrigated melon crop under semiarid conditions: A review.

NASA Astrophysics Data System (ADS)

Castellanos Serrano, María Teresa; Requejo Mariscal, María Isabel; Villena Gordo, Raquel; Cartagena Causapé, María Carmen; Arce Martínez, Augusto; Ribas Elcorobarrutia, Francisco; Jesús Cabello Cabello, María; María Tarquis Alfonso, Ana

2015-04-01

In recent times, there has been a major increase in the use of water and fertilizers in order to increase agricultural production, while at the same time there has increased evidence that aquifers are reducing their water level, enriched by nutrient and degraded as a result of pollution. So best management practices are needed for much of cropped, irrigated and fertirrigated land, to avoid contamination of fresh water and groundwater. The concept of "water footprint" (WF) was introduced as an indicator for the total volume of direct and indirect freshwater used, consumed and/or polluted [1]. The WF distinguishes between blue water (volume of surface and groundwater consumed), green water (rain-water consumed), and grey water (volume of freshwater that is required to assimilate the load of pollutants based on existing ambient water quality standards). This study is focused in calculating the crops WF using a real case of study in a fertirrigated melon crop under semiarid conditions which is principally cultivated in the centre of Spain declared vulnerable zone to nitrate pollution by applying the Directive 91/676/CEE. During successive years, a melon crop (Cucumis melo L.) was grown under field conditions applying mineral and organic fertilizers. Different doses of ammonium nitrate were used as well as compost derived from the wine-distillery industry which is relevant in this area. This application help us to review the different concepts in which is based WF. Acknowledgements: This project has been supported by INIA-RTA04-111-C3 and INIA-RTA2010-00110-C03-01. Keywords: Water footprint, nitrogen, fertirrigation, inorganic fertilizers, organic amendments, winery waste, semiarid conditions. [1] Hoekstra, A.Y. 2003. Virtual water trade. Proceedings of the International Expert Meeting on Virtual Water Trade, Delft, The Netherlands, 12-13 December 2002. Value of Water Research Report Series No. 12, UNESCO-IHE, Delft, The Netherlands.

Modeling tropical land-use and land-cover change related to sugarcane crops using remote sensing and soft computing techniques

NASA Astrophysics Data System (ADS)

Vicente, L. E.; Koga-Vicente, A.; Friedel, M. J.; Zullo, J.; Victoria, D.; Gomes, D.; Bayma, G.

2013-12-01

Agriculture is closely related to land-use/cover changes (LUCC). The increase in demand for ethanol necessitates the expansion of areas occupied by corn and sugar cane. In São Paulo state, the conversion of this land raises concern for impacts on food security, such as the decrease in traditional food crop production areas. We used remote sensing data to train and evaluate future land-cover scenarios using a machine-learning algorithm. The land cover classification procedure was based on Landsat 5 TM images, obtained from the Global Land Survey, covering three time periods over twenty years (1990 - 2010). Landsat images were segmented into homogeneous objects, which represent areas on the ground with similar spatial and spectral characteristics. These objects are related to the distinct land cover types that occur in each municipality. Based on the object shape, texture and spectral characteristics, land use/cover was visually identified, considering the following classes: sugarcane plantations, pasture lands, natural cover, forest plantation, permanent crop, short cycle crop, water bodies and urban areas. Results for the western regions of São Paulo state indicate that sugarcane crop area advanced mostly upon pasture areas with few areas of food crops being replaced by sugarcane.

Genetically modified crops: detection strategies and biosafety issues.

PubMed

Kamle, Suchitra; Ali, Sher

2013-06-15

Genetically modified (GM) crops are increasingly gaining acceptance but concurrently consumers' concerns are also increasing. The introduction of Bacillus thuringiensis (Bt) genes into the plants has raised issues related to its risk assessment and biosafety. The International Regulations and the Codex guidelines regulate the biosafety requirements of the GM crops. In addition, these bodies synergize and harmonize the ethical issues related to the release and use of GM products. The labeling of GM crops and their products are mandatory if the genetically modified organism (GMO) content exceeds the levels of a recommended threshold. The new and upcoming GM crops carrying multiple stacked traits likely to be commercialized soon warrant sensitive detection methods both at the DNA and protein levels. Therefore, traceability of the transgene and its protein expression in GM crops is an important issue that needs to be addressed on a priority basis. The advancement in the area of molecular biology has made available several bioanalytical options for the detection of GM crops based on DNA and protein markers. Since the insertion of a gene into the host genome may even cause copy number variation, this may be uncovered using real time PCR. Besides, assessing the exact number of mRNA transcripts of a gene, correlation between the template activity and expressed protein may be established. Here, we present an overview on the production of GM crops, their acceptabilities, detection strategies, biosafety issues and potential impact on society. Further, overall future prospects are also highlighted. Copyright © 2013 Elsevier B.V. All rights reserved.

Could Crop Height Affect the Wind Resource at Agriculturally Productive Wind Farm Sites?

NASA Astrophysics Data System (ADS)

Vanderwende, Brian; Lundquist, Julie K.

2016-03-01

The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length in a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. These considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.

Could crop height affect the wind resource at agriculturally productive wind farm sites?

DOE PAGES

Vanderwende, Brian; Lundquist, Julie K.

2015-11-07

The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length inmore » a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. As a result, these considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.« less

Could crop height affect the wind resource at agriculturally productive wind farm sites?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vanderwende, Brian; Lundquist, Julie K.

The collocation of cropland and wind turbines in the US Midwest region introduces complex meteorological interactions that could influence both agriculture and wind-power production. Crop management practices may affect the wind resource through alterations of land-surface properties. We use the weather research and forecasting (WRF) model to estimate the impact of crop height variations on the wind resource in the presence of a large turbine array. A hypothetical wind farm consisting of 121 1.8-MW turbines is represented using the WRF model wind-farm parametrization. We represent the impact of selecting soybeans rather than maize by altering the aerodynamic roughness length inmore » a region approximately 65 times larger than that occupied by the turbine array. Roughness lengths of 0.1 and 0.25 m represent the mature soy crop and a mature maize crop, respectively. In all but the most stable atmospheric conditions, statistically significant hub-height wind-speed increases and rotor-layer wind-shear reductions result from switching from maize to soybeans. Based on simulations for the entire month of August 2013, wind-farm energy output increases by 14 %, which would yield a significant monetary gain. Further investigation is required to determine the optimal size, shape, and crop height of the roughness modification to maximize the economic benefit and minimize the cost of such crop-management practices. As a result, these considerations must be balanced by other influences on crop choice such as soil requirements and commodity prices.« less

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

The Impacts of Various Environments Factors and Adaptive Management Strategies on Food Crops in the 21st Century Based on a Land Surface Model

NASA Astrophysics Data System (ADS)

Jain, A. K.; Lin, T. S.; Lawrence, P.; Kheshgi, H. S.

2017-12-01

Environmental factors - characterized by increasing levels of CO2, and changes in temperature and precipitation patterns - present potential risks to global food supply. To date, understanding of environmental factors' effects on crop production remains uncertain due to (1) uncertainties in projected trends of these factors and their spatial and temporal variability; (2) uncertainties in the physiological, genetic and molecular basis of crop adaptation to adaptive management practices (e.g. change in planting time, irrigation and N fertilization etc.) and (3) uncertainties in current land surface models to estimate the response of crop production to changes in environmental factors and management strategies. In this study we apply a process-based land surface model, the Integrated Science Assessment model (ISAM), to assess the impact of various environmental factors and management strategies on the production of row crops (corn, soybean and wheat) at regional and global scales. Results are compared to corresponding simulations performed with the crop model in the Community Land Model (CLM4.5). Each model is driven with historical atmospheric forcing data (1901-2005), and projected atmospheric forcing data under RCP 4.5 or RCP 8.5 (2006-2100) from CESM CMIP5 simulations to estimate the effects of different climate change projections on potential productivity of food crops at a global scale. For each set of atmospheric forcing data, production of each crop is simulated with and without inclusion of adaptive management practices (e.g. application of irrigation, N fertilization, change in planting time and crop cultivars etc.) to assess the effect of adaptation on projected crop production over the 21st century. In detail, three questions are addressed: (1) what is the impact of different climate change projections on global crop production; (2) what is the effect of adaptive management practices on projected crop production; and (3) how do differences in model mechanisms in ISAM and CLM4.5 impact projected global crop production and adaptive management practices (irrigation and N fertilizer) over the 21st century. The major outcomes of this study will help to understand the uncertainties in potential productivity of food crops under different environmental conditions and management practices.

Beyond conservation agriculture

PubMed Central

Giller, Ken E.; Andersson, Jens A.; Corbeels, Marc; Kirkegaard, John; Mortensen, David; Erenstein, Olaf; Vanlauwe, Bernard

2015-01-01

Global support for Conservation Agriculture (CA) as a pathway to Sustainable Intensification is strong. CA revolves around three principles: no-till (or minimal soil disturbance), soil cover, and crop rotation. The benefits arising from the ease of crop management, energy/cost/time savings, and soil and water conservation led to widespread adoption of CA, particularly on large farms in the Americas and Australia, where farmers harness the tools of modern science: highly-sophisticated machines, potent agrochemicals, and biotechnology. Over the past 10 years CA has been promoted among smallholder farmers in the (sub-) tropics, often with disappointing results. Growing evidence challenges the claims that CA increases crop yields and builds-up soil carbon although increased stability of crop yields in dry climates is evident. Our analyses suggest pragmatic adoption on larger mechanized farms, and limited uptake of CA by smallholder farmers in developing countries. We propose a rigorous, context-sensitive approach based on Systems Agronomy to analyze and explore sustainable intensification options, including the potential of CA. There is an urgent need to move beyond dogma and prescriptive approaches to provide soil and crop management options for farmers to enable the Sustainable Intensification of agriculture. PMID:26579139

Intensity of heat stress in winter wheat—phenology compensates for the adverse effect of global warming

NASA Astrophysics Data System (ADS)

Eyshi Rezaei, Ehsan; Siebert, Stefan; Ewert, Frank

2015-02-01

Higher temperatures during the growing season are likely to reduce crop yields with implications for crop production and food security. The negative impact of heat stress has also been predicted to increase even further for cereals such as wheat under climate change. Previous empirical modeling studies have focused on the magnitude and frequency of extreme events during the growth period but did not consider the effect of higher temperature on crop phenology. Based on an extensive set of climate and phenology observations for Germany and period 1951-2009, interpolated to 1 × 1 km resolution and provided as supplementary data to this article (available at stacks.iop.org/ERL/10/024012/mmedia), we demonstrate a strong relationship between the mean temperature in spring and the day of heading (DOH) of winter wheat. We show that the cooling effect due to the 14 days earlier DOH almost fully compensates for the adverse effect of global warming on frequency and magnitude of crop heat stress. Earlier heading caused by the warmer spring period can prevent exposure to extreme heat events around anthesis, which is the most sensitive growth stage to heat stress. Consequently, the intensity of heat stress around anthesis in winter crops cultivated in Germany may not increase under climate change even if the number and duration of extreme heat waves increase. However, this does not mean that global warning would not harm crop production because of other impacts, e.g. shortening of the grain filling period. Based on the trends for the last 34 years in Germany, heat stress (stress thermal time) around anthesis would be 59% higher in year 2009 if the effect of high temperatures on accelerating wheat phenology were ignored. We conclude that climate impact assessments need to consider both the effect of high temperature on grain set at anthesis but also on crop phenology.

[Effects of different crop rotations on growth of continuous cropping sorghum and its rhizosphere soil micro-environment.

PubMed

Wang, Jin Song; Fan, Fang Fang; Guo, Jun; Wu, Ai Lian; Dong, Er Wei; Bai, Wen Bin; Jiao, Xiao Yan

2016-07-01

The effects of crop rotation on sorghum [Sorghum biocolor (L) Moench] growth, rhizosphere microbial community and the activity of soil enzymes for successive crops of sorghum were evaluated. Five years of continuous monoculture sorghum as the control (CK) was compared to alfalfa and scallion planted in the fourth year. The results showed that incorporation of alfalfa and scallion into the rotation significantly improved sorghum shoot growth. Specifically, sorghum grain yield increased by 16.5% in the alfalfa rotation plots compared to the CK. The rotations also increased sorghum root system growth, with alfalfa or scallion rotation increasing sorghum total root length by 0.3 and 0.4 times, total root surface area by 0.6 and 0.5 times, root volume by 1.2 and 0.6 times, and root biomass by 1.0 and 0.3 times, respectively. Alfalfa rotation also expanded sorghum root distribution below the 10 cm soil depth. A Biolog analysis on biome functions in the sorghum flowering period indicated significantly higher microbial activity in the rotation plots. The alfalfa and scallion rotation increased the Shannon index by 0.2 and 0.1 times compared to the CK, and improved the sucrose activity in the rhizosphere soil. It was concluded that including alfalfa in rotation with sorghum improved sorghum rhizosphere soil environment, enhanced soil microbial enzyme activity, alleviated the obstacle of continuous cropping and thus increased the sorghum yield.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

NASA Astrophysics Data System (ADS)

Chen, Yaoliang; Lu, Dengsheng; Moran, Emilio; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, Ieda Del'Arco; da Silva, Ramon Felipe Bicudo; Huang, Jingfeng; Luiz, Alfredo José Barreto; de Oliveira, Maria Antonia Falcão

2018-07-01

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

Evaluating the Impacts of an Agricultural Water Market in the Guadalupe River Basin, Texas: An Agent-based Modeling Approach

NASA Astrophysics Data System (ADS)

Du, E.; Cai, X.; Minsker, B. S.

2014-12-01

Agriculture comprises about 80 percent of the total water consumption in the US. Under conditions of water shortage and fully committed water rights, market-based water allocations could be promising instruments for agricultural water redistribution from marginally profitable areas to more profitable ones. Previous studies on water market have mainly focused on theoretical or statistical analysis. However, how water users' heterogeneous physical attributes and decision rules about water use and water right trading will affect water market efficiency has been less addressed. In this study, we developed an agent-based model to evaluate the benefits of an agricultural water market in the Guadalupe River Basin during drought events. Agricultural agents with different attributes (i.e., soil type for crops, annual water diversion permit and precipitation) are defined to simulate the dynamic feedback between water availability, irrigation demand and water trading activity. Diversified crop irrigation rules and water bidding rules are tested in terms of crop yield, agricultural profit, and water-use efficiency. The model was coupled with a real-time hydrologic model and run under different water scarcity scenarios. Preliminary results indicate that an agricultural water market is capable of increasing crop yield, agricultural profit, and water-use efficiency. This capability is more significant under moderate drought scenarios than in mild and severe drought scenarios. The water market mechanism also increases agricultural resilience to climate uncertainty by reducing crop yield variance in drought events. The challenges of implementing an agricultural water market under climate uncertainty are also discussed.

How does spatial and temporal resolution of vegetation index impact crop yield estimation?

USDA-ARS?s Scientific Manuscript database

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...

Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model

NASA Astrophysics Data System (ADS)

Hu, Q.; Friedl, M. A.; Wu, W.

2017-12-01

Accurate and timely information regarding the spatial distribution of crop types and their changes is essential for acreage surveys, yield estimation, water management, and agricultural production decision-making. In recent years, increasing population, dietary shifts and climate change have driven drastic changes in China's agricultural land use. However, no maps are currently available that document the spatial and temporal patterns of these agricultural land use changes. Because of its short revisit period, rich spectral bands and global coverage, MODIS time series data has been shown to have great potential for detecting the seasonal dynamics of different crop types. However, its inherently coarse spatial resolution limits the accuracy with which crops can be identified from MODIS in regions with small fields or complex agricultural landscapes. To evaluate this more carefully and specifically understand the strengths and weaknesses of MODIS data for crop-type mapping, we used MODIS time-series imagery to map the sub-pixel fractional crop area for four major crop types (rice, corn, soybean and wheat) at 500-m spatial resolution for Heilongjiang province, one of the most important grain-production regions in China where recent agricultural land use change has been rapid and pronounced. To do this, a random forest regression (RF-g) model was constructed to estimate the percentage of each sub-pixel crop type in 2006, 2011 and 2016. Crop type maps generated through expert visual interpretation of high spatial resolution images (i.e., Landsat and SPOT data) were used to calibrate the regression model. Five different time series of vegetation indices (155 features) derived from different spectral channels of MODIS land surface reflectance (MOD09A1) data were used as candidate features for the RF-g model. An out-of-bag strategy and backward elimination approach was applied to select the optimal spectra-temporal feature subset for each crop type. The resulting crop maps were assessed in two ways: (1) wall-to-wall pixel comparison with corresponding high spatial resolution reference maps; and (2) county-level comparison with census data. Based on these derived maps, changes in crop type, total area, and spatial patterns of change in Heilongjiang province during 2006-2016 were analyzed.

Nitrous oxide emissions in cover crop-based corn production systems

NASA Astrophysics Data System (ADS)

Davis, Brian Wesley

Nitrous oxide (N2O) is a potent greenhouse gas; the majority of N2O emissions are the result of agricultural management, particularly the application of N fertilizers to soils. The relationship of N2O emissions to varying sources of N (manures, mineral fertilizers, and cover crops) has not been well-evaluated. Here we discussed a novel methodology for estimating precipitation-induced pulses of N2O using flux measurements; results indicated that short-term intensive time-series sampling methods can adequately describe the magnitude of these pulses. We also evaluated the annual N2O emissions from corn-cover crop (Zea mays; cereal rye [Secale cereale], hairy vetch [Vicia villosa ], or biculture) production systems when fertilized with multiple rates of subsurface banded poultry litter, as compared with tillage incorporation or mineral fertilizer. N2O emissions increased exponentially with total N rate; tillage decreased emissions following cover crops with legume components, while the effect of mineral fertilizer was mixed across cover crops.

A crops and soils data base for scene radiation research

NASA Technical Reports Server (NTRS)

Biehl, L. L.; Bauer, M. E.; Robinson, B. F.; Daughtry, C. S. T.; Silva, L. F.; Pitts, D. E.

1982-01-01

Management and planning activities with respect to food production require accurate and timely information on crops and soils on a global basis. The needed information can be obtained with the aid of satellite-borne sensors, if the relations between the spectral properties and the important biological-physical parameters of crops and soils are known. In order to obtain this knowledge, the development of a crops and soils scene radiation research data base was initiated. Work related to the development of this data base is discussed, taking into account details regarding the conducted experiments, the performed measurements, the calibration of spectral data, questions of data base access, and the expansion of the crops and soils scene radiation data base for 1982.

Envirotyping for deciphering environmental impacts on crop plants.

PubMed

Xu, Yunbi

2016-04-01

Global climate change imposes increasing impacts on our environments and crop production. To decipher environmental impacts on crop plants, the concept "envirotyping" is proposed, as a third "typing" technology, complementing with genotyping and phenotyping. Environmental factors can be collected through multiple environmental trials, geographic and soil information systems, measurement of soil and canopy properties, and evaluation of companion organisms. Envirotyping contributes to crop modeling and phenotype prediction through its functional components, including genotype-by-environment interaction (GEI), genes responsive to environmental signals, biotic and abiotic stresses, and integrative phenotyping. Envirotyping, driven by information and support systems, has a wide range of applications, including environmental characterization, GEI analysis, phenotype prediction, near-iso-environment construction, agronomic genomics, precision agriculture and breeding, and development of a four-dimensional profile of crop science involving genotype (G), phenotype (P), envirotype (E) and time (T) (developmental stage). In the future, envirotyping needs to zoom into specific experimental plots and individual plants, along with the development of high-throughput and precision envirotyping platforms, to integrate genotypic, phenotypic and envirotypic information for establishing a high-efficient precision breeding and sustainable crop production system based on deciphered environmental impacts.

Effect of the time interval from harvesting to the pre-drying step on natural fumonisin contamination in freshly harvested corn from the State of Parana, Brazil.

PubMed

Da Silva, M; Garcia, G T; Vizoni, E; Kawamura, O; Hirooka, E Y; Ono, E Y S

2008-05-01

Natural mycoflora and fumonisins were analysed in 490 samples of freshly harvested corn (Zea mays L.) (2003 and 2004 crops) collected at three points in the producing chain from the Northern region of Parana State, Brazil, and correlated to the time interval between the harvesting and the pre-drying step. The two crops showed a similar profile concerning the fungal frequency, and Fusarium sp. was the prevalent genera (100%) for the sampling sites from both crops. Fumonisins were detected in all samples from the three points of the producing chain (2003 and 2004 crops). The levels ranged from 0.11 to 15.32 microg g(-1)in field samples, from 0.16 to 15.90 microg g(-1)in reception samples, and from 0.02 to 18.78 microg g(-1)in pre-drying samples (2003 crop). Samples from the 2004 crop showed lower contamination and fumonisin levels ranged from 0.07 to 4.78 microg g(-1)in field samples, from 0.03 to 4.09 microg g(-1)in reception samples, and from 0.11 to 11.21 microg g(-1)in pre-drying samples. The mean fumonisin level increased gradually from < or = 5.0 to 19.0 microg g(-1)as the time interval between the harvesting and the pre-drying step increased from 3.22 to 8.89 h (2003 crop). The same profile was observed for samples from the 2004 crop. Fumonisin levels and the time interval (rho = 0.96) showed positive correlation (p < or = 0.05), indicating that delay in the drying process can increase fumonisin levels.

Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates

NASA Technical Reports Server (NTRS)

Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe;

Temperature increase reduces global yields of major crops in four independent estimates

PubMed Central

Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A.; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Peng, Shushi; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

2017-01-01

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population. PMID:28811375

Temperature increase reduces global yields of major crops in four independent estimates.

PubMed

Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Müller, Christoph; Peng, Shushi; Peñuelas, Josep; Ruane, Alex C; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

2017-08-29

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Conservation tillage issues: cover crop-based organic rotational no-till grain production in the mid-atlantic region

USDA-ARS?s Scientific Manuscript database

Organic producers in the mid-Atlantic region are interested in reducing tillage, labor, and time requirements for grain production. Cover crop-based organic rotational no-till grain production is one approach to accomplishing these goals. Advancements in a system for planting crops into a mat of cov...

E-precision agriculture for small scale cash crops in Tobasa regency

NASA Astrophysics Data System (ADS)

Putra Simanjuntak, Panca; Tiurniari Napitupulu, Pangeran; Pratama Silalahi, Soni; Kisno; Pasaribu, Norlina; Valešová, Libuše

2017-09-01

Cash crop is a promising sector in Tobasa regency; however, the trend showed a negative change of the cash crop production in. This research aims to develop an application which is based on Arduino for watering and fertilizing corn land. The result of using e-precision agriculture based on embedded system is 100% higher than the conventional one and the risk of harvesting failure using the embedded system decreased to 50%. Embedded system in this study acquired critical environment measurements which at last affected the yield raising and risk reduction. As the result, the use of e-precision agriculture provided a framework to be used by different stakeholders to implement e-agriculture platform that supports marketing of agricultural production since the system is proven to save the material and time which finally reduces the risk of harvesting failure and increases the yield. In other words, the system is able to economize the use of water and fertilizer on a small corn land. The system will be developed for more efficiency in material loss and the mobile-based application development to reach sustainable rural development particularly for cash-crop farmers.

A Novel Approach for Forecasting Crop Production and Yield Using Remotely Sensed Satellite Images

NASA Astrophysics Data System (ADS)

Singh, R. K.; Budde, M. E.; Senay, G. B.; Rowland, J.

2017-12-01

Forecasting crop production in advance of crop harvest plays a significant role in drought impact management, improved food security, stabilizing food grain market prices, and poverty reduction. This becomes essential, particularly in Sub-Saharan Africa, where agriculture is a critical source of livelihoods, but lacks good quality agricultural statistical data. With increasing availability of low cost satellite data, faster computing power, and development of modeling algorithms, remotely sensed images are becoming a common source for deriving information for agricultural, drought, and water management. Many researchers have shown that the Normalized Difference Vegetation Index (NDVI), based on red and near-infrared reflectance, can be effectively used for estimating crop production and yield. Similarly, crop production and yield have been closely related to evapotranspiration (ET) also as there are strong linkages between production/yield and transpiration based on plant physiology. Thus, we combined NDVI and ET information from remotely sensed images for estimating total production and crop yield prior to crop harvest for Niger and Burkina Faso in West Africa. We identified the optimum time (dekads 23-29) for cumulating NDVI and ET and developed a new algorithm for estimating crop production and yield. We used the crop data from 2003 to 2008 to calibrate our model and the data from 2009 to 2013 for validation. Our results showed that total crop production can be estimated within 5% of actual production (R2 = 0.98) about 30-45 days before end of the harvest season. This novel approach can be operationalized to provide a valuable tool to decision makers for better drought impact management in drought-prone regions of the world.

Study of Biometric Datum of Green Manure Crops in the Process of Biological Soil Reclamation on the Territory of Coal Producer Ojsc “Mine No 12”In The Kemerovo Region

NASA Astrophysics Data System (ADS)

Yakovchenko, M. A.; Pinchuk, L. G.; Kolosolapova, A. A.; Alankina, D. N.

2017-05-01

The article presents the results of a study of green manure crops of different kinds, it is determined that the best growth results were obtained with incorporation of hydrogel in the substratum, and particularly in the clay, so as due to the amount of moisture in the substratum, clay is more hygroscopic and physico-chemical properties of hydrogel significantly increase moisture capacity of the substratum. Sowing field germination of all crops is much higher in the clay then in soil. Territories with the hydrogel usage showed a greater plant density per 1m2. Almost all crops with the major growth of herb were sowed in the clay with hydrogel addition, the crops height increased by 2.5 times. The only exception was Rye, the difference in height between its plants in “soil + hydrogel” and “clay+hydrogel” was less than 1%. It was registered that the root growth of Phacelia in “clay+hydrogel” increased by 2.5 times. While the root growth of Rump, on the contrary, increased in mellow soil with hydrogel by 43%. Other kinds of crops did not perform any difference in their root length. The increase of herbage in mixtures of green manure crops was negligible, whereas mono-sowing of such crops as Esparcet, Rump and Buckwheat showed the greatest increase of herbage in comparison to the soil lots and other sowing variants. The greatest increase of herbage among lots without hydrogel addition was performed on the clay ones: Esparcet - 250%, Buckwheat - 172% Rump - 123% Phacelia - 77.5%. The best results of herbage accumulation were showed by Esparcet, Buckwheat, Rump, Phacelia.

Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H. D.

2011-12-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attachés, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly impact crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Although, historically, these analog years are identified through visual inspection, the qualitative nature of this methodology sometimes precludes the definitive identification of the best analog year. One goal of this study is to introduce a more rigorous, statistical approach for identifying analog years. This approach is based on a modified coefficient of determination, termed the analog index (AI). The derivation of AI will be described. Another goal of this study is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data). Five study areas and six growing seasons of data were analyzed (2003-2007 as potential analog years and 2008 as the target year). Results thus far show that, for all five areas, crop yield estimates derived from satellite-based precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include satellite-based surface soil moisture data and model-assimilated root zone soil moisture. This study is part of a larger effort to improve WAOB estimates by integrating NASA remote sensing observations and research results into WAOB's decision-making environment.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series.

PubMed

Zhao, Yan; Bai, Linyan; Feng, Jianzhong; Lin, Xiaosong; Wang, Li; Xu, Lijun; Ran, Qiyun; Wang, Kui

2016-04-19

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series

PubMed Central

Zhao, Yan; Bai, Linyan; Feng, Jianzhong; Lin, Xiaosong; Wang, Li; Xu, Lijun; Ran, Qiyun; Wang, Kui

2016-01-01

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province. PMID:27104536

[The new method monitoring crop water content based on NIR-Red spectrum feature space].

PubMed

Cheng, Xiao-juan; Xu, Xin-gang; Chen, Tian-en; Yang, Gui-jun; Li, Zhen-hai

2014-06-01

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

Effects of Cover Crops on Pratylenchus penetrans and the Nematode Community in Carrot Production

PubMed Central

Grabau, Zane J.; Zar Maung, Zin Thu; Noyes, D. Corey; Baas, Dean G.; Werling, Benjamin P.; Brainard, Daniel C.; Melakeberhan, Haddish

2017-01-01

Cover cropping is a common practice in U.S. Midwest carrot production for soil conservation, and may affect soil ecology and plant-parasitic nematodes—to which carrots are very susceptible. This study assessed the impact of cover crops—oats (Avena sativa), radish (Raphanus sativus) cv. Defender, rape (Brassica napus) cv. Dwarf Essex, and a mixture of oats and radish—on plant-parasitic nematodes and soil ecology based on the nematode community in Michigan carrot production systems. Research was conducted at two field sites where cover crops were grown in Fall 2014 preceding Summer 2015 carrot production. At Site 1, root-lesion (Pratylenchus penetrans) and stunt (Tylenchorhynchus sp.) nematodes were present at low population densities (less than 25 nematodes/100 cm3 soil), but were not significantly affected (P > 0.05) by cover crops. At Site 2, P. penetrans population densities were increased (P ≤ 0.05) by ‘Defender’ radish compared to other cover crops or fallow control during cover crop growth and midseason carrot production. At both sites, there were few short-term impacts of cover cropping on soil ecology based on the nematode community. At Site 1, only at carrot harvest, radish-oats mixture and ‘Dwarf Essex’ rape alone enriched the soil food web based on the enrichment index (P ≤ 0.05) while rape and radish increased structure index values. At Site 2, bacterivore abundance was increased by oats or radish cover crops compared to control, but only during carrot production. In general, cover crops did not affect the nematode community until nearly a year after cover crop growth suggesting that changes in the soil community following cover cropping may be gradual. PMID:28512383

Impacts of crop insurance on water withdrawals for irrigation

NASA Astrophysics Data System (ADS)

Deryugina, Tatyana; Konar, Megan

2017-12-01

Agricultural production remains particularly vulnerable to weather fluctuations and extreme events, such as droughts, floods, and heat waves. Crop insurance is a risk management tool developed to mitigate some of this weather risk and protect farmer income in times of poor production. However, crop insurance may have unintended consequences for water resources sustainability, as the vast majority of freshwater withdrawals go to agriculture. The causal impact of crop insurance on water use in agriculture remains poorly understood. Here, we determine the empirical relationship between crop insurance and irrigation water withdrawals in the United States. Importantly, we use an instrumental variables approach to establish causality. Our methodology exploits a major policy change in the crop insurance system - the 1994 Federal Crop Insurance Reform Act - which imposed crop insurance requirements on farmers. We find that a 1% increase in insured crop acreage leads to a 0.223% increase in irrigation withdrawals, with most coming from groundwater aquifers. We identify farmers growing more groundwater-fed cotton as an important mechanism contributing to increased withdrawals. A 1% increase in insured crop acreage leads to a 0.624% increase in cotton acreage, or 95,602 acres. These results demonstrate that crop insurance causally leads to more irrigation withdrawals. More broadly, this work underscores the importance of determining causality in the water-food nexus as we endeavor to achieve global food security and water resources sustainability.

Estimating Crop Growth Stage by Combining Meteorological and Remote Sensing Based Techniques

NASA Astrophysics Data System (ADS)

Champagne, C.; Alavi-Shoushtari, N.; Davidson, A. M.; Chipanshi, A.; Zhang, Y.; Shang, J.

2016-12-01

Estimations of seeding, harvest and phenological growth stage of crops are important sources of information for monitoring crop progress and crop yield forecasting. Growth stage has been traditionally estimated at the regional level through surveys, which rely on field staff to collect the information. Automated techniques to estimate growth stage have included agrometeorological approaches that use temperature and day length information to estimate accumulated heat and photoperiod, with thresholds used to determine when these stages are most likely. These approaches however, are crop and hybrid dependent, and can give widely varying results depending on the method used, particularly if the seeding date is unknown. Methods to estimate growth stage from remote sensing have progressed greatly in the past decade, with time series information from the Normalized Difference Vegetation Index (NDVI) the most common approach. Time series NDVI provide information on growth stage through a variety of techniques, including fitting functions to a series of measured NDVI values or smoothing these values and using thresholds to detect changes in slope that are indicative of rapidly increasing or decreasing `greeness' in the vegetation cover. The key limitations of these techniques for agriculture are frequent cloud cover in optical data that lead to errors in estimating local features in the time series function, and the incongruity between changes in greenness and traditional agricultural growth stages. There is great potential to combine both meteorological approaches and remote sensing to overcome the limitations of each technique. This research will examine the accuracy of both meteorological and remote sensing approaches over several agricultural sites in Canada, and look at the potential to integrate these techniques to provide improved estimates of crop growth stage for common field crops.

Interannual variability of crop water footprint

NASA Astrophysics Data System (ADS)

Tuninetti, M.; Tamea, S.; Laio, F.; Ridolfi, L.

2016-12-01

The crop water footprint, CWF, is a useful tool to investigate the water-food nexus, since it measures the water requirement for crop production. Heterogeneous spatial patterns of climatic conditions and agricultural practices have inspired a flourishing literature on the geographic assessment of CWF, mostly referred to a fixed (time-averaged) period. However, given that both climatic conditions and crop yield may vary substantially over time, also the CWF temporal dynamics need to be addressed. As other studies have done, we base the CWF variability on yield, while keeping the crop evapotranspiration constant over time. As a new contribution, we prove the feasibility of this approach by comparing these CWF estimates with the results obtained with a full model considering variations of crop evapotranspiration: overall, the estimates compare well showing high coefficients of determination that read 0.98 for wheat, 0.97 for rice, 0.97 for maize, and 0.91 for soybean. From this comparison, we derive also the precision of the method, which is around ±10% that is higher than the precision of the model used to evaluate the crop evapotranspiration (i.e., ±30%). Over the period between 1961 and 2013, the CWF of the most cultivated grains has sharply decreased on a global basis (i.e., -68% for wheat, -62% for rice, -66% for maize, and -52% for soybean), mainly driven by enhanced yield values. The higher water use efficiency in crop production implies a reduced virtual displacement of embedded water per ton of traded crop and as a result, the temporal variability of virtual water trade is different if considering constant or time-varying CWF. The proposed yield-based approach to estimate the CWF variability implies low computational costs and requires limited input data, thus, it represents a promising tool for time-dependent water footprint assessments.

Tuber yield and quality characteristics of potatoes for off-season crops in a Mediterranean environment.

PubMed

Ierna, Anita

2010-01-15

There is little research on evaluating the compatibility of potatoes for double cropping in southern Italy. The aim of this investigation was to assess tuber yield and some qualitative traits of tubers such as skin colour, tuber dry matter content and tuber nitrate content, both in winter-spring and in summer-autumn crops, as influenced by genotype and harvest time. Yield, skin colour and dry matter content of tubers were higher in the winter-spring crop than in the summer-autumn crop, attributable to the advantageous lag time in spring between solar radiation and temperatures and the disadvantageous lag in autumn. Spunta and Arinda performed well within each crop season, whereas Ninfa showed an important yield loss in autumn. In both off-season crops, delaying tuber harvest until leaf senescence increased yield and improved quality attributes such as tuber dry matter content and skin colour, whereas nitrate contents significantly decreased in the winter-spring crop and increased in the summer-autumn crop. Ninfa showed less tendency than Arinda and Spunta to accumulate nitrate in tubers in both off-season crops. It might be advantageous to examine in further research which mechanisms sustain compatibility to the autumn and assess other quality characteristics for the fresh market in the contrasting climatic conditions of the two off-season crops. Copyright (c) 2009 Society of Chemical Industry.

Crop Diversity for Yield Increase

PubMed Central

Li, Chengyun; He, Xiahong; Zhu, Shusheng; Zhou, Huiping; Wang, Yunyue; Li, Yan; Yang, Jing; Fan, Jinxiang; Yang, Jincheng; Wang, Guibin; Long, Yunfu; Xu, Jiayou; Tang, Yongsheng; Zhao, Gaohui; Yang, Jianrong; Liu, Lin; Sun, Yan; Xie, Yong; Wang, Haining; Zhu, Youyong

2009-01-01

Traditional farming practices suggest that cultivation of a mixture of crop species in the same field through temporal and spatial management may be advantageous in boosting yields and preventing disease, but evidence from large-scale field testing is limited. Increasing crop diversity through intercropping addresses the problem of increasing land utilization and crop productivity. In collaboration with farmers and extension personnel, we tested intercropping of tobacco, maize, sugarcane, potato, wheat and broad bean – either by relay cropping or by mixing crop species based on differences in their heights, and practiced these patterns on 15,302 hectares in ten counties in Yunnan Province, China. The results of observation plots within these areas showed that some combinations increased crop yields for the same season between 33.2 and 84.7% and reached a land equivalent ratio (LER) of between 1.31 and 1.84. This approach can be easily applied in developing countries, which is crucial in face of dwindling arable land and increasing food demand. PMID:19956624

Large scale maps of cropping intensity in Asia from MODIS

NASA Astrophysics Data System (ADS)

Gray, J. M.; Friedl, M. A.; Frolking, S. E.; Ramankutty, N.; Nelson, A.

2013-12-01

Agricultural systems are geographically extensive, have profound significance to society, and also affect regional energy, carbon, and water cycles. Since most suitable lands worldwide have been cultivated, there is growing pressure to increase yields on existing agricultural lands. In tropical and sub-tropical regions, multi-cropping is widely used to increase food production, but regional-to-global information related to multi-cropping practices is poor. Such information is of critical importance to ensure sustainable food production while mitigating against negative environmental impacts associated with agriculture such as contamination and depletion of freshwater resources. Unfortunately, currently available large-area inventory statistics are inadequate because they do not capture important spatial patterns in multi-cropping, and are generally not available in a timeframe that can be used to help manage cropping systems. High temporal resolution sensors such as MODIS provide an excellent source of information for addressing this need. However, relative to studies that document agricultural extensification, systematic assessment of agricultural intensification via multi-cropping has received relatively little attention. The goal of this work is to help close this methodological and information gap by developing methods that use multi-temporal remote sensing to map multi-cropping systems in Asia. Image time series analysis is especially challenging in Asia because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low quality remote sensing observations, especially during the Asian Monsoon. The methodology that we use for this work builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but employs refined methods to segment, smooth, and gap-fill 8-day EVI time series calculated from MODIS BRDF corrected surface reflectances. Crop cycle segments are identified based on changes in slope for linear regressions estimated for local windows, and constrained by the EVI amplitude and length of crop cycles that are identified. The procedure can be used to map seasonal or long-term average cropping strategies, and to characterize changes in cropping intensity over longer time periods. The datasets produced using this method therefore provide information related to global cropping systems, and more broadly, provide important information that is required to ensure sustainable management of Earth's resources and ensure food security. To test our algorithm, we applied it to time series of MODIS EVI images over Asia from 2000-2012. Our results demonstrate the utility of multi-temporal remote sensing for characterizing multi-cropping practices in some of the most important and intensely agricultural regions in the world. To evaluate our approach, we compared results from MODIS to field-scale survey data at the pixel scale, and agricultural inventory statistics at sub-national scales. We then mapped changes in multi-cropped area in Asia from the early MODIS period (2001-2004) to present (2009-2012), and characterizes the magnitude and location of changes in cropping intensity over the last 12 years. We conclude with a discussion of the challenges, future improvements, and broader impacts of this work.

New aspects of grassland recovery in old-fields revealed by trait-based analyses of perennial-crop-mediated succession.

PubMed

Kelemen, András; Tóthmérész, Béla; Valkó, Orsolya; Miglécz, Tamás; Deák, Balázs; Török, Péter

2017-04-01

Classical old-field succession studies focused on vegetation changes after the abandonment of annual croplands or on succession after the elimination of cultivated crops. Perennial-crop-mediated succession, where fields are initially covered by perennial crops, reveals alternative aspects of old-field succession theories. We tested the validity of classical theories of old-field succession for perennial-crop-mediated succession. We formulated the following hypotheses: (1) functional diversity increases with increasing field age; (2) resource acquisition versus conservation trade-off shifts toward conservation at community level during the succession; (3) the importance of spatial and temporal seed dispersal decreases during the succession; and (4) competitiveness and stress-tolerance increases and ruderality decreases at community level during the succession. We studied functional diversity, trait distributions and plant strategies in differently aged old-fields using chronosequence method. We found increasing functional richness and functional divergence, but also unchanged or decreasing functional evenness. We detected a shift from resource acquisition to resource conservation strategy of communities during the succession. The role of spatial and temporal seed dispersal was found to be important not only at the initial but also at latter successional stages. We found an increasing stress-tolerance and a decreasing ruderality during succession, while the competitiveness remained unchanged at the community level. Despite the markedly different starting conditions, we found that classical and perennial-crop-mediated old-field successions have some similarities regarding the changes of functional diversity, resource acquisition versus conservation trade-off, and seed dispersal strategies. However, we revealed also the subsequent differences. The competitive character of communities remained stable during the succession; hence, the initial stages of perennial-crop-mediated succession can be similar to the middle stages of classical old-field succession. Moreover, the occupied functional niche space and differentiation were larger in the older stages, but resources were not effectively utilized within this space, suggesting that the stabilization of the vegetation requires more time.

A quality assessment of the MARS crop yield forecasting system for the European Union

NASA Astrophysics Data System (ADS)

van der Velde, Marijn; Bareuth, Bettina

2015-04-01

Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

Predicting optimum crop designs using crop models and seasonal climate forecasts.

PubMed

Rodriguez, D; de Voil, P; Hudson, D; Brown, J N; Hayman, P; Marrou, H; Meinke, H

2018-02-02

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that "hindsight", by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer "What is the value of the skill in seasonal climate forecasting, to inform crop designs?" Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

Hand-held radiometer red and photographic infrared spectral measurements of agricultural crops

NASA Technical Reports Server (NTRS)

Tucker, C. J.; Fan, C. J.; Elgin, J. H., Jr.; Mcmurtrey, J. E., III

1978-01-01

Red and photographic infrared radiance data, collected under a variety of conditions at weekly intervals throughout the growing season using a hand-held radiometer, were used to monitor crop growth and development. The vegetation index transformation was used to effectively compensate for the different irradiational conditions encountered during the study period. These data, plotted against time, compared the different crops measured by comparing their green leaf biomass dynamics. This approach, based entirely upon spectral inputs, closely monitors crop growth and development and indicates the promise of ground-based hand-held radiometer measurements of crops.

Diverse effects of crop distribution and climate change on crop production in the agro-pastoral transitional zone of China

NASA Astrophysics Data System (ADS)

Qiao, Jianmin; Yu, Deyong; Wang, Qianfeng; Liu, Yupeng

2018-06-01

Both crop distribution and climate change are important drivers for crop production and can affect food security, which is an important requirement for sustainable development. However, their effects on crop production are confounded and warrant detailed investigation. As a key area for food production that is sensitive to climate change, the agro-pastoral transitional zone (APTZ) plays a significant role in regional food security. To investigate the respective effects of crop distribution and climate change on crop production, the well-established GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted with different scenario designs in this study. From 1980 to 2010, the crop distribution for wheat, maize, and rice witnessed a dramatic change due to agricultural policy adjustments and ecological engineering-related construction in the APTZ. At the same time, notable climate change was observed. The simulation results indicated that the climate change had a positive impact on the crop production of wheat, maize, and rice, while the crop distribution change led to an increase in the production of maize and rice, but a decrease in the wheat production. Comparatively, crop distribution change had a larger impact on wheat (-1.71 × 106 t) and maize (8.53 × 106 t) production, whereas climate change exerted a greater effect on rice production (0.58 × 106 t), during the period from 1980 to 2010 in the APTZ. This study is helpful to understand the mechanism of the effects of crop distribution and climate change on crop production, and aid policy makers in reducing the threat of future food insecurity.

Diverse effects of crop distribution and climate change on crop production in the agro-pastoral transitional zone of China

NASA Astrophysics Data System (ADS)

Qiao, Jianmin; Yu, Deyong; Wang, Qianfeng; Liu, Yupeng

2017-07-01

Both crop distribution and climate change are important drivers for crop production and can affect food security, which is an important requirement for sustainable development. However, their effects on crop production are confounded and warrant detailed investigation. As a key area for food production that is sensitive to climate change, the agro-pastoral transitional zone (APTZ) plays a significant role in regional food security. To investigate the respective effects of crop distribution and climate change on crop production, the well-established GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted with different scenario designs in this study. From 1980 to 2010, the crop distribution for wheat, maize, and rice witnessed a dramatic change due to agricultural policy adjustments and ecological engineering-related construction in the APTZ. At the same time, notable climate change was observed. The simulation results indicated that the climate change had a positive impact on the crop production of wheat, maize, and rice, while the crop distribution change led to an increase in the production of maize and rice, but a decrease in the wheat production. Comparatively, crop distribution change had a larger impact on wheat (-1.71 × 106 t) and maize (8.53 × 106 t) production, whereas climate change exerted a greater effect on rice production (0.58 × 106 t), during the period from 1980 to 2010 in the APTZ. This study is helpful to understand the mechanism of the effects of crop distribution and climate change on crop production, and aid policy makers in reducing the threat of future food insecurity.

How can we improve crop genotypes to increase stress resilience and productivity in a future climate? A new crop screening method based on productivity and resistance to abiotic stress

PubMed Central

Thiry, Arnauld A.; Chavez Dulanto, Perla N.; Reynolds, Matthew P.; Davies, William J.

2016-01-01

The need to accelerate the selection of crop genotypes that are both resistant to and productive under abiotic stress is enhanced by global warming and the increase in demand for food by a growing world population. In this paper, we propose a new method for evaluation of wheat genotypes in terms of their resilience to stress and their production capacity. The method quantifies the components of a new index related to yield under abiotic stress based on previously developed stress indices, namely the stress susceptibility index, the stress tolerance index, the mean production index, the geometric mean production index, and the tolerance index, which were created originally to evaluate drought adaptation. The method, based on a scoring scale, offers simple and easy visualization and identification of resilient, productive and/or contrasting genotypes according to grain yield. This new selection method could help breeders and researchers by defining clear and strong criteria to identify genotypes with high resilience and high productivity and provide a clear visualization of contrasts in terms of grain yield production under stress. It is also expected that this methodology will reduce the time required for first selection and the number of first-selected genotypes for further evaluation by breeders and provide a basis for appropriate comparisons of genotypes that would help reveal the biology behind high stress productivity of crops. PMID:27677299

Spectral variations of canopy reflectance in support of precision agriculture

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Georgiev, Georgi; Borisova, Denitsa; Nikolov, Hristo

2014-05-01

Agricultural monitoring is an important and continuously spreading activity in remote sensing and applied Earth observations. It supplies precise, reliable and valuable information on current crop condition and growth processes. In agriculture, the timing of seasonal cycles of crop activity is important for species classification and evaluation of crop development, growing conditions and potential yield. The correct interpretation of remotely sensed data, however, and the increasing demand for data reliability require ground-truth knowledge of the seasonal spectral behavior of different species and their relation to crop vigor. For this reason, we performed ground-based study of the seasonal response of winter wheat reflectance patterns to crop growth patterns. The goal was to quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties in main ontogenetic periods. Phenology and agro-specific relationships allow assessing crop condition during different portions of the growth cycle and thus effectively tracking plant development, and finally make yield predictions. The applicability of a number of vegetation indices (VIs) for monitoring crop seasonal dynamics, its health condition, and yield potential was examined. Special emphasis we put on narrow-band indices as the availability of data from hyperspectral imagers is unavoidable future. The temporal behavior of vegetation indices revealed increased sensitivity to crop growth. The derived spectral-biophysical relationships allowed extraction of quantitative information about crop variables and yield at different stages of the phenological development. Relating plant spectral and biophysical variables in a phenology-based manner allows crop monitoring, that is crop diagnosis and predictions to be performed multiple times during plant ontogenesis. During active vegetative periods spectral data was highly indicative of plant growth trends and yield potential. The VIs values contributed to reliable yield prediction and showed very good correspondence with the estimates from biophysical models. For dates before full maturity most of the examined VIs proved to be meaningful statistical predictors of crop state-indicative biophysical variables. High correlations were obtained for canopy cover fraction, LAI, and biomass. Sensitivity to red, near-infrared and green reflectance showed both vigorous and stressed plants. As crops attained advanced growth stages, decreased sensitivity of VIs and weaker correlations with bioparameters were observed, yet still significant in a statistical sense. The results highlight the capability of the presented approach to track the dynamics of crop growth from multitemporal spectral data, and illustrate the prediction accuracy of the spectral models. The results are useful in assessing the efficiency of various spectral band ratios and other vegetation indices often used in remote sensing studies of natural and agricultural vegetation. They suggest that the used algorithm for data processing is particularly suitable for airborne cropland monitoring and could be expanded to sites at farm or municipality scale. The results reported are from pilot study carried out on a plot located in one of the established polygons for experimental crop monitoring. In the mentioned research GIS database is established for supporting the experiments and modelling process. Recommendations on good farming practices for medium sized farms for monitoring stress conditions such as drought and overfertilizing are developed.

Transgenic, transplastomic and other genetically modified plants: a Canadian perspective.

PubMed

Belzile, François J

2002-11-01

Since the mid 1990s, genetically modified (GM) crops have been grown commercially in Canada on a scale that has increased steadily over the years. An intense debate ensued, as elsewhere, and many fears were expressed regarding not only the technology itself but some of the main GM crops being grown. It would seem appropriate at this time to examine how these novel crops compare to crops bred by more traditional means and what impacts these GM crops have had based on experience and not merely on conjecture. To begin, we will put things in a historical perspective and recall how domestication and conventional plant breeding have shaped the crops of today. Then, we will describe briefly the distinctive features of GM plants (obtained so far mainly by nuclear transgenesis) and how these novel crops are regulated in Canada. We will then give two examples of widely grown GM crops in Canada (insect-resistant corn and herbicide-tolerant canola) and examine the main questions that were raised as well as the actual impacts these crops have had on the farm. These examples will help us outline some of the limitations of the current generation of GM plants and, finally, we will try to get a glimpse of the future by examining some recent technical developments in the field of recombinant DNA technologies applied to plant breeding.

An integrated model for assessing both crop productivity and agricultural water resources at a large scale

NASA Astrophysics Data System (ADS)

Okada, M.; Sakurai, G.; Iizumi, T.; Yokozawa, M.

2012-12-01

Agricultural production utilizes regional resources (e.g. river water and ground water) as well as local resources (e.g. temperature, rainfall, solar energy). Future climate changes and increasing demand due to population increases and economic developments would intensively affect the availability of water resources for agricultural production. While many studies assessed the impacts of climate change on agriculture, there are few studies that dynamically account for changes in water resources and crop production. This study proposes an integrated model for assessing both crop productivity and agricultural water resources at a large scale. Also, the irrigation management to subseasonal variability in weather and crop response varies for each region and each crop. To deal with such variations, we used the Markov Chain Monte Carlo technique to quantify regional-specific parameters associated with crop growth and irrigation water estimations. We coupled a large-scale crop model (Sakurai et al. 2012), with a global water resources model, H08 (Hanasaki et al. 2008). The integrated model was consisting of five sub-models for the following processes: land surface, crop growth, river routing, reservoir operation, and anthropogenic water withdrawal. The land surface sub-model was based on a watershed hydrology model, SWAT (Neitsch et al. 2009). Surface and subsurface runoffs simulated by the land surface sub-model were input to the river routing sub-model of the H08 model. A part of regional water resources available for agriculture, simulated by the H08 model, was input as irrigation water to the land surface sub-model. The timing and amount of irrigation water was simulated at a daily step. The integrated model reproduced the observed streamflow in an individual watershed. Additionally, the model accurately reproduced the trends and interannual variations of crop yields. To demonstrate the usefulness of the integrated model, we compared two types of impact assessment of climate change on crop productivity in a watershed. The first was carried out by the large-scale crop model alone. The second was carried out by the integrated model of the large-scale crop model and the H08 model. The former projected that changes in temperature and precipitation due to future climate change would give rise to increasing the water stress in crops. Nevertheless, the latter projected that the increasing amount of agricultural water resources in the watershed would supply sufficient amount of water for irrigation, consequently reduce the water stress. The integrated model demonstrated the importance of taking into account the water circulation in watershed when predicting the regional crop production.

Adapting crop rotations to climate change in regional impact modelling assessments.

PubMed

Teixeira, Edmar I; de Ruiter, John; Ausseil, Anne-Gaelle; Daigneault, Adam; Johnstone, Paul; Holmes, Allister; Tait, Andrew; Ewert, Frank

2018-03-01

The environmental and economic sustainability of future cropping systems depends on adaptation to climate change. Adaptation studies commonly rely on agricultural systems models to integrate multiple components of production systems such as crops, weather, soil and farmers' management decisions. Previous adaptation studies have mostly focused on isolated monocultures. However, in many agricultural regions worldwide, multi-crop rotations better represent local production systems. It is unclear how adaptation interventions influence crops grown in sequences. We develop a catchment-scale assessment to investigate the effects of tactical adaptations (choice of genotype and sowing date) on yield and underlying crop-soil factors of rotations. Based on locally surveyed data, a silage-maize followed by catch-crop-wheat rotation was simulated with the APSIM model for the RCP 8.5 emission scenario, two time periods (1985-2004 and 2080-2100) and six climate models across the Kaituna catchment in New Zealand. Results showed that direction and magnitude of climate change impacts, and the response to adaptation, varied spatially and were affected by rotation carryover effects due to agronomical (e.g. timing of sowing and harvesting) and soil (e.g. residual nitrogen, N) aspects. For example, by adapting maize to early-sowing dates under a warmer climate, there was an advance in catch crop establishment which enhanced residual soil N uptake. This dynamics, however, differed with local environment and choice of short- or long-cycle maize genotypes. Adaptation was insufficient to neutralize rotation yield losses in lowlands but consistently enhanced yield gains in highlands, where other constraints limited arable cropping. The positive responses to adaptation were mainly due to increases in solar radiation interception across the entire growth season. These results provide deeper insights on the dynamics of climate change impacts for crop rotation systems. Such knowledge can be used to develop improved regional impact assessments for situations where multi-crop rotations better represent predominant agricultural systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives.

PubMed

Yang, Guijun; Liu, Jiangang; Zhao, Chunjiang; Li, Zhenhong; Huang, Yanbo; Yu, Haiyang; Xu, Bo; Yang, Xiaodong; Zhu, Dongmei; Zhang, Xiaoyan; Zhang, Ruyang; Feng, Haikuan; Zhao, Xiaoqing; Li, Zhenhai; Li, Heli; Yang, Hao

2017-01-01

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

PubMed Central

Yang, Guijun; Liu, Jiangang; Zhao, Chunjiang; Li, Zhenhong; Huang, Yanbo; Yu, Haiyang; Xu, Bo; Yang, Xiaodong; Zhu, Dongmei; Zhang, Xiaoyan; Zhang, Ruyang; Feng, Haikuan; Zhao, Xiaoqing; Li, Zhenhai; Li, Heli; Yang, Hao

2017-01-01

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping. PMID:28713402

A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, Katherine M.; Bell, Andrew M.; Barber, Quinn E.; Kourakos, George; Harter, Thomas

2018-05-01

This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Nitrogen loading rates for several crop types have been measured based on field-scale experiments, and recent research has calculated nitrogen loading rates for crops throughout the Central Valley based on a mass balance approach. However, research is lacking to infer nitrogen loading rates for the broad diversity of crop and land-use types directly from groundwater nitrate measurements. Relating groundwater nitrate measurements to specific crops must account for the uncertainty about and multiplicity in contributing crops (and other land uses) to individual well measurements, and for the variability of nitrogen loading within farms and from farm to farm for the same crop type. In this study, we developed a Bayesian regression model that allowed us to estimate land-use-specific groundwater nitrogen loading rate probability distributions for 15 crop and land-use groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water and natural, rice, and alfalfa and pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kg N ha-1 yr-1. Confined animal feeding operations (dairies) and citrus and subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kg N ha-1 yr-1, respectively. In general, our probability-based estimates compare favorably with previous direct measurements and with mass-balance-based estimates of nitrogen loading. Nitrogen mass-balance-based estimates are larger than our groundwater nitrate derived estimates for manured and nonmanured forage, nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to be due to groundwater age mixing, dilution from infiltrating river water, or denitrification between the time when nitrogen leaves the root zone (point of reference for mass-balance-derived loading) and the time and location of groundwater measurement.

A Fast Track approach to deal with the temporal dimension of crop water footprint

NASA Astrophysics Data System (ADS)

Tuninetti, Marta; Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

2017-07-01

Population growth, socio-economic development and climate changes are placing increasing pressure on water resources. Crop water footprint is a key indicator in the quantification of such pressure. It is determined by crop evapotranspiration and crop yield, which can be highly variable in space and time. While the spatial variability of crop water footprint has been the objective of several investigations, the temporal variability remains poorly studied. In particular, some studies approached this issue by associating the time variability of crop water footprint only to yield changes, while considering evapotranspiration patterns as marginal. Validation of this Fast Track approach has yet to be provided. In this Letter we demonstrate its feasibility through a comprehensive validation, an assessment of its uncertainty, and an example of application. Our results show that the water footprint changes are mainly driven by yield trends, while evapotranspiration plays a minor role. The error due to considering constant evapotranspiration is three times smaller than the uncertainty of the model used to compute the crop water footprint. These results confirm the suitability of the Fast Track approach and enable a simple, yet appropriate, evaluation of time-varying crop water footprint.

Relay cropping as a sustainable approach: problems and opportunities for sustainable crop production.

PubMed

Tanveer, Mohsin; Anjum, Shakeel Ahmad; Hussain, Saddam; Cerdà, Artemi; Ashraf, Umair

2017-03-01

Climate change, soil degradation, and depletion of natural resources are becoming the most prominent challenges for crop productivity and environmental sustainability in modern agriculture. In the scenario of conventional farming system, limited chances are available to cope with these issues. Relay cropping is a method of multiple cropping where one crop is seeded into standing second crop well before harvesting of second crop. Relay cropping may solve a number of conflicts such as inefficient use of available resources, controversies in sowing time, fertilizer application, and soil degradation. Relay cropping is a complex suite of different resource-efficient technologies, which possesses the capability to improve soil quality, to increase net return, to increase land equivalent ratio, and to control the weeds and pest infestation. The current review emphasized relay cropping as a tool for crop diversification and environmental sustainability with special focus on soil. Briefly, benefits, constraints, and opportunities of relay cropping keeping the goals of higher crop productivity and sustainability have also been discussed in this review. The research and knowledge gap in relay cropping was also highlighted in order to guide the further studies in future.

Productivity and sustainability of rainfed wheat-soybean system in the North China Plain: results from a long-term experiment and crop modelling

PubMed Central

Qin, Wei; Wang, Daozhong; Guo, Xisheng; Yang, Taiming; Oenema, Oene

2015-01-01

A quantitative understanding of yield response to water and nutrients is key to improving the productivity and sustainability of rainfed cropping systems. Here, we quantified the effects of rainfall, fertilization (NPK) and soil organic amendments (with straw and manure) on yields of a rainfed wheat-soybean system in the North China Plain (NCP), using 30-years’ field experimental data (1982–2012) and the simulation model-AquaCrop. On average, wheat and soybean yields were 5 and 2.5 times higher in the fertilized treatments than in the unfertilized control (CK), respectively. Yields of fertilized treatments increased and yields of CK decreased over time. NPK + manure increased yields more than NPK alone or NPK + straw. The additional effect of manure is likely due to increased availability of K and micronutrients. Wheat yields were limited by rainfall and can be increased through soil mulching (15%) or irrigation (35%). In conclusion, combined applications of fertilizer NPK and manure were more effective in sustaining high crop yields than recommended fertilizer NPK applications. Manure applications led to strong accumulation of NPK and relatively low NPK use efficiencies. Water deficiency in wheat increased over time due to the steady increase in yields, suggesting that the need for soil mulching increases. PMID:26627707

Impacts of climate change on cropping patterns in a tropical, sub-humid watershed

PubMed Central

Zwart, Sander J.; Hein, Lars

2018-01-01

In recent decades, there have been substantial increases in crop production in sub-Saharan Africa (SSA) as a result of higher yields, increased cropping intensity, expansion of irrigated cropping systems, and rainfed cropland expansion. Yet, to date much of the research focus of the impact of climate change on crop production in the coming decades has been on crop yield responses. In this study, we analyse the impact of climate change on the potential for increasing rainfed cropping intensity through sequential cropping and irrigation expansion in central Benin. Our approach combines hydrological modelling and scenario analysis involving two Representative Concentration Pathways (RCPs), two water-use scenarios for the watershed based on the Shared Socioeconomic Pathways (SSPs), and environmental water requirements leading to sustained streamflow. Our analyses show that in Benin, warmer temperatures will severely limit crop production increases achieved through the expansion of sequential cropping. Depending on the climate change scenario, between 50% and 95% of cultivated areas that can currently support sequential cropping or will need to revert to single cropping. The results also show that the irrigation potential of the watershed will be at least halved by mid-century in all scenario combinations. Given the urgent need to increase crop production to meet the demands of a growing population in SSA, our study outlines challenges and the need for planned development that need to be overcome to improve food security in the coming decades. PMID:29513753

An Individual-Based Model of the Evolution of Pesticide Resistance in Heterogeneous Environments: Control of Meligethes aeneus Population in Oilseed Rape Crops

PubMed Central

Stratonovitch, Pierre; Elias, Jan; Denholm, Ian; Slater, Russell; Semenov, Mikhail A.

2014-01-01

Preventing a pest population from damaging an agricultural crop and, at the same time, preventing the development of pesticide resistance is a major challenge in crop protection. Understanding how farming practices and environmental factors interact with pest characteristics to influence the spread of resistance is a difficult and complex task. It is extremely challenging to investigate such interactions experimentally at realistic spatial and temporal scales. Mathematical modelling and computer simulation have, therefore, been used to analyse resistance evolution and to evaluate potential resistance management tactics. Of the many modelling approaches available, individual-based modelling of a pest population offers most flexibility to include and analyse numerous factors and their interactions. Here, a pollen beetle (Meligethes aeneus) population was modelled as an aggregate of individual insects inhabiting a spatially heterogeneous landscape. The development of the pest and host crop (oilseed rape) was driven by climatic variables. The agricultural land of the landscape was managed by farmers applying a specific rotation and crop protection strategy. The evolution of a single resistance allele to the pyrethroid lambda cyhalothrin was analysed for different combinations of crop management practices and for a recessive, intermediate and dominant resistance allele. While the spread of a recessive resistance allele was severely constrained, intermediate or dominant resistance alleles showed a similar response to the management regime imposed. Calendar treatments applied irrespective of pest density accelerated the development of resistance compared to ones applied in response to prescribed pest density thresholds. A greater proportion of spring-sown oilseed rape was also found to increase the speed of resistance as it increased the period of insecticide exposure. Our study demonstrates the flexibility and power of an individual-based model to simulate how farming practices affect pest population dynamics, and the consequent impact of different control strategies on the risk and speed of resistance development. PMID:25531104

An individual-based model of the evolution of pesticide resistance in heterogeneous environments: control of Meligethes aeneus population in oilseed rape crops.

PubMed

Stratonovitch, Pierre; Elias, Jan; Denholm, Ian; Slater, Russell; Semenov, Mikhail A

2014-01-01

Preventing a pest population from damaging an agricultural crop and, at the same time, preventing the development of pesticide resistance is a major challenge in crop protection. Understanding how farming practices and environmental factors interact with pest characteristics to influence the spread of resistance is a difficult and complex task. It is extremely challenging to investigate such interactions experimentally at realistic spatial and temporal scales. Mathematical modelling and computer simulation have, therefore, been used to analyse resistance evolution and to evaluate potential resistance management tactics. Of the many modelling approaches available, individual-based modelling of a pest population offers most flexibility to include and analyse numerous factors and their interactions. Here, a pollen beetle (Meligethes aeneus) population was modelled as an aggregate of individual insects inhabiting a spatially heterogeneous landscape. The development of the pest and host crop (oilseed rape) was driven by climatic variables. The agricultural land of the landscape was managed by farmers applying a specific rotation and crop protection strategy. The evolution of a single resistance allele to the pyrethroid lambda cyhalothrin was analysed for different combinations of crop management practices and for a recessive, intermediate and dominant resistance allele. While the spread of a recessive resistance allele was severely constrained, intermediate or dominant resistance alleles showed a similar response to the management regime imposed. Calendar treatments applied irrespective of pest density accelerated the development of resistance compared to ones applied in response to prescribed pest density thresholds. A greater proportion of spring-sown oilseed rape was also found to increase the speed of resistance as it increased the period of insecticide exposure. Our study demonstrates the flexibility and power of an individual-based model to simulate how farming practices affect pest population dynamics, and the consequent impact of different control strategies on the risk and speed of resistance development.

Modeling and control for closed environment plant production systems

NASA Technical Reports Server (NTRS)

Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)

2002-01-01

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

Experimental evidence that wildflower strips increase pollinator visits to crops.

PubMed

Feltham, Hannah; Park, Kirsty; Minderman, Jeroen; Goulson, Dave

2015-08-01

Wild bees provide a free and potentially diverse ecosystem service to farmers growing pollination-dependent crops. While many crops benefit from insect pollination, soft fruit crops, including strawberries are highly dependent on this ecosystem service to produce viable fruit. However, as a result of intensive farming practices and declining pollinator populations, farmers are increasingly turning to commercially reared bees to ensure that crops are adequately pollinated throughout the season. Wildflower strips are a commonly used measure aimed at the conservation of wild pollinators. It has been suggested that commercial crops may also benefit from the presence of noncrop flowers; however, the efficacy and economic benefits of sowing flower strips for crops remain relatively unstudied. In a study system that utilizes both wild and commercial pollinators, we test whether wildflower strips increase the number of visits to adjacent commercial strawberry crops by pollinating insects. We quantified this by experimentally sowing wildflower strips approximately 20 meters away from the crop and recording the number of pollinator visits to crops with, and without, flower strips. Between June and August 2013, we walked 292 crop transects at six farms in Scotland, recording a total of 2826 pollinators. On average, the frequency of pollinator visits was 25% higher for crops with adjacent flower strips compared to those without, with a combination of wild and commercial bumblebees (Bombus spp.) accounting for 67% of all pollinators observed. This effect was independent of other confounding effects, such as the number of flowers on the crop, date, and temperature. Synthesis and applications. This study provides evidence that soft fruit farmers can increase the number of pollinators that visit their crops by sowing inexpensive flower seed mixes nearby. By investing in this management option, farmers have the potential to increase and sustain pollinator populations over time.

Experimental evidence that wildflower strips increase pollinator visits to crops

PubMed Central

Feltham, Hannah; Park, Kirsty; Minderman, Jeroen; Goulson, Dave

2015-01-01

Wild bees provide a free and potentially diverse ecosystem service to farmers growing pollination-dependent crops. While many crops benefit from insect pollination, soft fruit crops, including strawberries are highly dependent on this ecosystem service to produce viable fruit. However, as a result of intensive farming practices and declining pollinator populations, farmers are increasingly turning to commercially reared bees to ensure that crops are adequately pollinated throughout the season. Wildflower strips are a commonly used measure aimed at the conservation of wild pollinators. It has been suggested that commercial crops may also benefit from the presence of noncrop flowers; however, the efficacy and economic benefits of sowing flower strips for crops remain relatively unstudied. In a study system that utilizes both wild and commercial pollinators, we test whether wildflower strips increase the number of visits to adjacent commercial strawberry crops by pollinating insects. We quantified this by experimentally sowing wildflower strips approximately 20 meters away from the crop and recording the number of pollinator visits to crops with, and without, flower strips. Between June and August 2013, we walked 292 crop transects at six farms in Scotland, recording a total of 2826 pollinators. On average, the frequency of pollinator visits was 25% higher for crops with adjacent flower strips compared to those without, with a combination of wild and commercial bumblebees (Bombus spp.) accounting for 67% of all pollinators observed. This effect was independent of other confounding effects, such as the number of flowers on the crop, date, and temperature. Synthesis and applications. This study provides evidence that soft fruit farmers can increase the number of pollinators that visit their crops by sowing inexpensive flower seed mixes nearby. By investing in this management option, farmers have the potential to increase and sustain pollinator populations over time. PMID:26380683

Comparing soil functions for a wide range of agriculture soils focusing on production for bioenergy using a combined isotope-based observation and modelling approach

NASA Astrophysics Data System (ADS)

Leistert, Hannes; Herbstritt, Barbara; Weiler, Markus

2017-04-01

Increase crop production for bioenergy will result in changes in land use and the resulting soil functions and may generate new chances and risks. However, detailed data and information are still missing how soil function may be altered under changing crop productions for bioenergy, in particular for a wide range of agricultural soils since most data are currently derived from individual experimental sites studying different bioenergy crops at one location. We developed a new, rapid measurement approach to investigate the influence of bioenergy plants on the water cycle and different soil functions (filter and buffer of water and N-cycling). For this approach, we drilled 89 soil cores (1-3 m deep) in spring and fall at 11 sites with different soil properties and climatic conditions comparing different crops (grass, corn, willow, poplar, and other less common bioenergy crops) and analyzing 1150 soil samples for water content, nitrate concentration and stable water isotopes. We benchmarked a soil hydrological model (1-D numerical Richards equation, ADE, water isotope fractionation including liquid and vapor composition of isotopes) using longer-term climate variables and water isotopes in precipitation to derive crop specific parameterization and to specifically validate the differences in water transport and water partitioning into evaporation, transpiration and groundwater recharge among the sites and crops using the water isotopes in particular. The model simulation were in good agreement with the observed isotope profiles and allowed us to differentiate among the different crops. We defined different indicators for the soil functions considered in this study. These indicators included the proportion of groundwater recharge, transit time of water (different percentiles) though the upper 2m and nutrient leaching potential (e.g. nitrate) during the dormant season from the rooting zone. The parameterized model was first used to calculate the indicators for the sampled locations and to derive the changes in soil functions by altering the land cover among the different bioenergy crops in comparison to the grassland as a reference. We could show that percolation is strongly influenced by the crops and climate, the transit time is influenced by a combination of soil type, climate and land use, but the effect of soil type is very strong and the nitrate leaching is strongly influenced by soil type. The high variability of transit times and nitrate leaching are due to high variability of the temporal distribution of precipitation. Finally, the model was used to regionalized the indicators to a wide range of soils in the state of Baden-Württemberg and to assess if there are locations where bioenergy crops may improve the considered soil function. Our idea behind this was to propose location where specific bioenergy crops may be highly suitable to improve the current soil function to increase for example the protection of groundwater for drinking water, reduce erosion risk or increase water availability. The proposed method allows to assess the influence of different bioenergy crops on soil functions without costly multi-year measurement systems for assessing the soil functions using soil water content measurements or/and soil water suction devices.

One crop breeding cycle from starvation? How engineering crop photosynthesis for rising CO2 and temperature could be one important route to alleviation.

PubMed

Kromdijk, Johannes; Long, Stephen P

2016-03-16

Global climate change is likely to severely impact human food production. This comes at a time when predicted demand for primary foodstuffs by a growing human population and changing global diets is already outpacing a stagnating annual rate of increase in crop productivity. Additionally, the time required by crop breeding and bioengineering to release improved varieties to farmers is substantial, meaning that any crop improvements needed to mitigate food shortages in the 2040s would need to start now. In this perspective, the rationale for improvements in photosynthetic efficiency as a breeding objective for higher yields is outlined. Subsequently, using simple simulation models it is shown how predicted changes in temperature and atmospheric [CO2] affect leaf photosynthetic rates. The chloroplast accounts for the majority of leaf nitrogen in crops. Within the chloroplast about 25% of nitrogen is invested in the carboxylase, Rubisco, which catalyses the first step of CO2 assimilation. Most of the remaining nitrogen is invested in the apparatus to drive carbohydrate synthesis and regenerate ribulose-1:5-bisphosphate (RuBP), the CO2-acceptor molecule at Rubisco. At preindustrial [CO2], investment in these two aspects may have been balanced resulting in co-limitation. At today's [CO2], there appears to be over-investment in Rubisco, and despite the counter-active effects of rising temperature and [CO2], this imbalance is predicted to worsen with global climate change. By breeding or engineering restored optimality under future conditions increased productivity could be achieved in both tropical and temperate environments without additional nitrogen fertilizer. Given the magnitude of the potential shortfall, better storage conditions, improved crop management and better crop varieties will all be needed. With the short time-scale at which food demand is expected to outpace supplies, all available technologies to improve crop varieties, from classical crop breeding to crop genetic engineering should be employed. This will require vastly increased public and private investment to support translation of first discovery in laboratories to replicated field trials, and an urgent re-evaluation of regulation of crop genetic engineering. © 2016 The Authors.

Enhancing USDA's Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H. D.

2013-12-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attachés, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB has often, as part of its operational process, used historical time series of surface-based precipitation observations to visually identify growing seasons with similar (analog) weather patterns as, and help estimate crop yields for, the current growing season. As part of a larger effort to improve WAOB estimates by integrating NASA remote sensing observations and research results into WAOB's decision-making environment, a more rigorous, statistical method for identifying analog years was developed. This method, termed the analog index (AI), is based on the Nash-Sutcliffe model efficiency coefficient. The AI was computed for five study areas and six growing seasons of data analyzed (2003-2007 as potential analog years and 2008 as the target year). Previously reported results compared the performance of AI for time series derived from surface-based observations vs. satellite-retrieved precipitation data. Those results showed that, for all five areas, crop yield estimates derived from satellite-retrieved precipitation data are closer to measured yields than are estimates derived from surface-based precipitation observations. Subsequent work has compared the relative performance of AI for time series derived from satellite-retrieved surface soil moisture data and from root zone soil moisture derived from the assimilation of surface soil moisture data into a land surface model. These results, which also showed the potential benefits of satellite data for analog year analyses, will be presented.

Agricultural Industry Advanced Vehicle Technology: Benchmark Study for Reduction in Petroleum Use

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roger Hoy

2014-09-01

Diesel use on farms in the United States has remained relatively constant since 1985, decreasing slightly in 2009, which may be attributed to price increases and the economic recession. During this time, the United States’ harvested area also has remained relatively constant at roughly 300 million acres. In 2010, farm diesel use was 5.4% of the total United States diesel use. Crops accounting for an estimated 65% of United States farm diesel use include corn, soybean, wheat, hay, and alfalfa, respectively, based on harvested crop area and a recent analysis of estimated fuel use by crop. Diesel use in thesemore » cropping systems primarily is from tillage, harvest, and various other operations (e.g., planting and spraying) (Figure 3). Diesel efficiency is markedly variable due to machinery types, conditions of operation (e.g., soil type and moisture), and operator variability. Farm diesel use per acre has slightly decreased in the last two decades and diesel is now estimated to be less than 5% of farm costs per acre. This report will explore current trends in increasing diesel efficiency in the farm sector. The report combines a survey of industry representatives, a review of literature, and data analysis to identify nascent technologies for increasing diesel efficiency« less

Estimating yield gaps at the cropping system level.

PubMed

Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G

2017-05-01

Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems ( e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.

Integrated weed management strategies in cover crop-based, organic rotational no-till corn and soybean in the mid-Atlantic region

USDA-ARS?s Scientific Manuscript database

Cover crop-based, organic rotational no-till (CCORNT) corn and soybean systems have been developed in the mid-Atlantic region to build soil health, increase management flexibility, and reduce labor. In this system, a roll-crimped cover crop mulch provides within-season weed suppression in no-till co...

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

Management of Lignite Fly Ash for Improving Soil Fertility and Crop Productivity

NASA Astrophysics Data System (ADS)

Ram, Lal C.; Srivastava, Nishant K.; Jha, Sangeet K.; Sinha, Awadhesh K.; Masto, Reginald E.; Selvi, Vetrivel A.

2007-09-01

Lignite fly ash (LFA), being alkaline and endowed with excellent pozzolanic properties, a silt loam texture, and plant nutrients, has the potential to improve soil quality and productivity. Long-term field trials with groundnut, maize, and sun hemp were carried out to study the effect of LFA on growth and yield. Before crop I was sown, LFA was applied at various doses with and without press mud (an organic waste from the sugar industry, used as an amendment and source of nutrients). LFA with and without press mud was also applied before crops III and V were cultivated. Chemical fertilizer, along with gypsum, humic acid, and biofertilizer, was applied in all treatments, including the control. With one-time and repeat applications of LFA (with and without press mud), yield increased significantly (7.0-89.0%) in relation to the control crop. The press mud enhanced the yield (3.0-15.0%) with different LFA applications. The highest yield LFA dose was 200 t/ha for one-time and repeat applications, the maximum yield being with crop III (combination treatment). One-time and repeat application of LFA (alone and in combination with press mud) improved soil quality and the nutrient content of the produce. The highest dose of LFA (200 t/ha) with and without press mud showed the best residual effects (eco-friendly increases in the yield of succeeding crops). Some increase in trace- and heavy-metal contents and in the level of γ-emitters in soil and crop produce, but well within permissible limits, was observed. Thus, LFA can be used on a large scale to boost soil fertility and productivity with no adverse effects on the soil or crops, which may solve the problem of bulk disposal of fly ash in an eco-friendly manner.

Plant Adaptation to Acid Soils: The Molecular Basis for Crop Aluminum Resistance.

PubMed

Kochian, Leon V; Piñeros, Miguel A; Liu, Jiping; Magalhaes, Jurandir V

2015-01-01

Aluminum (Al) toxicity in acid soils is a significant limitation to crop production worldwide, as approximately 50% of the world's potentially arable soil is acidic. Because acid soils are such an important constraint to agriculture, understanding the mechanisms and genes conferring resistance to Al toxicity has been a focus of intense research interest in the decade since the last article on crop acid soil tolerance was published in this journal. An impressive amount of progress has been made during that time that has greatly increased our understanding of the diversity of Al resistance genes and mechanisms, how resistance gene expression is regulated and triggered by Al and Al-induced signals, and how the proteins encoded by these genes function and are regulated. This review examines the state of our understanding of the physiological, genetic, and molecular bases for crop Al tolerance, looking at the novel Al resistance genes and mechanisms that have been identified over the past ten years. Additionally, it examines how the integration of molecular and genetic analyses of crop Al resistance is starting to be exploited for the improvement of crop plants grown on acid soils via both molecular-assisted breeding and biotechnology approaches.

Variable-rate nitrogen application algorithm based on canopy reflected spectrum and its influence on wheat

NASA Astrophysics Data System (ADS)

Liang, Hongxia; Zhao, Chunjiang; Huang, Wenjiang; Liu, Liangyun; Wang, Jihua; Ma, Youhua

2005-01-01

This study was to develop the time-specific and time-critical method to overcome the limitations of traditional field sampling methods for variable rate fertilization. Farmers, agricultural managers and grain processing enterprises are interested in measuring and assessing soil and crop status in order to apply adequate fertilizer quantities to crop growth. This paper focused on studying the relationship between vegetation index (OSAVI) and nitrogen content to determine the amount of nitrogen fertilizer recommended for variable rate management in precision agriculture. The traditional even rate fertilizer management was chosen as the CK. The grain yield, ear numbers, 1000-grain weight and grain protein content were measured among the CK, uniform treatments and variable rate fertilizer treatments. It indicated that variable rate fertilization reduced the variability of wheat yield, ear numbers and dry biomass, but it didn't increased crop yield and grain protein content significantly and did not decrease the variety of 1000-grain weight, compared to traditional rate application. The nitrogen fertilizer use efficiency was improved, for this purpose, the variable rate technology based on vegetation index could be used to prevent under ground water pollution and environmental deterioration.

Impacts of enhanced fertilizer applications on tropospheric ozone and crop damage over sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Huang, Yaoxian; Hickman, Jonathan E.; Wu, Shiliang

2018-05-01

Fertilizer-induced nitrogen oxides (NOx) emissions in sub-Saharan Africa are expected to increase substantially in the coming decades, driven by increasing application of fertilizers to increase crop yields in an effort to attain food security across the continent. In many parts of sub-Saharan Africa, surface ozone (O3) is sensitive to increasing atmospheric concentrations of NOx. In this study, we employ the GEOS-Chem chemical transport model to conduct a preliminary investigation of the impacts on O3 air quality and the consequential crop damage associated with increasing fertilizer-induced NOx emissions in sub-Saharan Africa. Our simulation results, constrained by field NO flux measurements for the years 2011 and 2012 in response to a variety of fertilizer application rates in western Kenya, show that the enhancements in NO flux with fertilizer application rate of 150 kg N ha-1 can increase surface NOx and O3 concentrations by up to 0.36 and 2.8 ppbv respectively during the growing season. At the same time, accumulated O3 exposure during the crop growing season (expressed as AOT40 values) could increase by up to 496 ppb h, leading to crop yield decline of about 0.8% for O3-sensitive crops. Our results suggest that, when accounting for the consequential impacts on surface O3 air quality and crop damage over sub-Saharan Africa, agricultural intensification is possible without substantial impacts on crop productivity because the relatively small decline of crop yield resulting from O3 damage appears unlikely to outweigh the gain in crop yield from fertilization.

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

USGS Publications Warehouse

Thornton, P. K.; Bowen, W. T.; Ravelo, A.C.; Wilkens, P. W.; Farmer, G.; Brock, J.; Brink, J. E.

1997-01-01

Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet (Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

Manipulating microRNAs for improved biomass and biofuels from plant feedstocks.

PubMed

Trumbo, Jennifer Lynn; Zhang, Baohong; Stewart, Charles Neal

2015-04-01

Petroleum-based fuels are nonrenewable and unsustainable. Renewable sources of energy, such as lignocellulosic biofuels and plant metabolite-based drop-in fuels, can offset fossil fuel use and reverse environmental degradation through carbon sequestration. Despite these benefits, the lignocellulosic biofuels industry still faces many challenges, including the availability of economically viable crop plants. Cell wall recalcitrance is a major economic barrier for lignocellulosic biofuels production from biomass crops. Sustainability and biomass yield are two additional, yet interrelated, foci for biomass crop improvement. Many scientists are searching for solutions to these problems within biomass crop genomes. MicroRNAs (miRNAs) are involved in almost all biological and metabolic process in plants including plant development, cell wall biosynthesis and plant stress responses. Because of the broad functions of their targets (e.g. auxin response factors), the alteration of plant miRNA expression often results in pleiotropic effects. A specific miRNA usually regulates a biologically relevant bioenergy trait. For example, relatively low miR156 overexpression leads to a transgenic feedstock with enhanced biomass and decreased recalcitrance. miRNAs have been overexpressed in dedicated bioenergy feedstocks such as poplar and switchgrass yielding promising results for lignin reduction, increased plant biomass, the timing of flowering and response to harsh environments. In this review, we present the status of miRNA-related research in several major biofuel crops and relevant model plants. We critically assess published research and suggest next steps for miRNA manipulation in feedstocks for increased biomass and sustainability for biofuels and bioproducts. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Water-centric nexus for response to climate change on agriculture and forest sector: The case of the Korean Peninsula

NASA Astrophysics Data System (ADS)

Lim, C. H.; Choi, Y.; Jeon, S. W.; Lee, W. K.

2017-12-01

Given their complexity and the number of stakeholders involved, it is difficult to solve social issues or problems based on an analysis that focuses on a single dimension. In particular, research surrounding climate change is inherently multidisciplinary and there is a need for highly pluralistic nexuses that can be used as a framework for policy decisions. Here, we suggest to water-centric nexus on agriculture and forest sector to improve response to climate change. The nexus is composed agricultural water demand and forest water supply to enhancing water-related adaptation to climate change in the Korean Peninsula. Agricultural productivity and water use related variables was estimating by EPIC crop model, and InVEST model applied for estimation of forest water supply. Results under two climate change scenarios (RCP4.5 and 8.5) and time period (2050s and 2070s), the forest water supply for the all future climate scenarios will increase significantly. In case of agriculture, irrigated crops experienced only the benefits of climate change, but rainfed crops were negatively impacted. It was also found that crop irrigation demand in the future is expected to be around twice as high as baseline levels, thus making irrigation more difficult to successfully implement. These hydrological threats have the potential to greatly reduce food security. In the nexus perspectives, the drop in the productivity of rainfed crops and the increase in irrigation demand in the agriculture sector can be resolved through interconnections with the forest sector. Appropriate management of the water supply in future climatic conditions characterized by increasing precipitation can maintain and expand agricultural areas through irrigation. To achieve this, a time-series water supply versus demand analysis must be performed so that an accurate balance between supply and demand can be established. Water-centric interactions of the agriculture and forest are the basis of nexus-based adaptation and they can suggest effective climate change responses for the Korean peninsula. In particular, this approach will be effective in transforming sectors that experience trade-offs into ones that promote synergies.

Cultivar Mixture Cropping Increased Water Use Efficiency in Winter Wheat under Limited Irrigation Conditions

PubMed Central

Wang, Yunqi; Zhang, Yinghua; Ji, Wei; Yu, Peng; Wang, Bin; Li, Jinpeng; Han, Meikun; Xu, Xuexin; Wang, Zhimin

2016-01-01

The effects of cultivar mixture cropping on yield, biomass, and water use efficiency (WUE) in winter wheat (Triticum aestivum L.) were investigated under non-irrigation (W0, no irrigation during growth stage), one time irrigation (W1, irrigation applied at stem elongation) and two times irrigation (W2, irrigation applied at stem elongation and anthesis) conditions. Nearly 90% of cultivar mixture cropping treatments experienced an increase in grain yield as compared with the mean of the pure stands under W0, those for W1 and W2 were 80% and 85%, respectively. Over 75% of cultivar mixture cropping treatments got greater biomass than the mean of the pure stands under the three irrigation conditions. Cultivar mixture cropping cost more water than pure stands under W0 and W1, whereas the water consumption under W2 decreased by 5.9%–6.8% as compared with pure stands. Approximately 90% of cultivar mixtures showed an increase of 5.4%–34.5% in WUE as compared with the mean of the pure stands, and about 75% of cultivar mixtures had 0.8%–28.5% higher WUE than the better pure stands under W0. Similarly, there were a majority of mixture cropping treatments with higher WUE than the mean and the better one of the pure stands under W1 and W2. On the whole, proper cultivar mixture cropping could increase yield and WUE, and a higher increase in WUE occurred under limited irrigation condition. PMID:27362563

The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

2017-04-01

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

Analysis of climate signals in the crop yield record of sub-Saharan Africa.

PubMed

Hoffman, Alexis L; Kemanian, Armen R; Forest, Chris E

2018-01-01

Food security and agriculture productivity assessments in sub-Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale-compatible climate data for the 1962-2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates. © 2017 John Wiley & Sons Ltd.

Subsistence Agriculture versus Cash Cropping: The Social Repercussions.

ERIC Educational Resources Information Center

Rennie, Sandra Joy

1991-01-01

The introduction of cash cropping in the Solomon Islands and Tonga has had negative effects on women, leading to deterioration in their status, decreased leisure time, fewer opportunities to earn cash, increased birth rate (to help with the increased workload), and more sharply defined sex roles. (SV)

Biotech crops: imperative for achieving the millenium development goals and sustainability of agriculture in the climate change era.

PubMed

Husaini, Amjad M; Tuteja, Narendra

2013-01-01

Biotechnological intervention in the development of crops has opened new vistas in agriculture. Central to the accomplishment of the Millennium Development Goals (MDGs), biotech-agriculture is essential in meeting these targets. Biotech crops have already made modest contributions toward ensuring food and nutrition security by reducing losses and increasing productivity, with less pesticide input. These crops could help address some of the major challenges in agriculture-based economies created by climate change. Projections of global climate change expect the concentration of greenhouse gases to increase, aridization of the environment to increase, temperature fluctuations to occur sharply and frequently, and spatial and temporal distribution of rainfall to be disturbed-all of which will increase abiotic stress-related challenges to crops. Countering these challenges and to meet the food requirement of the ever-increasing world population (expected to reach 9 billion by 2030) we need to (1) develop and use biotech crops for mitigating adverse climatic changes; (2) develop biotech crops resilient to adverse environmental conditions; and (3) address the issues/non-issues raised by NGO's and educate the masses about the benefits of biotech crops.

Soil management practices under organic farming

NASA Astrophysics Data System (ADS)

Aly, Adel; Chami Ziad, Al; Hamdy, Atef

2015-04-01

Organic farming methods combine scientific knowledge of ecology and modern technology with traditional farming practices based on naturally occurring biological processes. Soil building practices such as crop rotations, intercropping, symbiotic associations, cover crops, organic fertilizers and minimum tillage are central to organic practices. Those practices encourage soil formation and structure and creating more stable systems. In farm nutrient and energy cycling is increased and the retentive abilities of the soil for nutrients and water are enhanced. Such management techniques also play an important role in soil erosion control. The length of time that the soil is exposed to erosive forces is decreased, soil biodiversity is increased, and nutrient losses are reduced, helping to maintain and enhance soil productivity. Organic farming as systematized and certifiable approach for agriculture, there is no surprise that it faces some challenges among both farmers and public sector. This can be clearly demonstrated particularly in the absence of the essential conditions needed to implement successfully the soil management practices like green manure and composting to improve soil fertility including crop rotation, cover cropping and reduced tillage. Those issues beside others will be fully discussed highlighting their beneficial impact on the environmental soil characteristics. Keywords: soil fertility, organic matter, plant nutrition

AgMIP: Next Generation Models and Assessments

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2014-12-01

Next steps in developing next-generation crop models fall into several categories: significant improvements in simulation of important crop processes and responses to stress; extension from simplified crop models to complex cropping systems models; and scaling up from site-based models to landscape, national, continental, and global scales. Crop processes that require major leaps in understanding and simulation in order to narrow uncertainties around how crops will respond to changing atmospheric conditions include genetics; carbon, temperature, water, and nitrogen; ozone; and nutrition. The field of crop modeling has been built on a single crop-by-crop approach. It is now time to create a new paradigm, moving from 'crop' to 'cropping system.' A first step is to set up the simulation technology so that modelers can rapidly incorporate multiple crops within fields, and multiple crops over time. Then the response of these more complex cropping systems can be tested under different sustainable intensification management strategies utilizing the updated simulation environments. Model improvements for diseases, pests, and weeds include developing process-based models for important diseases, frameworks for coupling air-borne diseases to crop models, gathering significantly more data on crop impacts, and enabling the evaluation of pest management strategies. Most smallholder farming in the world involves integrated crop-livestock systems that cannot be represented by crop modeling alone. Thus, next-generation cropping system models need to include key linkages to livestock. Livestock linkages to be incorporated include growth and productivity models for grasslands and rangelands as well as the usual annual crops. There are several approaches for scaling up, including use of gridded models and development of simpler quasi-empirical models for landscape-scale analysis. On the assessment side, AgMIP is leading a community process for coordinated contributions to IPCC AR6 that involves the key modeling groups from around the world including North America, Europe, South America, Sub-Saharan Africa, South Asia, East Asia, and Australia and Oceania. This community process will lead to mutually agreed protocols for coordinated global and regional assessments.

Crop yield monitoring in the Sahel using root zone soil moisture anomalies derived from SMOS soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Gibon, François; Pellarin, Thierry; Alhassane, Agali; Traoré, Seydou; Baron, Christian

2017-04-01

West Africa is greatly vulnerable, especially in terms of food sustainability. Mainly based on rainfed agriculture, the high variability of the rainy season strongly impacts the crop production driven by the soil water availability in the soil. To monitor this water availability, classical methods are based on daily precipitation measurements. However, the raingauge network suffers from the poor network density in Africa (1/10000km2). Alternatively, real-time satellite-derived precipitations can be used, but they are known to suffer from large uncertainties which produce significant error on crop yield estimations. The present study proposes to use root soil moisture rather than precipitation to evaluate crop yield variations. First, a local analysis of the spatiotemporal impact of water deficit on millet crop production in Niger was done, from in-situ soil moisture measurements (AMMA-CATCH/OZCAR (French Critical Zone exploration network)) and in-situ millet yield survey. Crop yield measurements were obtained for 10 villages located in the Niamey region from 2005 to 2012. The mean production (over 8 years) is 690 kg/ha, and ranges from 381 to 872 kg/ha during this period. Various statistical relationships based on soil moisture estimates were tested, and the most promising one (R>0.9) linked the 30-cm soil moisture anomalies from mid-August to mid-September (grain filling period) to the crop yield anomalies. Based on this local study, it was proposed to derive regional statistical relationships using 30-cm soil moisture maps over West Africa. The selected approach was to use a simple hydrological model, the Antecedent Precipitation Index (API), forced by real-time satellite-based precipitation (CMORPH, PERSIANN, TRMM3B42). To reduce uncertainties related to the quality of real-time rainfall satellite products, SMOS soil moisture measurements were assimilated into the API model through a Particular Filter algorithm. Then, obtained soil moisture anomalies were compared to 17 years of crop yield estimates from the FAOSTAT database (1998-2014). Results showed that the 30-cm soil moisture anomalies explained 89% of the crop yield variation in Niger, 72% in Burkina Faso, 82% in Mali and 84% in Senegal.

Overexpression of Arabidopsis VIT1 increases accumulation of iron in cassava roots and stems.

PubMed

Narayanan, Narayanan; Beyene, Getu; Chauhan, Raj Deepika; Gaitán-Solis, Eliana; Grusak, Michael A; Taylor, Nigel; Anderson, Paul

2015-11-01

Iron is extremely abundant in the soil, but its uptake in plants is limited due to low solubility in neutral or alkaline soils. Plants can rely on rhizosphere acidification to increase iron solubility. AtVIT1 was previously found to be involved in mediating vacuolar sequestration of iron, which indicates a potential application for iron biofortification in crop plants. Here, we have overexpressed AtVIT1 in the starchy root crop cassava using a patatin promoter. Under greenhouse conditions, iron levels in mature cassava storage roots showed 3-4 times higher values when compared with wild-type plants. Significantly, the expression of AtVIT1 showed a positive correlation with the increase in iron concentration of storage roots. Conversely, young leaves of AtVIT1 transgenic plants exhibit characteristics of iron deficiency such as interveinal chlorosis of leaves (yellowing) and lower iron concentration when compared with the wild type plants. Interestingly, the AtVIT1 transgenic plants showed 4 and 16 times higher values of iron concentration in the young stem and stem base tissues, respectively. AtVIT1 transgenic plants also showed 2-4 times higher values of iron content when compared with wild-type plants, with altered partitioning of iron between source and sink tissues. These results demonstrate vacuolar iron sequestration as a viable transgenic strategy to biofortify crops and to help eliminate micronutrient malnutrition in at-risk human populations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Soil moisture monitoring for crop management

NASA Astrophysics Data System (ADS)

Boyd, Dale

2015-07-01

The 'Risk management through soil moisture monitoring' project has demonstrated the capability of current technology to remotely monitor and communicate real time soil moisture data. The project investigated whether capacitance probes would assist making informed pre- and in-crop decisions. Crop potential and cropping inputs are increasingly being subject to greater instability and uncertainty due to seasonal variability. In a targeted survey of those who received regular correspondence from the Department of Primary Industries it was found that i) 50% of the audience found the information generated relevant for them and less than 10% indicted with was not relevant; ii) 85% have improved their knowledge/ability to assess soil moisture compared to prior to the project, with the most used indicator of soil moisture still being rain fall records; and iii) 100% have indicated they will continue to use some form of the technology to monitor soil moisture levels in the future. It is hoped that continued access to this information will assist informed input decisions. This will minimise inputs in low decile years with a low soil moisture base and maximise yield potential in more favourable conditions based on soil moisture and positive seasonal forecasts

Probabilistic Description of the Hydrologic Risk in Agriculture

NASA Astrophysics Data System (ADS)

Vico, G.; Porporato, A. M.

2011-12-01

Supplemental irrigation represents one of the main strategies to mitigate the effects of climatic variability on agroecosystems productivity and profitability, at the expenses of increasing water requirements for irrigation purposes. Optimizing water allocation for crop yield preservation and sustainable development needs to account for hydro-climatic variability, which is by far the main source of uncertainty affecting crop yields and irrigation water requirements. In this contribution, a widely applicable probabilistic framework is proposed to quantitatively define the hydrologic risk of yield reduction for both rainfed and irrigated agriculture. The occurrence of rainfall events and irrigation applications are linked probabilistically to crop development during the growing season. Based on these linkages, long-term and real-time yield reduction risk indices are defined as a function of climate, soil and crop parameters, as well as irrigation strategy. The former risk index is suitable for long-term irrigation strategy assessment and investment planning, while the latter risk index provides a rigorous probabilistic quantification of the emergence of drought conditions during a single growing season. This probabilistic framework allows also assessing the impact of limited water availability on crop yield, thus guiding the optimal allocation of water resources for human and environmental needs. Our approach employs relatively few parameters and is thus easily and broadly applicable to different crops and sites, under current and future climate scenarios, thus facilitating the assessment of the impact of increasingly frequent water shortages on agricultural productivity, profitability, and sustainability.

Impact of climate change on crop yield and role of model for achieving food security.

PubMed

Kumar, Manoj

2016-08-01

In recent times, several studies around the globe indicate that climatic changes are likely to impact the food production and poses serious challenge to food security. In the face of climate change, agricultural systems need to adapt measures for not only increasing food supply catering to the growing population worldwide with changing dietary patterns but also to negate the negative environmental impacts on the earth. Crop simulation models are the primary tools available to assess the potential consequences of climate change on crop production and informative adaptive strategies in agriculture risk management. In consideration with the important issue, this is an attempt to provide a review on the relationship between climate change impacts and crop production. It also emphasizes the role of crop simulation models in achieving food security. Significant progress has been made in understanding the potential consequences of environment-related temperature and precipitation effect on agricultural production during the last half century. Increased CO2 fertilization has enhanced the potential impacts of climate change, but its feasibility is still in doubt and debates among researchers. To assess the potential consequences of climate change on agriculture, different crop simulation models have been developed, to provide informative strategies to avoid risks and understand the physical and biological processes. Furthermore, they can help in crop improvement programmes by identifying appropriate future crop management practises and recognizing the traits having the greatest impact on yield. Nonetheless, climate change assessment through model is subjected to a range of uncertainties. The prediction uncertainty can be reduced by using multimodel, incorporating crop modelling with plant physiology, biochemistry and gene-based modelling. For devloping new model, there is a need to generate and compile high-quality field data for model testing. Therefore, assessment of agricultural productivity to sustain food security for generations is essential to maintain a collective knowledge and resources for preventing negative impact as well as managing crop practises.

Crop identification for the delineation of irrigated regions under scarce data conditions: a new approach based on chaos theory

NASA Astrophysics Data System (ADS)

Mangiarotti, S.; Muddu, S.; Sharma, A. K.; Corgne, S.; Ruiz, L.; Hubert-Moy, L.

2015-12-01

Groundwater is one of the main water reservoirs used for irrigation in regions of scarce water resources. For this reason, crop irrigation is expected to have a direct influence on this reservoir. To understand the time evolution of the groundwater table and its storage changes, it is important to delineate irrigated crops, whose evaporative demand is relatively higher. Such delineation may be performed based on classical classification approaches using optical remote sensing. However, it remains a difficult problem in regions where plots do not exceed a few hectares and exhibit a very heterogeneous pattern with multiple crops. This difficulty is emphasized in South India where two or three months of cloudy conditions during the monsoon period can hide crop growth during the year. An alternative approach is introduced here that takes advantage of such scarce signal. Ten different crops are considered in the present study. A bank of crop models is first established based on the global modeling technique [1]. These models are then tested using original time series (from which models were obtained) in order to evaluate the information that can be deduced from these models in an inverse approach. The approach is then tested on an independent data set and is finally applied to a large ensemble of 10,000 time series of plot data extracted from the Berambadi catchment (AMBHAS site) part of the Kabini River basin CZO, South India. Results show that despite the important two-month gap in satellite observations in the visible band, interpolated vegetation index remains an interesting indicator for identification of crops in South India. [1] S. Mangiarotti, R. Coudret, L. Drapeau, & L. Jarlan, Polynomial search and global modeling: Two algorithms for modeling chaos, Phys. Rev. E, 86(4), 046205 (2012).

Cover crops and crop residue management under no-till systems improve soils and environmental quality

NASA Astrophysics Data System (ADS)

Kumar, Sandeep; Wegner, Brianna; Vahyala, Ibrahim; Osborne, Shannon; Schumacher, Thomas; Lehman, Michael

2015-04-01

Crop residue harvest is a common practice in the Midwestern USA for the ethanol production. However, excessive removal of crop residues from the soil surface contributes to the degradation of important soil quality indicators such as soil organic carbon (SOC). Addition of a cover crop may help to mitigate these negative effects. The present study was set up to assess the impacts of corn (Zea mays L.) residue removal and cover crops on various soil quality indicators and surface greenhouse gas (GHG) fluxes. The study was being conducted on plots located at the North Central Agricultural Research Laboratory (NCARL) in Brookings, South Dakota, USA. Three plots of a corn and soybean (Glycine max (L.) Merr.) rotation under a no-till (NT) system are being monitored for soils and surface gas fluxes. Each plot has three residue removal (high residue removal, HRR; medium residue removal, MRR; and low residue removal, LRR) treatments and two cover crops (cover crops and no cover crops) treatments. Both corn and soybean are represented every year. Gas flux measurements were taken weekly using a closed static chamber method. Data show that residue removal significantly impacted soil quality indicators while more time was needed for an affect from cover crop treatments to be noticed. The LRR treatment resulted in higher SOC concentrations, increased aggregate stability, and increased microbial activity. The LRR treatment also increased soil organic matter (SOM) and particulate organic matter (POM) concentrations. Cover crops used in HRR (high corn residue removal) improved SOC (27 g kg-1) by 6% compared to that without cover crops (25.4 g kg-1). Cover crops significantly impacted POM concentration directly after the residue removal treatments were applied in 2012. CO2 fluxes were observed to increase as temperature increased, while N2O fluxes increased as soil moisture increased. CH4 fluxes were responsive to both increases in temperature and moisture. On average, soils under cover crop management had lower N2O fluxes than soils that did not have a cover crop. Results from this study concluded that it is important to allow crop residues to return to the soil as they help to improve soil quality indicators. The presence of cover crops also will contribute to the improvement of these indicators once established and may help mitigate greenhouse gas emissions.

Detecting crop growth stages of maize and soybeans by using time-series MODIS data

NASA Astrophysics Data System (ADS)

Sakamoto, T.; Wardlow, B. D.; Gitelson, A. A.; Verma, S. B.; Suyker, A. E.; Arkebauer, T. J.

2009-12-01

The crop phenological stages are one of essential parameters for evaluating crop productivity based on a crop simulation model. In this study, we improved a method named the Wavelet-based Filter for detecting Crop Phenology (WFCP) for detecting the specific phenological dates of maize and soybeans. The improved method was applied to MODIS-derived Wide Dynamic Range Vegetation Index (WDRVI) over a 6-year period (2003 to 2008) for three experimental fields planted to either maize or soybeans as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln (UNL). Using the ground-based crop growth stage observations collected by the CSP, it was confirmed that the improved method can estimate the specific phenological dates of maize (V2.5, R1, R5 and R6) and soybeans (V1, R5, R6 and R7) with reasonable accuracy.

Development of an irrigation scheduling software based on model predicted crop water stress

USDA-ARS?s Scientific Manuscript database

Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress which is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling software based on RZWQM2 model pr...

A Multi-sensor Approach to Identify Crop Sensitivity Related to Climate Variability in Central India

NASA Astrophysics Data System (ADS)

Mondal, P.; DeFries, R. S.; Jain, M.; Robertson, A. W.; Galford, G. L.; Small, C.

2012-12-01

Agriculture is a primary source of livelihood for over 70% of India's population, with staple crops (e.g. winter wheat) playing a pivotal role in satisfying an ever-increasing food-demand of a growing population. Agricultural yield in India has been reported to be highly correlated with the timing and total amount of monsoon rainfall and/or temperature depending on crop type. With expected change in future climate (temperature and precipitation), significant fluctuations in crop yields are projected for near future. To date, little work has identified the sensitivity of cropping intensity, or the number of crops planted in a given year, to climate variability. The objective of this study is to shed light on relative importance of different climate parameters through a statistical analysis of inter-annual variations in cropping intensity at a regional scale, which may help identify adaptive strategies in response to future climate anomalies. Our study focuses on a highly human-modified landscape in central India, and uses a multi-sensor approach to determine the sensitivity of agriculture to climate variability. First, we assembled the 16-day time-series of 250m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), and applied a spline function-based smoothing algorithm to develop maps of monsoon and winter crops in Central India for a decadal time-span. A hierarchical model involving moderate resolution Landsat (30m) data was used to estimate the heterogeneity of the spectral signature within the MODIS dataset (250m). We then compared the season-specific cropping patterns with spatio-temporal variability in climate parameters derived from the Tropical Rainfall Measuring Mission (TRMM) data. Initial data indicates that the existence of a monsoon crop has moderate to strong correlation with wet season end date (ρ = .522), wet season length (ρ = .522), and the number of rainy days during wet season (ρ = .829). Existence of a winter crop, however, has a moderately strong correlation with wet season start date (ρ = .577). In addition, winter crop yield (ton/ha) has a moderate correlation with wet season end date (ρ = .624), number of rainy days during the wet season (ρ = .492), and during the dry season (ρ = .410). Future work will assess which other factors influence cropping intensity (e.g. access to irrigation among many other), since a complex interplay of bio-physical and socio-economic factors governs the decision-making at the farm-level, ultimately leading to inter-annual variability in cropping intensity and/or yield.

Management of mine spoil for crop productivity with lignite fly ash and biological amendments.

PubMed

Ram, L C; Srivastava, N K; Tripathi, R C; Jha, S K; Sinha, A K; Singh, G; Manoharan, V

2006-04-01

Long-term field trials using lignite fly ash (LFA) were carried out in rice crops during the period 1996-2000 at Mine I, Neyveli Lignite Corporation, Tamil Nadu. LFA, being alkaline and endowed with an excellent pozzolanic nature, silt loam texture, and plant nutrients, has the potential to improve the texture, fertility, and crop productivity of mine spoil. The rice crops were the first, third, fifth, and sixth crops in rotation. The other crops, such as green gram (second) and sun hemp (fourth), were grown as green manure. For experimental trials, LFA was applied at various dosages (0, 5, 10, 20, 50, 100, and 200 t/ha), with and without press mud (10 t/ha), before cultivation of the first crop. Repeat applications of LFA were made at the same dosages in treatments of up to 50 t/ha (with and without press mud) before cultivation of the third and fifth crops. Press mud, a lightweight organic waste product from the sugar industry, was used as an organic amendment and source of plant nutrients. Also, a recommended dosage of chemical fertilizer, along with gypsum, humic acid, and biofertilizer as supplementing agents, was applied in all the treatments, including control. With one-time and repeat applications of LFA, from 5 to 20 t/ha (with and without press mud), the crop yield (grain and straw) increased significantly (p < 0.05), in the range from 3.0 to 42.0% over the corresponding control. The maximum yield was obtained with repeat applications of 20 t/ha of LFA with press mud in the third crop. The press mud enhanced the yield in the range of 1.5-10.2% with various dosages of LFA. The optimum dosage of LFA was 20 t/ha for both one-time and repeat applications. Repeat applications of LFA at lower dosages of up to 20 t/ha were more effective in increasing the yield than the corresponding one-time applications of up to 20 t/ha and repeat applications at 50 t/ha. One-time and repeat applications of LFA of up to 20 t/ha (with and without press mud), apart from increasing the yield, evinced improvement in the texture and fertility of mine spoil and the nutrient content of crop produce. Furthermore, some increase in the content of trace and heavy metals and the level of gamma-emitters in the mine spoil and crop produce was observed, but well within the permissible limits. The residual effect of LFA on succeeding crops was also encouraging in terms of eco-friendliness. Beyond 20 t/ha of LFA, the crop yield decreased significantly (p < 0.05), as a result of the formation of hardpan in the mine spoil and possibly the higher concentration of soluble salts in the LFA. However, the adverse effects of soluble salts were annulled progressively during the cultivation of succeeding crops. A plausible mechanism for the improved fertility of mine spoil and the carryover or uptake of toxic trace and heavy metals and gamma-emitters in mine spoil and crop produce is also discussed.

Globally Increased Crop Growth and Cropping Intensity from the Long-Term Satellite-Based Observations

NASA Astrophysics Data System (ADS)

Chen, Bin

2018-04-01

Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI) and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p < 0.001), and as for climatic drivers, the gradual temperature and precipitation changes have had a measurable impact on the crop growth trend.

AgroClimate: Simulating and Monitoring the Risk of Extreme Weather Events from a Crop Phenology Perspective

NASA Astrophysics Data System (ADS)

Fraisse, C.; Pequeno, D.; Staub, C. G.; Perry, C.

2016-12-01

Climate variability, particularly the occurrence of extreme weather conditions such as dry spells and heat stress during sensitive crop developmental phases can substantially increase the prospect of reduced crop yields. Yield losses or crop failure risk due to stressful weather conditions vary mainly due to stress severity and exposure time and duration. The magnitude of stress effects is also crop specific, differing in terms of thresholds and adaptation to environmental conditions. To help producers in the Southeast USA mitigate and monitor the risk of crop losses due to extreme weather events we developed a web-based tool that evaluates the risk of extreme weather events during the season taking into account the crop development stages. Producers can enter their plans for the upcoming season in a given field (e.g. crop, variety, planting date, acreage etc.), select or not a specific El Nino Southern Oscillation (ENSO) phase, and will be presented with the probabilities (ranging from 0 -100%) of extreme weather events occurring during sensitive phases of the growing season for the selected conditions. The DSSAT models CERES-Maize, CROPGRO-Soybean, CROPGRO-Cotton, and N-Wheat phenology models have been translated from FORTRAN to a standalone versions in R language. These models have been tested in collaboration with Extension faculty and producers during the 2016 season and their usefulness for risk mitigation and monitoring evaluated. A companion AgroClimate app was also developed to help producers track and monitor phenology development during the cropping season.

Mapping Agricultural Land-Use Change in the U.S. 2008-2012

NASA Astrophysics Data System (ADS)

Lark, T.; Salmon, M.; Gibbs, H.

2014-12-01

Cultivation of corn and soybeans in the United States reached record levels following the biofuels boom of the late 2000s. Debate churns about whether expansion of these crops caused conversion of carbon-rich natural ecosystems or instead replaced other crops on existing fields. Here we describe a novel trajectory-based methodology for analyzing satellite-derived land cover products that enables integration of all available and intermediate-year data to improve consistency across data sources, time, and geographic boundaries. Using this approach, we track crop-specific expansion pathways across the conterminous U.S., 2008-2012, and identify the types, amount, and locations of all land converted to and from cropland. We find total cropland area increased by a net of 3 million acres over the study period, with gross land conversion to cropland 2.5 times greater than net expansion. Grasslands were the source of 77% of all new cropland, and we estimate 1.6 million acres (22%) were virgin grasslands that had not been previously planted or plowed. Corn was the most common crop planted directly on new land, as well as the largest indirect contributor to change through its displacement of other crops. Results identify holes in federal policies including improper enforcement of the Renewable Fuels Standard and insufficient coverage of recent Farm Bill provisions, suggesting current implementations of federal policies are likely insufficient to protect remaining grassland habitat.

Assessment of real-time PCR based methods for quantification of pollen-mediated gene flow from GM to conventional maize in a field study.

PubMed

Pla, Maria; La Paz, José-Luis; Peñas, Gisela; García, Nora; Palaudelmàs, Montserrat; Esteve, Teresa; Messeguer, Joaquima; Melé, Enric

2006-04-01

Maize is one of the main crops worldwide and an increasing number of genetically modified (GM) maize varieties are cultivated and commercialized in many countries in parallel to conventional crops. Given the labeling rules established e.g. in the European Union and the necessary coexistence between GM and non-GM crops, it is important to determine the extent of pollen dissemination from transgenic maize to other cultivars under field conditions. The most widely used methods for quantitative detection of GMO are based on real-time PCR, which implies the results are expressed in genome percentages (in contrast to seed or grain percentages). Our objective was to assess the accuracy of real-time PCR based assays to accurately quantify the contents of transgenic grains in non-GM fields in comparison with the real cross-fertilization rate as determined by phenotypical analysis. We performed this study in a region where both GM and conventional maize are normally cultivated and used the predominant transgenic maize Mon810 in combination with a conventional maize variety which displays the characteristic of white grains (therefore allowing cross-pollination quantification as percentage of yellow grains). Our results indicated an excellent correlation between real-time PCR results and number of cross-fertilized grains at Mon810 levels of 0.1-10%. In contrast, Mon810 percentage estimated by weight of grains produced less accurate results. Finally, we present and discuss the pattern of pollen-mediated gene flow from GM to conventional maize in an example case under field conditions.

The relationship between extreme weather events and crop losses in central Taiwan

NASA Astrophysics Data System (ADS)

Lai, Li-Wei

2017-09-01

The frequency of extreme weather events, which cause severe crop losses, is increasing. This study investigates the relationship between crop losses and extreme weather events in central Taiwan from 2003 to 2015 and determines the main factors influencing crop losses. Data regarding the crop loss area and meteorological information were obtained from government agencies. The crops were categorised into the following five groups: `grains', `vegetables', `fruits', `flowers' and `other crops'. The extreme weather events and their synoptic weather patterns were categorised into six and five groups, respectively. The data were analysed using the z score, correlation coefficient and stepwise regression model. The results show that typhoons had the highest frequency of all extreme weather events (58.3%). The largest crop loss area (4.09%) was caused by two typhoons and foehn wind in succession. Extreme wind speed coupled with heavy rainfall is an important factor affecting the losses in the grain and vegetable groups. Extreme wind speed is a common variable that affects the loss of `grains', `vegetables', `fruits' and `flowers'. Consecutive extreme weather events caused greater crop losses than individual events. Crops with long production times suffered greater losses than those with short production times. This suggests that crops with physical structures that can be easily damaged and long production times would benefit from protected cultivation to maintain food security.

A GIS-based approach to prevent contamination of groundwater at regional scale

NASA Astrophysics Data System (ADS)

Balderacchi, M.; Vischetti, C.; di Guardo, A.; Trevisan, M.

2009-04-01

Sustainable development is a fundamental objective of the European Union. Since 1991, the use of numerical models has been used to assess the environmental fate of pesticides (directive 91/414 EC). Since then, new approaches to assess pesticide contamination have been developed. This is an ongoing process, with approaches getting increasingly close to reality. Actually, there is a new challenge to integrate the most advanced and cost-effective monitoring strategies with simulation models so that reliable indicators of unsaturated flow and transport can be suitably mapped and coupled with other indicators related to productivity and sustainability. The most relevant role of GIS in the analysis of pesticide fate in soil is its application to process together input data and the results of distribution model based simulations of pesticide transport. FitoMarche is a GIS-based software tool that estimates pesticide movement in the unsaturated zone using MACRO 5 and it is able to simulate complex and real crop rotations at the regional scale. Crop rotation involves the sequential production of different plant species on the same land, every crop is characterized by different agricultural practices that involve the use of different pesticides at different doses. FitoMarche extracts MACRO input data from a series of geographic data sets (shapefiles) and an internal database, writes input files for MACRO, executes the simulation and extracts solute and water fluxes from MACRO output files. The study has been performed in the Marche region, located in central Italy along the Adriatic coast. Soil, climate, land use shapefiles were provided from public authorities, crop rotation schemes were estimated from ISTAT (the national statistics institute) 5th agricultural census database using a municipality detail and agricultural practices following the local customs. Two herbicides have been tested: "A" is employed on maize crop, and "B" on maize, sunflower and sugarbeet. In the first part the study focused of a definition of an indicator of groundwater contamination. The probably to exceed the groundwater quality endpoint has been chosen and it has been developed according a probabilistic approach and following a lognormal distribution of the data. After that the effect of crop rotation on pesticide leaching has been evaluated by a stepwise procedure. The tier 1 was the worst case in which the whole region is considered cropped with maize, therefore the pesticide application is every year on the crop with the highest application rate, whereas the tier 2 was a first refinement of the previous tier, the pesticide application was still every year but only in to the areas with the presence of authorised crop fore the assessed pesticide and with a crop LUA (land under agriculture) ratio higher than 10%. In the passage from tier 1 to tier 2 a contemporaneous reduction of simulated surface and pesticide leaching occurred because a relationship exists between agriculture and pesticide use. The step 3 considered a pesticide timing based on typical crop rotations. Te application followed label doses and was every time an authorised crop was found in the rotation. The passage to step 3 allowed a further percolation reduction. Step 3 blind simulations have been plotted as maps and matched with the results of the regional environment agency monitoring plan. A good correspondence between prediction and observation has got. Nevertheless herbicide "A" was detected in a larger area than assumed to be cropped with maize. However, in the past this compound was authorized for application to crops other than maize and was also used extensively in non-agricultural applications. Herbicide "B" was also detected in two wells located in areas not considered vulnerable. In the first well, water was sampled three times and the compound was detected once, in the other water was sampled once and the compound was detected. In this case point contamination, could be the origin of that. These pesticides were also researched in areas in which there is not application. This suggest that GIS approach can allow the design of new agrochemical monitoring plans that focus resources in areas with the highest probability of detection, reducing the cost to the community, and increasing the scientific value of the data collected. In the future, when well validated geographic data sets are available, it will also be possible to distinguish between point and diffuse contamination.

High Resolution Modelling of Crop Response to Climate Change

NASA Astrophysics Data System (ADS)

Mirmasoudi, S. S.; Byrne, J. M.; MacDonald, R. J.; Lewis, D.

2014-12-01

Crop production is one of the most vulnerable sectors to climatic variability and change. Increasing atmospheric CO2 concentration and other greenhouse gases are causing increases in global temperature. In western North America, water supply is largely derived from mountain snowmelt. Climate change will have a significant impact on mountain snowpack and subsequently, the snow-derived water supply. This will strain water supplies and increase water demand in areas with substantial irrigation agriculture. Increasing temperatures may create heat stress for some crops regardless of soil water supply, and increasing surface O3 and other pollutants may damage crops and ecosystems. CO2 fertilization may or may not be an advantage in future. This work is part of a larger study that will address a series of questions based on a range of future climate scenarios for several watersheds in western North America. The key questions are: (1) how will snowmelt and rainfall runoff vary in future; (2) how will seasonal and inter-annual soil water supply vary, and how might that impacts food supplies; (3) how might heat stress impact (some) crops even with adequate soil water; (4) will CO2 fertilization alter crop yields; and (5) will pollution loads, particularly O3, cause meaningful changes to crop yields? The Generate Earth Systems Science (GENESYS) Spatial Hydrometeorological Model is an innovative, efficient, high-resolution model designed to assess climate driven changes in mountain snowpack derived water supplies. We will link GENESYS to the CROPWAT crop model system to assess climate driven changes in water requirement and associated crop productivity for a range of future climate scenarios. Literature bases studies will be utilised to develop approximate crop response functions for heat stress, CO2 fertilization and for O3 damages. The overall objective is to create modeling systems that allows meaningful assessment of agricultural productivity at a watershed scale under a range of climate scenarios.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

PubMed Central

Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

2017-01-01

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. PMID:28492515

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

PubMed

Calera, Alfonso; Campos, Isidro; Osann, Anna; D'Urso, Guido; Menenti, Massimo

2017-05-11

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Drought-related vulnerability and risk assessment of groundwater in Belgium: estimation of the groundwater recharge and crop yield vulnerability with the B-CGMS

NASA Astrophysics Data System (ADS)

Jacquemin, Ingrid; Verbeiren, Boud; Vanderhaegen, Sven; Canters, Frank; Vermeiren, Karolien; Engelen, Guy; Huysmans, Marijke; Batelaan, Okke; Tychon, Bernard

2016-04-01

Due to common belief that regions under temperate climate are not affected by (meteorological and groundwater) drought, these events and their impacts remain poorly studied: in the GroWaDRISK, we propose to take stock of this question. We aim at providing a better understanding of the influencing factors (land use and land cover changes, water demand and climate) and the drought-related impacts on the environment, water supply and agriculture. The study area is located in the North-East of Belgium, corresponding approximatively to the Dijle and Demer catchments. To establish an overview of the groundwater situation, we assess the system input: the recharge. To achieve this goal, two models, B-CGMS and WetSpass are used to evaluate the recharge, respectively, over agricultural land and over the remaining areas, as a function of climate and for various land uses and land covers. B-CGMS, which is an adapted version for Belgium of the European Crop Growth Monitoring System, is used for assessing water recharge at a daily timestep and under different agricultural lands: arable land (winter wheat, maize...), orchards, horticulture and floriculture and for grassland. B-CGMS is designed to foresee crop yield and obviously it studies the impact of drought on crop yield and raises issues for the potential need of irrigation. For both yields and water requirements, the model proposes a potential mode, driven by temperature and solar radiation, and a water-limited mode for which water availability can limit crop growth. By this way, we can identify where and when water consumption and yield are not optimal, in addition to the Crop Water Stress Index. This index is calculated for a given crop, as the number of days affected by water stress during the growth sensitive period. Both recharge and crop yield are assessed for the current situation (1980 - 2012), taking into account the changing land use/land cover, in terms of areas and localization of the agricultural land and where the proportion of the different crops had considerably evolved through time (e.g., increase of grain maize and potatoes while winter cereals decrease). The preliminary results of the recharge lead to an average value in the area showing a significant negative trend, in both simulations with fixed (base = 1980) and changing land cover. In the same time, we could observe an increasing number of water stress periods, especially for maize, one of the main crops in the area. Finally, a preliminary test will be presented for the horizon 2040, for which we use meteorological time series (for high and low hydrologic impacts) given by the CCI-HYDR Perturbation Tool (Ntegeka V. and Willems P., 2009). This preliminary test aims to (1) evaluate the amplitude of the potential recharge deficit and, (2) especially, to define vulnerability zones, affected by frequent water stress, in connection with irrigation needs which could possibly increase the groundwater extraction.

Food security: crops for people not for cars.

PubMed

Kullander, Sven

2010-05-01

Humankind is currently faced with the huge challenge of securing a sustainable energy supply and biofuels constitute one of the major options. However, the commercially traded edible crops are barely sufficient to meet food demand of the present world population. Certain regions, for example EU-27, do not even have a sufficient indigenous crop production. Of this follows that motor biofuels based on edible crops should be avoided. To replace more than some percent of the fossil motor fuels, non-edible biomass-rest products and wastes-should instead be considered for conversion to biofuels. In this way, about 10% of the current fossil fuels can be replaced. Feeding a world population expected to grow by some 50% during the next 50 years will be a major challenge. For environmental reasons it seems that agricultural land cannot be expanded very much, maybe not at all. The solution to the increasing food demand seems therefore to be using the present crop production more efficiently and increasing output from present agricultural land, maintaining biodiversity and climate stability within reasonable limits. In the future, agriculture will need more energy and more water irrigation. Food production is, however, already very energy demanding, requiring several times more externally provided energy than the energy content of the food itself. A sufficient energy supply will be a key issue for the future farming!

Ancient orphan crop joins modern era: gene-based SNP discovery and mapping in lentil.

PubMed

Sharpe, Andrew G; Ramsay, Larissa; Sanderson, Lacey-Anne; Fedoruk, Michael J; Clarke, Wayne E; Li, Rong; Kagale, Sateesh; Vijayan, Perumal; Vandenberg, Albert; Bett, Kirstin E

2013-03-18

The genus Lens comprises a range of closely related species within the galegoid clade of the Papilionoideae family. The clade includes other important crops (e.g. chickpea and pea) as well as a sequenced model legume (Medicago truncatula). Lentil is a global food crop increasing in importance in the Indian sub-continent and elsewhere due to its nutritional value and quick cooking time. Despite this importance there has been a dearth of genetic and genomic resources for the crop and this has limited the application of marker-assisted selection strategies in breeding. We describe here the development of a deep and diverse transcriptome resource for lentil using next generation sequencing technology. The generation of data in multiple cultivated (L. culinaris) and wild (L. ervoides) genotypes together with the utilization of a bioinformatics workflow enabled the identification of a large collection of SNPs and the subsequent development of a genotyping platform that was used to establish the first comprehensive genetic map of the L. culinaris genome. Extensive collinearity with M. truncatula was evident on the basis of sequence homology between mapped markers and the model genome and large translocations and inversions relative to M. truncatula were identified. An estimate for the time divergence of L. culinaris from L. ervoides and of both from M. truncatula was also calculated. The availability of the genomic and derived molecular marker resources presented here will help change lentil breeding strategies and lead to increased genetic gain in the future.

Annual and Perennial Alleyway Cover Crops Vary in Their Effects on Pratylenchus penetrans in Pacific Northwest Red Raspberry (Rubus idaeus)

PubMed Central

Rudolph, Rachel E.; Zasada, Inga A.; DeVetter, Lisa W.

2017-01-01

Cover crops can provide many benefits to agroecosystems, such as lessening soil erosion and increasing water infiltration. However, cover crop use is not common in established red raspberry (Rubus idaeus) fields in the Pacific Northwest. Raspberry growers are concerned about resource competition between the cover crop and raspberry crop, as well as increasing population densities of the plant-parasitic nematode Pratylenchus penetrans, which has a wide host range and has been shown to reduce raspberry plant vigor and yield. A 2-yr study was conducted in an established ‘Meeker’ raspberry field in northwest Washington to evaluate the effects of nine alleyway cover crops, mowed weed cover, and the industry standard of bare cultivated soil on P. penetrans population dynamics, raspberry yield, and fruit quality. The host status for P. penetrans of cover crops included in the field experiment, as well as Brassica juncea ‘Pacific Gold’ and Sinapis alba ‘Ida Gold’, was also evaluated in greenhouse experiments. In the field experiment, P. penetrans population densities did not increase in alleyway cover crop roots over time or in alleyway soil surrounding cover crop roots (means range from 0 to 116 P. penetrans/100 g of soil) compared with the bare cultivated control (means range from 2 to 55 P. penetrans/100 g of soil). Pratylenchus penetrans populations did not increase over time in raspberry grown adjacent to alleyways with cover crops (means range from 1,081 to 6,120 P. penetrans/g of root) compared with those grown adjacent to bare cultivated soil alleyways (means range from 2,391 to 5,536 P. penetrans/g of root). Raspberry grown adjacent to bare cultivated soil did not have significantly higher yield or fruit quality than raspberry grown adjacent to cover crops in either year of the experiment. In the greenhouse assays, ‘Norwest 553’ wheat and a perennial ryegrass mix were poor hosts for P. penetrans, whereas ‘Nora’ and ‘TAM 606’ oat and ‘Pacific Gold’ and ‘Ida Gold’ mustard were good hosts. These results support the idea that the potential benefits of alleyway cover crops outweigh the potential risk of increasing P. penetrans population densities and do not compromise raspberry yield or fruit quality. PMID:29353934

Crop monitoring & yield forecasting system based on Synthetic Aperture Radar (SAR) and process-based crop growth model: Development and validation in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.

2014-12-01

Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies and enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe. Such delivery of rice growth and yield information is made possible by regular earth observation using space-born Synthetic Aperture Radar (SAR) technology combined with crop modeling approach to estimate yield. Radar-based remote sensing is capable of observing rice vegetation growth irrespective of cloud coverage, an important feature given that in incidences of flooding the sky is often cloud-covered. The system allows rapid damage assessment over the area of interest. Rice yield monitoring is based on a crop growth simulation and SAR-derived key information, particularly start of season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of SAR data into crop model improves yield estimation for actual yields. Remote-sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. Such improvement of actual yield estimates offers practical application such as in a crop insurance program. Process-based crop simulation model is used in the system to ensure climate information is adequately captured and to enable mid-season yield forecast.

Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins.

PubMed

Belfry, Kimberly D; Trueman, Cheryl; Vyn, Richard J; Loewen, Steven A; Van Eerd, Laura L

2017-01-01

Much of cover crop research to date focuses on key indicators of impact without considering the implications over multiple years, in the absence of a systems-based approach. To evaluate the effect of three years of autumn cover crops on subsequent processing tomato (Solanum lycopersicum L.) production in 2010 and 2011, a field split-split-plot factorial design trial with effects of cover crop type, urea ammonium nitrate fertilizer rate (0 or 140 kg N ha-1 preplant broadcast incorporated) and tomato cultivar (early vs. late) was conducted. The main plot factor, cover crop, included a no cover crop control, oat (Avena sativa L.), winter cereal rye (hereafter referred to as rye) (Secale cereale L.), oilseed radish (OSR) (Raphanus sativus L. var. oleiferus Metzg Stokes), and mix of OSR and rye (OSR + rye) treatments. Cover crop biomass of 0.5 to 2.8 and 1.7 to 3.1 Mg ha-1 was attained in early Oct. and the following early May, respectively. In general, OSR increased soil mineral N during cover crop growth and into the succeeding summer tomato growing season, while the remaining cover crops did not differ from the no cover crop control. The lack of a cover crop by N rate interaction in soil and plant N analyses at harvest suggests that growers may not need to modify N fertilizer rates to tomatoes based on cover crop type. Processing tomato fruit quality at harvest (rots, insect or disease damage, Agtron colour, pH, or natural tomato soluble solids (NTSS)) was not affected by cover crop type. In both years, marketable yield in the no cover crop treatment was lower or not statistically different than all planted cover crops. Partial profit margins over both years were 1320 $ ha-1 higher with OSR and $960 higher with oat compared to the no cover crop control. Thus, results from a systems-based approach suggest that the cover crops tested had no observed negative impact on processing tomato production and have the potential to increase marketable yield and profit margins.

Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins

PubMed Central

Belfry, Kimberly D.; Trueman, Cheryl; Vyn, Richard J.; Loewen, Steven A.; Van Eerd, Laura L.

2017-01-01

Much of cover crop research to date focuses on key indicators of impact without considering the implications over multiple years, in the absence of a systems-based approach. To evaluate the effect of three years of autumn cover crops on subsequent processing tomato (Solanum lycopersicum L.) production in 2010 and 2011, a field split-split-plot factorial design trial with effects of cover crop type, urea ammonium nitrate fertilizer rate (0 or 140 kg N ha-1 preplant broadcast incorporated) and tomato cultivar (early vs. late) was conducted. The main plot factor, cover crop, included a no cover crop control, oat (Avena sativa L.), winter cereal rye (hereafter referred to as rye) (Secale cereale L.), oilseed radish (OSR) (Raphanus sativus L. var. oleiferus Metzg Stokes), and mix of OSR and rye (OSR + rye) treatments. Cover crop biomass of 0.5 to 2.8 and 1.7 to 3.1 Mg ha-1 was attained in early Oct. and the following early May, respectively. In general, OSR increased soil mineral N during cover crop growth and into the succeeding summer tomato growing season, while the remaining cover crops did not differ from the no cover crop control. The lack of a cover crop by N rate interaction in soil and plant N analyses at harvest suggests that growers may not need to modify N fertilizer rates to tomatoes based on cover crop type. Processing tomato fruit quality at harvest (rots, insect or disease damage, Agtron colour, pH, or natural tomato soluble solids (NTSS)) was not affected by cover crop type. In both years, marketable yield in the no cover crop treatment was lower or not statistically different than all planted cover crops. Partial profit margins over both years were 1320 $ ha-1 higher with OSR and $960 higher with oat compared to the no cover crop control. Thus, results from a systems-based approach suggest that the cover crops tested had no observed negative impact on processing tomato production and have the potential to increase marketable yield and profit margins. PMID:28683080

Application of a partitioning procedure based on Rao quadratic entropy index to characterize the temporal evolution of in situ varietal and genetic diversity of bread wheat in France over the period 1981-2006.

PubMed

Perronne, Rémi; Goldringer, Isabelle

2018-04-01

We present and highlight a partitioning procedure based on the Rao quadratic entropy index to assess temporal in situ inter-annual varietal and genetic changes of crop diversity. For decades, Western-European agroecosystems have undergone profound changes, among which a reduction of crop genetic diversity. These changes have been highlighted in numerous studies, but no unified partitioning procedure has been proposed to compute the inter-annual variability in both varietal and genetic diversity. To fill this gap, we tested, adjusted and applied a partitioning procedure based on the Rao quadratic entropy index that made possible to describe the different components of crop diversity as well as to account for the relative acreages of varieties. To emphasize the relevance of this procedure, we relied on a case study focusing on the temporal evolution of bread wheat diversity in France over the period 1981-2006 at both national and district scales. At the national scale, we highlighted a decrease of the weighted genetic replacement indicating that varieties sown in the most recent years were more genetically similar than older ones. At the district scale, we highlighted sudden changes in weighted genetic replacement in some agricultural regions that could be due to fast shifts of successive leading varieties over time. Other regions presented a relatively continuous increase of genetic similarity over time, potentially due to the coexistence of a larger number of co-leading varieties that got closer genetically. Based on the partitioning procedure, we argue that a tendency of in situ genetic homogenization could be compared to some of its potential causes, such as a decrease in the speed of replacement or an increase in between-variety genetic similarity over time.

Anticipating on amplifying water stress: Optimal crop production supported by anticipatory water management

NASA Astrophysics Data System (ADS)

Bartholomeus, Ruud; van den Eertwegh, Gé; Simons, Gijs

2015-04-01

Agricultural crop yields depend largely on the soil moisture conditions in the root zone. Drought but especially an excess of water in the root zone and herewith limited availability of soil oxygen reduces crop yield. With ongoing climate change, more prolonged dry periods alternate with more intensive rainfall events, which changes soil moisture dynamics. With unaltered water management practices, reduced crop yield due to both drought stress and waterlogging will increase. Therefore, both farmers and water management authorities need to be provided with opportunities to reduce risks of decreasing crop yields. In The Netherlands, agricultural production of crops represents a market exceeding 2 billion euros annually. Given the increased variability in meteorological conditions and the resulting larger variations in soil moisture contents, it is of large economic importance to provide farmers and water management authorities with tools to mitigate risks of reduced crop yield by anticipatory water management, both at field and at regional scale. We provide the development and the field application of a decision support system (DSS), which allows to optimize crop yield by timely anticipation on drought and waterlogging situations. By using this DSS, we will minimize plant water stress through automated drainage and irrigation management. In order to optimize soil moisture conditions for crop growth, the interacting processes in the soil-plant-atmosphere system need to be considered explicitly. Our study comprises both the set-up and application of the DSS on a pilot plot in The Netherlands, in order to evaluate its implementation into daily agricultural practice. The DSS focusses on anticipatory water management at the field scale, i.e. the unit scale of interest to a farmer. We combine parallel field measurements ('observe'), process-based model simulations ('predict'), and the novel Climate Adaptive Drainage (CAD) system ('adjust') to optimize soil moisture conditions. CAD is used both for controlled drainage practices and for sub-irrigation. The DSS has a core of the plot-scale SWAP model (soil-water-atmosphere-plant), extended with a process-based module for the simulation of oxygen stress for plant roots. This module involves macro-scale and micro-scale gas diffusion, as well as the plant physiological demand of oxygen, to simulate transpiration reduction due to limited oxygen availability. Continuous measurements of soil moisture content, groundwater level, and drainage level are used to calibrate the SWAP model each day. This leads to an optimal reproduction of the actual soil moisture conditions by data assimilation in the first step in the DSS process. During the next step, near-future (+10 days) soil moisture conditions and drought and oxygen stress are predicted using weather forecasts. Finally, optimal drainage levels to minimize stress are simulated, which can be established by CAD. Linkage to a grid-based hydrological simulation model (SPHY) facilitates studying the spatial dynamics of soil moisture and associated implications for management at the regional scale. Thus, by using local-scale measurements, process-based models and weather forecasts to anticipate on near-future conditions, not only field-scale water management but also regional surface water management can be optimized both in space and time.

Airborne and ground-based remote sensing for the estimation of evapotranspiration and yield of bean, potato, and sugar beet crops

NASA Astrophysics Data System (ADS)

Jayanthi, Harikishan

The focus of this research was two-fold: (1) extend the reflectance-based crop coefficient approach to non-grain (potato and sugar beet), and vegetable crops (bean), and (2) develop vegetation index (VI)-yield statistical models for potato and sugar beet crops using high-resolution aerial multispectral imagery. Extensive crop biophysical sampling (leaf area index and aboveground dry biomass sampling) and canopy reflectance measurements formed the backbone of developing of canopy reflectance-based crop coefficients for bean, potato, and sugar beet crops in this study. Reflectance-based crop coefficient equations were developed for the study crops cultivated in Kimberly, Idaho, and subsequently used in water availability simulations in the plant root zone during 1998 and 1999 seasons. The simulated soil water profiles were compared with independent measurements of actual soil water profiles in the crop root zone in selected fields. It is concluded that the canopy reflectance-based crop coefficient technique can be successfully extended to non-grain crops as well. While the traditional basal crop coefficients generally expect uniform growth in a region the reflectance-based crop coefficients represent the actual crop growth pattern (in less than ideal water availability conditions) in individual fields. Literature on crop canopy interactions with sunlight states that there is a definite correspondence between leaf area index progression in the season and the final yield. In case of crops like potato and sugar beet, the yield is influenced not only on how early and how quickly the crop establishes its canopy but also on how long the plant stands on the ground in a healthy state. The integrated area under the crop growth curve has shown excellent correlations with hand-dug samples of potato and sugar beet crops in this research. Soil adjusted vegetation index-yield models were developed, and validated using multispectral aerial imagery. Estimated yield images were compared with the actual yields extracted from the ground. The remote sensing-derived yields compared well with the actual yields sampled on the ground. This research has highlighted the importance of the date of spectral emergence, the need to know the duration for which the crops stand on the ground, and the need to identify critical periods of time when multispectral coverages are essential for reliable tuber yield estimation.

Effects of Bioreactor Retention Time on Aerobic Microbial Decomposition of CELSS Crop Residues

NASA Technical Reports Server (NTRS)

Strayer, R. F.; Finger, B. W.; Alazraki, M. P.

1997-01-01

The focus of resource recovery research at the KSC-CELSS Breadboard Project has been the evaluation of microbiologically mediated biodegradation of crop residues by manipulation of bioreactor process and environmental variables. We will present results from over 3 years of studies that used laboratory- and breadboard-scale (8 and 120 L working volumes, respectively) aerobic, fed-batch, continuous stirred tank reactors (CSTR) for recovery of carbon and minerals from breadboard grown wheat and white potato residues. The paper will focus on the effects of a key process variable, bioreactor retention time, on response variables indicative of bioreactor performance. The goal is to determine the shortest retention time that is feasible for processing CELSS crop residues, thereby reducing bioreactor volume and weight requirements. Pushing the lower limits of bioreactor retention times will provide useful data for engineers who need to compare biological and physicochemical components. Bioreactor retention times were manipulated to range between 0.25 and 48 days. Results indicate that increases in retention time lead to a 4-fold increase in crop residue biodegradation, as measured by both dry weight losses and CO2 production. A similar overall trend was also observed for crop residue fiber (cellulose and hemicellulose), with a noticeable jump in cellulose degradation between the 5.3 day and 10.7 day retention times. Water-soluble organic compounds (measured as soluble TOC) were appreciably reduced by more than 4-fold at all retention times tested. Results from a study of even shorter retention times (down to 0.25 days), in progress, will also be presented.

Changing Pattern of Crop Fraction in Late Blight Induced Potato Crops in Potato Bowl of West Bengal by using Multi-temporal Time Series AWiFs Data

NASA Astrophysics Data System (ADS)

Chakrabarty, Abhisek

2016-07-01

Crop fraction is the ratio of crop occupying a unit area in ground pixel, is very important for monitoring crop growth. One of the most important variables in crop growth monitoring is the fraction of available solar radiation intercepted by foliage. Late blight of potato (Solanum tuberosum), caused by the oomycete pathogen Phytophthora infestans, is considered to be the most destructive crop diseases of potato worldwide. Under favourable climatic conditions, and without intervention (i.e. fungicide sprays), the disease can destroy potato crop within few weeks. Therefore it is important to evaluate the crop fraction for monitoring the healthy and late blight affected potato crops. This study was conducted in potato bowl of West Bengal, which consists of districts of Hooghly, Howrah, Burdwan, Bankuara, and Paschim Medinipur. In this study different crop fraction estimation method like linear spectral un-mixing, Normalized difference vegetation index (NDVI) based DPM model (Zhang et al. 2013), Ratio vegetation index based DPM model, improved Pixel Dichotomy Model (Li et al. 2014) ware evaluated using multi-temporal IRS AWiFs data in two successive potato growing season of 2012-13 and 2013-14 over the study area and compared with measured crop fraction. The comparative study based on measured healthy and late blight affected potato crop fraction showed that improved Pixel Dichotomy Model maintain the high coefficient of determination (R2= 0.835) with low root mean square error (RMSE=0.21) whereas the correlation values of NDVI based DPM model and RVI based DPM model is 0.763 and 0.694 respectively. The changing pattern of crop fraction profile of late blight affected potato crop was studied in respect of healthy potato crop fraction which was extracted from the 269 GPS points of potato field. It showed that the healthy potato crop fraction profile maintained the normal phenological trend whereas the late blight affected potato crop fraction profile suddenly fallen after late blight disease affected in potato crops. Therefore, it can be concluded that based on the result of this study the improved Pixel Dichotomy Model is the most convenient method for crop fraction estimation for this region with satisfactory accuracy.

Global assessment of nitrogen losses and trade-offs with yields from major crop cultivations.

PubMed

Liu, Wenfeng; Yang, Hong; Liu, Junguo; Azevedo, Ligia B; Wang, Xiuying; Xu, Zongxue; Abbaspour, Karim C; Schulin, Rainer

2016-12-01

Agricultural application of reactive nitrogen (N) for fertilization is a cause of massive negative environmental problems on a global scale. However, spatially explicit and crop-specific information on global N losses into the environment and knowledge of trade-offs between N losses and crop yields are largely lacking. We use a crop growth model, Python-based Environmental Policy Integrated Climate (PEPIC), to determine global N losses from three major food crops: maize, rice, and wheat. Simulated total N losses into the environment (including water and atmosphere) are 44TgNyr -1 . Two thirds of these, or 29TgNyr -1 , are losses to water alone. Rice accounts for the highest N losses, followed by wheat and maize. The N loss intensity (NLI), defined as N losses per unit of yield, is used to address trade-offs between N losses and crop yields. The NLI presents high variation among different countries, indicating diverse N losses to produce the same amount of yields. Simulations of mitigation scenarios indicate that redistributing global N inputs and improving N management could significantly abate N losses and at the same time even increase yields without any additional total N inputs. Copyright © 2016 Elsevier B.V. All rights reserved.

Improving the use of crop models for risk assessment and climate change adaptation.

PubMed

Challinor, Andrew J; Müller, Christoph; Asseng, Senthold; Deva, Chetan; Nicklin, Kathryn Jane; Wallach, Daniel; Vanuytrecht, Eline; Whitfield, Stephen; Ramirez-Villegas, Julian; Koehler, Ann-Kristin

2018-01-01

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1.Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk?2.Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output.3.Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

Replacing fallow by cover crops: economic sustainability

NASA Astrophysics Data System (ADS)

Gabriel, José Luis; Garrido, Alberto; Quemada, Miguel

2013-04-01

Replacing fallow by cover crops in intensive fertilized systems has been demonstrated as an efficient tool for reducing nitrate leaching. However, despite the evident environmental services provided and the range of agronomic benefits documented in the literature, farmers' adoption of this new technology is still limited because they are either unwilling or unable, although adoption reluctance is frequently rooted in low economic profitability, low water se efficiency or poor knowledge. Economic analyses permit a comparison between the profit that farmers obtain from agricultural products and the cost of adopting specific agricultural techniques. The goal of this study was to evaluate the economic impact of replacing the usual winter fallow with cover crops (barley (Hordeum vulgare L., cv. Vanessa), vetch (Vicia villosa L., cv. Vereda) and rapeseed (Brassica napus L., cv. Licapo)) in irrigated maize systems and variable Mediterranean weather conditions using stochastic Monte-Carlo simulations of key farms' financial performance indicators. The three scenarios studied for each cover crop were: i) just leaving the cover crop residue in the ground, ii) leaving the cover crop residue but reduce following maize fertilization according to the N available from the previous cover crop and iii) selling the cover crop residue for animal feeding. All the scenarios were compared with respect to a typical maize-fallow rotation. With observed data from six different years and in various field trials, looking for different weather conditions, probability distribution functions of maize yield, cover crop biomass production and N fertilizer saving was fitted. Based in statistical sources maize grain price, different forage prices and the cost of fertilizer were fitted to probability distribution functions too. As result, introducing a cover crop involved extra costs with respect to fallow as the initial investment, because new seed, herbicide or extra field operations. Additional costs varied from 28 to 73 € ha-1 but, results suggest that barley and vetch as cover crops increases maize yields, being a strategy that stochastically dominates the fallow. In this case, even without selling residue and without fertilizer reduction, vetch treatment increased the benefits with respect to the fallow in almost two out of three years and barley treatment did so in one year out of two. When biomass was sold as forage, benefits increase in 80% of the years for the vetch and in 70% of years for the barley with respect to the fallow. However, rapeseed was not a good cover crop for the Mediterranean region because poorly adaptation to the weather conditions. Then, cover crops can lead to increase of economical benefits improving environmental conditions at the same time. Acknowledgements: Financial support by Spain CICYT (ref. AGL2005-00163 and AGL 2011-24732), Comunidad de Madrid (project AGRISOST, S2009/AGR-1630), Belgium FSR 2012 (ref. SPER/DST/340-1120525) and Marie Curie actions.

A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

PubMed

Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

2017-04-01

The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEI G90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding why the adoption of counter measures against drought is important and direct farmers to choose drought-resistant crops.

Soybean Physiology Calibration in the Community Land Model

NASA Astrophysics Data System (ADS)

Drewniak, B. A.; Bilionis, I.; Constantinescu, E. M.

2014-12-01

With the large influence of agricultural land use on biophysical and biogeochemical cycles, integrating cultivation into Earth System Models (ESMs) is increasingly important. The Community Land Model (CLM) was augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. However, the strong nonlinearity of ESMs makes parameter fitting a difficult task. In this study, our goal is to calibrate ten of the CLM-Crop parameters for one crop type, soybean, in order to improve model projection of plant development and carbon fluxes. We used measurements of gross primary productivity, net ecosystem exchange, and plant biomass from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). Our scheme can perform model calibration using very few evaluations and, by exploiting parallelism, at a fraction of the time required by plain vanilla Markov Chain Monte Carlo (MCMC). We present the results from a twin experiment (self-validation) and calibration results and validation using real observations from an AmeriFlux tower site in the Midwestern United States, for the soybean crop type. The improved model will help researchers understand how climate affects crop production and resulting carbon fluxes, and additionally, how cultivation impacts climate.

Using Geostatistical Data Fusion Techniques and MODIS Data to Upscale Simulated Wheat Yield

NASA Astrophysics Data System (ADS)

Castrignano, A.; Buttafuoco, G.; Matese, A.; Toscano, P.

2014-12-01

Population growth increases food request. Assessing food demand and predicting the actual supply for a given location are critical components of strategic food security planning at regional scale. Crop yield can be simulated using crop models because is site-specific and determined by weather, management, length of growing season and soil properties. Crop models require reliable location-specific data that are not generally available. Obtaining these data at a large number of locations is time-consuming, costly and sometimes simply not feasible. An upscaling method to extend coverage of sparse estimates of crop yield to an appropriate extrapolation domain is required. This work is aimed to investigate the applicability of a geostatistical data fusion approach for merging remote sensing data with the predictions of a simulation model of wheat growth and production using ground-based data. The study area is Capitanata plain (4000 km2) located in Apulia Region, mostly cropped with durum wheat. The MODIS EVI/NDVI data products for Capitanata plain were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC) remote for the whole crop cycle of durum wheat. Phenological development, biomass growth and grain quantity of durum wheat were simulated by the Delphi system, based on a crop simulation model linked to a database including soil properties, agronomical and meteorological data. Multicollocated cokriging was used to integrate secondary exhaustive information (multi-spectral MODIS data) with primary variable (sparsely distributed biomass/yield model predictions of durum wheat). The model estimates looked strongly spatially correlated with the radiance data (red and NIR bands) and the fusion data approach proved to be quite suitable and flexible to integrate data of different type and support.

Conservation Agriculture Practices in Rainfed Uplands of India Improve Maize-Based System Productivity and Profitability

PubMed Central

Pradhan, Aliza; Idol, Travis; Roul, Pravat K.

2016-01-01

Traditional agriculture in rainfed uplands of India has been experiencing low agricultural productivity as the lands suffer from poor soil fertility, susceptibility to water erosion and other external pressures of development and climate change. A shift toward more sustainable cropping systems such as conservation agriculture production systems (CAPSs) may help in maintaining soil quality as well as improving crop production and farmer’s net economic benefit. This research assessed the effects over 3 years (2011–2014) of reduced tillage, intercropping, and cover cropping practices customized for maize-based production systems in upland areas of Odisha, India. The study focused on crop yield, system productivity and profitability through maize equivalent yield and dominance analysis. Results showed that maize grain yield did not differ significantly over time or among CAPS treatments while cowpea yield was considered as an additional yield in intercropping systems. Mustard and horsegram grown in plots after maize cowpea intercropping recorded higher grain yields of 25 and 37%, respectively, as compared to those without intercropping. Overall, the full CAPS implementation, i.e., minimum tillage, maize–cowpea intercropping and mustard residue retention had significantly higher system productivity and net benefits than traditional farmer practices, i.e., conventional tillage, sole maize cropping, and no mustard residue retention. The dominance analysis demonstrated increasing benefits of combining conservation practices that exceeded thresholds for farmer adoption. Given the use of familiar crops and technologies and the magnitude of yield and income improvements, these types of CAPS should be acceptable and attractive for smallholder farmers in the area. This in turn should support a move toward sustainable intensification of crop production to meet future household income and nutritional needs. PMID:27471508

Conservation Agriculture Practices in Rainfed Uplands of India Improve Maize-Based System Productivity and Profitability.

PubMed

Pradhan, Aliza; Idol, Travis; Roul, Pravat K

2016-01-01

Traditional agriculture in rainfed uplands of India has been experiencing low agricultural productivity as the lands suffer from poor soil fertility, susceptibility to water erosion and other external pressures of development and climate change. A shift toward more sustainable cropping systems such as conservation agriculture production systems (CAPSs) may help in maintaining soil quality as well as improving crop production and farmer's net economic benefit. This research assessed the effects over 3 years (2011-2014) of reduced tillage, intercropping, and cover cropping practices customized for maize-based production systems in upland areas of Odisha, India. The study focused on crop yield, system productivity and profitability through maize equivalent yield and dominance analysis. Results showed that maize grain yield did not differ significantly over time or among CAPS treatments while cowpea yield was considered as an additional yield in intercropping systems. Mustard and horsegram grown in plots after maize cowpea intercropping recorded higher grain yields of 25 and 37%, respectively, as compared to those without intercropping. Overall, the full CAPS implementation, i.e., minimum tillage, maize-cowpea intercropping and mustard residue retention had significantly higher system productivity and net benefits than traditional farmer practices, i.e., conventional tillage, sole maize cropping, and no mustard residue retention. The dominance analysis demonstrated increasing benefits of combining conservation practices that exceeded thresholds for farmer adoption. Given the use of familiar crops and technologies and the magnitude of yield and income improvements, these types of CAPS should be acceptable and attractive for smallholder farmers in the area. This in turn should support a move toward sustainable intensification of crop production to meet future household income and nutritional needs.

Estimation of Remote Microclimates from Weather Station Data with Applications to Landscape Architecture.

NASA Astrophysics Data System (ADS)

Brown, Robert Douglas

Several components of a system for quantitative application of climatic statistics to landscape planning and design (CLIMACS) have been developed. One component model (MICROSIM) estimated the microclimate at the top of a remote crop using physically-based models and inputs of weather station data. Temperatures at the top of unstressed, uniform crops on flat terrain within 1600 m of a recording weather station were estimated within 1.0 C 96% of the time for a corn crop and 92% of the time for a soybean crop. Crop top winds were estimated within 0.4 m/s 92% of the time for corn and 100% of the time for soybean. This is of sufficient accuracy for application in landscape planning and design models. A physically-based model (COMFA) was developed for the determination of outdoor human thermal comfort from microclimate inputs. Estimated versus measured comfort levels in a wide range of environments agreed with a correlation coefficient of r = 0.91. Using these components, the CLIMACS concept has been applied to a typical planning example. Microclimate data were generated from weather station information using MICROSIM, then input to COMFA and to a house energy consumption model called HOTCAN to derive quantitative climatic justification for design decisions.

Modelling the perennial energy crop market: the role of spatial diffusion

PubMed Central

Alexander, Peter; Moran, Dominic; Rounsevell, Mark D. A.; Smith, Pete

2013-01-01

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies. PMID:24026474

Modelling the perennial energy crop market: the role of spatial diffusion.

PubMed

Alexander, Peter; Moran, Dominic; Rounsevell, Mark D A; Smith, Pete

2013-11-06

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies.

Influence of crop load on almond tree water status and its importance in irrigation scheduling

NASA Astrophysics Data System (ADS)

Puerto Conesa, Pablo; Domingo Miguel, Rafael; Torres Sánchez, Roque; Pérez Pastor, Alejandro

2014-05-01

In the Mediterranean area water is the main factor limiting crop production and therefore irrigation is essential to achieve economically viable yields. One of the fundamental techniques to ensure that irrigation water is managed efficiently with maximum productivity and minimum environmental impact is irrigation scheduling. The fact that the plant water status integrates atmospheric demand and soil water content conditions encourages the use of plant-based water status indicators. Some researchers have successfully scheduled irrigation in certain fruit trees by maintaining the maximum daily trunk diameter shrinkage (MDS) signal intensity at threshold values to generate (or not) water stress. However MDS not only depends on the climate and soil water content, but may be affected by tree factors such as age, size, phenological stage and fruit load. There is therefore a need to quantify the influence of these factors on MDS. The main objective of this work was to study the effects of crop load on tree water relations for scheduling purposes. We particularly focused on MDS vs VPD10-15 (mean air vapor pressure deficit during the period 10.00-15.00 h solar time) for different loads and phenological phases under non-limiting soil water conditions. The experiment was carried out in 2011 in a 1 ha plot in SE Spain with almond trees (Prunus dulcis (Mill.) D.A. Webb cv. 'Marta'). Three crop load treatments were studied according to three crop load levels, i) T100, high crop load, characteristic crop load, ii) T50, medium crop load, in which 50% of the fruits were removed and iii) T0, practically without fruits. Fruits were manually thinned. Each treatment, randomly distributed in blocks, was run in triplicate. Plant water status was assessed from midday stem water potential (Ψs), MDS, daily trunk growth rate (TGR), leaf turgor potential Ψp, fruit water potential (Ψf), stomatal conductance (gs) and photosynthesis (Pn) and transpiration rates (E). Yield, pruning weights and reserve sugar concentration were also evaluated. Trees were drip irrigated in order to satisfy the maximum crop water requirements. Variations in MDS were compared with changes in Ψs and VPD10-15 in the three treatments at the end of fruit growth stage (stage III), kernel filling stage (stage IV) and postharvest (stage V). Our results highlighted that crop load affects almond tree water status. We observed a greater effect of crop load on MDS and TGR than on Ψs. In T0 trees, Ψs was 16% higher than in T50 and T100. MDS was 36% and 49% lower in the low (T50) and almost nil-cropping trees (T0) than in the high-cropping trees (T100). The slope of MDS vs VPD10-15 forced to the origin increased with crop load, suggesting that different relationships are needed to estimate tree water status. TGR was 33% higher in T0 than in the cropping trees. In the same way, the presence of fruits, as reflected by the source/sink relationship, increased gas exchange parameters. Also pruning weights reflected competition between fruits and shoots for photoassimilates. Nevertheless the reserve sugar concentration at the base of the main branches was unaffected by the crop load. All this implies that it is necessary to consider the crop load in irrigation scheduling based on MDS signal intensity.

Increased food production and reduced water use through optimized crop distribution

NASA Astrophysics Data System (ADS)

Davis, Kyle Frankel; Rulli, Maria Cristina; Seveso, Antonio; D'Odorico, Paolo

2017-12-01

Growing demand for agricultural commodities for food, fuel and other uses is expected to be met through an intensification of production on lands that are currently under cultivation. Intensification typically entails investments in modern technology — such as irrigation or fertilizers — and increases in cropping frequency in regions suitable for multiple growing seasons. Here we combine a process-based crop water model with maps of spatially interpolated yields for 14 major food crops to identify potential differences in food production and water use between current and optimized crop distributions. We find that the current distribution of crops around the world neither attains maximum production nor minimum water use. We identify possible alternative configurations of the agricultural landscape that, by reshaping the global distribution of crops within current rainfed and irrigated croplands based on total water consumption, would feed an additional 825 million people while reducing the consumptive use of rainwater and irrigation water by 14% and 12%, respectively. Such an optimization process does not entail a loss of crop diversity, cropland expansion or impacts on nutrient and feed availability. It also does not necessarily invoke massive investments in modern technology that in many regions would require a switch from smallholder farming to large-scale commercial agriculture with important impacts on rural livelihoods.

Interactive effects of pests increase seed yield.

PubMed

Gagic, Vesna; Riggi, Laura Ga; Ekbom, Barbara; Malsher, Gerard; Rusch, Adrien; Bommarco, Riccardo

2016-04-01

Loss in seed yield and therefore decrease in plant fitness due to simultaneous attacks by multiple herbivores is not necessarily additive, as demonstrated in evolutionary studies on wild plants. However, it is not clear how this transfers to crop plants that grow in very different conditions compared to wild plants. Nevertheless, loss in crop seed yield caused by any single pest is most often studied in isolation although crop plants are attacked by many pests that can cause substantial yield losses. This is especially important for crops able to compensate and even overcompensate for the damage. We investigated the interactive impacts on crop yield of four insect pests attacking different plant parts at different times during the cropping season. In 15 oilseed rape fields in Sweden, we estimated the damage caused by seed and stem weevils, pollen beetles, and pod midges. Pest pressure varied drastically among fields with very low correlation among pests, allowing us to explore interactive impacts on yield from attacks by multiple species. The plant damage caused by each pest species individually had, as expected, either no, or a negative impact on seed yield and the strongest negative effect was caused by pollen beetles. However, seed yield increased when plant damage caused by both seed and stem weevils was high, presumably due to the joint plant compensatory reaction to insect attack leading to overcompensation. Hence, attacks by several pests can change the impact on yield of individual pest species. Economic thresholds based on single species, on which pest management decisions currently rely, may therefore result in economically suboptimal choices being made and unnecessary excessive use of insecticides.

Closing Yield Gaps: How Sustainable Can We Be?

PubMed

Pradhan, Prajal; Fischer, Günther; van Velthuizen, Harrij; Reusser, Dominik E; Kropp, Juergen P

2015-01-01

Global food production needs to be increased by 60-110% between 2005 and 2050 to meet growing food and feed demand. Intensification and/or expansion of agriculture are the two main options available to meet the growing crop demands. Land conversion to expand cultivated land increases GHG emissions and impacts biodiversity and ecosystem services. Closing yield gaps to attain potential yields may be a viable option to increase the global crop production. Traditional methods of agricultural intensification often have negative externalities. Therefore, there is a need to explore location-specific methods of sustainable agricultural intensification. We identified regions where the achievement of potential crop calorie production on currently cultivated land will meet the present and future food demand based on scenario analyses considering population growth and changes in dietary habits. By closing yield gaps in the current irrigated and rain-fed cultivated land, about 24% and 80% more crop calories can respectively be produced compared to 2000. Most countries will reach food self-sufficiency or improve their current food self-sufficiency levels if potential crop production levels are achieved. As a novel approach, we defined specific input and agricultural management strategies required to achieve the potential production by overcoming biophysical and socioeconomic constraints causing yield gaps. The management strategies include: fertilizers, pesticides, advanced soil management, land improvement, management strategies coping with weather induced yield variability, and improving market accessibility. Finally, we estimated the required fertilizers (N, P2O5, and K2O) to attain the potential yields. Globally, N-fertilizer application needs to increase by 45-73%, P2O5-fertilizer by 22-46%, and K2O-fertilizer by 2-3 times compared to the year 2010 to attain potential crop production. The sustainability of such agricultural intensification largely depends on the way management strategies for closing yield gaps are chosen and implemented.

Closing Yield Gaps: How Sustainable Can We Be?

PubMed Central

Pradhan, Prajal; Fischer, Günther; van Velthuizen, Harrij; Reusser, Dominik E.; Kropp, Juergen P.

2015-01-01

Global food production needs to be increased by 60–110% between 2005 and 2050 to meet growing food and feed demand. Intensification and/or expansion of agriculture are the two main options available to meet the growing crop demands. Land conversion to expand cultivated land increases GHG emissions and impacts biodiversity and ecosystem services. Closing yield gaps to attain potential yields may be a viable option to increase the global crop production. Traditional methods of agricultural intensification often have negative externalities. Therefore, there is a need to explore location-specific methods of sustainable agricultural intensification. We identified regions where the achievement of potential crop calorie production on currently cultivated land will meet the present and future food demand based on scenario analyses considering population growth and changes in dietary habits. By closing yield gaps in the current irrigated and rain-fed cultivated land, about 24% and 80% more crop calories can respectively be produced compared to 2000. Most countries will reach food self-sufficiency or improve their current food self-sufficiency levels if potential crop production levels are achieved. As a novel approach, we defined specific input and agricultural management strategies required to achieve the potential production by overcoming biophysical and socioeconomic constraints causing yield gaps. The management strategies include: fertilizers, pesticides, advanced soil management, land improvement, management strategies coping with weather induced yield variability, and improving market accessibility. Finally, we estimated the required fertilizers (N, P2O5, and K2O) to attain the potential yields. Globally, N-fertilizer application needs to increase by 45–73%, P2O5-fertilizer by 22–46%, and K2O-fertilizer by 2–3 times compared to the year 2010 to attain potential crop production. The sustainability of such agricultural intensification largely depends on the way management strategies for closing yield gaps are chosen and implemented. PMID:26083456

Crop advisor perceptions of giant ragweed distribution, herbicide-resistance, and management in the Corn Belt

USDA-ARS?s Scientific Manuscript database

Giant ragweed has been increasing as a major weed of row crops in North America. We conducted a web-based survey of Certified Crop Advisors in the Corn Belt to determine the current distribution of giant ragweed, the distribution of herbicide resistant populations, and management and ecological fact...

Short-term winter wheat (Triticum aestivum L.) cover crop grazing influence on calf growth, grain yield, and soil properties

USDA-ARS?s Scientific Manuscript database

Winter cover cropping has many agronomic benefits and can provide forages base for spring livestock grazing. Winter cover crop grazing has shown immediate economic benefits through increased animal production. Winter wheat pasture grazing is common in beef cow-calf production and stocker operations....

Improved Satellite-based Crop Yield Mapping by Spatially Explicit Parameterization of Crop Phenology

NASA Astrophysics Data System (ADS)

Jin, Z.; Azzari, G.; Lobell, D. B.

2016-12-01

Field-scale mapping of crop yields with satellite data often relies on the use of crop simulation models. However, these approaches can be hampered by inaccuracies in the simulation of crop phenology. Here we present and test an approach to use dense time series of Landsat 7 and 8 acquisitions data to calibrate various parameters related to crop phenology simulation, such as leaf number and leaf appearance rates. These parameters are then mapped across the Midwestern United States for maize and soybean, and for two different simulation models. We then implement our recently developed Scalable satellite-based Crop Yield Mapper (SCYM) with simulations reflecting the improved phenology parameterizations, and compare to prior estimates based on default phenology routines. Our preliminary results show that the proposed method can effectively alleviate the underestimation of early-season LAI by the default Agricultural Production Systems sIMulator (APSIM), and that spatially explicit parameterization for the phenology model substantially improves the SCYM performance in capturing the spatiotemporal variation in maize and soybean yield. The scheme presented in our study thus preserves the scalability of SCYM, while significantly reducing its uncertainty.

Food security: increasing yield and improving resource use efficiency.

PubMed

Parry, Martin A J; Hawkesford, Malcolm J

2010-11-01

Food production and security will be a major issue for supplying an increasing world population. The problem will almost certainly be exacerbated by climate change. There is a projected need to double food production by 2050. In recent times, the trend has been for incremental modest yield increases for most crops. There is an urgent need to develop integrated and sustainable approaches that will significantly increase both production per unit land area and the resource use efficiency of crops. This review considers some key processes involved in plant growth and development with some examples of ways in which molecular technology, plant breeding and genetics may increase the yield and resource use efficiency of wheat. The successful application of biotechnology to breeding is essential to provide the major increases in production required. However, each crop and each specific agricultural situation presents specific requirements and targets for optimisation. Some increases in production will come about as new varieties are developed which are able to produce satisfactory crops on marginal land presently not considered appropriate for arable crops. Other new varieties will be developed to increase both yield and resource use efficiency on the best land.

Salt and N leaching and soil accumulation due to cover cropping practices

NASA Astrophysics Data System (ADS)

Gabriel, J. L.; Quemada, M.

2012-04-01

Nitrate leaching beyond the root zone can increase water contamination hazards and decrease crop available N. Cover crops used in spite of fallow are an alternative to reduce nitrate contamination in the vadose zone, because reducing drainage and soil mineral N accumulation. Cover crops can improve important characteristics in irrigated land as water retention capacity or soil aggregate stability. However, increasing evapotranspiration and consequent drainage below the root system reduction, could lead to soil salt accumulation. Salinity affects more than 80 million ha of arable land in many areas of the world, and one of the principal causes for yield reduction and even land degradation in the Mediterranean region. Few studies dealt with both problems at the same time. Therefore, it is necessary a long-term evaluation of the potential effect on soil salinity and nitrate leaching, in order to ensure that potential disadvantages that could originate from soil salt accumulation are compensated with all advantages of cover cropping. A study of the soil salinity and nitrate leaching was conducted during 4 years in a semiarid irrigated agricultural area of Central Spain. Three treatments were studied during the intercropping period of maize (Zea mays L.): barley (Hordeum vulgare L.), vetch (Vicia villosa L.) and fallow. Cover crops were killed in March allowing seeding of maize of the entire trial in April, and all treatments were irrigated and fertilised following the same procedure. Before sowing, and after harvesting maize and cover crops, soil salt and nitrate accumulation was determined along the soil profile. Soil analysis was conducted at six depths every 0.20 m in each plot in samples from four 0 to 1.2-m depth holes dug. The electrical conductivity of the saturated paste extract and soil mineral nitrogen was measured in each soil sample. A numerical model based on the Richards water balance equation was applied in order to calculate drainage at 1.2 m depth, using daily soil water content measurements, based on calibrated capacitance probes. Our results showed that drainage during the irrigated period was minimized, because irrigation water was adjusted to crop needs, leading to soil salt and nitrate accumulation on the upper layers after maize harvest. Then, during the intercrop period, most of salt and nitrate leaching occurred. Cover crops use led to shorter drainage period, lower drainage water amount and lower nitrate and salt leaching than treatment with fallow. These effects were related with a larger nitrate accumulation in the upper layers of the soil after cover crop treatments. But there was not soil salt accumulation increase in treatments with cover crops, and even decreased after years with a large cover crop biomass production. Then, adoption of cover crops in this kind of irrigated cropping system reduced water drainage beyond the root zone, salt and nitrate leaching diminished as a consequence but did not lead to salt accumulation in the upper soil layers. Acknowledgements: Financial support by CICYT, Spain (ref. AGL2005-00163 and AGL 2011-24732) and Comunidad de Madrid (project AGRISOST, S2009/AGR-1630).

Development and characterization of transgenic dominant male sterile rice toward an outcross-based breeding system.

PubMed

Abe, Kiyomi; Oshima, Masao; Akasaka, Maiko; Konagaya, Ken-Ichi; Nanasato, Yoshihiko; Okuzaki, Ayako; Taniguchi, Yojiro; Tanaka, Junichi; Tabei, Yutaka

2018-03-01

Genomic selection is attracting attention in the field of crop breeding. To apply genomic selection effectively for autogamous (self-pollinating) crops, an efficient outcross system is desired. Since dominant male sterility is a powerful tool for easy and successive outcross of autogamous crops, we developed transgenic dominant male sterile rice ( Oryza sativa L.) using the barnase gene that is expressed by the tapetum-specific promoter BoA9 . Barnase -induced male sterile rice No. 10 (BMS10) was selected for its stable male sterility and normal growth characteristics. The BMS10 flowering habits, including heading date, flowering date, and daily flowering time of BMS10 tended to be delayed compared to wild type. When BMS10 and wild type were placed side-by-side and crossed under an open-pollinating condition, the seed-setting rate was <1.5%. When the clipping method was used to avoid the influence of late flowering habits, the seed-setting rate of BMS10 increased to a maximum of 86.4%. Although flowering synchronicity should be improved to increase the seed-setting rate, our results showed that this system can produce stable transgenic male sterility with normal female fertility in rice. The transgenic male sterile rice would promote a genomic selection-based breeding system in rice.

The use of seasonal forecasts in a crop failure early warning system for West Africa

NASA Astrophysics Data System (ADS)

Nicklin, K. J.; Challinor, A.; Tompkins, A.

2011-12-01

Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

Rainfall and crop modeling-based water stress assessment for rainfed maize cultivation in peninsular India

NASA Astrophysics Data System (ADS)

Manivasagam, V. S.; Nagarajan, R.

2018-04-01

Water stress due to uneven rainfall distribution causes a significant impact on the agricultural production of monsoon-dependent peninsular India. In the present study, water stress assessment for rainfed maize crop is carried out for kharif (June-October) and rabi (October-February) cropping seasons which coincide with two major Indian monsoons. Rainfall analysis (1976-2010) shows that the kharif season receives sufficient weekly rainfall (28 ± 32 mm) during 26th-39th standard meteorological weeks (SMWs) from southwest monsoon, whereas the rabi season experiences a major portion of its weekly rainfall due to northeast monsoon between the 42nd and 51st SMW (31 ± 42 mm). The later weeks experience minimal rainfall (5.5 ± 15 mm) and thus expose the late sown maize crops to a severe water stress during its maturity stage. Wet and dry spell analyses reveal a substantial increase in the rainfall intensity over the last few decades. However, the distribution of rainfall shows a striking decrease in the number of wet spells, with prolonged dry spells in both seasons. Weekly rainfall classification shows that the flowering and maturity stages of kharif maize (33rd-39th SMWs) can suffer around 30-40% of the total water stress. In the case of rabi maize, the analysis reveals that a shift in the sowing time from the existing 42nd SMW (16-22 October) to the 40th SMW (1-7 October) can avoid terminal water stress. Further, AquaCrop modeling results show that one or two minimal irrigations during the flowering and maturity stages (33rd-39th SMWs) of kharif maize positively avoid the mild water stress exposure. Similarly, rabi maize requires an additional two or three lifesaving irrigations during its flowering and maturity stages (48th-53rd SMWs) to improve productivity. Effective crop planning with appropriate sowing time, short duration crop, and high yielding drought-resistant varieties will allow for better utilization of the monsoon rain, thus reducing water stress with an increase in rainfed maize productivity.

Mapping Cropland and Crop-type Distribution Using Time Series MODIS Data

NASA Astrophysics Data System (ADS)

Lu, D.; Chen, Y.; Moran, E. F.; Batistella, M.; Luo, L.; Pokhrel, Y.; Deb, K.

2016-12-01

Mapping regional and global cropland distribution has attracted great attention in the past decade, but the separation of crop types is challenging due to the spectral confusion and cloud cover problems during the growing season in Brazil. The objective of this study is to develop a new approach to identify crop types (including soybean, cotton, maize) and planting patterns (soybean-maize, soybean-cotton, and single crop) in Mato Grosso, Goias and Tocantins States, Brazil. The time series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (MOD13Q1) in 2015/2016 were used in this research and field survey data were collected in May 2016. The major steps include: (1) reconstruct time series NDVI data contaminated by noise and clouds using the temporal interpolation algorithm; (2) identify the best periods and develop temporal indices and phenology parameters to distinguish cropland from other land cover types based on time series NDVI data; (3) develop a crop temporal difference index (CTDI) to extract crop types and patterns using time series NDVI data. This research shows that (1) the cropland occupied approximately 16.85% of total land in these three states; (2) soybean-maize and soybean-cotton were two major crop patterns which occupied 54.80% and 19.30% of total cropland area. This research indicates that the proposed approach is promising for accurately and rapidly mapping cropland and crop-type distribution in these three states of Brazil.

Crop water productivity under increasing irrigation capacities in Romania. A spatially-explicit assessment of winter wheat and maize cropping systems in the southern lowlands of the country

NASA Astrophysics Data System (ADS)

Dogaru, Diana

2016-04-01

Improved water use efficiency in agriculture is a key issue in terms of sustainable management and consumption of water resources in the context of peoples' increasing food demands and preferences, economic growth and agricultural adaptation options to climate variability and change. Crop Water Productivity (CWP), defined as the ratio of yield (or value of harvested crop) to actual evapotranspiration or as the ratio of yield (or value of harvested crop) to volume of supplied irrigation water (Molden et al., 1998), is a useful indicator in the evaluation of water use efficiency and ultimately of cropland management, particularly in the case of regions affected by or prone to drought and where irrigation application is essential for achieving expected productions. The present study investigates the productivity of water in winter wheat and maize cropping systems in the Romanian Plain (49 594 sq. km), an important agricultural region in the southern part of the country which is increasingly affected by drought and dry spells (Sandu and Mateescu, 2014). The scope of the analysis is to assess the gains and losses in CWP for the two crops, by considering increased irrigated cropland and improved fertilization, these being the most common measures potentially and already implemented by the farmers. In order to capture the effects of such measures on agricultural water use, the GIS-based EPIC crop-growth model (GEPIC) (Williams et al., 1989; Liu, 2009) was employed to simulate yields, seasonal evapotranspiration from crops and volume of irrigation water in the Romanian Plain for the 2002 - 2013 interval with focus on 2007 and 2010, two representative years for dry and wet periods, respectively. The GEPIC model operates on a daily time step, while the geospatial input datasets for this analysis (e.g. climate data, soil classes and soil parameters, land use) were harmonized at 1km resolution grid cell. The sources of the spatial data are mainly the national profile agencies/institutes, providing the data at fine resolutions. The increased irrigated area was accounted according to the reported increased percentages of the irrigated area out of the total area equipped for irrigation, as an expected outcome of public irrigation systems rehabilitation schemes (MADR, 2011), while the optimum Nitrogen fertilizer rates for wheat and maize were established according to several field experiments made on irrigated and rain-fed wheat and maize plots in south Romania (Hera and Borlan, 1980). The effects of such farming measures on yields were compared to a baseline condition given by actual irrigated area and fertilization rates. The preliminary results show that potential gains in CWP could be obtained through improved fertilizer management and water allocation in winter wheat cropping systems, particularly in the dry periods, while in maize cropping systems CWP is more sensitive to water than to optimum fertilization rates. Irrigation water supply increases the stability of yields in both cropping systems, although regional differences can be observed across the study area, thus augmenting the relevance and the need for investigations on sustainable use of irrigation water in Romania. As such, this study could represent an information base for further analyses on yield potential under current and future climatic conditions, on impacts of land use patterns and farming practices on crop production in Romania, etc. Keywords: agricultural water use, crop water productivity, irrigation water, GEPIC, Romania References: Molden, D.J., Sakthivadivel, R., Perry, C.J., de Fraiture, C., Kloezen, W.H. (1998). Indicators for comparing performance of irrigated agricultural systems, Research Report 20, IWMI: Colombo, Sri Lanka. Sandu, I., Mateescu E. (2014). Current and prospective climate changes in Romania (in Romanian), in vol. Climate change: a major challenge for research in agriculture (ed. Saulescu, N.), Romanian Academy Publishing House, 17-36. Williams, J.R., Jones, C.A., Kiniry, J.R., Spanel, D.A. (1989). The EPIC crop growth model. Trans. ASAE 32 (2), 497-511. Liu, J. (2009). A GIS-based tool for modelling large-scale crop-water relations, Environmental Modelling & Software, 24, 411-422. MADR (Ministry of Agriculture and Rural Development), (2011). Rehabilitation and reform in the irrigation sector. Strategy of investment in the irrigation sector (in Romanian), Fidman Merk at., Bucharest, http://old.madr.ro/pages/strategie/strategie-investitii-irigatii.pdf. Hera, C., Borlan, Z. (1980). Guide for fertilization planning (in Romanian), 2nd edition, CERES Publishing House, Bucharest, Romania, 341p.

A high-throughput method for GMO multi-detection using a microfluidic dynamic array.

PubMed

Brod, Fábio Cristiano Angonesi; van Dijk, Jeroen P; Voorhuijzen, Marleen M; Dinon, Andréia Zilio; Guimarães, Luis Henrique S; Scholtens, Ingrid M J; Arisi, Ana Carolina Maisonnave; Kok, Esther J

2014-02-01

The ever-increasing production of genetically modified crops generates a demand for high-throughput DNA-based methods for the enforcement of genetically modified organisms (GMO) labelling requirements. The application of standard real-time PCR will become increasingly costly with the growth of the number of GMOs that is potentially present in an individual sample. The present work presents the results of an innovative approach in genetically modified crops analysis by DNA based methods, which is the use of a microfluidic dynamic array as a high throughput multi-detection system. In order to evaluate the system, six test samples with an increasing degree of complexity were prepared, preamplified and subsequently analysed in the Fluidigm system. Twenty-eight assays targeting different DNA elements, GM events and species-specific reference genes were used in the experiment. The large majority of the assays tested presented expected results. The power of low level detection was assessed and elements present at concentrations as low as 0.06 % were successfully detected. The approach proposed in this work presents the Fluidigm system as a suitable and promising platform for GMO multi-detection.

Expansion of mass-flowering crops leads to transient pollinator dilution and reduced wild plant pollination

PubMed Central

Holzschuh, Andrea; Dormann, Carsten F.; Tscharntke, Teja; Steffan-Dewenter, Ingolf

2011-01-01

Agricultural land use results in direct biodiversity decline through loss of natural habitat, but may also cause indirect cross-habitat effects on conservation areas. We conducted three landscape-scale field studies on 67 sites to test the hypothesis that mass flowering of oilseed rape (Brassica napus) results in a transient dilution of bees in crop fields, and in increased competition between crop plants and grassland plants for pollinators. Abundances of bumble-bees, which are the main pollinators of the grassland plant Primula veris, but also pollinate oilseed rape (OSR), decreased with increasing amount of OSR. This landscape-scale dilution affected bumble-bee abundances strongly in OSR fields and marginally in grasslands, where bumble-bee abundances were generally low at the time of Primula flowering. Seed set of Primula veris, which flowers during OSR bloom, was reduced by 20 per cent when the amount of OSR within 1 km radius increased from 0 to 15 per cent. Hence, the current expansion of bee-attractive biofuel crops results in transient dilution of crop pollinators, which means an increased competition for pollinators between crops and wild plants. In conclusion, mass-flowering crops potentially threaten fitness of concurrently flowering wild plants in conservation areas, despite the fact that, in the long run, mass-flowering crops can enhance abundances of generalist pollinators and their pollination service. PMID:21471115

Prediction of seasonal climate-induced variations in global food production

NASA Astrophysics Data System (ADS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-10-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop--rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

Uav-Based Crops Classification with Joint Features from Orthoimage and Dsm Data

NASA Astrophysics Data System (ADS)

Liu, B.; Shi, Y.; Duan, Y.; Wu, W.

2018-04-01

Accurate crops classification remains a challenging task due to the same crop with different spectra and different crops with same spectrum phenomenon. Recently, UAV-based remote sensing approach gains popularity not only for its high spatial and temporal resolution, but also for its ability to obtain spectraand spatial data at the same time. This paper focus on how to take full advantages of spatial and spectrum features to improve crops classification accuracy, based on an UAV platform equipped with a general digital camera. Texture and spatial features extracted from the RGB orthoimage and the digital surface model of the monitoring area are analysed and integrated within a SVM classification framework. Extensive experiences results indicate that the overall classification accuracy is drastically improved from 72.9 % to 94.5 % when the spatial features are combined together, which verified the feasibility and effectiveness of the proposed method.

Comment on 'Shang S. 2012. Calculating actual crop evapotranspiration under soil water stress conditions with appropriate numerical methods and time step. Hydrological Processes 26: 3338-3343. DOI: 10.1002/hyp.8405'

NASA Technical Reports Server (NTRS)

Yatheendradas, Soni; Narapusetty, Balachandrudu; Peters-Lidard, Christa; Funk, Christopher; Verdin, James

2014-01-01

A previous study analyzed errors in the numerical calculation of actual crop evapotranspiration (ET(sub a)) under soil water stress. Assuming no irrigation or precipitation, it constructed equations for ET(sub a) over limited soil-water ranges in a root zone drying out due to evapotranspiration. It then used a single crop-soil composite to provide recommendations about the appropriate usage of numerical methods under different values of the time step and the maximum crop evapotranspiration (ET(sub c)). This comment reformulates those ET(sub a) equations for applicability over the full range of soil water values, revealing a dependence of the relative error in numerical ET(sub a) on the initial soil water that was not seen in the previous study. It is shown that the recommendations based on a single crop-soil composite can be invalid for other crop-soil composites. Finally, a consideration of the numerical error in the time-cumulative value of ET(sub a) is discussed besides the existing consideration of that error over individual time steps as done in the previous study. This cumulative ET(sub a) is more relevant to the final crop yield.

Adapting rice production to climate change for sustainable blue water consumption: an economic and virtual water analysis

NASA Astrophysics Data System (ADS)

Darzi-Naftchali, Abdullah; Karandish, Fatemeh

2017-12-01

Sustainable utilization of blue water resources under climate change is of great significance especially for producing high water-consuming crops in water-scarce regions. Based on the virtual water concept, we carried out a comprehensive field-modeling research to find the optimal agricultural practices regarding rice blue water consumption under prospective climate change. The DSSAT-CERES-Rice model was used in combination with 20 GCMs under three Representative Concentration Pathways of low (RCP2.6), intermediate (RCP4.6), and very high (RCP8.5) greenhouse concentrations to predict rice yield and water requirement and related virtual water and economic return for the base and future periods. The crop model was calibrated and validated based on the 2-year field data obtained from consolidated paddy fields of the Sari Agricultural Sciences and Natural Resources University during 2011 and 2012 rice cropping cycles. Climate change imposes an increase of 0.02-0.04 °C in air temperature which consequently shifts rice growing seasons to winter season, and shorten the length of rice physiological maturity period by 2-15 days. While rice virtual water reduces by 0.1-20.6% during 2011-2070, reduced rice yield by 3.8-22.6% over the late twenty-first century results in a considerable increase in rice virtual water. By increasing the contribution of green water in supplying crop water requirement, earlier cropping could diminish blue water consumption for rice production in the region while cultivation postponement increases irrigation water requirement by 2-195 m3 ha-1. Forty days delay in rice cultivation in future will result in 29.9-40.6% yield reduction and 43.9-60% increase in rice virtual water under different scenarios. Earlier cropping during the 2011-2040 and 2041-2070 periods would increase water productivity, unit value of water, and economic value of blue water compared to the base period. Based on the results, management of rice cultivation calendar is a suitable strategy for sustainable blue water consumption for producing rice under future climate.

Impact of Climate Change on Food Security in Kenya

NASA Astrophysics Data System (ADS)

Yator, J. J.

2016-12-01

This study sought to address the existing gap on the impact of climate change on food security in support of policy measures to avert famine catastrophes. Fixed and random effects regressions for crop food security were estimated. The study simulated the expected impact of future climate change on food insecurity based on the Representative Concentration Pathways scenario (RCPs). The study makes use of county-level yields estimates (beans, maize, millet and sorghum) and daily climate data (1971 to 2010). Climate variability affects food security irrespective of how food security is defined. Rainfall during October-November-December (OND), as well as during March-April-May (MAM) exhibit an inverted U-shaped relationship with most food crops; the effects are most pronounced for maize and sorghum. Beans and Millet are found to be largely unresponsive to climate variability and also to time-invariant factors. OND rains and fall and summer temperature exhibit a U-shaped relationship with yields for most crops, while MAM rains temperature exhibits an inverted U-shaped relationship. However, winter temperatures exhibit a hill-shaped relationship with most crops. Project future climate change scenarios on crop productivity show that climate change will adversely affect food security, with up to 69% decline in yields by the year 2100. Climate variables have a non-linear relationship with food insecurity. Temperature exhibits an inverted U-shaped relationship with food insecurity, suggesting that increased temperatures will increase crop food insecurity. However, maize and millet, benefit from increased summer and winter temperatures. The simulated effects of different climate change scenarios on food insecurity suggest that adverse climate change will increase food insecurity in Kenya. The largest increases in food insecurity are predicted for the RCP 8.5Wm2, compared to RCP 4.5Wm2. Climate change is likely to have the greatest effects on maize insecurity, which is likely to increase by between 8.56% and 21% by the year 2100. There exists a need for policies that safeguard agriculture against the adverse effects of climate change to alleviate food insecurity in Kenya. Therefore, it is important that climate change mitigation is given much more priority in policy planning and also implementation.

Effects of crop load on distribution and utilization of 13C and 15N and fruit quality for dwarf apple trees.

PubMed

Ding, Ning; Chen, Qian; Zhu, Zhanling; Peng, Ling; Ge, Shunfeng; Jiang, Yuanmao

2017-10-26

In order to define the effects of fruit crop load on the distribution and utilization of carbon and nitrogen in dwarf apple trees, we conducted three crop load levels (High-crop load, 6 fruits per trunk cross-sectional area (cm 2 , TCA)), Medium-crop load (4 fruits cm -2 TCA), Low-crop load (2 fruits cm -2 TCA)) in 2014 and 2015. The results indicated that the 15 N derived from fertilizer (Ndff) values of fruits decreased with the reduction of crop load, but the Ndff values of annual branches, leaves and roots increased. The plant 15 N-urea utilization rates on Medium and Low-crop load were 1.12-1.35 times higher than the High-crop load. With the reduction of crop load, the distribution rate of 13 C and 15 N in fruits was gradually reduced, but in contrast, the distribution of 13 C and 15 N gradually increased in annual branches, leaves and roots. Compared with High-crop load, the Medium and Low-crop load significantly improved fruit quality p < 0.05. Hence, controlling fruit load effectively regulated the distribution of carbon and nitrogen in plants, improved the nitrogen utilization rate and fruit quality. The appropriate crop load level for mature M.26 interstocks apple orchards was deemed to be 4.0 fruits cm -2 TCA.

Detection and identification of genetically modified EE-1 brinjal (Solanum melongena) by single, multiplex and SYBR® real-time PCR.

PubMed

Ballari, Rajashekhar V; Martin, Asha; Gowda, Lalitha R

2013-01-01

Brinjal is an important vegetable crop. Major crop loss of brinjal is due to insect attack. Insect-resistant EE-1 brinjal has been developed and is awaiting approval for commercial release. Consumer health concerns and implementation of international labelling legislation demand reliable analytical detection methods for genetically modified (GM) varieties. End-point and real-time polymerase chain reaction (PCR) methods were used to detect EE-1 brinjal. In end-point PCR, primer pairs specific to 35S CaMV promoter, NOS terminator and nptII gene common to other GM crops were used. Based on the revealed 3' transgene integration sequence, primers specific for the event EE-1 brinjal were designed. These primers were used for end-point single, multiplex and SYBR-based real-time PCR. End-point single PCR showed that the designed primers were highly specific to event EE-1 with a sensitivity of 20 pg of genomic DNA, corresponding to 20 copies of haploid EE-1 brinjal genomic DNA. The limits of detection and quantification for SYBR-based real-time PCR assay were 10 and 100 copies respectively. The prior development of detection methods for this important vegetable crop will facilitate compliance with any forthcoming labelling regulations. Copyright © 2012 Society of Chemical Industry.

Large and Small-Scale Cropland Classification on the Foothills of Mount Kenya Based on SPOT-5 Take-5 Data Time Series

NASA Astrophysics Data System (ADS)

Eckert, Sandra

2016-08-01

The SPOT-5 Take 5 campaign provided SPOT time series data of an unprecedented spatial and temporal resolution. We analysed 29 scenes acquired between May and September 2015 of a semi-arid region in the foothills of Mount Kenya, with two aims: first, to distinguish rainfed from irrigated cropland and cropland from natural vegetation covers, which show similar reflectance patterns; and second, to identify individual crop types. We tested several input data sets in different combinations: the spectral bands and the normalized difference vegetation index (NDVI) time series, principal components of NDVI time series, and selected NDVI time series statistics. For the classification we used random forests (RF). In the test differentiating rainfed cropland, irrigated cropland, and natural vegetation covers, the best classification accuracies were achieved using spectral bands. For the differentiation of crop types, we analysed the phenology of selected crop types based on NDVI time series. First results are promising.

Changes in the potential multiple cropping system in response to climate change in China from 1960-2010.

PubMed

Liu, Luo; Xu, Xinliang; Zhuang, Dafang; Chen, Xi; Li, Shuang

2013-01-01

The multiple cropping practice is essential to agriculture because it has been shown to significantly increase the grain yield and promote agricultural economic development. In this study, potential multiple cropping systems in China are calculated based on meteorological observation data by using the Agricultural Ecology Zone (AEZ) model. Following this, the changes in the potential cropping systems in response to climate change between the 1960s and the 2010s were subsequently analyzed. The results indicate that the changes of potential multiple cropping systems show tremendous heterogeneity in respect to the spatial pattern in China. A key finding is that the magnitude of change of the potential cropping systems showed a pattern of increase both from northern China to southern China and from western China to eastern China. Furthermore, the area found to be suitable only for single cropping decreased, while the area suitable for triple cropping increased significantly from the 1960s to the 2000s. During the studied period, the potential multiple cropping index (PMCI) gap between rain-fed and irrigated scenarios increased from 18% to 24%, which indicated noticeable growth of water supply limitations under the rain-fed scenario. The most significant finding of this research was that from the 1960s to the 2000s climate change had led to a significant increase of PMCI by 13% under irrigated scenario and 7% under rain-fed scenario across the whole of China. Furthermore, the growth of the annual mean temperature is identified as the main reason underlying the increase of PMCI. It has also been noticed that across China the changes of potential multiple cropping systems under climate change were different from region to region.

Environmental health impacts of feeding crops to farmed fish.

PubMed

Fry, Jillian P; Love, David C; MacDonald, Graham K; West, Paul C; Engstrom, Peder M; Nachman, Keeve E; Lawrence, Robert S

2016-05-01

Half of the seafood consumed globally now comes from aquaculture, or farmed seafood. Aquaculture therefore plays an increasingly important role in the global food system, the environment, and human health. Traditionally, aquaculture feed has contained high levels of wild fish, which is unsustainable for ocean ecosystems as demand grows. The aquaculture industry is shifting to crop-based feed ingredients, such as soy, to replace wild fish as a feed source and allow for continued industry growth. This shift fundamentally links seafood production to terrestrial agriculture, and multidisciplinary research is needed to understand the ecological and environmental health implications. We provide basic estimates of the agricultural resource use associated with producing the top five crops used in commercial aquaculture feed. Aquaculture's environmental footprint may now include nutrient and pesticide runoff from industrial crop production, and depending on where and how feed crops are produced, could be indirectly linked to associated negative health outcomes. We summarize key environmental health research on health effects associated with exposure to air, water, and soil contaminated by industrial crop production. Our review also finds that changes in the nutritional content of farmed seafood products due to altered feed composition could impact human nutrition. Based on our literature reviews and estimates of resource use, we present a conceptual framework describing the potential links between increasing use of crop-based ingredients in aquaculture and human health. Additional data and geographic sourcing information for crop-based ingredients are needed to fully assess the environmental health implications of this trend. This is especially critical in the context of a food system that is using both aquatic and terrestrial resources at unsustainable rates. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Applications of satellite 'hyper-sensing' in Chinese agriculture: Challenges and opportunities

NASA Astrophysics Data System (ADS)

Onojeghuo, Alex Okiemute; Blackburn, George Alan; Huang, Jingfeng; Kindred, Daniel; Huang, Wenjiang

2018-02-01

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of 'hyper-sensing' (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite 'hyper-sensing' to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite 'hyper-sensing' in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing 'hyper-sensing' approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China.

Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era

NASA Astrophysics Data System (ADS)

Parkes, Ben; Defrance, Dimitri; Sultan, Benjamin; Ciais, Philippe; Wang, Xuhui

2018-02-01

The ability of a region to feed itself in the upcoming decades is an important issue. The West African population is expected to increase significantly in the next 30 years. The responses of crops to short-term climate change is critical to the population and the decision makers tasked with food security. This leads to three questions: how will crop yields change in the near future? What influence will climate change have on crop failures? Which adaptation methods should be employed to ameliorate undesirable changes? An ensemble of near-term climate projections are used to simulate maize, millet and sorghum in West Africa in the recent historic period (1986-2005) and a near-term future when global temperatures are 1.5 K above pre-industrial levels to assess the change in yield, yield variability and crop failure rate. Four crop models were used to simulate maize, millet and sorghum in West Africa in the historic and future climates. Across the majority of West Africa the maize, millet and sorghum yields are shown to fall. In the regions where yields increase, the variability also increases. This increase in variability increases the likelihood of crop failures, which are defined as yield negative anomalies beyond 1 standard deviation during the historic period. The increasing variability increases the frequency of crop failures across West Africa. The return time of crop failures falls from 8.8, 9.7 and 10.1 years to 5.2, 6.3 and 5.8 years for maize, millet and sorghum respectively. The adoption of heat-resistant cultivars and the use of captured rainwater have been investigated using one crop model as an idealized sensitivity test. The generalized doption of a cultivar resistant to high-temperature stress during flowering is shown to be more beneficial than using rainwater harvesting.

Performance of Vegetation Indices for Wheat Yield Forecasting for Punjab, Pakistan

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Becker-Reshef, I.; Adusei, B.; Barker, B.

2013-12-01

Forecasting wheat yield in major producer countries early in the growing season allows better planning for harvest deficits and surplus with implications for food security, world market transactions, sustaining adequate grain stocks, policy making and other matters. Remote sensing imagery is well suited for yield forecasting over large areas. The Normalized Difference Vegetation Index (NDVI) has been the most-used spectral index derived from remote sensing imagery for assessing crop condition of major crops and forecasting crop yield. Many authors have found that the highest correlation between NDVI and yield of wheat crops occurs at the height of the growing season when NDVI values and photosynthetic activity of the wheat plants are at their relative maximum. At the same time NDVI saturates in very dense and vigorous (healthy, green) canopies such as wheat fields during the seasonal peak and shows significantly reduced sensitivity to further increases in photosynthetic activity. In this study we compare the performance of different vegetation indices derived from space-borne red and near-infrared spectral reflectance measurements for wheat yield forecasting in the Punjab Province, Pakistan. Areas covered by wheat crop each year were determined using a time series of MODIS 8-day composites at 250 m resolution converted to temporal metrics and classified using a bagged decision tree approach, driven by classified multi-temporal Landsat scenes. Within the wheat areas we analyze and compare wheat yield forecasts derived from three different satellite-based vegetation indices at the peak of the growing season. We regressed in turn NDVI, Wide Dynamic Range Vegetation Index (WDRVI) and the Vegetation Condition Index (VCI) from the four years preceding the wheat growing season 2011/12 against reported yield values and applied the regression equations to forecast wheat yield for the 2011/12 season per district for each of 36 Punjab districts. Yield forecasts overall corresponded well with reported values. NDVI-based forecasts showed high correlations of r squared = 0.881 and RMSE 11%. The VCI performed similarly well with r squared = 0.886 and RMSE 11%. WDRVI performed better than either of the other indices with r squared = 0.909 and RMSE 10%, probably due to the increased sensitivity of the index at high values. Wheat yields in Pakistan show on average a slow but steady annual increase but overall are comparatively stable due to the fact that the majority of fields are irrigated. The next steps in this study will be to compare NDVI- with WDRVI-based yield forecasts in other environments dominated by rain-fed agriculture, such as Ukraine, Australia and the United States.

Investigating the (Mis)Match between Natural Pest Control Knowledge and the Intensity of Pesticide Use.

PubMed

Mall, David; Larsen, Ashley E; Martin, Emily A

2018-01-05

Transforming modern agriculture towards both higher yields and greater sustainability is critical for preserving biodiversity in an increasingly populous and variable world. However, the intensity of agricultural practices varies strongly between crop systems. Given limited research capacity, it is crucial to focus efforts to increase sustainability in the crop systems that need it most. In this study, we investigate the match (or mismatch) between the intensity of pesticide use and the availability of knowledge on the ecosystem service of natural pest control across various crop systems. Using a systematic literature search on pest control and publicly available pesticide data, we find that pest control literature is not more abundant in crops where insecticide input per hectare is highest. Instead, pest control literature is most abundant, with the highest number of studies published, in crops with comparatively low insecticide input per hectare but with high world harvested area. These results suggest that a major increase of interest in agroecological research towards crops with high insecticide input, particularly cotton and horticultural crops such as citrus and high value-added vegetables, would help meet knowledge needs for a timely ecointensification of agriculture.

Investigating the (Mis)Match between Natural Pest Control Knowledge and the Intensity of Pesticide Use

PubMed Central

Larsen, Ashley E.

2018-01-01

Transforming modern agriculture towards both higher yields and greater sustainability is critical for preserving biodiversity in an increasingly populous and variable world. However, the intensity of agricultural practices varies strongly between crop systems. Given limited research capacity, it is crucial to focus efforts to increase sustainability in the crop systems that need it most. In this study, we investigate the match (or mismatch) between the intensity of pesticide use and the availability of knowledge on the ecosystem service of natural pest control across various crop systems. Using a systematic literature search on pest control and publicly available pesticide data, we find that pest control literature is not more abundant in crops where insecticide input per hectare is highest. Instead, pest control literature is most abundant, with the highest number of studies published, in crops with comparatively low insecticide input per hectare but with high world harvested area. These results suggest that a major increase of interest in agroecological research towards crops with high insecticide input, particularly cotton and horticultural crops such as citrus and high value-added vegetables, would help meet knowledge needs for a timely ecointensification of agriculture. PMID:29304005

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

A data-oriented semi-process model for evaluating the yields of major crops at global scale (PRYSBI-2)

NASA Astrophysics Data System (ADS)

Sakurai, G.; Iizumi, T.; Yokozawa, M.

2013-12-01

Demand for major cereal crops will double by 2050 compared to the amount in 2005 due to the population growth, dietary change, and increase in biofuel use. This requires substantial efforts to increase crop yields under changing climate, water resources, and land use. In order to explore possible paths to meet the supply target, global crop modeling is a useful approach. To that end, we developed a process-based large-area crop model (called PRYSBIE-2) for major crops, including soybean. This model consisted of the enzyme kinetics model for photosynthetic carbon assimilation and soil water balance model from SWAT. The parameter values on water stress, nitrogen stress were calibrated over global croplands from one grid cell to another (1.125° in latitude and longitude) using Markov Chain Monte Carlo (MCMC) methods. The historical yield data collected from major crop-producing countries on a state, county, or prefecture scale were used as the calibration data. Then we obtained the model parameter sets that can give high correlation coefficients between the historical and estimated yield time series for the period 1980-2006. We analyzed the impacts on soybean yields in the three top soybean-producing countries (the USA, China, and Brazil) associated with the changes in climate and CO2 during the period 1980-2006, using the model. We found that, given the simulated yields and reported harvested areas, the estimated average net benefit from the CO2 fertilization effect (with one standard deviation) in the USA, Brazil, and China in the years was 42.70×32.52 Mt, 35.30×28.55 Mt, and 12.52×15.11 Mt, respectively. Results suggest that the CO2-induced increases in soybean yields in the USA and China likely offset a part of the negative impacts on yields due to the historical temperature rise. In contrast, the net effect of the past change in climate and CO2 in Brazil appeared to be positive. This study demonstrates a quantitative estimation of the impacts of the changes in climate and CO2 during the past few decades using a new global crop model.

Development of a European Ensemble System for Seasonal Prediction: Application to crop yield

NASA Astrophysics Data System (ADS)

Terres, J. M.; Cantelaube, P.

2003-04-01

Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.

Dynamic drought risk assessment using crop model and remote sensing techniques

NASA Astrophysics Data System (ADS)

Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

2017-02-01

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

USGS Publications Warehouse

Glenn, E.P.; Neale, C. M. U.; Hunsaker, D.J.; Nagler, P.L.

2011-01-01

Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil evaporation. Copyright ?? 2011 John Wiley & Sons, Ltd.

Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

NASA Technical Reports Server (NTRS)

Zhang, Ping; Anderson, Bruce; Tan, Bin; Barlow, Mathew; Myneni, Ranga

2011-01-01

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity.

A half-century analysis of landscape dynamics in southern Québec, Canada.

PubMed

Jobin, Benoît; Latendresse, Claudie; Baril, Alain; Maisonneuve, Charles; Boutin, Céline; Côté, Dominique

2014-04-01

We studied landscape dynamics for three time periods (<1950, 1965, and 1997) along a gradient of agricultural intensity from highly intensive agriculture to forested areas in southern Québec. Air photos were analyzed to obtain long-term information on land cover (crop and habitat types) and linear habitats (hedgerows and riparian habitats) and landscape metrics were calculated to quantify changes in habitat configuration. Anthropogenic areas increased in all types of landscapes but mostly occurred in the highly disturbed cash crop dominated landscape. Perennial crops (pasture and hayfields) were largely converted into annual crops (corn and soybean) between 1965 and 1997. The coalescence of annual crop fields resulted in a more homogeneous agricultural landscape. Old fields and forest cover was consistently low and forest fragmentation remained stable through time in the intensive agriculture landscapes. However, forest cover increased and forest fragmentation receded in the forest-dominated landscapes following farm abandonment and the transition of old fields into forests. Tree-dominated hedgerows and riparian habitats increased in areas with intensive agriculture. Observed changes in land cover classes are related to proximate factors, such as surficial deposits and topography. Agriculture intensification occurred in areas highly suitable for agriculture whereas farm abandonment was observed in poor-quality agriculture terrains. Large-scale conversion of perennial crops into annual crops along with continued urbanization exerts strong pressures on residual natural habitats and their inhabiting wildlife. The afforestation process occurring in the more forested landscapes along with the addition of tree-dominated hedgerows and riparian habitats in the agriculture-dominated landscapes should improve landscape ecological value.

Examining the Resilience of Crop Production, Livestock Carrying Capacity, and Woodland Density in a Rural Zimbabwean Socio-Ecological System Using Agent-Based Models Representing Human Management Decisions

NASA Astrophysics Data System (ADS)

Eitzel Solera, M. V.; Neves, K.; Veski, A.; Solera, J.; Omoju, O. E.; Mawere Ndlovu, A.; Wilson, K.

2016-12-01

As climate change increases the pressures on arid ecosystems by changing timing and amount of rainfall, understanding the ways in which human management choices affect the resilience of these systems becomes key to their sustainability. On marginal farmland in Mazvihwa, Midlands Province, the historical carrying capacity of livestock has been consistently surprisingly high. We explore this phenomenon by building an agent-based model in NetLogo from a wealth of long-term data generated by the community-based participatory research team of The Muonde Trust, a Zimbabwean non-governmental organization. We combine the accumulated results of 35 years of indigenous and local knowledge with national datasets such as rainfall records. What factors keep the carrying capacity high? What management choices can maintain crops, livestock, and woodland at levels necessary for the community's survival? How do these choices affect long-term sustainability, and does increasing resilience at one scale reduce resilience at another scale? We use our agent-based model to explore the feedbacks between crops, livestock, and woodland and the impacts of various human choices as well as temporal and spatial ecological variation. By testing different scenarios, we disentangle the complex interactions between these components. We find that some factors out of the community's control can strongly affect the sustainability of the system through times of drought, and that supplementary feed may maintain livestock potentially at the expense of other resources. The challenges to resilience encountered by the farmers in Mazvihwa are not unique - many indigenous and rural people face drought and the legacies of colonialism, which contribute to lowered resilience to external challenges such as climate change, epidemics, and political instability. Using the agent-based model as a tool for synthesis and exploration initiates discussion about resilience-enhancing management choices for Mazvihwa's farmer-researchers.

Assessment of time-series MODIS data for cropland mapping in the U.S. central Great Plains

NASA Astrophysics Data System (ADS)

Masialeti, Iwake

This study had three general objectives. First, to explore ways of creating and refining a reference data set when reference data set is unobtainable. Second, extend work previously done in Kansas by Wardlow et al. (2007) to Nebraska, several exploratory approaches were used to further investigate the potential of MODIS NDVI 250-m data in agricultural-related land cover research other parts of the Great Plains. The objective of this part of the research was to evaluate the applicability of time-series MODIS 250-m NDVI data for crop-type discrimination by spectrally characterizing and discriminating major crop types in Nebraska using the reference data set collected and refined under research performed for the first objective. Third, conduct an initial investigation into whether time-series NDVI response curves for crops over a growing season for one year could be used to classify crops for a different year. In this case, time-series NDVI response curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 data set could be used to classify crops for 2005. GIS operations, and reference data refinement using clustering and visual assessment of each crop's NDVI cluster profiles in Nebraska, demonstrated that it is possible to devise an alternative reference data set and refinement plan that redresses the unexpected loss of training and validation data. The analysis enabled the identification and removal of crop pattern outliers and sites atypical of crop phenology under consideration, and after editing, a total of 1,288 field sites remained, which were used as a reference data set for classification of Nebraska crop types. A pixel-level analysis of the time-series MODIS 250-m NDVI for 1,288 field sites representing each of the eight cover types under investigation across Nebraska found that each crop type had a distinctive MODIS 250-m NDVI profile corresponding to the crop calendar. A visual and statistical comparison of the average NDVI profiles showed that the crop types were separable at different times of the growing season based on their phenology-driven spectral-temporal differences. Winter wheat and alfalfa, winter wheat and summer crops, and alfalfa and summer crops were clearly separable. Specific summer crop types were not easily distinguishable from each other due to their similar crop calendars. Their greatest separability however occurred during the initial spring green up and/or senescence plant growth phases. In Kansas, an initial investigation revealed that there was near-complete agreement between the winter wheat crop profiles but that there were some minor differences in the crop profiles for alfalfa and summer crops between 2001 and 2005. However, the profiles of summer crops---corn, grain sorghum, and soybeans---displayed a shift to the right by at least 1 composite date, indicative of possible late crop planting and emergence. Alfalfa and summer crops, seem to suggest that time series NDVI response curves for crops over a growing period for one year of valid ground reference data may not be used to map crops for a different year without taking into account the climatic and/or environmental conditions of each year.

Coupling of phenological information and synthetically generated time-series for crop types as indicator for vegetation coverage information

USDA-ARS?s Scientific Manuscript database

It is widely believed that in Germany and Europe the risk of soil erosion by water increases as a result of changes in climate. Especially, an increase of the frequency of extreme precipitation events during phenological crop phases with reduced soil cover is very likely for the near future. A monit...

Strategic system development toward biofuel, desertification, and crop production monitoring in continental scales using satellite-based photosynthesis models

NASA Astrophysics Data System (ADS)

Kaneko, Daijiro

2013-10-01

The author regards fundamental root functions as underpinning photosynthesis activities by vegetation and as affecting environmental issues, grain production, and desertification. This paper describes the present development of monitoring and near real-time forecasting of environmental projects and crop production by approaching established operational monitoring step-by-step. The author has been developing a thematic monitoring structure (named RSEM system) which stands on satellite-based photosynthesis models over several continents for operational supports in environmental fields mentioned above. Validation methods stand not on FLUXNET but on carbon partitioning validation (CPV). The models demand continuing parameterization. The entire frame system has been built using Reanalysis meteorological data, but model accuracy remains insufficient except for that of paddy rice. The author shall accomplish the system that incorporates global environmental forces. Regarding crop production applications, industrialization in developing countries achieved through direct investment by economically developed nations raises their income, resulting in increased food demand. Last year, China began to import rice as it had in the past with grains of maize, wheat, and soybeans. Important agro-potential countries make efforts to cultivate new crop lands in South America, Africa, and Eastern Europe. Trends toward less food sustainability and stability are continuing, with exacerbation by rapid social and climate changes. Operational monitoring of carbon sequestration by herbaceous and bore plants converges with efforts at bio-energy, crop production monitoring, and socio-environmental projects such as CDM A/R, combating desertification, and bio-diversity.

A process-based agricultural model for the irrigated agriculture sector in Alberta, Canada

NASA Astrophysics Data System (ADS)

Ammar, M. E.; Davies, E. G.

2015-12-01

Connections between land and water, irrigation, agricultural productivity and profitability, policy alternatives, and climate change and variability are complex, poorly understood, and unpredictable. Policy assessment for agriculture presents a large potential for development of broad-based simulation models that can aid assessment and quantification of policy alternatives over longer temporal scales. The Canadian irrigated agriculture sector is concentrated in Alberta, where it represents two thirds of the irrigated land-base in Canada and is the largest consumer of surface water. Despite interest in irrigation expansion, its potential in Alberta is uncertain given a constrained water supply, significant social and economic development and increasing demands for both land and water, and climate change. This paper therefore introduces a system dynamics model as a decision support tool to provide insights into irrigation expansion in Alberta, and into trade-offs and risks associated with that expansion. It is intended to be used by a wide variety of users including researchers, policy analysts and planners, and irrigation managers. A process-based cropping system approach is at the core of the model and uses a water-driven crop growth mechanism described by AquaCrop. The tool goes beyond a representation of crop phenology and cropping systems by permitting assessment and quantification of the broader, long-term consequences of agricultural policies for Alberta's irrigation sector. It also encourages collaboration and provides a degree of transparency that gives confidence in simulation results. The paper focuses on the agricultural component of the systems model, describing the process involved; soil water and nutrients balance, crop growth, and water, temperature, salinity, and nutrients stresses, and how other disciplines can be integrated to account for the effects of interactions and feedbacks in the whole system. In later stages, other components such as livestock production systems and agricultural production economics will be integrated to the agricultural model to make the systems tool. It will capture feedback loops, time delays, and the nonlinearities of the system. Moreover, the model is designed for quick reconfiguration to different regions given parametrized crop data.

Validation of Leaf Area Index measurements based on the Wireless Sensor Network platform

NASA Astrophysics Data System (ADS)

Song, Q.; Li, X.; Liu, Q.

2017-12-01

The leaf area index (LAI) is one of the important parameters for estimating plant canopy function, which has significance for agricultural analysis such as crop yield estimation and disease evaluation. The quick and accurate access to acquire crop LAI is particularly vital. In the study, LAI measurement of corn crops is mainly through three kinds of methods: the leaf length and width method (LAILLW), the instruments indirect measurement method (LAII) and the leaf area index sensor method(LAIS). Among them, LAI value obtained from LAILLW can be regarded as approximate true value. LAI-2200,the current widespread LAI canopy analyzer,is used in LAII. LAIS based on wireless sensor network can realize the automatic acquisition of crop images,simplifying the data collection work,while the other two methods need person to carry out field measurements.Through the comparison of LAIS and other two methods, the validity and reliability of LAIS observation system is verified. It is found that LAI trend changes are similar in three methods, and the rate of change of LAI has an increase with time in the first two months of corn growth when LAIS costs less manpower, energy and time. LAI derived from LAIS is more accurate than LAII in the early growth stage,due to the small blade especially under the strong light. Besides, LAI processed from a false color image with near infrared information is much closer to the true value than true color picture after the corn growth period up to one and half months.

Incorporating Grasslands into Cropping Systems: What are the Keys?

USDA-ARS?s Scientific Manuscript database

American agriculture in the 20th century has been shaped by social/political, economic, environmental and technological drivers. During this time, American agricultural systems became increasingly specialized and input driven resulting in agricultural production being dominated by ‘commodity crop p...

Application of water footprint combined with a unified virtual crop pattern to evaluate crop water productivity in grain production in China.

PubMed

Wang, Y B; Wu, P T; Engel, B A; Sun, S K

2014-11-01

Water shortages are detrimental to China's grain production while food production consumes a great deal of water causing water crises and ecological impacts. Increasing crop water productivity (CWP) is critical, so China is devoting significant resources to develop water-saving agricultural systems based on crop planning and agricultural water conservation planning. A comprehensive CWP index is necessary for such planning. Existing indices such as water use efficiency (WUE) and irrigation efficiency (IE) have limitations and are not suitable for the comprehensive evaluation of CWP. The water footprint (WF) index, calculated using effective precipitation and local water use, has advantages for CWP evaluation. Due to regional differences in crop patterns making the CWP difficult to compare directly across different regions, a unified virtual crop pattern is needed to calculate the WF. This project calculated and compared the WF of each grain crop and the integrated WFs of grain products with actual and virtual crop patterns in different regions of China for 2010. The results showed that there were significant differences for the WF among different crops in the same area or among different areas for the same crop. Rice had the highest WF at 1.39 m(3)/kg, while corn had the lowest at 0.91 m(3)/kg among the main grain crops. The WF of grain products was 1.25 m(3)/kg in China. Crop patterns had an important impact on WF of grain products because significant differences in WF were found between actual and virtual crop patterns in each region. The CWP level can be determined based on the WF of a virtual crop pattern, thereby helping optimize spatial distribution of crops and develop agricultural water savings to increase CWP. Copyright © 2014 Elsevier B.V. All rights reserved.

Integration of lessons from recent research for “Earth to Mars” life support systems

NASA Astrophysics Data System (ADS)

Nelson, M.; Dempster, W. F.; Allen, J. P.

Development of reliable and robust strategies for long-term life support for planetary exploration must be built from real-time experimentation to verify and improve system components. Also critical is incorporating a range of viable options to handle potential short-term life system imbalances. This paper revisits some of the conceptual framework for a Mars base prototype which has been developed by the authors along with others previously advanced ("Mars on Earth ®") in the light of three years of experimentation in the Laboratory Biosphere, further investigation of system alternatives and the advent of other innovative engineering and agri-ecosystem approaches. Several experiments with candidate space agriculture crops have demonstrated the higher productivity possible with elevated light levels and improved environmental controls. For example, crops of sweet potatoes exceeded original Mars base prototype projections by an average of 46% (53% for best crop) ultradwarf (Apogee) wheat by 9% (23% for best crop), pinto bean by 13% (31% for best crop). These production levels, although they may be increased with further optimization of lighting regimes, environmental parameters, crop density etc. offer evidence that a soil-based system can be as productive as the hydroponic systems which have dominated space life support scenarios and research. But soil also offers distinct advantages: the capability to be created on the Moon or Mars using in situ space resources, reduces long-term reliance on consumables and imported resources, and more readily recycling and incorporating crew and crop waste products. In addition, a living soil contains a complex microbial ecosystem which helps prevent the buildup of trace gases or compounds, and thus assist with air and water purification. The atmospheric dynamics of these crops were studied in the Laboratory Biosphere adding to the database necessary for managing the mixed stands of crops essential for supplying a nutritionally adequate diet in space. This paper explores some of the challenges of small bioregenerative life support: air-sealing and facility architecture/design, balance of short-term variations of carbon dioxide and oxygen through staggered plantings, options for additional atmospheric buffers and sinks, lighting/energy efficiency engineering, crop and waste product recycling approaches, and human factor considerations in the design and operation of a Mars base. An "Earth to Mars" project, forging the ability to live sustainably in space (as on Earth) requires continued research and testing of these components and integrated subsystems; and developing a step-by-step learning process.

Plant-based assessment of inherent soil productivity and contributions to China's cereal crop yield increase since 1980.

PubMed

Fan, Mingsheng; Lal, Rattan; Cao, Jian; Qiao, Lei; Su, Yansen; Jiang, Rongfeng; Zhang, Fusuo

2013-01-01

China's food production has increased 6-fold during the past half-century, thanks to increased yields resulting from the management intensification, accomplished through greater inputs of fertilizer, water, new crop strains, and other Green Revolution's technologies. Yet, changes in underlying quality of soils and their effects on yield increase remain to be determined. Here, we provide a first attempt to quantify historical changes in inherent soil productivity and their contributions to the increase in yield. The assessment was conducted based on data-set derived from 7410 on-farm trials, 8 long-term experiments and an inventory of soil organic matter concentrations of arable land. Results show that even without organic and inorganic fertilizer addition crop yield from on-farm trials conducted in the 2000s was significantly higher compared with those in the 1980s - the increase ranged from 0.73 to 1.76 Mg/ha for China's major irrigated cereal-based cropping systems. The increase in on-farm yield in control plot since 1980s was due primarily to the enhancement of soil-related factors, and reflected inherent soil productivity improvement. The latter led to higher and stable yield with adoption of improved management practices, and contributed 43% to the increase in yield for wheat and 22% for maize in the north China, and, 31%, 35% and 22% for early and late rice in south China and for single rice crop in the Yangtze River Basin since 1980. Thus, without an improvement in inherent soil productivity, the 'Agricultural Miracle in China' would not have happened. A comprehensive strategy of inherent soil productivity improvement in China, accomplished through combining engineering-based measures with biological-approaches, may be an important lesson for the developing world. We propose that advancing food security in 21st century for both China and other parts of world will depend on continuously improving inherent soil productivity.

Plant-Based Assessment of Inherent Soil Productivity and Contributions to China’s Cereal Crop Yield Increase since 1980

PubMed Central

Fan, Mingsheng; Lal, Rattan; Cao, Jian; Qiao, Lei; Su, Yansen; Jiang, Rongfeng; Zhang, Fusuo

2013-01-01

Objective China’s food production has increased 6-fold during the past half-century, thanks to increased yields resulting from the management intensification, accomplished through greater inputs of fertilizer, water, new crop strains, and other Green Revolution’s technologies. Yet, changes in underlying quality of soils and their effects on yield increase remain to be determined. Here, we provide a first attempt to quantify historical changes in inherent soil productivity and their contributions to the increase in yield. Methods The assessment was conducted based on data-set derived from 7410 on-farm trials, 8 long-term experiments and an inventory of soil organic matter concentrations of arable land. Results Results show that even without organic and inorganic fertilizer addition crop yield from on-farm trials conducted in the 2000s was significantly higher compared with those in the 1980s — the increase ranged from 0.73 to 1.76 Mg/ha for China’s major irrigated cereal-based cropping systems. The increase in on-farm yield in control plot since 1980s was due primarily to the enhancement of soil-related factors, and reflected inherent soil productivity improvement. The latter led to higher and stable yield with adoption of improved management practices, and contributed 43% to the increase in yield for wheat and 22% for maize in the north China, and, 31%, 35% and 22% for early and late rice in south China and for single rice crop in the Yangtze River Basin since 1980. Conclusions Thus, without an improvement in inherent soil productivity, the ‘Agricultural Miracle in China’ would not have happened. A comprehensive strategy of inherent soil productivity improvement in China, accomplished through combining engineering-based measures with biological-approaches, may be an important lesson for the developing world. We propose that advancing food security in 21st century for both China and other parts of world will depend on continuously improving inherent soil productivity. PMID:24058605

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

USGS Publications Warehouse

Howard, Daniel M.; Wylie, Bruce K.; Tieszen, Larry L.

2012-01-01

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R 2 = 0.90).

Implications for water use of a shift from annual to perennial crops - A stochastic modelling approach based on a trait meta-analysis

NASA Astrophysics Data System (ADS)

Vico, Giulia; Brunsell, Nathaniel

2017-04-01

The projected population growth and changes in climate and dietary habits will further increase the pressure on water resources globally. Within precision farming, a host of technical solutions has been developed to reduce water consumption for agricultural uses. The next frontier for a more sustainable agriculture is the combination of reduced water requirements with enhanced ecosystem services. Currently, staple grains are obtained from annuals crops. A shift from annual to perennial crops has been suggested as a way to enhance ecosystem services. In fact, perennial plants, with their continuous soil cover and the higher allocation of resources to the below ground, contribute to the reduction of soil erosion and nutrient losses, while enhancing carbon sequestration in the root zone. Nevertheless, the net effect of a shift to perennial crops on water use for agriculture is still unknown, despite its relevance for the sustainability of such a shift. We explore here the implications for water management at the field- to farm-scale of a shift from annual to perennial crops, under rainfed and irrigated agriculture. A probabilistic description of the soil water balance and crop development is employed to quantify water requirements and yields and their inter-annual variability, as a function of rainfall patterns, soil and crop features. Optimal irrigation strategies are thus defined in terms of maximization of yield and minimization of required irrigation volumes and their inter-annual variability. The probabilistic model is parameterized based on an extensive meta-analysis of traits of co-generic annual and perennial species to explore the consequences for water requirements of shifting from annual to perennial crops under current and future climates. We show that the larger and more developed roots of perennial crops may allow a better exploitation of soil water resources and a reduction of yield variability with respect to annual species. At the same time, perennial crops are larger and may require adequate water supply for longer periods, thus leading to higher water requirements. Furthermore, they lead to lower yields per unit area, thus requiring irrigation of larger areas.

Effects of seeding rate and poultry litter on weed suppression from a rolled cereal rye cover crop

USDA-ARS?s Scientific Manuscript database

Growing enough cover crop biomass to adequately suppress weeds is one of the primary challenges in reduced-tillage systems that rely on mulch-based weed suppression. We investigated two approaches to increasing cereal rye biomass for improved weed suppression: (1) increasing soil fertility and (2) i...

Remote sensing of global croplands for food security

USGS Publications Warehouse

Thenkabail, Prasad S.; Biradar, Chandrashekhar M.; Turral, Hugh; Lyon, John G.

2009-01-01

Increases in populations have created an increasing demand for food crops while increases in demand for biofuels have created an increase in demand for fuel crops. What has not increased is the amount of croplands and their productivity. These and many other factors such as decreasing water resources in a changing climate have created a crisis like situation in global food security. Decision makers in these situations need accurate information based on science. Remote Sensing of Global Croplands for Food Security provides a comprehensive knowledge base in use of satellite sensor-based maps and statistics that can be used to develop strategies for croplands (irrigated and rainfed) and their water use for food security.

Climate change and farmers’ cropping patterns in Cemoro watershed area, Central Java, Indonesia

NASA Astrophysics Data System (ADS)

Sugihardjo; Sutrisno, J.; Setyono, P.; Suntoro

2018-03-01

Cropping pattern applied by farmers is usually based on the availability of water. Farmers cultivate rice when water is available. If it is unavailable, farmers will choose to plant crops that need less water. Climate change greatly affects to farmers in determining the cropping pattern as it alters the rainfall pattern and distribution in the region. This condition requires farmers to adjust the cropping pattern so that they can do the farming successfully. This study aims to examine the application of cropping patterns applied by the farmers in the Cemoro Watershed, Central Java, Indonesia. Descriptive analysis approach is employed in this research. The results showed that farmers’ cropping pattern is not based on the availability of water. However, it adopts a habit that has been practiced since long time ago or just adopt others farmer's habit. The cropping pattern applied by irrigated paddy farmers in Cemoro watershed area consists of two types: rice-rice-rice and rice-rice-secondary crops. Among those two types, most farmers apply the rice-rice-rice pattern. Meanwhile, there are three cropping patterns applied in the rain-land, namely rice-rice-rice, rice-rice-secondary crop, and rice-rice-fallow. The majority of farmers apply the second pattern (rice-rice-secondary crops). It was also found that farmers’ cropping pattern was not in accordance with the recommendation of the local government.

Estimation of different data compositions for early-season crop type classification.

PubMed

Hao, Pengyu; Wu, Mingquan; Niu, Zheng; Wang, Li; Zhan, Yulin

2018-01-01

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer's accuracies (PAs) and user's accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

Estimation of different data compositions for early-season crop type classification

PubMed Central

Wu, Mingquan; Wang, Li; Zhan, Yulin

2018-01-01

Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study. PMID:29868265

Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

NASA Astrophysics Data System (ADS)

Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

The green, blue and grey water footprint of crops and derived crop products

NASA Astrophysics Data System (ADS)

Mekonnen, M. M.; Hoekstra, A. Y.

2011-01-01

This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996-2005. The assessment is global and improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc min grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the water footprint network. Considering the water footprints of primary crops, we see that global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton-1), vegetables (300 m3 ton-1), roots and tubers (400 m3 ton-1), fruits (1000 m3 ton-1), cereals} (1600 m3 ton-1), oil crops (2400 m3 ton-1) to pulses (4000 m3 ton-1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ-1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ-1, while this is 121 m3 GJ-1 for maize. The global water footprint related to crop production in the period 1996-2005 was 7404 billion cubic meters per year (78% green, 12% blue, 10% grey). A large total water footprint was calculated for wheat (1087 Gm3 yr-1), rice (992 Gm3 yr-1) and maize (770 Gm3 yr-1). Wheat and rice have the largest blue water footprints, together accounting for 45% of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm3 yr-1), China (967 Gm3 yr-1) and the USA (826 Gm3 yr-1). A relatively large total blue water footprint as a result of crop production is observed in the Indus River Basin (117 Gm3 yr-1) and the Ganges River Basin (108 Gm3 yr-1). The two basins together account for 25% of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm3 yr-1 (91% green, 9% grey); irrigated agriculture has a water footprint of 2230 Gm3 yr-1 (48% green, 40% blue, 12% grey).

The green, blue and grey water footprint of crops and derived crop products

NASA Astrophysics Data System (ADS)

Mekonnen, M. M.; Hoekstra, A. Y.

2011-05-01

This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996-2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network. Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton-1), vegetables (300 m3 ton-1), roots and tubers (400 m3 ton-1), fruits (1000 m3 ton-1), cereals (1600 m3 ton-1), oil crops (2400 m3 ton-1) to pulses (4000 m3 ton-1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ-1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ-1, while this is 121 m3 GJ-1 for maize. The global water footprint related to crop production in the period 1996-2005 was 7404 billion cubic meters per year (78 % green, 12 % blue, 10 % grey). A large total water footprint was calculated for wheat (1087 Gm3 yr-1), rice (992 Gm3 yr-1) and maize (770 Gm3 yr-1). Wheat and rice have the largest blue water footprints, together accounting for 45 % of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm3 yr-1), China (967 Gm3 yr-1) and the USA (826 Gm3 yr-1). A relatively large total blue water footprint as a result of crop production is observed in the Indus river basin (117 Gm3 yr-1) and the Ganges river basin (108 Gm3 yr-1). The two basins together account for 25 % of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm3 yr-1 (91 % green, 9 % grey); irrigated agriculture has a water footprint of 2230 Gm3 yr-1 (48 % green, 40 % blue, 12 % grey).

Crop-specific seasonal estimates of irrigation-water demand in South Asia

NASA Astrophysics Data System (ADS)

Biemans, Hester; Siderius, Christian; Mishra, Ashok; Ahmad, Bashir

2016-05-01

Especially in the Himalayan headwaters of the main rivers in South Asia, shifts in runoff are expected as a result of a rapidly changing climate. In recent years, our insight into these shifts and their impact on water availability has increased. However, a similar detailed understanding of the seasonal pattern in water demand is surprisingly absent. This hampers a proper assessment of water stress and ways to cope and adapt. In this study, the seasonal pattern of irrigation-water demand resulting from the typical practice of multiple cropping in South Asia was accounted for by introducing double cropping with monsoon-dependent planting dates in a hydrology and vegetation model. Crop yields were calibrated to the latest state-level statistics of India, Pakistan, Bangladesh and Nepal. The improvements in seasonal land use and cropping periods lead to lower estimates of irrigation-water demand compared to previous model-based studies, despite the net irrigated area being higher. Crop irrigation-water demand differs sharply between seasons and regions; in Pakistan, winter (rabi) and monsoon summer (kharif) irrigation demands are almost equal, whereas in Bangladesh the rabi demand is ~ 100 times higher. Moreover, the relative importance of irrigation supply versus rain decreases sharply from west to east. Given the size and importance of South Asia improved regional estimates of food production and its irrigation-water demand will also affect global estimates. In models used for global water resources and food-security assessments, processes like multiple cropping and monsoon-dependent planting dates should not be ignored.

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.

2012-12-01

Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production and driving crop water balance models. We present a series of derived rainfall products and provide an update on efforts to improve satellite-based estimates. We also present advancements in monitoring tools, namely, the Early Warning eXplorer (EWX) and interactive rainfall and NDVI time series viewers. The EWX is a data analysis and visualization tool that allows users to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The interactive time series viewers allow users to analyze rainfall and NDVI time series over multiple spatial domains. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

Using Remote Sensing to Determine Timing of High Altitude Grass Hay Growth Stages

NASA Astrophysics Data System (ADS)

Mefford, B.

2015-12-01

Remote sensing has become the standard for collecting data to determine potential irrigation consumptive use in Wyoming for the Green River Basin. The Green River Basin within Wyoming is around 10.8 million acres, located in south western Wyoming and is a sub-basin of the Colorado River Basin. Grass hay is the main crop grown in the basin. The majority of the hay is grown at elevations 7,000 feet above mean sea level. Daily potential irrigation consumptive use is calculated for the basin during the growing season (May 1st to September 30th). To determine potential irrigation consumptive use crop coefficients, reference evapotranspiration (ET) and effective precipitation are required. Currently crop coefficients are the hardest to determine as most research on crop coefficients are based at lower elevations. Values for crop coefficients for grass hay still apply to high altitude grass hay, but the hay grows at a much slower rate than low elevation grass hay. To be able to more accurately determine the timing of the growth stages of hay in this basin, time-lapse cameras were installed at two different irrigated hay fields in the basin for the 2015 growing season and took pictures automatically once a day at 1 P.M.. Both of the fields also contained a permanent research grade weather station. Imagery obtained from these cameras was used as indicators of timing of the major growth stages of the hay and the length of days between the stages. A crop coefficient value was applied every day in the growing season based on the results from the imagery. Daily potential ET was calculated using the crop coefficients and the data from the on-site weather stations. The final result was potential irrigation induced crop consumptive use for each site. Using remote sensing provided necessary information that normally would be applied arbitrarily in determining irrigation induced consumptive use in the Green River Basin.

Soil microbial biomass and function are altered by 12 years of crop rotation

NASA Astrophysics Data System (ADS)

McDaniel, Marshall D.; Grandy, A. Stuart

2016-11-01

Declines in plant diversity will likely reduce soil microbial biomass, alter microbial functions, and threaten the provisioning of soil ecosystem services. We examined whether increasing temporal plant biodiversity in agroecosystems (by rotating crops) can partially reverse these trends and enhance soil microbial biomass and function. We quantified seasonal patterns in soil microbial biomass, respiration rates, extracellular enzyme activity, and catabolic potential three times over one growing season in a 12-year crop rotation study at the W. K. Kellogg Biological Station LTER. Rotation treatments varied from one to five crops in a 3-year rotation cycle, but all soils were sampled under a corn year. We hypothesized that crop diversity would increase microbial biomass, activity, and catabolic evenness (a measure of functional diversity). Inorganic N, the stoichiometry of microbial biomass and dissolved organic C and N varied seasonally, likely reflecting fluctuations in soil resources during the growing season. Soils from biodiverse cropping systems increased microbial biomass C by 28-112 % and N by 18-58 % compared to low-diversity systems. Rotations increased potential C mineralization by as much as 53 %, and potential N mineralization by 72 %, and both were related to substantially higher hydrolase and lower oxidase enzyme activities. The catabolic potential of the soil microbial community showed no, or slightly lower, catabolic evenness in more diverse rotations. However, the catabolic potential indicated that soil microbial communities were functionally distinct, and microbes from monoculture corn preferentially used simple substrates like carboxylic acids, relative to more diverse cropping systems. By isolating plant biodiversity from differences in fertilization and tillage, our study illustrates that crop biodiversity has overarching effects on soil microbial biomass and function that last throughout the growing season. In simplified agricultural systems, relatively small increases in crop diversity can have large impacts on microbial community size and function, with cover crops appearing to facilitate the largest increases.

Frost Damage Detection in Sugarcane Crop Using Modis Images and Srtm Data

NASA Astrophysics Data System (ADS)

Rudorff, B.; Alves de Aguiar, D.; Adami, M.

2011-12-01

Brazil is the largest world producer of sugarcane which is used to produce almost equal proportions of either sugar (food) or ethanol (biofuel). In recent years sugarcane crop production has increased fast to meet the growing market demand for sugar and ethanol. This increase has been mainly due to expansion in crop area, but sugarcane production is also subjected to several factors that influence both the agricultural crop yield (tons of stalks/ha) and the industrial yield (kg of sugar/ton of stalks). Sugarcane is a semi-perennial crop that experiences major growth during spring and summer seasons with large demands for water and high temperatures to produce good stalk formation (crop yield). The harvest is performed mainly during fall and winter seasons when water availability and temperature should be low in order to accumulate sucrose in the stalks (industrial yield). These favorable climatic conditions for sugarcane crop are found in several regions in Brazil, particularly in São Paulo state, which is the major sugarcane producer in Brazil being responsible for almost 60% of its production. Despite the favorable climate in São Paulo state there is a certain probability of frost occurrence from time to time that has a negative impact on sugarcane crop, particularly on industrial yield, reducing the amount of sugar in the stalks; having consequences on price increase and product shortage. To evaluate the impact of frost on sugarcane crop, in the field, on a state level, is not a trivial task; however, this information is relevant due to its direct impact on the consumer market. Remote sensing images allow a synoptic view and present great potential to monitor large sugarcane plantations as has been done since 2003 in São Paulo state by the Canasat Project with Landsat type images (http://www.dsr.inpe.br/laf/canasat/en/). Images acquired from sensors with high temporal resolution such as MODIS (Moderate-Resolution Imaging Spectroradiometer) present the potential to detect the impact of climatic effects, such as frost, on crop growth, which is relevant information to evaluate the negative impact on sugarcane production. Thus, the objective of the present study is to detect the impact of the frost occurred on 28 June 2011 in the sugarcane production region of São Paulo state, using MODIS images acquired on board of Terra and Aqua satellites before and after the frost event. Also, Landsat type images were used to map the harvested sugarcane fields up to the frost event based on a sugarcane crop map for year 2011. The remaining sugarcane fields available for harvest in 2011 were monitored with the MODIS images acquired on 17, 19, 27, 28 June and 8 and 9 July, to detect frost damage. Field work was conducted shortly after frost occurrence to identify sugarcane fields with frost damage for training and validation purposes. MODIS images transformed to vegetation indices and morphometric variables extracted from SRTM (Shuttle Radar Topography Mission) data are being analyzed to detect and quantify the damage of the frost from 28 July 2011 on sugarcane crop.

A National Crop Progress Monitoring and Decision Support System Based on NASA Earth Science Results

NASA Astrophysics Data System (ADS)

di, L.; Yang, Z.

2009-12-01

Timely and accurate information on weekly crop progress and development is essential to a dynamic agricultural industry in the U. S. and the world. By law, the National Agricultural Statistics Service (NASS) of the U. S. Department of Agriculture’s (USDA) is responsible for monitoring and assessing U.S. agricultural production. Currently NASS compiles and issues weekly state and national crop progress and development reports based on reports from knowledgeable state and county agricultural officials and farmers. Such survey-based reports are subjectively estimated for an entire county, lack spatial coverage, and are labor intensive. There has been limited use of remote sensing data to assess crop conditions. NASS produces weekly 1-km resolution un-calibrated AVHRR-based NDVI static images to represent national vegetation conditions but there is no quantitative crop progress information. This presentation discusses the early result for developing a National Crop Progress Monitoring and Decision Support System. The system will overcome the shortcomings of the existing systems by integrating NASA satellite and model-based land surface and weather products, NASS’ wealth of internal crop progress and condition data and Cropland Data Layers (CDL), and the Farm Service Agency’s (FSA) Common Land Units (CLU). The system, using service-oriented architecture and web service technologies, will automatically produce and disseminate quantitative national crop progress maps and associated decision support data at 250-m resolution, as well as summary reports to support NASS and worldwide users in their decision-making. It will provide overall and specific crop progress for individual crops from the state level down to CLU field level to meet different users’ needs on all known croplands. This will greatly enhance the effectiveness and accuracy of the NASS aggregated crop condition data and charts of and provides objective and scientific evidence and guidance for the adjustment of NASS survey data. This presentation will discuss the architecture, Earth observation data, and the crop progress model used in the decision support system.

Performance of the CELSS Antarctic Analog Project (CAAP) crop production system.

PubMed

Bubenheim, D L; Schlick, G; Wilson, D; Bates, M

2003-01-01

Regenerative life support systems potentially offer a level of self-sufficiency and a decrease in logistics and associated costs in support of space exploration and habitation missions. Current state-of-the-art in plant-based, regenerative life support requires resources in excess of allocation proposed for candidate mission scenarios. Feasibility thresholds have been identified for candidate exploration missions. The goal of this paper is to review recent advances in performance achieved in the CELSS Antarctic Analog Project (CAAP) in light of the likely resource constraints. A prototype CAAP crop production chamber has been constructed and operated at the Ames Research Center. The chamber includes a number of unique hardware and software components focused on attempts to increase production efficiency, increase energy efficiency, and control the flow of energy and mass through the system. Both single crop, batch production and continuous cultivation of mixed crops production studies have been completed. The crop productivity as well as engineering performance of the chamber are described. For each scenario, energy required and partitioned for lighting, cooling, pumping, fans, etc. is quantified. Crop production and the resulting lighting efficiency and energy conversion efficiencies are presented. In the mixed-crop scenario, with 27 different crops under cultivation, 17 m2 of crop area provided a mean of 515 g edible biomass per day (85% of the approximate 620 g required for one person). Enhanced engineering and crop production performance achieved with the CAAP chamber, compared with current state-of-the-art, places plant-based life support systems at the threshold of feasibility. c2002 Published by Elsevier Science Ltd on behalf of COSPAR.

Performance of the CELSS Antarctic Analog Project (CAAP) crop production system

NASA Astrophysics Data System (ADS)

Bubenheim, D. L.; Schlick, G.; Wilson, D.; Bates, M.

Regenerative life support systems potentially offer a level of self-sufficiency and a decrease in logistics and associated costs in support of space exploration and habitation missions. Current state-of-the-art in plant-based, regenerative life support requires resources in excess of allocation proposed for candidate mission scenarios. Feasibility thresholds have been identified for candidate exploration missions. The goal of this paper is to review recent advances in performance achieved in the CELSS Antarctic Analog Project (CAAP) in light of the likely resource constraints. A prototype CAAP crop production chamber has been constructed and operated at the Ames Research Center. The chamber includes a number of unique hardware and software components focused on attempts to increase production efficiency, increase energy efficiency, and control the flow of energy and mass through the system. Both single crop, batch production and continuous cultivation of mixed crops production studies have been completed. The crop productivity as well as engineering performance of the chamber are described. For each scenario, energy required and partitioned for lighting, cooling, pumping, fans, etc. is quantified. Crop production and the resulting lighting efficiency and energy conversion efficiencies are presented. In the mixed-crop scenario, with 27 different crops under cultivation, 17 m2 of crop area provided a mean of 515g edible biomass per day (85% of the approximate 620 g required for one person). Enhanced engineering and crop production performance achieved with the CAAP chamber, compared with current state-of-the-art, places plant-based life support systems at the threshold of feasibility.

Performance of the CELSS Antarctic Analog Project (CAAP) crop production system

NASA Technical Reports Server (NTRS)

Bubenheim, D. L.; Schlick, G.; Wilson, D.; Bates, M.

2003-01-01

Regenerative life support systems potentially offer a level of self-sufficiency and a decrease in logistics and associated costs in support of space exploration and habitation missions. Current state-of-the-art in plant-based, regenerative life support requires resources in excess of allocation proposed for candidate mission scenarios. Feasibility thresholds have been identified for candidate exploration missions. The goal of this paper is to review recent advances in performance achieved in the CELSS Antarctic Analog Project (CAAP) in light of the likely resource constraints. A prototype CAAP crop production chamber has been constructed and operated at the Ames Research Center. The chamber includes a number of unique hardware and software components focused on attempts to increase production efficiency, increase energy efficiency, and control the flow of energy and mass through the system. Both single crop, batch production and continuous cultivation of mixed crops production studies have been completed. The crop productivity as well as engineering performance of the chamber are described. For each scenario, energy required and partitioned for lighting, cooling, pumping, fans, etc. is quantified. Crop production and the resulting lighting efficiency and energy conversion efficiencies are presented. In the mixed-crop scenario, with 27 different crops under cultivation, 17 m2 of crop area provided a mean of 515 g edible biomass per day (85% of the approximate 620 g required for one person). Enhanced engineering and crop production performance achieved with the CAAP chamber, compared with current state-of-the-art, places plant-based life support systems at the threshold of feasibility. c2002 Published by Elsevier Science Ltd on behalf of COSPAR.

Predcition of Long term Water table Trends in Response to Groundwater Irrigation and Climate Change in an Indian Context

NASA Astrophysics Data System (ADS)

Thekkemeppilly Sivakumar, I.; Steenhuis, T. S.; Walter, M. F.; Ghosh, S.; Salvi, K. A.

2015-12-01

Intensified groundwater irrigation is a major factor that contributes to water table decline. This phenomenon has been documented in many parts of the world. This study investigates trends in water table in response to agriculture intensification to meet increasing food demand, water management practices and climate change. A shallow-aquifer model based on the extended Thornthwaite-Mather procedure is used to predict groundwater levels in response to precipitation, evapotranspiration, and groundwater pumping for irrigation. Krishna district in the state of Andhra Pradesh in southern India which has a sub-humid, monsoon climate and Calicut district of Kerala state with a wet tropical monsoon climate have been chosen as sites for this study. The effect of increasing food demand by a growing population is investigated by increasing the number of crops per year from one to three. We consider three climate scenarios and two water management practices in this study. The three climate scenarios are the ones those envisaged by the Intergovernmental Panel for Climate Change (IPCC). The two water management practices considered are the traditional flooded agriculture and the system of rice intensification method which does not use total flooding. The results show that single crop agriculture in Krishna district is sustainable for all climate scenarios and water management practices with a maximum depth to water table around 6 - 7 m at the end of dry season and the water table recovers to the surface most of the time. Increasing crop production with two or three crops per year with groundwater irrigation is unsustainable with the water table levels dropping potentially to 200 - 1000 m at the end of 21st century. We found that climate change and better irrigation water management practices affected ground water levels only minimally compared to the growing more than one crop per year. Our study leads to the conclusion that ground water irrigated rice can only be sustainable when crop evaporation is less then precipitation and in order to meet increasing food demands the rice yield per unit water should be improved.

Can genomics deliver climate-change ready crops?

PubMed

Varshney, Rajeev K; Singh, Vikas K; Kumar, Arvind; Powell, Wayne; Sorrells, Mark E

2018-04-20

Development of climate resilient crops with accelerating genetic gains in crops will require integration of different disciplines/technologies, to see the impact in the farmer's field. In this review, we summarize how we are utilizing our germplasm collections to identify superior alleles/haplotypes through NGS based sequencing approaches and how genomics-enabled technologies together with precise phenotyping are being used in crop breeding. Pre-breeding and genomics-assisted breeding approaches are contributing to the more efficient development of climate-resilient crops. It is anticipated that the integration of several disciplines/technologies will result in the delivery of climate change ready crops in less time. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Changes in ecosystem services associated with planting structures of cropland: A case study in Minle County in China

NASA Astrophysics Data System (ADS)

Liu, Yaqun; Song, Wei; Mu, Fengyun

2017-12-01

The cropland ecosystem provides essential direct and indirect products and services to mankind such as food, fiber, biodiversity and soil conservation. A change of crop planting structure can change the ecosystem services of cropland by affecting land use type. In recent years, under the influence of regional comparative advantage and consumer demand changes, the crop planting structure in China has changed rapidly. However, there is still a lack of deep understanding on the effect of such a change in planting structure on the ecosystem services of cropland. Therefore, this research selected Minle County in the Heihe River Basin, which has small scattered croplands and a complex planting structure, as a study area. Based on the key time phase and optimal threshold of the normalized difference vegetation index (NDVI) data of the Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) images, this study used the decision tree algorithm to classify and extract the crop planting structure in Minle County in 2007 and 2012 and to analyze the changes in its temporal and spatial patterns. Then, the market value method was adopted to estimate the effect of the change in crop planting structure on the ecosystem services of the cropland. From 2007 to 2012, the planting area of corn and rapeseed in Minle County increased by 5.86 × 103 ha and 5.10 × 103 ha, respectively. Conversely, the planting area of wheat and barley was reduced by 3.26 × 103 ha and 6.34 × 103 ha, respectively. These changes directly caused the increase of the ecosystem services value of corn and rapeseed by 1062.82 × 104 USD and 842.54 × 104 USD, respectively. The resulting reduction in the ecosystem services value of wheat and barley was 488.24 × 104 USD and 828.29 × 104 USD, respectively. Besides, the total ecosystem services value of cropland increased by 1564.98 × 104 USD. Further analysis found that the change in the crop planting structure caused an increase in the ecosystem services value of cropland of 359.44 × 104 USD, with a contribution rate of 22.97% to the total increase. The expansion of corn caused the increase of the ecosystem services value of cropland by 151.65 × 104 USD, with a contribution rate of 9.69% to the total increase. The change in crop planting structure in Minle County increased not only the economic benefits of crop planting, but also the ecosystem services of cropland.

7 CFR 457.106 - Texas citrus tree crop insurance provisions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... proper times. Root stock—A root or a piece of a root of one tree variety onto which a bud from another... will be increased by 46 percent as a result of the additional six months of coverage for that crop year...

Simulated crop yield in response to changes in climate and agricultural practices: results from a simple process based model

NASA Astrophysics Data System (ADS)

Caldararu, S.; Smith, M. J.; Purves, D.; Emmott, S.

2013-12-01

Global agriculture will, in the future, be faced with two main challenges: climate change and an increase in global food demand driven by an increase in population and changes in consumption habits. To be able to predict both the impacts of changes in climate on crop yields and the changes in agricultural practices necessary to respond to such impacts we currently need to improve our understanding of crop responses to climate and the predictive capability of our models. Ideally, what we would have at our disposal is a modelling tool which, given certain climatic conditions and agricultural practices, can predict the growth pattern and final yield of any of the major crops across the globe. We present a simple, process-based crop growth model based on the assumption that plants allocate above- and below-ground biomass to maintain overall carbon optimality and that, to maintain this optimality, the reproductive stage begins at peak nitrogen uptake. The model includes responses to available light, water, temperature and carbon dioxide concentration as well as nitrogen fertilisation and irrigation. The model is data constrained at two sites, the Yaqui Valley, Mexico for wheat and the Southern Great Plains flux site for maize and soybean, using a robust combination of space-based vegetation data (including data from the MODIS and Landsat TM and ETM+ instruments), as well as ground-based biomass and yield measurements. We show a number of climate response scenarios, including increases in temperature and carbon dioxide concentrations as well as responses to irrigation and fertiliser application.

Genetically modified (GM) crops: milestones and new advances in crop improvement.

PubMed

Kamthan, Ayushi; Chaudhuri, Abira; Kamthan, Mohan; Datta, Asis

2016-09-01

New advances in crop genetic engineering can significantly pace up the development of genetically improved varieties with enhanced yield, nutrition and tolerance to biotic and abiotic stresses. Genetically modified (GM) crops can act as powerful complement to the crops produced by laborious and time consuming conventional breeding methods to meet the worldwide demand for quality foods. GM crops can help fight malnutrition due to enhanced yield, nutritional quality and increased resistance to various biotic and abiotic stresses. However, several biosafety issues and public concerns are associated with cultivation of GM crops developed by transgenesis, i.e., introduction of genes from distantly related organism. To meet these concerns, researchers have developed alternative concepts of cisgenesis and intragenesis which involve transformation of plants with genetic material derived from the species itself or from closely related species capable of sexual hybridization, respectively. Recombinase technology aimed at site-specific integration of transgene can help to overcome limitations of traditional genetic engineering methods based on random integration of multiple copy of transgene into plant genome leading to gene silencing and unpredictable expression pattern. Besides, recently developed technology of genome editing using engineered nucleases, permit the modification or mutation of genes of interest without involving foreign DNA, and as a result, plants developed with this technology might be considered as non-transgenic genetically altered plants. This would open the doors for the development and commercialization of transgenic plants with superior phenotypes even in countries where GM crops are poorly accepted. This review is an attempt to summarize various past achievements of GM technology in crop improvement, recent progress and new advances in the field to develop improved varieties aimed for better consumer acceptance.

Genetic Architecture of Flowering Phenology in Cereals and Opportunities for Crop Improvement

PubMed Central

Hill, Camilla B.; Li, Chengdao

2016-01-01

Cereal crop species including bread wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), rice (Oryza sativa L.), and maize (Zea mays L.) provide the bulk of human nutrition and agricultural products for industrial use. These four cereals are central to meet future demands of food supply for an increasing world population under a changing climate. A prerequisite for cereal crop production is the transition from vegetative to reproductive and grain-filling phases starting with flower initiation, a key developmental switch tightly regulated in all flowering plants. Although studies in the dicotyledonous model plant Arabidopsis thaliana build the foundations of our current understanding of plant phenology genes and regulation, the availability of genome assemblies with high-confidence sequences for rice, maize, and more recently bread wheat and barley, now allow the identification of phenology-associated gene orthologs in monocots. Together with recent advances in next-generation sequencing technologies, QTL analysis, mutagenesis, complementation analysis, and RNA interference, many phenology genes have been functionally characterized in cereal crops and conserved as well as functionally divergent genes involved in flowering were found. Epigenetic and other molecular regulatory mechanisms that respond to environmental and endogenous triggers create an enormous plasticity in flowering behavior among cereal crops to ensure flowering is only induced under optimal conditions. In this review, we provide a summary of recent discoveries of flowering time regulators with an emphasis on four cereal crop species (bread wheat, barley, rice, and maize), in particular, crop-specific regulatory mechanisms and genes. In addition, pleiotropic effects on agronomically important traits such as grain yield, impact on adaptation to new growing environments and conditions, genetic sequence-based selection and targeted manipulation of phenology genes, as well as crop growth simulation models for predictive crop breeding, are discussed. PMID:28066466

Hydrological and sedimentation implications of landscape changes in a Himalayan catchment due to bioenergy cropping

NASA Astrophysics Data System (ADS)

Remesan, Renji; Holman, Ian; Janes, Victoria

2015-04-01

There is a global effort to focus on the development of bioenergy and energy cropping, due to the generally increasing demand for crude oil, high oil price volatility and climate change mitigation challenges. Second generation energy cropping is expected to increase greatly in India as the Government of India has recently approved a national policy of 20 % biofuel blending by 2017; furthermore, the country's biomass based power generation potential is estimated as around ~24GW and large investments are expected in coming years to increase installed capacity. In this study, we have modelled the environmental influences (e.g.: hydrology and sediment) of scenarios of increased biodiesel cropping (Jatropha curcas) using the Soil and Water Assessment Tool (SWAT) in a northern Indian river basin. SWAT has been applied to the River Beas basin, using daily Tropical Rainfall Measuring Mission (TRMM) precipitation and NCEP Climate Forecast System Reanalysis (CFSR) meteorological data to simulate the river regime and crop yields. We have applied Sequential Uncertainty Fitting Ver. 2 (SUFI-2) to quantify the parameter uncertainty of the stream flow modelling. The model evaluation statistics for daily river flows at the Jwalamukhi and Pong gauges show good agreement with measured flows (Nash Sutcliffe efficiency of 0.70 and PBIAS of 7.54 %). The study has applied two land use change scenarios of (1) increased bioenergy cropping in marginal (grazing) lands in the lower and middle regions of catchment (2) increased bioenergy cropping in low yielding areas of row crops in the lower and middle regions of the catchment. The presentation will describe the improved understanding of the hydrological, erosion and sediment delivery and food production impacts arising from the introduction of a new cropping variety to a marginal area; and illustrate the potential prospects of bioenergy production in Himalayan valleys.

The Potential for Cereal Rye Cover Crops to Host Corn Seedling Pathogens.

PubMed

Bakker, Matthew G; Acharya, Jyotsna; Moorman, Thomas B; Robertson, Alison E; Kaspar, Thomas C

2016-06-01

Cover cropping is a prevalent conservation practice that offers substantial benefits to soil and water quality. However, winter cereal cover crops preceding corn may diminish beneficial rotation effects because two grass species are grown in succession. Here, we show that rye cover crops host pathogens capable of causing corn seedling disease. We isolated Fusarium graminearum, F. oxysporum, Pythium sylvaticum, and P. torulosum from roots of rye and demonstrate their pathogenicity on corn seedlings. Over 2 years, we quantified the densities of these organisms in rye roots from several field experiments and at various intervals of time after rye cover crops were terminated. Pathogen load in rye roots differed among fields and among years for particular fields. Each of the four pathogen species increased in density over time on roots of herbicide-terminated rye in at least one field site, suggesting the broad potential for rye cover crops to elevate corn seedling pathogen densities. The radicles of corn seedlings planted following a rye cover crop had higher pathogen densities compared with seedlings following a winter fallow. Management practices that limit seedling disease may be required to allow corn yields to respond positively to improvements in soil quality brought about by cover cropping.

Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence

NASA Technical Reports Server (NTRS)

Guanter, Luis; Zhang, Yongguang; Jung, Martin; Joiner, Joanna; Voigt, Maximilian; Berry, Joseph A.; Frankenberg, Christian; Huete, Alfredo R.; Zarco-Tejada, Pablo; Lee, Jung-Eun;

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence

PubMed Central

Guanter, Luis; Zhang, Yongguang; Jung, Martin; Joiner, Joanna; Voigt, Maximilian; Berry, Joseph A.; Frankenberg, Christian; Huete, Alfredo R.; Zarco-Tejada, Pablo; Lee, Jung-Eun; Moran, M. Susan; Ponce-Campos, Guillermo; Beer, Christian; Camps-Valls, Gustavo; Buchmann, Nina; Gianelle, Damiano; Klumpp, Katja; Cescatti, Alessandro; Baker, John M.; Griffis, Timothy J.

2014-01-01

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50–75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle. PMID:24706867

A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition

PubMed Central

Schmittmann, Oliver; Schulze Lammers, Peter

2017-01-01

Plant-specific herbicide application requires sensor systems for plant recognition and differentiation. A literature review reveals a lack of sensor systems capable of recognizing small weeds in early stages of development (in the two- or four-leaf stage) and crop plants, of making spraying decisions in real time and, in addition, are that are inexpensive and ready for practical use in sprayers. The system described in this work is based on free cascadable and programmable true-color sensors for real-time recognition and identification of individual weed and crop plants. The application of this type of sensor is suitable for municipal areas and farmland with and without crops to perform the site-specific application of herbicides. Initially, databases with reflection properties of plants, natural and artificial backgrounds were created. Crop and weed plants should be recognized by the use of mathematical algorithms and decision models based on these data. They include the characteristic color spectrum, as well as the reflectance characteristics of unvegetated areas and areas with organic material. The CIE-Lab color-space was chosen for color matching because it contains information not only about coloration (a- and b-channel), but also about luminance (L-channel), thus increasing accuracy. Four different decision making algorithms based on different parameters are explained: (i) color similarity (ΔE); (ii) color similarity split in ΔL, Δa and Δb; (iii) a virtual channel ‘d’ and (iv) statistical distribution of the differences of reflection backgrounds and plants. Afterwards, the detection success of the recognition system is described. Furthermore, the minimum weed/plant coverage of the measuring spot was calculated by a mathematical model. Plants with a size of 1–5% of the spot can be recognized, and weeds in the two-leaf stage can be identified with a measuring spot size of 5 cm. By choosing a decision model previously, the detection quality can be increased. Depending on the characteristics of the background, different models are suitable. Finally, the results of field trials on municipal areas (with models of plants), winter wheat fields (with artificial plants) and grassland (with dock) are shown. In each experimental variant, objects and weeds could be recognized. PMID:28786922

A True-Color Sensor and Suitable Evaluation Algorithm for Plant Recognition.

PubMed

Schmittmann, Oliver; Schulze Lammers, Peter

2017-08-08

Plant-specific herbicide application requires sensor systems for plant recognition and differentiation. A literature review reveals a lack of sensor systems capable of recognizing small weeds in early stages of development (in the two- or four-leaf stage) and crop plants, of making spraying decisions in real time and, in addition, are that are inexpensive and ready for practical use in sprayers. The system described in this work is based on free cascadable and programmable true-color sensors for real-time recognition and identification of individual weed and crop plants. The application of this type of sensor is suitable for municipal areas and farmland with and without crops to perform the site-specific application of herbicides. Initially, databases with reflection properties of plants, natural and artificial backgrounds were created. Crop and weed plants should be recognized by the use of mathematical algorithms and decision models based on these data. They include the characteristic color spectrum, as well as the reflectance characteristics of unvegetated areas and areas with organic material. The CIE-Lab color-space was chosen for color matching because it contains information not only about coloration (a- and b-channel), but also about luminance (L-channel), thus increasing accuracy. Four different decision making algorithms based on different parameters are explained: (i) color similarity (ΔE); (ii) color similarity split in ΔL, Δa and Δb; (iii) a virtual channel 'd' and (iv) statistical distribution of the differences of reflection backgrounds and plants. Afterwards, the detection success of the recognition system is described. Furthermore, the minimum weed/plant coverage of the measuring spot was calculated by a mathematical model. Plants with a size of 1-5% of the spot can be recognized, and weeds in the two-leaf stage can be identified with a measuring spot size of 5 cm. By choosing a decision model previously, the detection quality can be increased. Depending on the characteristics of the background, different models are suitable. Finally, the results of field trials on municipal areas (with models of plants), winter wheat fields (with artificial plants) and grassland (with dock) are shown. In each experimental variant, objects and weeds could be recognized.

Irrigation management strategies to improve Water Use Efficiency of potatoes crop in Central Tunisia

NASA Astrophysics Data System (ADS)

Ghazouani, Hiba; Provenzano, Giuseppe; Rallo, Giovanni; Mguidiche, Amel; Douh, Boutheina; Boujelben, Abdelhamid

2015-04-01

In Tunisia, the expansion of irrigated area and the semiarid climate make it compulsory to adopt strategies of water management to increase water use efficiency. Subsurface drip irrigation (SDI), providing the application of high frequency small irrigation volumes below the soil surface have been increasingly used to enhance irrigation efficiency. At the same time, deficit irrigation (DI) has shown successful results with a large number of crop in various countries. However, for some crops like potatoes, DI is difficult to manage due to the rapid effect of water stress on tuber yield. Irrigation frequency is a key factor to schedule subsurface drip irrigation because, even maintaining the total seasonal volume, soil wetting patterns can result different during the growth period, with consequence on crop yield. Despite the need to enhance water use efficiency, only a few studies related to deficit irrigation of horticultural crops have been made in Tunisia. Objective of the paper was to assess the effects of different on-farm irrigation strategies on water use efficiency of potatoes crop irrigated with subsurface drip irrigation in a semiarid area of central Tunisia. After validation, Hydrus-2D model was used to simulate soil water status in the root zone, to evaluate actual crop evapotranspiration and then to estimate indirectly water use efficiency (IWUE), defined as the ratio between crop yield and total amount of water supplied with irrigation. Field experiments, were carried out in Central Tunisia (10° 33' 47.0" E, 35° 58' 8.1° N, 19 m a.s.l) on a potatoes crop planted in a sandy loam soil, during the growing season 2014, from January 15 (plantation of tubers) to May 6 (harvesting). Soil water status was monitored in two plots (T1 and T2) maintained under the same management, but different irrigation volumes, provided by a SDI system. In particular, irrigation was scheduled according to the average water content measured in the root zone, with a total of 8 watering, with timing ranging between one and three hours in T1, and between about half-an-hour and one-hour and a-half, in T2. The validity of Hydrus-2D model was initially assessed based on the comparison between measured and estimated soil water content at different distances from the emitter (RMSE values were not higher than 0.036). Then, model simulations allowed to verify that it is possible to enhance irrigation water use efficiency by increasing the frequency of irrigation even maintaining limited water deficit conditions during the full development stage subsequent the crop tuberization. Experimental results, joined to model simulations can therefore provide useful guidelines for a more sustainable use of irrigation water in countries characterised by semi-arid environments and limited availability of water resources.

CROP type analysis using Landsat digital data

NASA Technical Reports Server (NTRS)

Brown, C. E.; Thomas, R. W.; Wall, S. L.

1981-01-01

Classification and statistical sampling techniques for crop type discrimination using Landsat digital data have been developed by the University of California in cooperation with NASA and the California Department of Water Resources. Ratioed bands (MSS 7/5 and 5/4) and a sun-angle corrected Euclidean albedo band were prepared from data for the Sacramento Valley for five different dates. The test area was stratified into general crop groupings based on the particular patterns of irrigation timing for each crop. Data classified within each stratum were used to produce a crop type map. Comparison with ground data indicates that certain crops and crop groups are discernable. Small grains and rice are easily identifiable, as are deciduous fruit varieties as a group. However, it is not feasible to separate various fruit and nut varieties, or separate vegetable crops with these techniques at present.

An integrated approach to monitoring ecosystem services and agriculture: implications for sustainable agricultural intensification in Rwanda.

PubMed

Rosa, Melissa F; Bonham, Curan A; Dempewolf, Jan; Arakwiye, Bernadette

2017-01-01

Maintaining the long-term sustainability of human and natural systems across agricultural landscapes requires an integrated, systematic monitoring system that can track crop productivity and the impacts of agricultural intensification on natural resources. This study presents the design and practical implementation of a monitoring framework that combines satellite observations with ground-based biophysical measurements and household surveys to provide metrics on ecosystem services and agricultural production at multiple spatial scales, reaching from individual households and plots owned by smallholder farmers to 100-km 2 landscapes. We developed a set of protocols for monitoring and analyzing ecological and agricultural household parameters within two 10 × 10-km landscapes in Rwanda, including soil fertility, crop yield, water availability, and fuelwood sustainability. Initial results suggest providing households that rely on rainfall for crop irrigation with timely climate information and improved technical inputs pre-harvest could help increase crop productivity in the short term. The value of the monitoring system is discussed as an effective tool for establishing a baseline of ecosystem services and agriculture before further change in land use and climate, identifying limitations in crop production and soil fertility, and evaluating food security, economic development, and environmental sustainability goals set forth by the Rwandan government.

From Crop Domestication to Super-domestication

PubMed Central

Vaughan, D. A.; Balázs, E.; Heslop-Harrison, J. S.

2007-01-01

Research related to crop domestication has been transformed by technologies and discoveries in the genome sciences as well as information-related sciences that are providing new tools for bioinformatics and systems' biology. Rapid progress in archaeobotany and ethnobotany are also contributing new knowledge to understanding crop domestication. This sense of rapid progress is encapsulated in this Special Issue, which contains 18 papers by scientists in botanical, crop sciences and related disciplines on the topic of crop domestication. One paper focuses on current themes in the genetics of crop domestication across crops, whereas other papers have a crop or geographic focus. One feature of progress in the sciences related to crop domestication is the availability of well-characterized germplasm resources in the global network of genetic resources centres (genebanks). Germplasm in genebanks is providing research materials for understanding domestication as well as for plant breeding. In this review, we highlight current genetic themes related to crop domestication. Impressive progress in this field in recent years is transforming plant breeding into crop engineering to meet the human need for increased crop yield with the minimum environmental impact – we consider this to be ‘super-domestication’. While the time scale of domestication of 10 000 years or less is a very short evolutionary time span, the details emerging of what has happened and what is happening provide a window to see where domestication might – and can – advance in the future. PMID:17940074

Crop physiology calibration in the CLM

DOE PAGES

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

2015-04-15

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Crop physiology calibration in the CLM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Crop and varietal diversification of rainfed rice based cropping systems for higher productivity and profitability in Eastern India

PubMed Central

Panda, B. B.; Raja, R.; Singh, Teekam; Tripathi, R.; Shahid, M.; Nayak, A. K.

2017-01-01

Rice-rice system and rice fallows are no longer productive in Southeast Asia. Crop and varietal diversification of the rice based cropping systems may improve the productivity and profitability of the systems. Diversification is also a viable option to mitigate the risk of climate change. In Eastern India, farmers cultivate rice during rainy season (June–September) and land leftovers fallow after rice harvest in the post-rainy season (November–May) due to lack of sufficient rainfall or irrigation amenities. However, in lowland areas, sufficient residual soil moistures are available in rice fallow in the post-rainy season (November–March), which can be utilized for raising second crops in the region. Implementation of suitable crop/varietal diversification is thus very much vital to achieve this objective. To assess the yield performance of rice varieties under timely and late sown conditions and to evaluate the performance of dry season crops following them, three different duration rice cultivars were transplanted in July and August. In dry season several non-rice crops were sown in rice fallow to constitute a cropping system. The results revealed that tiller occurrence, biomass accumulation, dry matter remobilization, crop growth rate, and ultimately yield were significantly decreased under late transplanting. On an average, around 30% yield reduction obtained under late sowing may be due to low temperature stress and high rainfall at reproductive stages of the crop. Dry season crops following short duration rice cultivars performed better in terms of grain yield. In the dry season, toria was profitable when sown earlier and if sowing was delayed greengram was suitable. Highest system productivity and profitability under timely sown rice may be due to higher dry matter remobilization from source to sink. A significant correlation was observed between biomass production and grain yield. We infer that late transplanting decrease the tiller occurrence and assimilate remobilization efficiency, which may be responsible for the reduced grain yield. PMID:28437487

Crop and varietal diversification of rainfed rice based cropping systems for higher productivity and profitability in Eastern India.

PubMed

Lal, B; Gautam, Priyanka; Panda, B B; Raja, R; Singh, Teekam; Tripathi, R; Shahid, M; Nayak, A K

2017-01-01

Rice-rice system and rice fallows are no longer productive in Southeast Asia. Crop and varietal diversification of the rice based cropping systems may improve the productivity and profitability of the systems. Diversification is also a viable option to mitigate the risk of climate change. In Eastern India, farmers cultivate rice during rainy season (June-September) and land leftovers fallow after rice harvest in the post-rainy season (November-May) due to lack of sufficient rainfall or irrigation amenities. However, in lowland areas, sufficient residual soil moistures are available in rice fallow in the post-rainy season (November-March), which can be utilized for raising second crops in the region. Implementation of suitable crop/varietal diversification is thus very much vital to achieve this objective. To assess the yield performance of rice varieties under timely and late sown conditions and to evaluate the performance of dry season crops following them, three different duration rice cultivars were transplanted in July and August. In dry season several non-rice crops were sown in rice fallow to constitute a cropping system. The results revealed that tiller occurrence, biomass accumulation, dry matter remobilization, crop growth rate, and ultimately yield were significantly decreased under late transplanting. On an average, around 30% yield reduction obtained under late sowing may be due to low temperature stress and high rainfall at reproductive stages of the crop. Dry season crops following short duration rice cultivars performed better in terms of grain yield. In the dry season, toria was profitable when sown earlier and if sowing was delayed greengram was suitable. Highest system productivity and profitability under timely sown rice may be due to higher dry matter remobilization from source to sink. A significant correlation was observed between biomass production and grain yield. We infer that late transplanting decrease the tiller occurrence and assimilate remobilization efficiency, which may be responsible for the reduced grain yield.

[Agricultural biotechnology safety assessment].

PubMed

McClain, Scott; Jones, Wendelyn; He, Xiaoyun; Ladics, Gregory; Bartholomaeus, Andrew; Raybould, Alan; Lutter, Petra; Xu, Haibin; Wang, Xue

2015-01-01

Genetically modified (GM) crops were first introduced to farmers in 1995 with the intent to provide better crop yield and meet the increasing demand for food and feed. GM crops have evolved to include a thorough safety evaluation for their use in human food and animal feed. Safety considerations begin at the level of DNA whereby the inserted GM DNA is evaluated for its content, position and stability once placed into the crop genome. The safety of the proteins coded by the inserted DNA and potential effects on the crop are considered, and the purpose is to ensure that the transgenic novel proteins are safe from a toxicity, allergy, and environmental perspective. In addition, the grain that provides the processed food or animal feed is also tested to evaluate its nutritional content and identify unintended effects to the plant composition when warranted. To provide a platform for the safety assessment, the GM crop is compared to non-GM comparators in what is typically referred to as composition equivalence testing. New technologies, such as mass spectrometry and well-designed antibody-based methods, allow better analytical measurements of crop composition, including endogenous allergens. Many of the analytical methods and their intended uses are based on regulatory guidance documents, some of which are outlined in globally recognized documents such as Codex Alimentarius. In certain cases, animal models are recommended by some regulatory agencies in specific countries, but there is typically no hypothesis or justification of their use in testing the safety of GM crops. The quality and standardization of testing methods can be supported, in some cases, by employing good laboratory practices (GLP) and is recognized in China as important to ensure quality data. Although the number of recommended, in some cases, required methods for safety testing are increasing in some regulatory agencies, it should be noted that GM crops registered to date have been shown to be comparable to their nontransgenic counterparts and safe . The crops upon which GM development are based are generally considered safe.

Comparison of Sub-pixel Classification Approaches for Crop-specific Mapping

EPA Science Inventory

The Moderate Resolution Imaging Spectroradiometer (MODIS) data has been increasingly used for crop mapping and other agricultural applications. Phenology-based classification approaches using the NDVI (Normalized Difference Vegetation Index) 16-day composite (250 m) data product...

An evaluation of time-series MODIS 250-meter vegetation index data for crop mapping in the United States Central Great Plains

NASA Astrophysics Data System (ADS)

Wardlow, Brian Douglas

The objectives of this research were to: (1) investigate time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-meter EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) data for regional-scale crop-related land use/land cover characterization in the U.S. Central Great Plains and (2) develop and test a MODIS-based crop mapping protocol. A pixel-level analysis of the time-series MODIS 250-m VIs for 2,000+ field sites across Kansas found that unique spectral-temporal signatures were detected for the region's major crop types, consistent with the crops' phenology. Intra-class variations were detected in VI data associated with different land use practices, climatic conditions, and planting dates for the crops. The VIs depicted similar seasonal variations and were highly correlated. A pilot study in southwest Kansas found that accurate and detailed cropping patterns could be mapped using the MODIS 250-m VI data. Overall and class-specific accuracies were generally greater than 90% for mapping crop/non-crop, general crops (alfalfa, summer crops, winter wheat, and fallow), summer crops (corn, sorghum, and soybeans), and irrigated/non-irrigated crops using either VI dataset. The classified crop areas also had a high level of agreement (<5% difference) with the USDA reported crop areas. Both VIs produced comparable crop maps with only a 1-2% difference between their classification accuracies and a high level of pixel-level agreement (>90%) between their classified crop patterns. This hierarchical crop mapping protocol was tested for Kansas and produced similar classification results over a larger and more diverse area. Overall and class-specific accuracies were typically between 85% and 95% for the crop maps. At the state level, the maps had a high level of areal agreement (<5% difference) with the USDA crop area figures and their classified patterns were consistent with the state's cropping practices. In general, the protocol's performance was relatively consistent across the state's range of environmental conditions, landscape patterns, and cropping practices. The largest areal differences occurred in eastern Kansas due to the omission of many small cropland areas that were not resolvable at MODIS' 250-m resolution. Notable regional deviations in classified areas also occurred for selected classes due to localized precipitation patterns and specific cropping practices.

Genome editing using CRISPR/Cas9-targeted mutagenesis: An opportunity for yield improvements of crop plants grown under environmental stresses.

PubMed

Abdelrahman, Mostafa; Al-Sadi, Abdullah M; Pour-Aboughadareh, Alireza; Burritt, David J; Tran, Lam-Son Phan

2018-03-12

Developing more crops able to sustainably produce high yields when grown under biotic/abiotic stresses is an important goal, if crop production and food security are to be guaranteed in the face of ever-increasing human population and unpredictable global climatic conditions. However, conventional crop improvement, through random mutagenesis or genetic recombination, is time-consuming and cannot keep pace with increasing food demands. Targeted genome editing (GE) technologies, especially clustered regularly interspaced short palindromic repeats (CRISPR)/(CRISPR)-associated protein 9 (Cas9), have great potential to aid in the breeding of crops that are able to produce high yields under conditions of biotic/abiotic stress. This is due to their high efficiency, accuracy and low risk of off-target effects, compared with conventional random mutagenesis methods. The use of CRISPR/Cas9 system has grown very rapidly in recent years with numerous examples of targeted mutagenesis in crop plants, including gene knockouts, modifications, and the activation and repression of target genes. The potential of the GE approach for crop improvement has been clearly demonstrated. However, the regulation and social acceptance of GE crops still remain a challenge. In this review, we evaluate the recent applications of the CRISPR/Cas9-mediated GE, as a means to produce crop plants with greater resilience to the stressors they encounter when grown under increasing stressful environmental conditions. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

NASA Astrophysics Data System (ADS)

Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

2015-04-01

Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple component in the water balance of a catchment, as outstanding future development, the model could also offer an advanced support for water resources management decisions at catchment scale.

Pesticides reduce symbiotic efficiency of nitrogen-fixing rhizobia and host plants

PubMed Central

Fox, Jennifer E.; Gulledge, Jay; Engelhaupt, Erika; Burow, Matthew E.; McLachlan, John A.

2007-01-01

Unprecedented agricultural intensification and increased crop yield will be necessary to feed the burgeoning world population, whose global food demand is projected to double in the next 50 years. Although grain production has doubled in the past four decades, largely because of the widespread use of synthetic nitrogenous fertilizers, pesticides, and irrigation promoted by the “Green Revolution,” this rate of increased agricultural output is unsustainable because of declining crop yields and environmental impacts of modern agricultural practices. The last 20 years have seen diminishing returns in crop yield in response to increased application of fertilizers, which cannot be completely explained by current ecological models. A common strategy to reduce dependence on nitrogenous fertilizers is the production of leguminous crops, which fix atmospheric nitrogen via symbiosis with nitrogen-fixing rhizobia bacteria, in rotation with nonleguminous crops. Here we show previously undescribed in vivo evidence that a subset of organochlorine pesticides, agrichemicals, and environmental contaminants induces a symbiotic phenotype of inhibited or delayed recruitment of rhizobia bacteria to host plant roots, fewer root nodules produced, lower rates of nitrogenase activity, and a reduction in overall plant yield at time of harvest. The environmental consequences of synthetic chemicals compromising symbiotic nitrogen fixation are increased dependence on synthetic nitrogenous fertilizer, reduced soil fertility, and unsustainable long-term crop yields. PMID:17548832

Crops Models for Varying Environmental Conditions

NASA Technical Reports Server (NTRS)

Jones, Harry; Cavazzoni, James; Keas, Paul

2001-01-01

New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.

Net global warming potential and greenhouse gas intensity of conventional and conservation agriculture system in rainfed semi arid tropics of India

NASA Astrophysics Data System (ADS)

Pratibha, G.; Srinivas, I.; Rao, K. V.; Shanker, Arun K.; Raju, B. M. K.; Choudhary, Deepak K.; Srinivas Rao, K.; Srinivasarao, Ch.; Maheswari, M.

2016-11-01

Agriculture has been considered as one of the contributors to greenhouse gas (GHG) emissions and it continues to increase with increase in crop production. Hence development of sustainable agro techniques with maximum crop production, and low global warming potential is need of the hour. Quantifying net global warming potential (NGWP) and greenhouse gas intensity (GHGI) of an agricultural activity is a method to assess the mitigation potential of the activity. But there is dearth of information on NGWP of conservation agriculture under rainfed conditions. Hence in this study two methods such as crop based (NGWPcrop) and soil based (NGWPsoil) were estimated from the data of the experiment initiated in 2009 in rainfed semiarid regions of Hyderabad, India with different tillage practices like conventional tillage (CT), reduced tillage (RT), zero tillage (ZT) and residue retention levels by harvesting at different heights which includes 0, 10 and 30 cm anchored residue in pigeonpea-castor systems. The results of the study revealed that under rainfed conditions CT recorded 24% higher yields over ZT, but CT and RT were on par with each other. However, the yield gap between the tillage treatments is narrowing down over 5 years of study. ZT and RT recorded 26 and 11% lower indirect GHG emissions (emissions from farm operations and input use) over CT, respectively. The percent contribution of CO2 eq. N2O emission is higher to total GHG emissions in both the crops. Both NGWPcrop, NGWPsoil, GHGIcrop, and GHGIsoil based were influenced by tillage and residue treatments. Further, castor grown on pigeonpea residue recorded 20% higher GHG emissions over pigeonpea grown on castor residues. The fuel consumption in ZT was reduced by 58% and 81% as compared to CT in pigeonpea and castor, respectively. Lower NGWP and GHGI based on crop and soil was observed with increase in crop residues and decrease in tillage intensity in both the crops. The results of the study indicate that, there is scope to reduce the NGWP emissions by reducing one tillage operation as in RT and increase in crop residue by harvesting at 10 and 30 cm height with minimal impact on the crop yields. However, the trade-off between higher yield and soil health versus GHG emissions should be considered while promoting conservation agriculture. The NGWPcrop estimation method indicated considerable benefits of residues to the soil and higher potential of GHG mitigation than by the NGWPsoil method and may overestimate the potential of GHG mitigation in agriculture system.

MS-based analytical methodologies to characterize genetically modified crops.

PubMed

García-Cañas, Virginia; Simó, Carolina; León, Carlos; Ibáñez, Elena; Cifuentes, Alejandro

2011-01-01

The development of genetically modified crops has had a great impact on the agriculture and food industries. However, the development of any genetically modified organism (GMO) requires the application of analytical procedures to confirm the equivalence of the GMO compared to its isogenic non-transgenic counterpart. Moreover, the use of GMOs in foods and agriculture faces numerous criticisms from consumers and ecological organizations that have led some countries to regulate their production, growth, and commercialization. These regulations have brought about the need of new and more powerful analytical methods to face the complexity of this topic. In this regard, MS-based technologies are increasingly used for GMOs analysis to provide very useful information on GMO composition (e.g., metabolites, proteins). This review focuses on the MS-based analytical methodologies used to characterize genetically modified crops (also called transgenic crops). First, an overview on genetically modified crops development is provided, together with the main difficulties of their analysis. Next, the different MS-based analytical approaches applied to characterize GM crops are critically discussed, and include "-omics" approaches and target-based approaches. These methodologies allow the study of intended and unintended effects that result from the genetic transformation. This information is considered to be essential to corroborate (or not) the equivalence of the GM crop with its isogenic non-transgenic counterpart. Copyright © 2010 Wiley Periodicals, Inc.

Photoperiod shift effects on yield characteristics of rice

NASA Technical Reports Server (NTRS)

Volk, G. M.; Mitchell, C. A.

1995-01-01

Edible yield must be maximized for each crop species selected for inclusion in the Controlled Ecological Life-Support System (CELSS) proposed by NASA to support long-term manned space missions. In a greenhouse study aimed at increasing biomass partitioning to rice (Oryza sativa L.) grain, plants of the high yielding semi-dwarf rice cultivar Ai-Nan-Tsao were started in pots under 8-h photoperiods at a density of 212 plants m-2. After different periods of time under 8-h photoperiods, pots were switched to continuous light for the remainder of the cropping cycle. Continuous light did not delay time to first panicle emergence (60 d) or time to harvest (83 d). There was a positive correlation between the length of continuous light treatments and nongrain biomass. Grain yield (1.6 +/- 0.2 g plant-1) did not increase in continuous light. Yield-efficiency rate (grain weight per length of cropping cycle, canopy volume, and weight of nongrain shoot biomass) was used to compare treatments. Small Ai-Nan-Tsao rice canopies grown under 8-h photoperiods were more efficient producers of grain than canopies grown under continuous light for a portion of the rice cropping cycle.

Long-Term Coffee Monoculture Alters Soil Chemical Properties and Microbial Communities.

PubMed

Zhao, Qingyun; Xiong, Wu; Xing, Yizhang; Sun, Yan; Lin, Xingjun; Dong, Yunping

2018-04-17

Long-term monoculture severely inhibits coffee plant growth, decreases its yield and results in serious economic losses in China. Here, we selected four replanted coffee fields with 4, 18, 26 and 57 years of monoculture history in Hainan China to investigate the influence of continuous cropping on soil chemical properties and microbial communities. Results showed long-term monoculture decreased soil pH and organic matter content and increased soil EC. Soil bacterial and fungal richness decreased with continuous coffee cropping. Principal coordinate analysis suggested monoculture time was a major determinant of bacterial and fungal community structures. Relative abundances of bacterial Proteobacteria, Bacteroidetes and Nitrospira and fungal Ascomycota phyla decreased over time. At genus level, potentially beneficial microbes such as Nitrospira and Trichoderma, significantly declined over time and showed positive relationships with coffee plant growth in pots. In conclusion, continuous coffee cropping decreased soil pH, organic matter content, potentially beneficial microbes and increased soil EC, which might lead to the poor growth of coffee plants in pots and decline of coffee yields in fields. Thus, developing sustainable agriculture to improve soil pH, organic matter content, microbial activity and reduce the salt stress under continuous cropping system is important for coffee production in China.

Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment.

PubMed

Papadopoulos, Antonis; Kalivas, Dionissios; Theocharopoulos, Sid

2017-07-01

Multispectral sensor capability of capturing reflectance data at several spectral channels, together with the inherent reflectance responses of various soils and especially plant surfaces, has gained major interest in crop production. In present study, two multispectral sensing systems, a ground-based and an aerial-based, were applied for the multispatial and temporal monitoring of two cotton fields in central Greece. The ground-based system was Crop Circle ACS-430, while the aerial consisted of a consumer-level quadcopter (Phantom 2) and a modified Hero3+ Black digital camera. The purpose of the research was to monitor crop growth with the two systems and investigate possible interrelations between the derived well-known normalized difference vegetation index (NDVI). Five data collection campaigns were conducted during the cultivation period and concerned scanning soil and plants with the ground-based sensor and taking aerial photographs of the fields with the unmanned aerial system. According to the results, both systems successfully monitored cotton growth stages in terms of space and time. The mean values of NDVI changes through time as retrieved by the ground-based system were satisfactorily modelled by a second-order polynomial equation (R 2 0.96 in Field 1 and 0.99 in Field 2). Further, they were highly correlated (r 0.90 in Field 1 and 0.74 in Field 2) with the according values calculated via the aerial-based system. The unmanned aerial system (UAS) can potentially substitute crop scouting as it concerns a time-effective, non-destructive and reliable way of soil and plant monitoring.

Temporal changes in climatic variables and their impact on crop yields in southwestern China

NASA Astrophysics Data System (ADS)

Liu, Hong-Bin; Gou, Yu; Wang, Hong-Ye; Li, Hong-Mei; Wu, Wei

2014-08-01

Knowledge of variability in climatic variables changes and its impact on crop yields is important for farmers and policy makers, especially in southwestern China where rainfed agriculture is dominant. In the current study, six climatic parameters (mean temperature, rainfall, relative humidity, sunshine hours, temperature difference, and rainy days) and aggregated yields of three main crops (rice: Oryza sativa L., oilseed rape: Brassica napus L., and tobacco: Nicotiana tabacum L.) during 1985-2010 were collected and analyzed for Chongqing—a large agricultural municipality of China. Climatic variables changes were detected by Mann-Kendall test. Increased mean temperature and temperature difference and decreased relative humidity were found in annual and oilseed rape growth time series ( P < 0.05). Increased sunshine hours were observed during the oilseed rape growth period ( P < 0.05). Rainy days decreased slightly in annual and oilseed rape growth time series ( P < 0.10). Correlation analysis showed that yields of all three crops could benefit from changes in climatic variables in this region. Yield of rice increased with rainfall ( P < 0.10). Yield of oilseed rape increased with mean temperature and temperature difference but decreased with relative humidity ( P < 0.01). Tobacco yield increased with mean temperature ( P < 0.05). Path analysis provided additional information about the importance and contribution paths of climatic variables to crop yields. Temperature difference and sunshine hours had higher direct and indirect effects via other climatic variables on yields of rice and tobacco. Mean temperature, relative humidity, rainy days, and temperature difference had higher direct and indirect effects via others on yield of oilseed rape.

Temporal changes in climatic variables and their impact on crop yields in southwestern China.

PubMed

Liu, Hong-Bin; Gou, Yu; Wang, Hong-Ye; Li, Hong-Mei; Wu, Wei

2014-08-01

Knowledge of variability in climatic variables changes and its impact on crop yields is important for farmers and policy makers, especially in southwestern China where rainfed agriculture is dominant. In the current study, six climatic parameters (mean temperature, rainfall, relative humidity, sunshine hours, temperature difference, and rainy days) and aggregated yields of three main crops (rice: Oryza sativa L., oilseed rape: Brassica napus L., and tobacco: Nicotiana tabacum L.) during 1985-2010 were collected and analyzed for Chongqing-a large agricultural municipality of China. Climatic variables changes were detected by Mann-Kendall test. Increased mean temperature and temperature difference and decreased relative humidity were found in annual and oilseed rape growth time series (P<0.05). Increased sunshine hours were observed during the oilseed rape growth period (P<0.05). Rainy days decreased slightly in annual and oilseed rape growth time series (P<0.10). Correlation analysis showed that yields of all three crops could benefit from changes in climatic variables in this region. Yield of rice increased with rainfall (P<0.10). Yield of oilseed rape increased with mean temperature and temperature difference but decreased with relative humidity (P<0.01). Tobacco yield increased with mean temperature (P<0.05). Path analysis provided additional information about the importance and contribution paths of climatic variables to crop yields. Temperature difference and sunshine hours had higher direct and indirect effects via other climatic variables on yields of rice and tobacco. Mean temperature, relative humidity, rainy days, and temperature difference had higher direct and indirect effects via others on yield of oilseed rape.

Food Crops Response to Climate Change

NASA Astrophysics Data System (ADS)

Butler, E.; Huybers, P.

2009-12-01

Projections of future climate show a warming world and heterogeneous changes in precipitation. Generally, warming temperatures indicate a decrease in crop yields where they are currently grown. However, warmer climate will also open up new areas at high latitudes for crop production. Thus, there is a question whether the warmer climate with decreased yields but potentially increased growing area will produce a net increase or decrease of overall food crop production. We explore this question through a multiple linear regression model linking temperature and precipitation to crop yield. Prior studies have emphasised temporal regression which indicate uniformly decreased yields, but neglect the potentially increased area opened up for crop production. This study provides a compliment to the prior work by exploring this spatial variation. We explore this subject with a multiple linear regression model from temperature, precipitation and crop yield data over the United States. The United States was chosen as the training region for the model because there are good crop data available over the same time frame as climate data and presumably the yield from crops in the United States is optimized with respect to potential yield. We study corn, soybeans, sorghum, hard red winter wheat and soft red winter wheat using monthly averages of temperature and precipitation from NCEP reanalysis and yearly yield data from the National Agriculture Statistics Service for 1948-2008. The use of monthly averaged temperature and precipitation, which neglect extreme events that can have a significant impact on crops limits this study as does the exclusive use of United States agricultural data. The GFDL 2.1 model under a 720ppm CO2 scenario provides temperature and precipitation fields for 2040-2100 which are used to explore how the spatial regions available for crop production will change under these new conditions.

Biomass Logistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

J. Richard Hess; Kevin L. Kenney; William A. Smith

Equipment manufacturers have made rapid improvements in biomass harvesting and handling equipment. These improvements have increased transportation and handling efficiencies due to higher biomass densities and reduced losses. Improvements in grinder efficiencies and capacity have reduced biomass grinding costs. Biomass collection efficiencies (the ratio of biomass collected to the amount available in the field) as high as 75% for crop residues and greater than 90% for perennial energy crops have also been demonstrated. However, as collection rates increase, the fraction of entrained soil in the biomass increases, and high biomass residue removal rates can violate agronomic sustainability limits. Advancements inmore » quantifying multi-factor sustainability limits to increase removal rate as guided by sustainable residue removal plans, and mitigating soil contamination through targeted removal rates based on soil type and residue type/fraction is allowing the use of new high efficiency harvesting equipment and methods. As another consideration, single pass harvesting and other technologies that improve harvesting costs cause biomass storage moisture management challenges, which challenges are further perturbed by annual variability in biomass moisture content. Monitoring, sampling, simulation, and analysis provide basis for moisture, time, and quality relationships in storage, which has allowed the development of moisture tolerant storage systems and best management processes that combine moisture content and time to accommodate baled storage of wet material based upon “shelf-life.” The key to improving biomass supply logistics costs has been developing the associated agronomic sustainability and biomass quality technologies and processes that allow the implementation of equipment engineering solutions.« less

Quantitative inheritance of crop timing traits in interspecific hybrid Petunia populations and interactions with crop quality parameters.

PubMed

Warner, Ryan M; Walworth, Aaron E

2010-01-01

The leaf unfolding rate (i.e., development rate) and the number of nodes forming prior to floral initiation are 2 factors determining production times for floriculture crops. Wild relative species of the cultivated petunia (Petunia x hybrida Vilm.) that exhibited faster development rates than modern cultivars and may therefore be useful genetic sources to develop cultivars with decreased production time were identified. Three interspecific F(2) families, Petunia exserta Stehmann x P. axillaris (Lam.) Britton et al., P. x hybrida 'Mitchell' x P. axillaris, and P. axillaris x P. integrifolia (Hook.) Schinz & Thell. all exhibited transgressive segregation for development rate and node number below the first flower. Development rate and time to flower segregated independently in all families. Leaf number below the first flower was positively correlated with leaf unfolding rate in all families except P. axillaris x P. integrifolia. Time to flower was positively correlated with flower bud number in the P. x hybrida 'Mitchell' x P. axillaris and P. axillaris x P. integrifolia families only. Based on these results, wild Petunia germplasm should be useful for developing petunia cultivars with reduced crop production times, but some negative effects on crop quality parameters may need to be overcome.

Liming impacts on soils, crops and biodiversity in the UK: A review.

PubMed

Holland, J E; Bennett, A E; Newton, A C; White, P J; McKenzie, B M; George, T S; Pakeman, R J; Bailey, J S; Fornara, D A; Hayes, R C

2018-01-01

Fertile soil is fundamental to our ability to achieve food security, but problems with soil degradation (such as acidification) are exacerbated by poor management. Consequently, there is a need to better understand management approaches that deliver multiple ecosystem services from agricultural land. There is global interest in sustainable soil management including the re-evaluation of existing management practices. Liming is a long established practice to ameliorate acidic soils and many liming-induced changes are well understood. For instance, short-term liming impacts are detected on soil biota and in soil biological processes (such as in N cycling where liming can increase N availability for plant uptake). The impacts of liming on soil carbon storage are variable and strongly relate to soil type, land use, climate and multiple management factors. Liming influences all elements in soils and as such there are numerous simultaneous changes to soil processes which in turn affect the plant nutrient uptake; two examples of positive impact for crops are increased P availability and decreased uptake of toxic heavy metals. Soil physical conditions are at least maintained or improved by liming, but the time taken to detect change varies significantly. Arable crops differ in their sensitivity to soil pH and for most crops there is a positive yield response. Liming also introduces implications for the development of different crop diseases and liming management is adjusted according to crop type within a given rotation. Repeated lime applications tend to improve grassland biomass production, although grassland response is variable and indirect as it relates to changes in nutrient availability. Other indicators of liming response in grassland are detected in mineral content and herbage quality which have implications for livestock-based production systems. Ecological studies have shown positive impacts of liming on biodiversity; such as increased earthworm abundance that provides habitat for wading birds in upland grasslands. Finally, understanding of liming impacts on soil and crop processes are explored together with functional aspects (in terms of ecosystems services) in a new qualitative framework that includes consideration of how liming impacts change with time. This holistic approach provides insights into the far-reaching impacts that liming has on ecosystems and the potential for liming to enhance the multiple benefits from agriculturally managed land. Recommendations are given for future research on the impact of liming and the implications for ecosystem services. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Unmanned Aerial Vehicle (UAV) data analysis for fertilization dose assessment

NASA Astrophysics Data System (ADS)

Kavvadias, Antonis; Psomiadis, Emmanouil; Chanioti, Maroulio; Tsitouras, Alexandros; Toulios, Leonidas; Dercas, Nicholas

2017-10-01

The growth rate monitoring of crops throughout their biological cycle is very important as it contributes to the achievement of a uniformly optimum production, a proper harvest planning, and reliable yield estimation. Fertilizer application often dramatically increases crop yields, but it is necessary to find out which is the ideal amount that has to be applied in the field. Remote sensing collects spatially dense information that may contribute to, or provide feedback about, fertilization management decisions. There is a potential goal to accurately predict the amount of fertilizer needed so as to attain an ideal crop yield without excessive use of fertilizers cause financial loss and negative environmental impacts. The comparison of the reflectance values at different wavelengths, utilizing suitable vegetation indices, is commonly used to determine plant vigor and growth. Unmanned Aerial Vehicles (UAVs) have several advantages; because they can be deployed quickly and repeatedly, they are flexible regarding flying height and timing of missions, and they can obtain very high-resolution imagery. In an experimental crop field in Eleftherio Larissa, Greece, different dose of pre-plant and in-season fertilization was applied in 27 plots. A total of 102 aerial photos in two flights were taken using an Unmanned Aerial Vehicle based on the scheduled fertilization. Α correlation of experimental fertilization with the change of vegetation indices values and with the increase of the vegetation cover rate during those days was made. The results of the analysis provide useful information regarding the vigor and crop growth rate performance of various doses of fertilization.

Socio-economic, Biophysical, and Perceptional Factors Associated with Agricultural Adaptation of Smallholder Farmers in Gujarat, Northwest India

NASA Astrophysics Data System (ADS)

Jain, M.; DeFries, R. S.

2012-12-01

Climate change is predicted to negatively impact many agricultural communities across the globe, particularly smallholder farmers who often do not have access to appropriate technologies to reduce their vulnerability. To better predict which farmers will be most impacted by future climate change at a regional scale, we use remote sensing and agricultural census data to examine how cropping intensity and crop type have shifted based on rainfall variability across Gujarat, India from 1990 to 2010. Using household-level interviews, we then identify the socio-economic, biophysical, perceptional, and psychological factors associated with smallholder farmers who are the most impacted and the least able to adapt to contemporaneous rainfall variability. We interviewed 750 farmers in 2011 and 2012 that span a rainfall, irrigation, socio-economic, and caste gradient across central Gujarat. Our results show that farmers shift cropping practices in several ways based on monsoon onset, which farmers state is the main observable rainfall signal influencing cropping decisions during the monsoon season. When monsoon onset is delayed, farmers opt to plant more drought-tolerant crops, push back the date of sowing, and increase the number of irrigations used. Comparing self-reported income and yields, we find that switching crops does not improve agricultural income, shifting planting date does not influence crop yield, yet increasing the number of irrigations significantly increases yield. Future work will identify which social (e.g. social networks), psychological (e.g. risk preference), and knowledge (e.g. information sources) factors are associated with farmers who are best able to adapt to rainfall variability.

Decomposition rate and enzymatic activity of composted municipal waste and poultry manure in the soil in a biofuel crops field.

PubMed

Cordovil, Cláudia Marques-Dos-Santos; de Varennes, Amarilis; Pinto, Renata Machado Dos Santos; Alves, Tiago Filipe; Mendes, Pedro; Sampaio, Sílvio César

2017-05-01

Biofuel crops are gaining importance because of the need to replace non-renewable sources. Also, due to the increasing amounts of wastes generated, there is the need to recycle them to the soil, both to fertilize crops and to improve soil physical properties through organic matter increase and microbiological changes in the rhizosphere. We therefore studied the influence of six biofuel crops (elephant grass, giant cane, sugarcane, blue gum, black cottonwood, willow) on the decomposition rate and enzymatic activity of composted municipal solid waste and poultry manure. Organic amendments were incubated in the field (litterbag method), buried near each plant or bare soil. Biomass decrease and dehydrogenase, urease and acid phosphatase level in amendments was monitored over a 180-day period. Soil under the litterbags was analysed for the same enzymatic activity and organic matter fractions (last sampling). After 365 days, a fractionation of organic matter was carried out in both amendments and soil under the litterbags. For compost, willow and sugarcane generally led to the greatest enzymatic activity, at the end of the experiment. For manure, dehydrogenase activity decreased sharply with time, the smallest value near sugarcane, while phosphatase and urease generally presented the highest values, at the beginning or after 90 days' incubation. Clustering showed that plant species could be grouped based on biomass and enzymes measured over time. Plant species influenced the decomposition rate and enzymatic activities of the organic amendments. Overall, mineralization of both amendments was associated with a greater urease activity in soils. Dehydrogenase activity in manure was closely associated with urease activity. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Achieving Lower Nitrogen Balance and Higher Nitrogen Recovery Efficiency Reduces Nitrous Oxide Emissions in North America's Maize Cropping Systems

PubMed Central

Omonode, Rex A.; Halvorson, Ardell D.; Gagnon, Bernard; Vyn, Tony J.

2017-01-01

Few studies have assessed the common, yet unproven, hypothesis that an increase of plant nitrogen (N) uptake and/or recovery efficiency (NRE) will reduce nitrous oxide (N2O) emission during crop production. Understanding the relationships between N2O emissions and crop N uptake and use efficiency parameters can help inform crop N management recommendations for both efficiency and environmental goals. Analyses were conducted to determine which of several commonly used crop N uptake-derived parameters related most strongly to growing season N2O emissions under varying N management practices in North American maize systems. Nitrogen uptake-derived variables included total aboveground N uptake (TNU), grain N uptake (GNU), N recovery efficiency (NRE), net N balance (NNB) in relation to GNU [NNB(GNU)] and TNU [NNB(TNU)], and surplus N (SN). The relationship between N2O and N application rate was sigmoidal with relatively small emissions for N rates <130 kg ha−1, and a sharp increase for N rates from 130 to 220 kg ha−1; on average, N2O increased linearly by about 5 g N per kg of N applied for rates up to 220 kg ha−1. Fairly strong and significant negative relationships existed between N2O and NRE when management focused on N application rate (r2 = 0.52) or rate and timing combinations (r2 = 0.65). For every percentage point increase, N2O decreased by 13 g N ha−1 in response to N rates, and by 20 g N ha−1 for NRE changes in response to rate-by-timing treatments. However, more consistent positive relationships (R2 = 0.73–0.77) existed between N2O and NNB(TNU), NNB(GNU), and SN, regardless of rate and timing of N application; on average N2O emission increased by about 5, 7, and 8 g N, respectively, per kg increase of NNB(GNU), NNB(TNU), and SN. Neither N source nor placement influenced the relationship between N2O and NRE. Overall, our analysis indicated that a careful selection of appropriate N rate applied at the right time can both increase NRE and reduce N2O. However, N2O reduction benefits of optimum N rate-by-timing practices were achieved most consistently with management systems that reduced NNB through an increase of grain N removal or total plant N uptake relative to the total fertilizer N applied to maize. Future research assessing crop or N management effects on N2O should include N uptake parameter measurements to better understand N2O emission relationships to plant NRE and N uptake. PMID:28690623

A Data-driven Approach to Integrate Crop Rotation Agronomic Practices in a Global Gridded Land-use Forcing Dataset

NASA Astrophysics Data System (ADS)

Sahajpal, R.; Hurtt, G. C.; Chini, L. P.; Frolking, S. E.; Izaurralde, R. C.

2016-12-01

Agro-ecosystems are the dominant land-use type on Earth, covering more than a third of ice-free land surface. Agricultural practices have influenced the Earth's climate system by significantly altering the biogeophysical and biogeochemical properties from hyper-local to global scales. While past work has focused largely on characterizing the effects of net land cover changes, the magnitude and nature of gross transitions and agricultural management practices on climate remains highly uncertain. To address this issue, a new set of global gridded land-use forcing datasets (LUH2) have been developed in a standard format required by climate models for CMIP6. For the first time, this dataset includes information on key agricultural management practices including crop rotations. Crop rotations describe the practice of growing crops on the same land in sequential seasons and are essential to agronomic management as they influence key ecosystem services such as crop yields, water quality, carbon and nutrient cycling, pest and disease control. Here, we present a data-driven approach to infer crop rotations based on crop specific land cover data, derived from moderate resolution satellite imagery and created at an annual time-step for the continental United States. Our approach compresses the more than 100,000 unique crop rotations prevalent in the United States from 2013 - 2015 to about 200 representative crop rotations that account for nearly 80% of the spatio-temporal variability. Further simplification is achieved by mapping individual crops to crop functional types, which identify crops based on their photosynthetic pathways (C3/C4), life strategy (annual/perennial) and whether they are N-fixing or not. The resulting matrix of annual transitions between crop functional types averages 41,000 km2/yr for rotations between C3 and C4 annual crops, and 140,000 km2/yr between C3 N-fixing and C4 annual crops. The crop rotation matrix is combined with information on other land-use states to compute annual changes between these states, thereby producing a detailed land-use transition information that can help close regional and global carbon budgets. We also validate the quality of the crop rotations identified in our product in countries with agronomic practices different from the United States.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Benefits of supplementing an industrial waste anaerobic digester with energy crops for increased biogas production.

PubMed

Nges, Ivo Achu; Escobar, Federico; Fu, Xinmei; Björnsson, Lovisa

2012-01-01

Currently, there is increasing competition for waste as feedstock for the growing number of biogas plants. This has led to fluctuation in feedstock supply and biogas plants being operated below maximum capacity. The feasibility of supplementing a protein/lipid-rich industrial waste (pig manure, slaughterhouse waste, food processing and poultry waste) mesophilic anaerobic digester with carbohydrate-rich energy crops (hemp, maize and triticale) was therefore studied in laboratory scale batch and continuous stirred tank reactors (CSTR) with a view to scale-up to a commercial biogas process. Co-digesting industrial waste and crops led to significant improvement in methane yield per ton of feedstock and carbon-to-nitrogen ratio as compared to digestion of the industrial waste alone. Biogas production from crops in combination with industrial waste also avoids the need for micronutrients normally required in crop digestion. The batch co-digestion methane yields were used to predict co-digestion methane yield in full scale operation. This was done based on the ratio of methane yields observed for laboratory batch and CSTR experiments compared to full scale CSTR digestion of industrial waste. The economy of crop-based biogas production is limited under Swedish conditions; therefore, adding crops to existing industrial waste digestion could be a viable alternative to ensure a constant/reliable supply of feedstock to the anaerobic digester. Copyright © 2011 Elsevier Ltd. All rights reserved.

The release of genetically modified crops into the environment. Part I. Overview of current status and regulations.

PubMed

Nap, Jan-Peter; Metz, Peter L J; Escaler, Marga; Conner, Anthony J

2003-01-01

In the past 6 years, the global area of commercially grown, genetically modified (GM) crops has increased more than 30-fold to over 52 million hectares. The number of countries involved has more than doubled. Especially in developing countries, the GM crop area is anticipated to increase rapidly in the coming years. Despite this high adoption rate and future promises, there is a multitude of concerns about the impact of GM crops on the environment. Regulatory approaches in Europe and North America are essentially different. In the EU, it is based on the process of making GM crops; in the US, on the characteristics of the GM product. Many other countries are in the process of establishing regulation based on either system or a mixture. Despite these differences, the information required for risk assessment tends to be similar. Each risk assessment considers the possibility, probability and consequence of harm on a case-by-case basis. For GM crops, the impact of non-use should be added to this evaluation. It is important that the regulation of risk should not turn into the risk of regulation. The best and most appropriate baseline for comparison when performing risk assessment on GM crops is the impact of plants developed by traditional breeding. The latter is an integral and accepted part of agriculture.

Global growth and stability of agricultural yield decrease with pollinator dependence

PubMed Central

Garibaldi, Lucas A.; Aizen, Marcelo A.; Klein, Alexandra M.; Cunningham, Saul A.; Harder, Lawrence D.

2011-01-01

Human welfare depends on the amount and stability of agricultural production, as determined by crop yield and cultivated area. Yield increases asymptotically with the resources provided by farmers’ inputs and environmentally sensitive ecosystem services. Declining yield growth with increased inputs prompts conversion of more land to cultivation, but at the risk of eroding ecosystem services. To explore the interdependence of agricultural production and its stability on ecosystem services, we present and test a general graphical model, based on Jensen's inequality, of yield–resource relations and consider implications for land conversion. For the case of animal pollination as a resource influencing crop yield, this model predicts that incomplete and variable pollen delivery reduces yield mean and stability (inverse of variability) more for crops with greater dependence on pollinators. Data collected by the Food and Agriculture Organization of the United Nations during 1961–2008 support these predictions. Specifically, crops with greater pollinator dependence had lower mean and stability in relative yield and yield growth, despite global yield increases for most crops. Lower yield growth was compensated by increased land cultivation to enhance production of pollinator-dependent crops. Area stability also decreased with pollinator dependence, as it correlated positively with yield stability among crops. These results reveal that pollen limitation hinders yield growth of pollinator-dependent crops, decreasing temporal stability of global agricultural production, while promoting compensatory land conversion to agriculture. Although we examined crop pollination, our model applies to other ecosystem services for which the benefits to human welfare decelerate as the maximum is approached. PMID:21422295

Adaptation to the Local Environment by Modifications of the Photoperiod Response in Crops

PubMed Central

Nakamichi, Norihito

2015-01-01

Flowering plants produce a meristem at the shoot tip where specialized tissue generates shoot apical meristems at the appropriate time to differentiate into reproductive structures, pollinate and efficiently generate seeds. The complex set of molecular and phenological events culminating in development of a flowering meristem is referred to as ‘flowering time’. Flowering time affects plant productivity because plants dedicate energy to produce flowers and seeds rather than vegetative tissue once the molecular decision to initiate flowering has been taken. Thus, initiation of flowering time is an important decision in plants, especially in annual plants including crops. Humans have introduced crops into latitudes and climate areas far from their origin or natural ecosystem, requiring in many cases modification of native flowering times. Recent molecular–genetic studies shed light on the genetic basis related to such introductions. In this review, recent progress regarding crop introductions and their genetic bases are summarized, as well as the potential of other agricultural plants to be introduced into different climatic zones. PMID:25432974

[Effects of crop tree release on stand growth and stand structure of Cunninghamia lanceolata plantation].

PubMed

Wu, Jian-qiang; Wang, Yi-xiang; Yang, Yi; Zhu, Ting-ting; Zhu, Xu-dan

2015-02-01

Crop trees were selected in a 26-year-old even-aged Cunninghamia lanceolata plantation in Lin' an, and compared in plots that were released and unreleased to examine growth and structure responses for 3 years after thinning. Crop tree release significantly increased the mean increments of diameter and volume of individual tree by 1.30 and 1.25 times relative to trees in control stands, respectively. The increments of diameter and volume of crop trees were significantly higher than those of general trees in thinning plots, crop trees and general trees in control plots, which suggested that the responses from different tree types to crop tree release treatment were different. Crop tree release increased the average distances of crop trees to the nearest neighboring trees, reducing competition among crop trees by about 68.2%. 3-year stand volume increment for thinning stands had no significant difference with that of control stands although the number of trees was only 81.5% of the control. Crop trees in thinned plots with diameters over than 14 cm reached 18.0% over 3 years, compared with 12.0% for trees without thinning, suggesting that crop tree release benefited the larger individual trees. The pattern of tree locations in thinning plots tended to be random, complying with the rule that tree distribution pattern changes with growth. Crop tree release in C. lanceolata plantation not only promoted the stand growth, but also optimized the stand structure, benefiting crop trees sustained rapid growth and larger diameter trees production.

Assessing Honey Bee (Hymenoptera: Apidae) Foraging Populations and the Potential Impact of Pesticides on Eight U.S. Crops

PubMed Central

Frazier, Maryann T.; Mullin, Chris A.; Frazier, Jim L.; Ashcraft, Sara A.; Leslie, Tim W.; Mussen, Eric C.; Drummond, Frank A.

2015-01-01

Beekeepers who use honey bees (Apis mellifera L.) for crop pollination services, or have colonies making honey on or in close proximity to agricultural crops, are concerned about the reductions of colony foragers and ultimate weakening of their colonies. Pesticide exposure is a potential factor in the loss of foragers. During 2009–2010, we assessed changes in the field force populations of 9–10 colonies at one location per crop on each of the eight crops by counting departing foragers leaving colonies at regular intervals during the respective crop blooming periods. The number of frames of adult bees was counted before and after bloom period. For pesticide analysis, we collected dead and dying bees near the hives, returning foragers, crop flowers, trapped pollen, and corn-flowers associated with the cotton crop. The number of departing foragers changed over time in all crops except almonds; general patterns in foraging activity included declines (cotton), noticeable peaks and declines (alfalfa, blueberries, cotton, corn, and pumpkins), and increases (apples and cantaloupes). The number of adult bee frames increased or remained stable in all crops except alfalfa and cotton. A total of 53 different pesticide residues were identified in samples collected across eight crops. Hazard quotients (HQ) were calculated for the combined residues for all crop-associated samples and separately for samples of dead and dying bees. A decrease in the number of departing foragers in cotton was one of the most substantial crop-associated impacts and presented the highest pesticide risk estimated by a summed pesticide residue HQ. PMID:26453703

A framework for standardized calculation of weather indices in Germany

NASA Astrophysics Data System (ADS)

Möller, Markus; Doms, Juliane; Gerstmann, Henning; Feike, Til

2018-05-01

Climate change has been recognized as a main driver in the increasing occurrence of extreme weather. Weather indices (WIs) are used to assess extreme weather conditions regarding its impact on crop yields. Designing WIs is challenging, since complex and dynamic crop-climate relationships have to be considered. As a consequence, geodata for WI calculations have to represent both the spatio-temporal dynamic of crop development and corresponding weather conditions. In this study, we introduce a WI design framework for Germany, which is based on public and open raster data of long-term spatio-temporal availability. The operational process chain enables the dynamic and automatic definition of relevant phenological phases for the main cultivated crops in Germany. Within the temporal bounds, WIs can be calculated for any year and test site in Germany in a reproducible and transparent manner. The workflow is demonstrated on the example of a simple cumulative rainfall index for the phenological phase shooting of winter wheat using 16 test sites and the period between 1994 and 2014. Compared to station-based approaches, the major advantage of our approach is the possibility to design spatial WIs based on raster data characterized by accuracy metrics. Raster data and WIs, which fulfill data quality standards, can contribute to an increased acceptance and farmers' trust in WI products for crop yield modeling or weather index-based insurances (WIIs).

Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data to Improve USDA's World Agricultural Supply and Demand Estimates

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H.

2010-12-01

The USDA World Agricultural Outlook Board (WAOB) coordinates the development of the monthly World Agricultural Supply and Demand Estimates (WASDE) for the U.S. and major foreign producing countries. Given the significant effect of weather on crop progress, conditions, and production, WAOB prepares frequent agricultural weather assessments in the Global Agricultural Decision Support Environment (GLADSE). Because the timing of the precipitation is often as important as the amount, in their effects on crop production, WAOB frequently examines precipitation time series to estimate crop productivity. An effective method for such assessment is the use of analog year comparisons, where precipitation time series, based on surface weather stations, from several historical years are compared with the time series from the current year. Once analog years are identified, crop yields can be estimated for the current season based on observed yields from the analog years, because of the similarities in the precipitation patterns. In this study, NASA satellite precipitation and soil moisture time series are used to identify analog years. Given that soil moisture often has a more direct effect than does precipitation on crop water availability, the time series of soil moisture could be more effective than that of precipitation, in identifying those years with similar crop yields. Retrospective analyses of analogs will be conducted to determine any reduction in the level of uncertainty in identifying analog years, and any reduction in false negatives or false positives. The comparison of analog years could potentially be improved by quantifying the selection of analogs, instead of the current visual inspection method. Various approaches to quantifying are currently being evaluated. This study is part of a larger effort to improve WAOB estimates by integrating NASA remote sensing soil moisture observations and research results into GLADSE, including (1) the integration of the Land Parameter Retrieval Model (LPRM) soil moisture algorithm for operational production and (2) the assimilation of LPRM soil moisture into the USDA Environmental Policy Integrated Climate (EPIC) crop model.

[Water-saving mechanisms of intercropping system in improving cropland water use efficiency].

PubMed

Zhang, Feng-Yun; Wu, Pu-Te; Zhao, Xi-Ning; Cheng, Xue-Feng

2012-05-01

Based on the multi-disciplinary researches, and in terms of the transformation efficiency of surface water to soil water, availability of cropland soil water, crop canopy structure, total irrigation volume needed on a given area, and crop yield, this paper discussed the water-saving mechanisms of intercropping system in improving cropland water use efficiency. Intercropping system could promote the full use of cropland water by plant roots, increase the water storage in root zone, reduce the inter-row evaporation and control excessive transpiration, and create a special microclimate advantageous to the plant growth and development. In addition, intercropping system could optimize source-sink relationship, provide a sound foundation for intensively utilizing resources temporally and spatially, and increase the crop yield per unit area greatly without increase of water consumption, so as to promote the crop water use efficiency effectively.

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides.

PubMed

Topping, Chris J; Craig, Peter S; de Jong, Frank; Klein, Michael; Laskowski, Ryszard; Manachini, Barbara; Pieper, Silvia; Smith, Rob; Sousa, José Paulo; Streissl, Franz; Swarowsky, Klaus; Tiktak, Aaldrik; van der Linden, Ton

2015-12-15

Pesticides are regulated in Europe and this process includes an environmental risk assessment (ERA) for non-target arthropods (NTA). Traditionally a non-spatial or field trial assessment is used. In this study we exemplify the introduction of a spatial context to the ERA as well as suggest a way in which the results of complex models, necessary for proper inclusion of spatial aspects in the ERA, can be presented and evaluated easily using abundance and occupancy ratios (AOR). We used an agent-based simulation system and an existing model for a widespread carabid beetle (Bembidion lampros), to evaluate the impact of a fictitious highly-toxic pesticide on population density and the distribution of beetles in time and space. Landscape structure and field margin management were evaluated by comparing scenario-based ERAs for the beetle. Source-sink dynamics led to an off-crop impact even when no pesticide was present off-crop. In addition, the impacts increased with multi-year application of the pesticide whereas current ERA considers only maximally one year. These results further indicated a complex interaction between landscape structure and pesticide effect in time, both in-crop and off-crop, indicating the need for NTA ERA to be conducted at landscape- and multi-season temporal-scales. Use of AOR indices to compare ERA outputs facilitated easy comparison of scenarios, allowing simultaneous evaluation of impacts and planning of mitigation measures. The landscape and population ERA approach also demonstrates that there is a potential to change from regulation of a pesticide in isolation, towards the consideration of pesticide management at landscape scales and provision of biodiversity benefits via inclusion and testing of mitigation measures in authorisation procedures. Copyright © 2015 Elsevier B.V. All rights reserved.

NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

NASA Astrophysics Data System (ADS)

Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

2007-12-01

National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high spatial and temporal resolution remote sensing datasets; improved time-series meteorological inputs required for crop growth models; and regional prediction capability through geo-processing-based yield modeling.

IRRIMET: a web 2.0 advisory service for irrigation water management

NASA Astrophysics Data System (ADS)

De Michele, Carlo; Anzano, Enrico; Colandrea, Marco; Marotta, Luigi; Mula, Ileana; Pelosi, Anna; D'Urso, Guido; Battista Chirico, Giovanni

2016-04-01

Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Irrigating according to reliable crop water requirement estimates is one of the most convincing solution to decrease agricultural water use. Here we present an innovative irrigation advisory service, applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. The advisory service is based on the optimal combination of VIS-NIR high resolution satellite images (Landsat, Deimos, Rapideye) to map crop vigour, and high resolution numerical weather prediction for assessing the meteorological variables driving the crop water needs in the short-medium range. The advisory service is broadcasted with a simple and intuitive web app interface which makes daily real time irrigation and evapotranspiration maps and customized weather forecasts (based on Cosmo Leps model) accessible from desktop computers, tablets and smartphones.

New soil water sensors for irrigation management

USDA-ARS?s Scientific Manuscript database

Effective irrigation management is key to obtaining the most crop production per unit of water applied and increasing production in the face of competing demands on water resources. Management methods have included calculating crop water needs based on weather station measurements, calculating soil ...

Quantitative Resistance to Plant Pathogens in Pyramiding Strategies for Durable Crop Protection.

PubMed

Pilet-Nayel, Marie-Laure; Moury, Benoît; Caffier, Valérie; Montarry, Josselin; Kerlan, Marie-Claire; Fournet, Sylvain; Durel, Charles-Eric; Delourme, Régine

2017-01-01

Quantitative resistance has gained interest in plant breeding for pathogen control in low-input cropping systems. Although quantitative resistance frequently has only a partial effect and is difficult to select, it is considered more durable than major resistance (R) genes. With the exponential development of molecular markers over the past 20 years, resistance QTL have been more accurately detected and better integrated into breeding strategies for resistant varieties with increased potential for durability. This review summarizes current knowledge on the genetic inheritance, molecular basis, and durability of quantitative resistance. Based on this knowledge, we discuss how strategies that combine major R genes and QTL in crops can maintain the effectiveness of plant resistance to pathogens. Combining resistance QTL with complementary modes of action appears to be an interesting strategy for breeding effective and potentially durable resistance. Combining quantitative resistance with major R genes has proven to be a valuable approach for extending the effectiveness of major genes. In the plant genomics era, improved tools and methods are becoming available to better integrate quantitative resistance into breeding strategies. Nevertheless, optimal combinations of resistance loci will still have to be identified to preserve resistance effectiveness over time for durable crop protection.

Investigating Engineered Ribonucleoprotein Particles to Improve Oral RNAi Delivery in Crop Insect Pests

PubMed Central

Gillet, François-Xavier; Garcia, Rayssa A.; Macedo, Leonardo L. P.; Albuquerque, Erika V. S.; Silva, Maria C. M.; Grossi-de-Sa, Maria F.

2017-01-01

Genetically modified (GM) crops producing double-stranded RNAs (dsRNAs) are being investigated largely as an RNA interference (RNAi)-based resistance strategy against crop insect pests. However, limitations of this strategy include the sensitivity of dsRNA to insect gut nucleases and its poor insect cell membrane penetration. Working with the insect pest cotton boll weevil (Anthonomus grandis), we showed that the chimeric protein PTD-DRBD (peptide transduction domain—dsRNA binding domain) combined with dsRNA forms a ribonucleoprotein particle (RNP) that improves the effectiveness of the RNAi mechanism in the insect. The RNP slows down nuclease activity, probably by masking the dsRNA. Furthermore, PTD-mediated internalization in insect gut cells is achieved within minutes after plasma membrane contact, limiting the exposure time of the RNPs to gut nucleases. Therefore, the RNP provides an approximately 2-fold increase in the efficiency of insect gene silencing upon oral delivery when compared to naked dsRNA. Taken together, these data demonstrate the role of engineered RNPs in improving dsRNA stability and cellular entry, representing a path toward the design of enhanced RNAi strategies in GM plants against crop insect pests. PMID:28503153

Investigating Engineered Ribonucleoprotein Particles to Improve Oral RNAi Delivery in Crop Insect Pests.

PubMed

Gillet, François-Xavier; Garcia, Rayssa A; Macedo, Leonardo L P; Albuquerque, Erika V S; Silva, Maria C M; Grossi-de-Sa, Maria F

2017-01-01

Genetically modified (GM) crops producing double-stranded RNAs (dsRNAs) are being investigated largely as an RNA interference (RNAi)-based resistance strategy against crop insect pests. However, limitations of this strategy include the sensitivity of dsRNA to insect gut nucleases and its poor insect cell membrane penetration. Working with the insect pest cotton boll weevil ( Anthonomus grandis ), we showed that the chimeric protein PTD-DRBD (peptide transduction domain-dsRNA binding domain) combined with dsRNA forms a ribonucleoprotein particle (RNP) that improves the effectiveness of the RNAi mechanism in the insect. The RNP slows down nuclease activity, probably by masking the dsRNA. Furthermore, PTD-mediated internalization in insect gut cells is achieved within minutes after plasma membrane contact, limiting the exposure time of the RNPs to gut nucleases. Therefore, the RNP provides an approximately 2-fold increase in the efficiency of insect gene silencing upon oral delivery when compared to naked dsRNA. Taken together, these data demonstrate the role of engineered RNPs in improving dsRNA stability and cellular entry, representing a path toward the design of enhanced RNAi strategies in GM plants against crop insect pests.

Impacts on Water Management and Crop Production of Regional Cropping System Adaptation to Climate Change

NASA Astrophysics Data System (ADS)

Zhong, H.; Sun, L.; Tian, Z.; Liang, Z.; Fischer, G.

2014-12-01

China is one of the most populous and fast developing countries, also faces a great pressure on grain production and food security. Multi-cropping system is widely applied in China to fully utilize agro-climatic resources and increase land productivity. As the heat resource keep improving under climate warming, multi-cropping system will also shifting northward, and benefit crop production. But water shortage in North China Plain will constrain the adoption of new multi-cropping system. Effectiveness of multi-cropping system adaptation to climate change will greatly depend on future hydrological change and agriculture water management. So it is necessary to quantitatively express the water demand of different multi-cropping systems under climate change. In this paper, we proposed an integrated climate-cropping system-crops adaptation framework, and specifically focused on: 1) precipitation and hydrological change under future climate change in China; 2) the best multi-cropping system and correspondent crop rotation sequence, and water demand under future agro-climatic resources; 3) attainable crop production with water constraint; and 4) future water management. In order to obtain climate projection and precipitation distribution, global climate change scenario from HADCAM3 is downscaled with regional climate model (PRECIS), historical climate data (1960-1990) was interpolated from more than 700 meteorological observation stations. The regional Agro-ecological Zone (AEZ) model is applied to simulate the best multi-cropping system and crop rotation sequence under projected climate change scenario. Finally, we use the site process-based DSSAT model to estimate attainable crop production and the water deficiency. Our findings indicate that annual land productivity may increase and China can gain benefit from climate change if multi-cropping system would be adopted. This study provides a macro-scale view of agriculture adaptation, and gives suggestions to national agriculture adaptation strategy decisions.

A generic probability based model to derive regional patterns of crops in time and space

NASA Astrophysics Data System (ADS)

Wattenbach, Martin; Luedtke, Stefan; Redweik, Richard; van Oijen, Marcel; Balkovic, Juraj; Reinds, Gert Jan

2015-04-01

Croplands are not only the key to human food supply, they also change the biophysical and biogeochemical properties of the land surface leading to changes in the water cycle, energy portioning, they influence soil erosion and substantially contribute to the amount of greenhouse gases entering the atmosphere. The effects of croplands on the environment depend on the type of crop and the associated management which both are related to the site conditions, economic boundary settings as well as preferences of individual farmers. The method described here is designed to predict the most probable crop to appear at a given location and time. The method uses statistical crop area information on NUTS2 level from EUROSTAT and the Common Agricultural Policy Regionalized Impact Model (CAPRI) as observation. These crops are then spatially disaggregated to the 1 x 1 km grid scale within the region, using the assumption that the probability of a crop appearing at a given location and a given year depends on a) the suitability of the land for the cultivation of the crop derived from the MARS Crop Yield Forecast System (MCYFS) and b) expert knowledge of agricultural practices. The latter includes knowledge concerning the feasibility of one crop following another (e.g. a late-maturing crop might leave too little time for the establishment of a winter cereal crop) and the need to combat weed infestations or crop diseases. The model is implemented in R and PostGIS. The quality of the generated crop sequences per grid cell is evaluated on the basis of the given statistics reported by the joint EU/CAPRI database. The assessment is given on NUTS2 level using per cent bias as a measure with a threshold of 15% as minimum quality. The results clearly indicates that crops with a large relative share within the administrative unit are not as error prone as crops that allocate only minor parts of the unit. However, still roughly 40% show an absolute per cent bias above the 15% threshold. This highlights the discrepancy between the best practice given the soil properties within an administrative unit and the effectively cultivated crops.

Recombinase Polymerase Amplification (RPA) of CaMV-35S Promoter and nos Terminator for Rapid Detection of Genetically Modified Crops

PubMed Central

Xu, Chao; Li, Liang; Jin, Wujun; Wan, Yusong

2014-01-01

Recombinase polymerase amplification (RPA) is a novel isothermal DNA amplification and detection technology that enables the amplification of DNA within 30 min at a constant temperature of 37–42 °C by simulating in vivo DNA recombination. In this study, based on the regulatory sequence of the cauliflower mosaic virus 35S (CaMV-35S) promoter and the Agrobacterium tumefaciens nopaline synthase gene (nos) terminator, which are widely incorporated in genetically modified (GM) crops, we designed two sets of RPA primers and established a real-time RPA detection method for GM crop screening and detection. This method could reliably detect as few as 100 copies of the target molecule in a sample within 15–25 min. Furthermore, the real-time RPA detection method was successfully used to amplify and detect DNA from samples of four major GM crops (maize, rice, cotton, and soybean). With this novel amplification method, the test time was significantly shortened and the reaction process was simplified; thus, this method represents an effective approach to the rapid detection of GM crops. PMID:25310647

Recombinase polymerase amplification (RPA) of CaMV-35S promoter and nos terminator for rapid detection of genetically modified crops.

PubMed

Xu, Chao; Li, Liang; Jin, Wujun; Wan, Yusong

2014-10-10

Recombinase polymerase amplification (RPA) is a novel isothermal DNA amplification and detection technology that enables the amplification of DNA within 30 min at a constant temperature of 37-42 °C by simulating in vivo DNA recombination. In this study, based on the regulatory sequence of the cauliflower mosaic virus 35S (CaMV-35S) promoter and the Agrobacterium tumefaciens nopaline synthase gene (nos) terminator, which are widely incorporated in genetically modified (GM) crops, we designed two sets of RPA primers and established a real-time RPA detection method for GM crop screening and detection. This method could reliably detect as few as 100 copies of the target molecule in a sample within 15-25 min. Furthermore, the real-time RPA detection method was successfully used to amplify and detect DNA from samples of four major GM crops (maize, rice, cotton, and soybean). With this novel amplification method, the test time was significantly shortened and the reaction process was simplified; thus, this method represents an effective approach to the rapid detection of GM crops.

Mapping large-area landscape suitability for honey bees to assess the influence of land-use change on sustainability of national pollination services.

PubMed

Gallant, Alisa L; Euliss, Ned H; Browning, Zac

2014-01-01

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops.

Mapping large-area landscape suitability for honey bees to assess the influence of land-use change on sustainability of national pollination services

USGS Publications Warehouse

Gallant, Alisa L.; Euliss, Ned H.; Browning, Zac

2014-01-01

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops.

Mapping Large-Area Landscape Suitability for Honey Bees to Assess the Influence of Land-Use Change on Sustainability of National Pollination Services

PubMed Central

Gallant, Alisa L.; Euliss, Ned H.; Browning, Zac

2014-01-01

Pollination is a critical ecosystem service affected by various drivers of land-use change, such as policies and programs aimed at land resources, market values for crop commodities, local land-management decisions, and shifts in climate. The United States is the world's most active market for pollination services by honey bees, and the Northern Great Plains provide the majority of bee colonies used to meet the Nation's annual pollination needs. Legislation requiring increased production of biofuel crops, increasing commodity prices for crops of little nutritional value for bees in the Northern Great Plains, and reductions in government programs aimed at promoting land conservation are converging to alter the regional landscape in ways that challenge beekeepers to provide adequate numbers of hives for national pollination services. We developed a spatially explicit model that identifies sites with the potential to support large apiaries based on local-scale land-cover requirements for honey bees. We produced maps of potential apiary locations for North Dakota, a leading producer of honey, based on land-cover maps representing (1) an annual time series compiled from existing operational products and (2) a realistic scenario of land change. We found that existing land-cover products lack sufficient local accuracy to monitor actual changes in landscape suitability for honey bees, but our model proved informative for evaluating effects on suitability under scenarios of land change. The scenario we implemented was aligned with current drivers of land-use change in the Northern Great Plains and highlighted the importance of conservation lands in landscapes intensively and extensively managed for crops. PMID:24919181

Monitoring irrigation water consumption using high resolution NDVI image time series (Sentinel-2 like). Calibration and validation in the Kairouan plain (Tunisia)

NASA Astrophysics Data System (ADS)

Saadi, Sameh; Simonneaux, Vincent; Boulet, Gilles; Mougenot, Bernard; Zribi, Mehrez; Lili Chabaane, Zohra

2015-04-01

Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. It is thus of major importance to design tools allowing a better management of this resource. Remote sensing has long been used for computing evapotranspiration estimates, which is an input for crop water balance monitoring. Up to now, only medium and low resolution data (e.g. MODIS) are available on regular basis to monitor cultivated areas. However, the increasing availability of high resolution high repetitivity VIS-NIR remote sensing, like the forthcoming Sentinel-2 mission to be lunched in 2015, offers unprecedented opportunity to improve this monitoring. In this study, regional crops water consumption was estimated with the SAMIR software (Satellite of Monitoring Irrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient (Kcb) and the vegetation fraction cover. The model includes a soil water model, requiring the knowledge of soil water holding capacity, maximum rooting depth, and water inputs. As irrigations are usually not known on large areas, they are simulated based on rules reproducing the farmer practices. The main objective of this work is to assess the operationality and accuracy of SAMIR at plot and perimeter scales, when several land use types (winter cereals, summer vegetables…), irrigation and agricultural practices are intertwined in a given landscape, including complex canopies such as sparse orchards. Meteorological ground stations were used to compute the reference evapotranspiration and get the rainfall depths. Two time series of ten and fourteen high-resolution SPOT5 have been acquired for the 2008-2009 and 2012-2013 hydrological years over an irrigated area in central Tunisia. They span the various successive crop seasons. The images were radiometrically corrected, first, using the SMAC6s Algorithm, second, using invariant objects located on the scene, based on visual observation of the images. From these time series, a Normalized Difference Vegetation Index (NDVI) profile was generated for each pixel. SAMIR was first calibrated based on ground measurements of evapotranspiration achieved using eddy-correlation devices installed on irrigated wheat and barley plots. After calibration, the model was run to spatialize irrigation over the whole area and a validation was done using cumulated seasonal water volumes obtained from ground survey at both plot and perimeter scales. The results show that although determination of model parameters was successful at plot scale, irrigation rules required an additional calibration which was achieved at perimeter scale.

Using the USDA Weekly Crop Progress Record to Document Trends in Corn Planting Date From 1979 to 2005

NASA Astrophysics Data System (ADS)

Kucharik, C. J.

2005-12-01

Agriculture is a dominant driver of land surface phenology in the United States Corn Belt. The timing of planting and harvest, along with the rate of plant development, are influenced by crop type, technology, land management decisions, and weather and soil conditions. Collectively, these integrated factors affect the spatial and temporal spectral signature of crops captured by remote sensing. While many studies have used the historical satellite record of vegetation activity to detect changes across the land surface, there has been less emphasis on using ground-based or remote sensing data to depict the contemporary phenology of individual US agro-ecosystems. The objectives of this study were twofold: (1) demonstrate how weekly USDA-NASS 'Crop Progress' data and 'Weekly Weather and Crop Bulletins' could be useful to remote sensing science when characterizing changing land surface phenology over the US; and (2) quantify long-term trends in corn planting progress from 1979 to 2005 across 12 states in the US Corn Belt. Examination of the weekly NASS crop progress data shows that the initiation of corn planting has become significantly (P < 0.01) earlier by 6 to 24 days since 1979, potentially contributing to about 10% to 64% of the linear increase in corn yields during this period. The magnitude of earlier planting date trend varies regionally, and not all of this change can be attributed to an earlier arrival of spring or warmer springtime temperatures. Rather, the change appears to be related to increased farmer planting efficiency in spring attributed to the increased adoption of no-tillage or reduced-tillage practices and plowing soils in fall. Regardless of the exact cause of this trend, we have a legitimate reason to suspect that 'greening' of the Corn Belt since about 1980, according to remote sensing observations, is not entirely due to climate change, but rather arises from human land-use change in combination with climate factors. In the future, crop progress data may provide an ideal blueprint for selecting the ideal MODIS scene (i.e., 8-day period) that can separate various crop phenologies (e.g., corn vs. soybean) at high resolution, and offer a means to help validate or parameterize ecosystem model algorithms.

Disaggregating and mapping crop statistics using hypertemporal remote sensing

NASA Astrophysics Data System (ADS)

Khan, M. R.; de Bie, C. A. J. M.; van Keulen, H.; Smaling, E. M. A.; Real, R.

2010-02-01

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998-2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered "small scale maps". These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.

Altered cropping pattern and cultural continuation with declined prosperity following abrupt and extreme arid event at ~4,200 yrs BP: Evidence from an Indus archaeological site Khirsara, Gujarat, western India.

PubMed

Pokharia, Anil K; Agnihotri, Rajesh; Sharma, Shalini; Bajpai, Sunil; Nath, Jitendra; Kumaran, R N; Negi, Bipin Chandra

2017-01-01

Archaeological sites hold important clues to complex climate-human relationships of the past. Human settlements in the peripheral zone of Indus culture (Gujarat, western India) are of considerable importance in the assessment of past monsoon-human-subsistence-culture relationships and their survival thresholds against climatic stress exerted by abrupt changes. During the mature phase of Harappan culture between ~4,600-3,900yrsBP, the ~4,100±100yrsBP time slice is widely recognized as one of the major, abrupt arid-events imprinted innumerous well-dated palaeo records. However, the veracity of this dry event has not been established from any archaeological site representing the Indus (Harappan) culture, and issues concerning timing, changes in subsistence pattern, and the likely causes of eventual abandonment (collapse) continue to be debated. Here we show a significant change in crop-pattern (from barley-wheat based agriculture to 'drought-resistant' millet-based crops) at ~4,200 yrs BP, based on abundant macrobotanical remains and C isotopes of soil organic matter (δ13CSOM) in an archaeological site at Khirsara, in the Gujarat state of western India. The crop-change appears to be intentional and was likely used as an adaptation measure in response to deteriorated monsoonal conditions. The ceramic and architectural remains of the site indicate that habitation survived and continued after the ~4,200yrsBP dry climatic phase, but with declined economic prosperity. Switching to millet-based crops initially helped inhabitants to avoid immediate collapse due to climatic stresses, but continued aridity and altered cropping pattern led to a decline in prosperity levels of inhabitants and eventual abandonment of the site at the end of the mature Harappan phase.

Calibration of Daycent biogeochemical model for rice paddies in three agro-ecological zones in Peninsular India to optimize cropping practices and predict GHG emissions

NASA Astrophysics Data System (ADS)

Rajan, S.; Kritee, K.; Keough, C.; Parton, W. J.; Ogle, S. M.

2014-12-01

Rice is a staple for nearly half of the world population with irrigated and rainfed lowland rice accounting for about 80% of the worldwide harvested rice area. Increased atmospheric CO2 and rising temperatures are expected to adversely affect rice yields by the end of the 21st century. In addition, different crop management practices affect methane and nitrous oxide emissions from rice paddies antagonistically warranting a review of crop management practices such that farmers can adapt to the changing climate and also help mitigate climate change. The Daily DayCent is a biogeochemical model that operates on a daily time step, driven by four ecological drivers, i.e. climate, soil, vegetation, and management practices. The model is widely used to simulate daily fluxes of various gases, plant productivity, nutrient availability, and other ecosystem parameters in response to changes in land management and climate. We employed the DayCent model as a tool to optimize rice cropping practices in Peninsular India so as to develop a set of farming recommendations to ensure a triple win (i.e. higher yield, higher profit and lower GHG emissions). We applied the model to simulate both N2O and CH4 emissions, and crop yields from four rice paddies in three different agro-ecological zones under different management practices, and compared them with measured GHG and yield data from these plots. We found that, like all process based models, the biggest constraint in using the model was input data acquisition. Lack of accurate documentation of historic land use and management practices, missing historical daily weather data, and difficulty in obtaining digital records of soil and crop/vegetation parameters related to our experimental plots came in the way of our execution of this model. We will discuss utilization of estimates based on available literature, or knowledge-based values in lieu of missing measured parameters in our simulations with DayCent which could prove to be a solution to overcome data limitations in modeling with DayCent and other process based models for developing regions of the world.

Nitrogen use efficiency and crop production: Patterns of regional variation in the United States, 1987-2012.

PubMed

Swaney, Dennis P; Howarth, Robert W; Hong, Bongghi

2018-04-17

National-level summaries of crop production and nutrient use efficiency, important for international comparisons, only partially elucidate agricultural dynamics within a country. Agricultural production and associated environmental impacts in large countries vary significantly because of regional differences in crops, climate, resource use and production practices. Here, we review patterns of regional crop production, nitrogen use efficiency (NUE), and major inputs of nitrogen to US crops over 1987-2012, based on the Farm Resource Regions developed by the Economic Research Service (USDA-ERS). Across the US, NUE generally decreased over time over the period studied, mainly due to increased use in mineral N fertilizer above crop N requirements. The Heartland region dominates production of major crops and thus tends to drive national patterns, showing linear response of crop production to nitrogen inputs broadly consistent with an earlier analysis of global patterns of country-scale data by Lassaletta et al. (2014). Most other regions show similar responses, but the Eastern Uplands region shows a negative response to nitrogen inputs, and the Southern Seaboard shows no significant relationship. The regional differences appear as two branches in the response of aggregate production to N inputs on a cropland area basis, but not on a total area basis, suggesting that the type of scaling used is critical under changing cropland area. Nitrogen use efficiency (NUE) is positively associated with fertilizer as a percentage of N inputs in four regions, and all regions considered together. NUE is positively associated with crop N fixation in all regions except Northern Great Plains. It is negatively associated with manure (livestock excretion); in the US, manure is still treated largely as a waste to be managed rather than a nutrient resource. This significant regional variation in patterns of crop production and NUE vs N inputs, has implications for environmental quality and food security. Copyright © 2018 Elsevier B.V. All rights reserved.

Automated observation of diurnal solar-induced chlorophyll fluorescence for better understanding of crop photosynthesis

NASA Astrophysics Data System (ADS)

Huang, C.; Zhang, L.; Wang, S.; Qiao, N.

2016-12-01

Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been considered an ideal probe in monitoring vegetation photosynthesis. However, numerous challenges have greatly limited its wide applications, including accurate estimate of faint SIF from the observed apparent reflected radiation, uncertainties in inferring the vegetation photosynthesis as well as lack of validation. These difficulties should be resolved at ground-based controlled scales before the launch of SIF satellite platforms such as ESA's FLEX (to be launched 2021). Currently, increasing continuous and long-term automated SIF measurement systems have been reported for better understanding the diurnal and seasonal changes of vegetation photosynthesis. This study introduces a newly developed automated SIF field measurement system (Auto-SIF, 500-800 nm, FWHM=0.74 nm, SSI=0.38 nm, SNR=1000:1, see figure 1) in China and its initial results for inferring photosynthesis of different crops including soybean (three types), maize (two types) and rice (two types). The experiments were conducted at the test crop field affiliated to the Institute of Genetics and Development Biology, Chinese Academy of Sciences. The Auto-SIF incorporates two observation modes, i.e., reference panel mode and target mode (see figure 1), and the two modes can be switched very quickly through an electrical motor. All diurnal super-spectra data and SIFs of crops were collected in clear days and with a finer time interval of 1minute, therefore they can be easily resampled to different time intervals (see figure 2) in order for convenient comparisons with other data from different observation platforms, like 30-minute tower-flux GPP data. For better understanding of crop photosynthesis, Li-6400 XT (LI-COR, Inc.) and TES-1339R light meter were respectively used in this study to simultaneously obtain diurnal dynamics of leaf-level SIFs and sun incoming flux. Due to the availability of wide spectral range of Auto-SIF (500-800 nm), the photochemical reflectance index (PRI) and NDVI were also calculated to assess the diurnal SIFs and photosynthesis performances among different crops. This study presents a primary analyses of field diurnal canopy/leaf SIFs, PRI, NDVI of different crops , and may provide a better understanding of crop photosynthesis.

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

NASA Technical Reports Server (NTRS)

Elliott, Joshua; Muller, Christoff

2015-01-01

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

Genetic diversity in Malus ×domestica (Rosaceae) through time in response to domestication.

PubMed

Gross, Briana L; Henk, Adam D; Richards, Christopher M; Fazio, Gennaro; Volk, Gayle M

2014-10-01

• Patterns of genetic diversity in domesticated plants are affected by geographic region of origin and cultivation, intentional artificial selection, and unintentional genetic bottlenecks. While bottlenecks are mainly associated with the initial domestication process, they can also affect diversity during crop improvement. Here, we investigate the impact of the improvement process on the genetic diversity of domesticated apple in comparison with other perennial and annual fruit crops.• Apple cultivars that were developed at various times (ranging from the 13th through the 20th century) and 11 of the 15 apple cultivars that are used for 90% of the apple production in the United States were surveyed for genetic diversity based on either 9 or 19 simple sequence repeats (SSRs). Diversity was compared using standard metrics and model-based approaches based on expected heterozygosity (He) at equilibrium. Improvement bottleneck data for fruit crops were also collected from the literature.• Domesticated apples showed no significant reduction in genetic diversity through time across the last eight centuries. Diversity was generally high, with an average He > 0.7 for apples from all centuries. However, diversity of the apples currently used for the bulk of commercial production was lower.• The improvement bottleneck in domesticated apples appears to be mild or nonexistent, in contrast to improvement bottlenecks in many annual and perennial fruit crops, as documented from the literature survey. The low diversity of the subset of cultivars used for commercial production, however, indicates that an improvement bottleneck may be in progress for this perennial crop. © 2014 Botanical Society of America, Inc.

A global sensitivity analysis of crop virtual water content

NASA Astrophysics Data System (ADS)

Tamea, S.; Tuninetti, M.; D'Odorico, P.; Laio, F.; Ridolfi, L.

2015-12-01

The concepts of virtual water and water footprint are becoming widely used in the scientific literature and they are proving their usefulness in a number of multidisciplinary contexts. With such growing interest a measure of data reliability (and uncertainty) is becoming pressing but, as of today, assessments of data sensitivity to model parameters, performed at the global scale, are not known. This contribution aims at filling this gap. Starting point of this study is the evaluation of the green and blue virtual water content (VWC) of four staple crops (i.e. wheat, rice, maize, and soybean) at a global high resolution scale. In each grid cell, the crop VWC is given by the ratio between the total crop evapotranspiration over the growing season and the crop actual yield, where evapotranspiration is determined with a detailed daily soil water balance and actual yield is estimated using country-based data, adjusted to account for spatial variability. The model provides estimates of the VWC at a 5x5 arc minutes and it improves on previous works by using the newest available data and including multi-cropping practices in the evaluation. The model is then used as the basis for a sensitivity analysis, in order to evaluate the role of model parameters in affecting the VWC and to understand how uncertainties in input data propagate and impact the VWC accounting. In each cell, small changes are exerted to one parameter at a time, and a sensitivity index is determined as the ratio between the relative change of VWC and the relative change of the input parameter with respect to its reference value. At the global scale, VWC is found to be most sensitive to the planting date, with a positive (direct) or negative (inverse) sensitivity index depending on the typical season of crop planting date. VWC is also markedly dependent on the length of the growing period, with an increase in length always producing an increase of VWC, but with higher spatial variability for rice than for other crops. The sensitivity to the reference evapotranspiration is highly variable with the considered crop and ranges from positive values (for soybean), to negative values (for rice and maize) and near-zero values for wheat. This variability reflects the different yield response factors of crops, which expresses their tolerance to water stress.

The C-simulator as a tool to investigate the potential of household waste compost to increase soil organic matter in Flanders

NASA Astrophysics Data System (ADS)

Tits, Mia; Hermans, Inge; Elsen, Annemie; Vandendriessche, Hilde

2010-05-01

Soil organic matter (SOM) is an important parameter of the quality of arable land. At the global scale, agricultural soils are considered to be a major sink of carbon dioxide. Results of thousands of soil analyses carried out annually by the Soil Service of Belgium have shown that carbon stocks in Flemish agricultural land have dwindled in the past decades, and this in spite of the increased use of animal manure from intensive livestock holdings. In the framework of the improvement of the SOM content and at the same time the idea of organic waste recycling ("cradle to cradle"-principle), a long-term field experiment with household waste compost (HWC) was set up in 1997 by the Soil Service of Belgium. In this trial different HWC application rates and timings were realized yearly, in order to investigate its nutritive value for arable crops, its effect on crop yield and its long-term effect on soil fertility, pH and soil organic matter content. Yearly data on crop rotation, crop development and yield as well as soil and HWC analyses were obtained for each trial treatment. Climatic data were obtained from nearby weather stations. Also in the context of the SOM-problem, the Soil Service of Belgium and the University of Ghent have developed, at the request of the Flemish government, the C-simulator, a simple but efficient interactive tool to assist farmers with the carbon stock management on their arable land. By providing input on the current carbon status of a particular field, the crop rotation and the (organic) fertiliser plan, the program calculates the expected evolution of the soil organic carbon over a thirty year period. By consulting comparative lists of characteristics of different crops and organic manures the farmer can adjust his strategy for a more efficient organic matter management. The calculations of the C-simulator are based on the RothC model, which was calibrated for Flemish conditions through an extensive literature study. Specific data on the characteristics of plant residues of most common arable crops and organic fertilisers used in Flanders were obtained from the Soil Service of Belgium database and from literature. Based on a series of test runs, four initial RothC carbon pool distributions were developed for relevant soil-rotation combinations in Flanders. The objective of our study was twofold: firstly, both the calibrated RothC-model and the C-simulator were validated using the data of the long-term HWC-trial. Secondly, the C-simulator was used to simulate future carbon evolution in the different HWC-trial treatments, in order to obtain a deeper insight in the built-up of soil carbon by the use of HWC.

Land Husbandry: Biochar application to reduce land degradation and erosion on cassava production

NASA Astrophysics Data System (ADS)

Yuniwati, E. D.

2017-12-01

This field experiment was carried out to examine the effect of increasing crop yield on land degradation and erosion in cassava-based cropping systems. The experiment was also aimed at showing that with proper crop management, the planting of cassava does not result in land degradation, and therefore, a sustainable production system can be obtained. The experiment was done in a farmer's fields in Batu, about 15 km south east of Malang, East Java, Indonesia. The soils are Alfisols with a surface slope of about 8%. There were 8 experimental treatments with two replications. The experiment results show that biochar applications reduce of soil erosion rate of the cassava field were not necessarily higher than those of maize in terms of crop yield and crop management. At low-to-medium yield, also observed the nutrient uptake of cassava was lower than that of maize. At high yield, only the K uptake of cassava was higher than that of maize, whereas the N and P uptake was more or less similar. Soil erosion on the cassava field was significantly higher than that on the maize field; however, this only occurred when there was no suitable crop management. Simple crop managements, such as ridging, biochar application, or manure application could significantly reduce soil erosion. The results also revealed that proper management could prevent land degradation and increase crop yield. In turn, the increase in crop yield could decrease soil erosion and plant nutrient depletion.

Evaluation of preservation methods for improving biogas production and enzymatic conversion yields of annual crops

PubMed Central

2011-01-01

Background The use of energy crops and agricultural residues is expected to increase to fulfil the legislative demands of bio-based components in transport fuels. Ensiling methods, adapted from the feed sector, are suitable storage methods to preserve fresh crops throughout the year for, for example, biogas production. Various preservation methods, namely ensiling with and without acid addition for whole crop maize, fibre hemp and faba bean were investigated. For the drier fibre hemp, alkaline urea treatment was studied as well. These treatments were also explored as mild pretreatment methods to improve the disassembly and hydrolysis of these lignocellulosic substrates. Results The investigated storage treatments increased the availability of the substrates for biogas production from hemp and in most cases from whole maize but not from faba bean. Ensiling of hemp, without or with addition of formic acid, increased methane production by more than 50% compared to fresh hemp. Ensiling resulted in substantially increased methane yields also from maize, and the use of formic acid in ensiling of maize further enhanced methane yields by 16%, as compared with fresh maize. Ensiled faba bean, in contrast, yielded somewhat less methane than the fresh material. Acidic additives preserved and even increased the amount of the valuable water-soluble carbohydrates during storage, which affected most significantly the enzymatic hydrolysis yield of maize. However, preservation without additives decreased the enzymatic hydrolysis yield especially in maize, due to its high content of soluble sugars that were already converted to acids during storage. Urea-based preservation significantly increased the enzymatic hydrolysability of hemp. Hemp, preserved with urea, produced the highest carbohydrate increase of 46% in enzymatic hydrolysis as compared to the fresh material. Alkaline pretreatment conditions of hemp improved also the methane yields. Conclusions The results of the present work show that ensiling and alkaline preservation of fresh crop materials are useful pretreatment methods for methane production. Improvements in enzymatic hydrolysis were also promising. While all three crops still require a more powerful pretreatment to release the maximum amount of carbohydrates, anaerobic preservation is clearly a suitable storage and pretreatment method prior to production of platform sugars from fresh crops. PMID:21771298

Accelerating plant breeding.

PubMed

De La Fuente, Gerald N; Frei, Ursula K; Lübberstedt, Thomas

2013-12-01

The growing demand for food with limited arable land available necessitates that the yield of major food crops continues to increase over time. Advances in marker technology, predictive statistics, and breeding methodology have allowed for continued increases in crop performance through genetic improvement. However, one major bottleneck is the generation time of plants, which is biologically limited and has not been improved since the introduction of doubled haploid technology. In this opinion article, we propose to implement in vitro nurseries, which could substantially shorten generation time through rapid cycles of meiosis and mitosis. This could prove a useful tool for speeding up future breeding programs with the aim of sustainable food production. Copyright © 2013 Elsevier Ltd. All rights reserved.

Separability of agricultural crops with airborne scatterometry

NASA Technical Reports Server (NTRS)

Mehta, N. C.

1983-01-01

Backscattering measurements were acquired with airborne scatterometers over a site in Cass County, North Dakota on four days in the 1981 crop growing season. Data were acquired at three frequencies (L-, C- and Ku-bands), two polarizations (like and cross) and ten incidence angles (5 degrees to 50 degrees in 5 degree steps). Crop separability is studied in an hierarchical fashion. A two-class separability measure is defined, which compares within-class to between-class variability, to determine crop separability. The scatterometer channels with the best potential for crop separability are determined, based on this separability measure. Higher frequencies are more useful for discriminating small grains, while lower frequencies tend to separate non-small grains better. Some crops are more separable when row direction is taken into account. The effect of pixel purity is to increase the separability between all crops while not changing the order of useful scatterometer channels. Crude estimates of separability errors are calculated based on these analyses. These results are useful in selecting the parameters of active microwave systems in agricultural remote sensing.

Mapping Crop Yield and Sow Date Using High Resolution Imagery

NASA Astrophysics Data System (ADS)

Royal, K.

2015-12-01

Keitasha Royal, Meha Jain, Ph.D., David Lobell, Ph.D Mapping Crop Yield and Sow Date Using High Resolution ImageryThe use of satellite imagery in agriculture is becoming increasingly more significant and valuable. Due to the emergence of new satellites, such as Skybox, these satellites provide higher resolution imagery (e.g 1m) therefore improving the ability to map smallholder agriculture. For the smallholder farm dominated area of northern India, Skybox high-resolution satellite imagery can aid in understanding how to improve farm yields. In particular, we are interested in mapping winter wheat in India, as this region produces approximately 80% of the country's wheat crop, which is important given that wheat is a staple crop that provides approximately 20% of household calories. In northeast India, the combination of increased heat stress, limited irrigation access, and the difficulty for farmers to access advanced farming technologies results in farmers only producing about 50% of their potential crop yield. The use of satellite imagery can aid in understanding wheat yields through time and help identify ways to increase crop yields in the wheat belt of India. To translate Skybox satellite data into meaningful information about wheat fields, we examine vegetation indices, such as the normalized difference vegetation index (NDVI), to measure the "greenness" of plants to help determine the health of the crops. We test our ability to predict crop characteristics, like sow date and yield, using vegetation indices of 59 fields for which we have field data in Bihar, India.

Remote sensing in precision farming: real-time monitoring of water and fertilizer requirements of agricultural crops

NASA Astrophysics Data System (ADS)

Zilberman, Arkadi; Ben Asher, Jiftah; Kopeika, Norman S.

2016-10-01

The advancements in remote sensing in combination with sensor technology (both passive and active) enable growers to analyze an entire crop field as well as its local features. In particular, changes of actual evapo-transpiration (ET) as a function of water availability can be measured remotely with infrared radiometers. Detection of crop water stress and ET and combining it with the soil water flow model enable rational irrigation timing and application amounts. Nutrient deficiency, and in particular nitrogen deficiency, causes substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield. In the present work, to retrieve ET a novel deterministic approach was used which is based on the remote sensing data. The algorithm can automatically provide timely valuable information on plant and soil water status, which can improve the management of irrigated crops. The solution is capable of bridging between Penman-Monteith ET model and Richards soil water flow model. This bridging can serve as a preliminary tool for expert irrigation system. To support decisions regarding fertilizers the greenness of plant canopies is assessed and quantified by using the spectral reflectance sensors and digital color imaging. Fertilization management can be provided on the basis of sampling and monitoring of crop nitrogen conditions using RS technique and translating measured N concentration in crop to kg/ha N application in the field.

Role of modern chemistry in sustainable arable crop protection.

PubMed

Smith, Keith; Evans, David A; El-Hiti, Gamal A

2008-02-12

Organic chemistry has been, and for the foreseeable future will remain, vitally important for crop protection. Control of fungal pathogens, insect pests and weeds is crucial to enhanced food provision. As world population continues to grow, it is timely to assess the current situation, anticipate future challenges and consider how new chemistry may help meet those challenges. In future, agriculture will increasingly be expected to provide not only food and feed, but also crops for conversion into renewable fuels and chemical feedstocks. This will further increase the demand for higher crop yields per unit area, requiring chemicals used in crop production to be even more sophisticated. In order to contribute to programmes of integrated crop management, there is a requirement for chemicals to display high specificity, demonstrate benign environmental and toxicological profiles, and be biodegradable. It will also be necessary to improve production of those chemicals, because waste generated by the production process mitigates the overall benefit. Three aspects are considered in this review: advances in the discovery process for new molecules for sustainable crop protection, including tests for environmental and toxicological properties as well as biological activity; advances in synthetic chemistry that may offer efficient and environmentally benign manufacturing processes for modern crop protection chemicals; and issues related to energy use and production through agriculture.

Advances in molecular-based diagnostics in meeting crop biosecurity and phytosanitary issues.

PubMed

Schaad, Norman W; Frederick, Reid D; Shaw, Joe; Schneider, William L; Hickson, Robert; Petrillo, Michael D; Luster, Douglas G

2003-01-01

Awareness of crop biosecurity and phytosanitation has been heightened since 9/11 and the unresolved anthrax releases in October 2001. Crops are highly vulnerable to accidental or deliberate introductions of crop pathogens from outside U.S. borders. Strategic thinking about protection against deliberate or accidental release of a plant pathogen is an urgent priority. Rapid detection will be the key to success. This review summarizes recent progress in the development of rapid real-time PCR protocols and evaluates their effectiveness in a proposed nationwide network of diagnostic laboratories that will facilitate rapid diagnostics and improved communication.

Impacts of Farmers' Knowledge Increase on Farm Profit and Watershed Water Quality

NASA Astrophysics Data System (ADS)

Ding, D.; Bennett, D. A.

2013-12-01

This study explores the impact that an increase in real-time data might have on farmers' nitrogen management, on-farm profit, and watershed water quality in the Midwestern US. In this study, an agent-based model (ABM) is used to simulate farmers' decisions about nitrogen application rate and timing in corn fields. SWAT (soil-water assessment tool) is used to generate a database that characterizes the response of corn yields to nitrogen fertilizer application and the dynamics of nitrogen loss under different scenarios of rainfall events. The database simulates a scenario where farmers would receive real-time feedback about the fate and impact of nitrogen applied to their fields from in-situ sensors. The ability to transform these data into optimal actions is simulated at multiple levels for farmer agents. In a baseline scenario, the farmer agent is only aware of the yield potential of the land field and single values of N rates for achieving the yield potential and is not aware of N loss from farm fields. Knowledge increase is represented by greater accuracy in predicting rainfall events, and the increase of the number of discrete points in a field-specific quadratic curve that captures crop yield response to various levels of nitrogen perceived by farmer agents. In addition, agents perceive N loss from farm fields at increased temporal resolutions. Correspondingly, agents make adjustments to the rate of N application for crops and the timing of fertilizer application given the rainfall events predictions. Farmers' decisions simulated by the ABM are input into SWAT to model nitrogen concentration in impacted streams. Farm profit statistics and watershed-level nitrogen loads are compared among different scenarios of knowledge increase. The hypothesis that the increase of farmers' knowledge benefits both farm profits and watershed water quality is tested through the comparison.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Recent evolution of China's virtual water trade: analysis of selected crops and considerations for policy

NASA Astrophysics Data System (ADS)

Shi, J.; Liu, J.; Pinter, L.

2013-09-01

China has dramatically increased its virtual water import unconsciously for recent years. Many studies have focused on the quantity of traded virtual water but very few go into analysing geographic distribution and the properties of China's virtual water trade network. This paper provides a calculation and analysis of the crop-related virtual water trade network of China based on 27 major primary crops between 1986 and 2009. The results show that China is a net importer of virtual water from water-abundant areas of North and South America, and a net virtual water exporter to water-stressed areas of Asia, Africa, and Europe. Virtual water import is far larger than virtual water export and in both import and export a small number of trade partners control the supply chain. Grain crops are the major contributors to virtual water trade, and among grain crops soybeans, mostly imported from the US, Brazil and Argentina are the most significant. As crop yield and crop water productivity in North and South America are generally higher than those in Asia and Africa, the effect of China's crop-related virtual water trade positively contributes to optimizing crop water use efficiency at the global scale. In order to mitigate water scarcity and secure the food supply, virtual water should be actively incorporated into national water management strategies. From the national perspective, China should reduce the export and increase the import of water-intensive crops. But the sources of virtual water import need to be further diversified to reduce supply chain risks and increase resilience.

MicroRNA-mediated gene regulation: potential applications for plant genetic engineering.

PubMed

Zhou, Man; Luo, Hong

2013-09-01

Food security is one of the most important issues challenging the world today. Any strategies to solve this problem must include increasing crop yields and quality. MicroRNA-based genetic modification technology (miRNA-based GM tech) can be one of the most promising solutions that contribute to agricultural productivity directly by developing superior crop cultivars with enhanced biotic and abiotic stress tolerance and increased biomass yields. Indirectly, the technology may increase usage of marginal soils and decrease pesticide use, among other benefits. This review highlights the most recent progress of transgenic studies utilizing various miRNAs and their targets for plant trait modifications, and analyzes the potential of miRNA-mediated gene regulation for use in crop improvement. Strategies for manipulating miRNAs and their targets in transgenic plants including constitutive, stress-induced, or tissue-specific expression of miRNAs or their targets, RNA interference, expressing miRNA-resistant target genes, artificial target mimic and artificial miRNAs were discussed. We also discussed potential risks of utilizing miRNA-based GM tech. In general, miRNAs and their targets not only provide an invaluable source of novel transgenes, but also inspire the development of several new GM strategies, allowing advances in breeding novel crop cultivars with agronomically useful characteristics.

Assimilation of LAI time-series in crop production models

NASA Astrophysics Data System (ADS)

Kooistra, Lammert; Rijk, Bert; Nannes, Louis

2014-05-01

Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor techniques shows promising results for precision agriculture application and thereby for reduction of the footprint agriculture has on the world's resources.

Characterizing drought stress and trait influence on maize yield under current and future conditions.

PubMed

Harrison, Matthew T; Tardieu, François; Dong, Zhanshan; Messina, Carlos D; Hammer, Graeme L

2014-03-01

Global climate change is predicted to increase temperatures, alter geographical patterns of rainfall and increase the frequency of extreme climatic events. Such changes are likely to alter the timing and magnitude of drought stresses experienced by crops. This study used new developments in the classification of crop water stress to first characterize the typology and frequency of drought-stress patterns experienced by European maize crops and their associated distributions of grain yield, and second determine the influence of the breeding traits anthesis-silking synchrony, maturity and kernel number on yield in different drought-stress scenarios, under current and future climates. Under historical conditions, a low-stress scenario occurred most frequently (ca. 40%), and three other stress types exposing crops to late-season stresses each occurred in ca. 20% of cases. A key revelation shown was that the four patterns will also be the most dominant stress patterns under 2050 conditions. Future frequencies of low drought stress were reduced by ca. 15%, and those of severe water deficit during grain filling increased from 18% to 25%. Despite this, effects of elevated CO2 on crop growth moderated detrimental effects of climate change on yield. Increasing anthesis-silking synchrony had the greatest effect on yield in low drought-stress seasonal patterns, whereas earlier maturity had the greatest effect in crops exposed to severe early-terminal drought stress. Segregating drought-stress patterns into key groups allowed greater insight into the effects of trait perturbation on crop yield under different weather conditions. We demonstrate that for crops exposed to the same drought-stress pattern, trait perturbation under current climates will have a similar impact on yield as that expected in future, even though the frequencies of severe drought stress will increase in future. These results have important ramifications for breeding of maize and have implications for studies examining genetic and physiological crop responses to environmental stresses. © 2013 John Wiley & Sons Ltd.

Development and application of SINE multilocus and quantitative genetic markers to study oilseed rape (Brassica napus L.) crops.

PubMed

Allnutt, T R; Roper, K; Henry, C

2008-01-23

A genetic marker system based on the S1 Short Interspersed Elements (SINEs) in the important commercial crop, oilseed rape ( Brassica napus L.) has been developed. SINEs provided a successful multilocus, dominant marker system that was capable of clearly delineating winter- and spring-type crop varieties. Sixteen of 20 varieties tested showed unique profiles from the 17 polymorphic SINE markers generated. The 3' or 5' flank region of nine SINE markers were cloned, and DNA was sequenced. In addition, one putative pre-transposition SINE allele was cloned and sequenced. Two SINE flanking sequences were used to design real-time PCR assays. These quantitative SINE assays were applied to study the genetic structure of eight fields of oilseed rape crops. Studied fields were more genetically diverse than expected for the chosen loci (mean H T = 0.23). The spatial distribution of SINE marker frequencies was highly structured in some fields, suggesting locations of volunteer impurities within the crop. In one case, the assay identified a mislabeling of the crop variety. SINE markers were a useful tool for crop genetics, phylogenetics, variety identification, and purity analysis. The use and further application of quantitative, real-time PCR markers are discussed.

CQESTR Simulation of Soil Organic Matter Dynamics in Long-term Agricultural Experiments across USA

NASA Astrophysics Data System (ADS)

Gollany, H.; Liang, Y.; Albrecht, S.; Rickman, R.; Follett, R.; Wilhelm, W.; Novak, J.

2009-04-01

Soil organic matter (SOM) has important chemical (supplies nutrients, buffers and adsorbs harmful chemical compounds), biological (supports the growth of microorganisms and micro fauna), and physical (improves soil structure and soil tilth, stores water, and reduces surface crusting, water runoff) functions. The loss of 20 to 50% of soil organic carbon (SOC) from USA soils after converting native prairie or forest to production agriculture is well documented. Sustainable management practices for SOC is critical for maintaining soil productivity and responsible utilization of crop residues. As crop residues are targeted for additional uses (e.g., cellulosic ethanol feedstock) developing C models that predict change in SOM over time with change in management becomes increasingly important. CQESTR, pronounced "sequester," is a process-based C balance model that relates organic residue additions, crop management and soil tillage to SOM accretion or loss. The model works on daily time-steps and can perform long-term (100-year) simulations. Soil organic matter change is computed by maintaining a soil C budget for additions, such as crop residue or added amendments like manure, and organic C losses through microbial decomposition. Our objective was to simulate SOM changes in agricultural soils under a range of soil parent materials, climate and management systems using the CQESTR model. Long-term experiments (e.g. Champaign, IL, >100 yrs; Columbia, MO, >100 yrs; Lincoln, NE, 20 yrs) under various tillage practices, organic amendments, crop rotations, and crop residue removal treatments were selected for their documented history of the long-term effects of management practice on SOM dynamics. Simulated and observed values from the sites were significantly related (r2 = 94%, P < 0.001) with slope not significantly different from 1. Recent interest in crop residue removal for biofuel feedstock prompted us to address that as a management issue. CQESTR successfully simulated a substantial decline in SOM with 90% of crop residue removal for 50 years under various rotations at Columbia, MO and Champaign, IL. An increase in SOM following addition of manure was also well simulated. However, the model underestimated SOM for a fertilized treatment at Columbia. We estimated that a minimum of 8.0 Mg/ha/yr of crop residue and organic amendments (4.0 Mg C ha/yr) was required to prevent a decline in SOM at the Morrow Plots in Champaign, IL. More studies are needed to evaluate the CQESTR model's performance in predicting the amount of crop residue required to maintain the SOM concentration in different soils under a wide range of management and climatic conditions. Given the high correlation of simulated and observed SOM changes, CQESTR can be used to consider a wide range of scenarios before making recommendations or implementing proposed changes. CQESTR in conjunction with the local conditions can guide planning and development of sustainable crop and soil management practices.

Influence of future cropland expansion on regional and global tropospheric ozone

NASA Astrophysics Data System (ADS)

Squire, Oliver; Archibald, Alex; Telford, Paul; Pyle, John

2013-04-01

With the global population set to rise over the next 100 years, the fraction of land used for crop cultivation is likely to increase, the trend being most pronounced in developing regions such as Brazil and South East Asia. In these regions currently there stands natural rainforest, a high emitter of isoprene. As many staple crops, such as soy bean, are low emitters of isoprene, increasing the crop fraction in these regions will decrease isoprene emissions. Ozone over ~35 ppb has been shown to be damaging to plants, and as ground level ozone is sensitive to isoprene concentrations, altering isoprene emissions could increase ground level ozone, potentially resulting in crop damage. This mechanism was investigated by comparing two configurations of an atmospheric chemistry-climate model (UM-UKCA) under a 2100 climate following an IPCC scenario of moderate climate change. The first run had a present day crop distribution but isoprene emissions concurrent with 2100 temperatures and climatic conditions. The second run had isoprene emissions representative of both a 2100 climate and a 2100 crop distribution in accordance with the IMAGE model. By comparing these runs it was established that ozone increased by up to 8 ppb (~30%) due to crop land expansion. Over the Amazon (the most affected region) it was found that crops were exposed to a daily maximum 8-hour (DM8H) ozone above the 35 ppb threshold for up to 65 days more per year than in the base case. These conclusions suggest that increasing the crop fraction in current areas of natural rainforest could increase regional ground level ozone, having a significant effect on crop yield and air quality. The sensitivity of such conclusions to isoprene chemistry was examined by varying the isoprene chemistry scheme within the model. The CheT isoprene scheme used here (50 reactions) was compared with the AQUM (23 reactions) and CESM Superfast (2 reactions) isoprene schemes, all of which are currently used in Earth-system models. It was found that the effect of transplanting these isoprene schemes into the base CheT chemistry scheme lead, in both cases, to higher ozone over isoprene rich regions by up to ~40 ppb. Furthermore, upon repeating the land use change experiment with these other isoprene schemes, it was found that the AQUM scheme produced more ozone (up to ~20 ppb more) in isoprene rich regions due to crop expansion than CheT. However the CESM Superfast scheme showed the opposite effect, producing less ozone than the CheT scheme in isoprene-rich regions. These varied responses highlight the sensitivity of future trends in surface ozone to isoprene chemistry within the range of some currently used chemical schemes, and suggest that further research is needed in order to most effectively parameterise this complex chemistry.

Generation-based life table analysis reveals manifold effects of inbreeding on the population fitness in Plutella xylostella

PubMed Central

Peng, Lu; Zou, Mingmin; Ren, Nana; Xie, Miao; Vasseur, Liette; Yang, Yifan; He, Weiyi; Yang, Guang; Gurr, Geoff M.; Hou, Youming; You, Shijun; You, Minsheng

2015-01-01

Understanding how inbreeding affects fitness is biologically important for conservation and pest management. Despite being a worldwide pest of many economically important cruciferous crops, the influence of inbreeding on diamondback moth, Plutella xylostella (L.), populations is currently unknown. Using age-stage-specific life tables, we quantified the inbreeding effects on fitness-related traits and demographic parameters of P. xylostella. Egg hatching rate, survival and fecundity of the inbred line significantly declined compared to those of the outbred line over time. The inbred P. xylostella line showed significantly lower intrinsic rate of increase (r), net reproduction rate (R0), and finite increase rate (λ), and increasing generation time (T). Inbreeding effects vary with developmental stages and the fitness-related traits can be profoundly affected by the duration of inbreeding. Our work provides a foundation for further studies on molecular and genetic bases of the inbreeding depression for P. xylostella. PMID:26227337

Performance of the CELSS Antarctic Analog Project (CAAP) Crop Production System

NASA Technical Reports Server (NTRS)

Bubenheim, David L.; Flynn, Michael T.; Bates, Maynard; Schlick, Greg; Kliss, Mark (Technical Monitor)

1998-01-01

Regenerative life support systems potentially offer a level of self-sufficiency and a concomitant decrease in logistics and associated costs in support of space exploration and habitation missions. Current state-of-the-art in plant based, regenerative life support requires resources in excess of resource allocations proposed for candidate mission scenarios. Feasibility thresholds have been identified for candidate exploration missions. The goal of this paper is to review recent advances in performance achieved in the CELSS Antarctic Analog Project (CAAP) in light of likely resource constraints. A prototype CAAP crop production chamber has been constructed and operated at the Ames Research Center. The chamber includes a number of unique hardware and software components focused on attempts to increase production efficiency, increase energy efficiency, and control the flow of energy and mass through the system to achieve enhanced performance efficiency. Both single crop, batch production, and continuous cultivation of mixed crops Product ion scenarios have been completed. The crop productivity as well as engineering performance of the chamber will be described. For each scenario, energy required and partitioned for lighting, cooling, pumps, fans, etc. is quantified. Crop production and the resulting lighting efficiency and energy conversion efficiencies are presented. In the mixed-crop scenario, with up to 25 different crops under cultivation, 17 sq m of crop area provided a mean of 515 g edible biomass per day (83% of the approximately 620 g required for one person). Lighting efficiency (moles on photons kWh-1) approached 4 and the conversion efficiency of light energy to biomass was greatly enhanced compared with conventional growing systems. Engineering and biological performance achieved place plant-based life support systems at the threshold of feasibility.

Eleven years of crop diversification alters decomposition dynamics of litter mixtures incubated with soil

DOE PAGES

McDaniel, M. D.; Grandy, A. S.; Tiemann, L. K.; ...

2016-08-11

Agricultural crop rotations have been shown to increase soil carbon (C), nitrogen (N), and microbial biomass. The mechanisms behind these increases remain unclear, but may be linked to the diversity of crop residue inputs to soil organic matter (SOM). Here, we used a residue mixture incubation to examine how variation in long-term diversity of plant communities in agroecosystems influences decomposition of residue mixtures, thus providing a comparison of the effects of plant diversification on decomposition in the long term (via crop rotation) and short term (via residue mixtures). Three crop residue mixtures, ranging in diversity from two to four species,more » were incubated for 360 d with soils from five crop rotations, ranging from monoculture corn (mC) to a complex five-crop rotation. In response, we measured fundamental soil pools and processes underlying C and N cycling. These included soil respiration, inorganic N, microbial biomass, and extracellular enzymes. We hypothesized that soils with more diverse cropping histories would show greater synergistic mixture effects than mC. For most variables (except extracellular enzymes), crop rotation history, or the long-term history of plant diversity in the field, had a stronger effect on soil processes than mixture composition. In contrast to our hypothesis, the mC soil had nearly three and seven times greater synergistic mixture effects for respiration and microbial biomass N, respectively, compared with soils from crop rotations. This was due to the low response of the mC soils to poor quality residues (corn and wheat), likely resulting from a lack of available C and nutrients to cometabolize these residues. These results indicate that diversifying crop rotations in agricultural systems alter the decomposition dynamics of new residue inputs, which may be linked to the benefits of increasing crop rotation diversity on soil nutrient cycling, SOM dynamics, and yields.« less

Conservation Agriculture Improves Soil Quality, Crop Yield, and Incomes of Smallholder Farmers in North Western Ghana

PubMed Central

Naab, Jesse B.; Mahama, George Y.; Yahaya, Iddrisu; Prasad, P. V. V.

2017-01-01

Conservation agriculture (CA) practices are being widely promoted in many areas in sub-Saharan Africa to recuperate degraded soils and improve ecosystem services. This study examined the effects of three tillage practices [conventional moldboard plowing (CT), hand hoeing (MT) and no-tillage (NT)], and three cropping systems (continuous maize, soybean–maize annual rotation, and soybean/maize intercropping) on soil quality, crop productivity, and profitability in researcher and farmer managed on-farm trials from 2010 to 2013 in northwestern Ghana. In the researcher managed mother trial, the CA practices of NT, residue retention and crop rotation/intercropping maintained higher soil organic carbon, and total soil N compared to conventional tillage practices after 4 years. Soil bulk density was higher under NT than under CT soils in the researcher managed mother trails or farmers managed baby trials after 4 years. In the researcher managed mother trial, there was no significant difference between tillage systems or cropping systems in maize or soybean yields in the first three seasons. In the fourth season, crop rotation had the greatest impact on maize yields with CT maize following soybean increasing yields by 41 and 49% compared to MT and NT maize, respectively. In the farmers’ managed trials, maize yield ranged from 520 to 2700 kg ha-1 and 300 to 2000 kg ha-1 for CT and NT, respectively, reflecting differences in experience of farmers with NT. Averaged across farmers, CT cropping systems increased maize and soybean yield ranging from 23 to 39% compared with NT cropping systems. Partial budget analysis showed that the cost of producing maize or soybean is 20–29% cheaper with NT systems and gives higher returns to labor compared to CT practice. Benefit-to-cost ratios also show that NT cropping systems are more profitable than CT systems. We conclude that with time, implementation of CA practices involving NT, crop rotation, intercropping of maize and soybean along with crop residue retention presents a win–win scenario due to improved crop yield, increased economic return, and trends of increasing soil fertility. The biggest challenge, however, remains with producing enough biomass and retaining same on the field. PMID:28680427

Forest amount affects soybean productivity in Brazilian agricultural frontier

NASA Astrophysics Data System (ADS)

Rattis, L.; Brando, P. M.; Marques, E. Q.; Queiroz, N.; Silverio, D. V.; Macedo, M.; Coe, M. T.

2017-12-01

Over the past three decades, large tracts of tropical forests have been converted to crop and pasturelands across southern Amazonia, largely to meet the increasing worldwide demand for protein. As the world's population continue to grow and consume more protein per capita, forest conversion to grow more crops could be a potential solution to meet such demand. However, widespread deforestation is expected to negatively affect crop productivity via multiple pathways (e.g., thermal regulation, rainfall, local moisture, pest control, among others). To quantify how deforestation affects crop productivity, we modeled the relationship between forest amount and enhanced vegetation index (EVI—a proxy for crop productivity) during the soybean planting season across southern Amazonia. Our hypothesis that forest amount causes increased crop productivity received strong support. We found that the maximum MODIS-based EVI in soybean fields increased as a function of forest amount across three spatial-scales, 0.5 km, 1 km, 2 km, 5 km, 10 km, 15 km and 20 km. However, the strength of this relationship varied across years and with precipitation, but only at the local scale (e.g., 500 meters and 1 km radius). Our results highlight the importance of considering forests to design sustainable landscapes.

Shifts and disruptions in resource-use trait syndromes during the evolution of herbaceous crops.

PubMed

Milla, Rubén; Morente-López, Javier; Alonso-Rodrigo, J Miguel; Martín-Robles, Nieves; Chapin, F Stuart

2014-10-22

Trait-based ecology predicts that evolution in high-resource agricultural environments should select for suites of traits that enable fast resource acquisition and rapid canopy closure. However, crop breeding targets specific agronomic attributes rather than broad trait syndromes. Breeding for specific traits, together with evolution in high-resource environments, might lead to reduced phenotypic integration, according to predictions from the ecological literature. We provide the first comprehensive test of these hypotheses, based on a trait-screening programme of 30 herbaceous crops and their wild progenitors. During crop evolution plants became larger, which enabled them to compete more effectively for light, but they had poorly integrated phenotypes. In a subset of six herbaceous crop species investigated in greater depth, competitiveness for light increased during early plant domestication, whereas diminished phenotypic integration occurred later during crop improvement. Mass-specific leaf and root traits relevant to resource-use strategies (e.g. specific leaf area or tissue density of fine roots) changed during crop evolution, but in diverse and contrasting directions and magnitudes, depending on the crop species. Reductions in phenotypic integration and overinvestment in traits involved in competition for light may affect the chances of upgrading modern herbaceous crops to face current climatic and food security challenges. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Mapping cropland GPP in the north temperate region with space measurements of chlorophyll fluorescence

NASA Astrophysics Data System (ADS)

Guanter, L.; Zhang, Y.; Jung, M.; Joiner, J.; Voigt, M.; Huete, A. R.; Zarco-Tejada, P.; Frankenberg, C.; Lee, J.; Berry, J. A.; Moran, S. M.; Ponce-Campos, G.; Beer, C.; Camps-Valls, G.; Buchmann, N. C.; Gianelle, D.; Klumpp, K.; Cescatti, A.; Baker, J. M.; Griffis, T.

2013-12-01

Monitoring agricultural productivity is important for optimizing management practices in a world under a continuous increase of food and biofuel demand. We used new space measurements of sun-induced chlorophyll fluorescence (SIF), a vegetation parameter intrinsically linked to photosynthesis, to capture photosynthetic uptake of the crop belts in the north temperate region. The following data streams and procedures have been used in this analysis: (1) SIF retrievals have been derived from measurements of the MetOp-A / GOME-2 instrument in the 2007-2011 time period; (2) ensembles of process-based and data-driven biogeochemistry models have been analyzed in order to assess the capability of global models to represent crop gross primary production (GPP); (3) flux tower-based GPP estimates covering the 2007-2011 time period have been extracted over 18 cropland and grassland sites in the Midwest US and Western Europe from the Ameriflux and the European Fluxes Database networks; (4) large-scale NPP estimates have been derived by the agricultural inventory data sets developed by USDA-NASS and Monfreda et al. The strong linear correlation between the SIF space retrievals and the flux tower-based GPP, found to be significantly higher than that between reflectance-based vegetation indices (EVI, NDVI and MTCI) and GPP, has enabled the direct upscaling of SIF to cropland GPP maps at the synoptic scale. The new crop GPP estimates we derive from the scaling of SIF space retrievals are consistent with both flux tower GPP estimates and agricultural inventory data. These new GPP estimates show that crop productivity in the US Western Corn Belt, and most likely also in the rice production areas in the Indo-Gangetic plain and China, is up to 50-75% higher than estimates by state-of-the-art data-driven and process-oriented biogeochemistry models. From our analysis we conclude that current carbon models have difficulties in reproducing the special conditions of those highly productive crops subject to an intense management. Observational inputs closely linked to physiological condition and the photosynthetic dynamics of the vegetation, such as the fluorescence measurements presented in this study, can be an essential complement to existing models and remotely-sensed observations for the evaluation of global agricultural yields.

Increasing productivity by matching farming system management and genotype in water-limited environments.

PubMed

Kirkegaard, J A; Hunt, J R

2010-10-01

Improvements in water productivity and yield arise from interactions between varieties (G) and their management (M). Most G×M interactions considered by breeders and physiologists focus on in-crop management (e.g. sowing time, plant density, N management). However, opportunities exist to capture more water and use it more effectively that involve judicious management of prior crops and fallows (e.g. crop sequence, weed control, residue management). The dry-land wheat production system of southern Australia, augmented by simulation studies, is used to demonstrate the relative impacts and interactions of a range of pre-crop and in-crop management decisions on water productivity. A specific case study reveals how a novel genetic trait, long coleoptiles that enable deeper sowing, can interact with different management options to increase the water-limited yield of wheat from 1.6 t ha(-1) to 4.5 t ha(-1), reflecting the experience of leading growers. Understanding such interactions will be necessary to capture benefits from new varieties within the farming systems of the future.

Trends in global approvals of biotech crops (1992–2014)

PubMed Central

Aldemita, Rhodora R; Reaño, Ian Mari E; Solis, Renando O; Hautea, Randy A

2015-01-01

ABSTRACT With the increasing number of genetically modified (GM) events, traits, and crops that are developed to benefit the global population, approval of these technologies for food, feed, cultivation and import in each country may vary depending on needs, demand and trade interest. ISAAA established a GMO Approval Database to document global approvals of biotech crops. GM event name, crops, traits, developer, year of approval for cultivation, food/feed, import, and relevant dossiers were sourced from credible government regulatory websites and biosafety clearinghouses. This paper investigates the trends in GM approvals for food, feed and cultivation based on the number of approving countries, GM crops, events, and traits in the last 23 y (1992–2014), rationale for approval, factors influencing approvals, and their implications in GM crop adoption. Results show that in 2014, there was an accumulative increase in the number of countries granting approvals at 29 (79% developing countries) for commercial cultivation and 31 (70% developing countries) for food and 19 (80% developing developing) for feed; 2012 had the highest number of approving countries and cultivation approvals; 2011 had the highest number of country approvals for feed, and 2014 for food approvals. Herbicide tolerance trait had the highest events approved, followed by insect tolerance traits. Approvals for food product quality increased in the second decade. Maize had the highest number of events approved (single and stacked traits), and stacked traits product gradually increased which is already 30% of the total trait approvals. These results may indicate understanding and acceptance of countries to enhance regulatory capability to be able to benefit from GM crop commercialization. Hence, the paper provided information on the trends on the growth of the GM crop industry in the last 23 y which may be vital in predicting future GM crops and traits. PMID:26039675

Trends in global approvals of biotech crops (1992-2014).

PubMed

Aldemita, Rhodora R; Reaño, Ian Mari E; Solis, Renando O; Hautea, Randy A

2015-01-01

With the increasing number of genetically modified (GM) events, traits, and crops that are developed to benefit the global population, approval of these technologies for food, feed, cultivation and import in each country may vary depending on needs, demand and trade interest. ISAAA established a GMO Approval Database to document global approvals of biotech crops. GM event name, crops, traits, developer, year of approval for cultivation, food/feed, import, and relevant dossiers were sourced from credible government regulatory websites and biosafety clearinghouses. This paper investigates the trends in GM approvals for food, feed and cultivation based on the number of approving countries, GM crops, events, and traits in the last 23 y (1992-2014), rationale for approval, factors influencing approvals, and their implications in GM crop adoption. Results show that in 2014, there was an accumulative increase in the number of countries granting approvals at 29 (79% developing countries) for commercial cultivation and 31 (70% developing countries) for food and 19 (80% developing developing) for feed; 2012 had the highest number of approving countries and cultivation approvals; 2011 had the highest number of country approvals for feed, and 2014 for food approvals. Herbicide tolerance trait had the highest events approved, followed by insect tolerance traits. Approvals for food product quality increased in the second decade. Maize had the highest number of events approved (single and stacked traits), and stacked traits product gradually increased which is already 30% of the total trait approvals. These results may indicate understanding and acceptance of countries to enhance regulatory capability to be able to benefit from GM crop commercialization. Hence, the paper provided information on the trends on the growth of the GM crop industry in the last 23 y which may be vital in predicting future GM crops and traits.

Tradeoffs between vigor and yield for crops grown under different management systems

NASA Astrophysics Data System (ADS)

Simic Milas, Anita; Keller Vincent, Robert; Romanko, Matthew; Feitl, Melina; Rupasinghe, Prabha

2016-04-01

Remote sensing can provide an effective means for rapid and non-destructive monitoring of crop status and biochemistry. Monitoring pattern of traditional vigor algorithms generated from Landsat 8 OLI satellite data represents a robust method that can be widely used to differentiate the status of crops, as well as to monitor nutrient uptake functionality of differently treated seeds grown under different managements. This study considers 24 factorial parcels of winter wheat in 2013, corn in 2014, and soybeans in 2015, grown under four different types of agricultural management. The parcels are located at the Kellogg Biological Station, Long-Term Ecological Research site in the State of Michigan USA. At maturity, the organic crops exhibit significantly higher vigor and significantly lower yield than conventionally managed crops under different treatments. While organic crops invest in their metabolism at the expense of their yield, the conventional crops manage to increase their yield at the expense of their vigor. Landsat 8 OLI is capable of 1) differentiating the biochemical status of crops under different treatments at maturity, and 2) monitoring the tradeoff between crop yield and vigor that can be controlled by the seed treatments and proper conventional applications, with the ultimate goal of increasing food yield and food availability, and 3) distinguishing between organic and conventionally treated crops. Timing, quantity and types of herbicide applications have a great impact on early and pre-harvest vigor, maturity and yield of conventionally treated crops. Satellite monitoring using Landsat 8 is an optimal tool for coordinating agricultural applications, soil practices and genetic coding of the crop to produce higher yield as well as have early crop maturity, desirable in northern climates.

Extraction and Analysis of Major Autumn Crops in Jingxian County Based on Multi - Temporal gf - 1 Remote Sensing Image and Object-Oriented

NASA Astrophysics Data System (ADS)

Ren, B.; Wen, Q.; Zhou, H.; Guan, F.; Li, L.; Yu, H.; Wang, Z.

2018-04-01

The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.

Nitrogen Recycling and Flowering Time in Perennial Bioenergy Crops

PubMed Central

Schwartz, Christopher; Amasino, Richard

2013-01-01

Perennials have a number of traits important for profitability and sustainability of a biofuel crop. Perennialism is generally defined as the ability to grow and reproduce in multiple years. In temperate climates, many perennial plants enter dormancy during winter and recycle nutrients, such as nitrogen, to below ground structures for the next growing season. Nitrogen is expensive to produce and application of nitrogen increases the potent greenhouse gas NOx. Perennial bioenergy crops have been evaluated for biomass yields with nitrogen fertilization, location, year, and genotype as variables. Flowering time and dormancy are closely related to the N recycling program. Substantial variation for flowering time and dormancy has been identified in the switchgrass (Panicum virgatum L.) species, which provides a source to identify the genetic components of N recycling, and for use in breeding programs. Some studies have addressed recycling specifically, but flowering time and developmental differences were largely ignored, complicating interpretation of the results. Future studies on recycling need to appreciate plant developmental stage to allow comparison between experiments. A perennial/annual model(s) and more environmentally controlled experiments would be useful to determine the genetic components of nitrogen recycling. Increasing biomass yield per unit of nitrogen by maximizing recycling might mean the difference for profitability of a biofuel crop and has the added benefit of minimizing negative environmental effects from agriculture. PMID:23626592

Early forecasting of crop condition using an integrative remote sensing method for corn and soybeans in Iowa and Illinois, USA

NASA Astrophysics Data System (ADS)

Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

2017-04-01

The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotely sense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE < 15.0%) and the average accuracy of the normal/bad year classification was well (> 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remote sensing method for crop condition and crop phenology contributes to reducing the growing risk of crop production in the Earth.

Estimating 20-year land-use change and derived CO2 emissions associated with crops, pasture and forestry in Brazil and each of its 27 states.

PubMed

Novaes, Renan M L; Pazianotto, Ricardo A A; Brandão, Miguel; Alves, Bruno J R; May, André; Folegatti-Matsuura, Marília I S

2017-09-01

Land-use change (LUC) in Brazil has important implications on global climate change, ecosystem services and biodiversity, and agricultural expansion plays a critical role in this process. Concerns over these issues have led to the need for estimating the magnitude and impacts associated with that, which are increasingly reported in the environmental assessment of products. Currently, there is an extensive debate on which methods are more appropriate for estimating LUC and related emissions and regionalized estimates are lacking for Brazil, which is a world leader in agricultural production (e.g. food, fibres and bioenergy). We developed a method for estimating scenarios of past 20-year LUC and derived CO 2 emission rates associated with 64 crops, pasture and forestry in Brazil as whole and in each of its 27 states, based on time-series statistics and in accordance with most used carbon-footprinting standards. The scenarios adopted provide a range between minimum and maximum rates of CO 2 emissions from LUC according to different possibilities of land-use transitions, which can have large impacts in the results. Specificities of Brazil, like multiple cropping and highly heterogeneous carbon stocks, are also addressed. The highest CO 2 emission rates are observed in the Amazon biome states and crops with the highest rates are those that have undergone expansion in this region. Some states and crops showing large agricultural areas have low emissions associated, especially in southern and eastern Brazil. Native carbon stocks and time of agricultural expansion are the most decisive factors to the patterns of emissions. Some implications on LUC estimation methods and standards and on agri-environmental policies are discussed. © 2017 John Wiley & Sons Ltd.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

NASA Astrophysics Data System (ADS)

Battisti, R.; Sentelhas, P. C.; Boote, K. J.

2017-12-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil

NASA Astrophysics Data System (ADS)

Battisti, R.; Sentelhas, P. C.; Boote, K. J.

2018-05-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha-1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2.

[Effects of tobacco garlic crop rotation and intercropping on tobacco yield and rhizosphere soil phosphorus fractions].

PubMed

Tang, Biao; Zhang, Xi-zhou; Yang, Xian-bin

2015-07-01

A field plot experiment was conducted to investigate the tobacco yield and different forms of soil phosphorus under tobacco garlic crop rotation and intercropping patterns. The results showed that compared with tobacco monoculture, the tobacco yield and proportion of middle/high class of tobacco leaves to total leaves were significantly increased in tobacco garlic crop rotation and intercropping, and the rhizosphere soil available phosphorus contents were 1.3 and 1.7 times as high as that of tobacco monoculture at mature stage of lower leaf. For the inorganic phosphorus in rhizosphere and non-rhizosphere soil in different treatments, the contents of O-P and Fe-P were the highest, followed by Ca2-P and Al-P, and Ca8-P and Ca10-P were the lowest. Compared with tobacco monoculture and tobacco garlic crop intercropping, the Ca2-P concentration in rhizosphere soil under tobacco garlic crop rotation at mature stage of upper leaf, the Ca8-P concentration at mature stage of lower leaf, and the Ca10-P concentration at mature stage of middle leaf were lowest. The Al-P concentrations under tobacco garlic crop rotation and intercropping were 1.6 and 1.9 times, and 1.2 and 1.9 times as much as that under tobacco monoculture in rhizosphere soil at mature stages of lower leaf and middle leaf, respectively. The O-P concentrations in rhizosphere soil under tobacco garlic crop rotation and intercropping were significantly lower than that under tobacco monoculture. Compared with tobacco garlic crop intercropping, the tobacco garlic crop rotation could better improve tobacco yield and the proportion of high and middle class leaf by activating O-P, Ca10-P and resistant organic phosphorus in soil.

Developing a global crop model for maize, wheat, and soybean production

NASA Astrophysics Data System (ADS)

Deryng, D.; Ramankutty, N.; Sacks, W. J.

2008-12-01

Recently, the world food supply has faced a crisis due to increasing food prices driven by rising food demand, increasing fuel prices, poor harvests due to climate factors, and the use of crops such as maize and soybean to produce biofuel. In order to assess the future of global food availability, there is a need for understanding the factors underlying food production. Farmer management practices along with climatic conditions are the main elements directly influencing crop yield. As a consequence, estimations of future world food production require the use of a global crop model that simulates reasonably the effect of both climate and management practices on yield. Only a few global crop models have been developed to date, and currently none of them represent management factors adequately, principally due to the lack of spatially explicit datasets at the global scale. In this study, we present a global crop model designed for maize, wheat, and soybean production that incorporates planting and harvest decisions, along with irrigation options based on newly available data. The crop model is built on a simple water-balance algorithm based on the Penman- Monteith equation combined with a light use efficiency approach that calculates biomass production under non-nutrient-limiting conditions. We used a world crop calendar dataset to develop statistical relationships between climate variables and planting dates for different regions of the world. Development stages are defined based on total growing degree days required to reach the beginning of each phase. Irrigation options are considered in regions where water stress occurs and irrigation infrastructures exist. We use a global dataset on irrigated areas for each crop type. The quantity of water applied is then calculated in order to avoid water stress but with an upper threshold derived from total irrigation withdrawal quantity estimated by the global water use model WaterGAP 2. Our analysis will present the model sensitivity to different scenarios of management practices, e.g. planting date and water supply, under non-nutrient limited conditions. With this study, we hope to clarify the importance of planting date and irrigation versus climate for crop yield.

Exploring economically and environmentally viable northeastern US dairy farm strategies for coping with rising corn grain prices.

PubMed

Ghebremichael, L T; Veith, T L; Cerosaletti, P E; Dewing, D E; Rotz, C A

2009-08-01

In 2008, corn grain prices rose $115/t of DM above the 2005 average. Such an increase creates tight marginal profits for small (<100) and medium-sized (100 to 199) dairy farms in the northeastern United States importing corn grain as animal feed supplement. Particularly in New York State, dairy farmers are attempting to avoid or minimize profit losses by growing more corn silage and reducing corn grain purchases. This study applies the Integrated Farm Systems Model to 1 small and 1 medium-sized New York State dairy farm to predict 1) sediment and P loss impacts from expanding corn fields, 2) benefits of no-till or cover cropping on corn fields, and 3) alternatives to the economic challenge of the current farming system as the price ratio of milk to corn grain continues to decline. Based on the simulation results, expanding corn silage production by 3% of the cultivated farm area increased sediment and sediment-bound P losses by 41 and 18%, respectively. Implementing no-till controlled about 84% of the erosion and about 75% of the sediment-bound P that would have occurred from the conventionally tilled, expanded corn production scenario. Implementing a conventionally tilled cover crop with the conventionally tilled, expanded corn production scenario controlled both erosion and sediment-bound P, but to a lesser extent than no-till corn with no cover crop. However, annual farm net return using cover crops was slightly less than when using no-till. Increasing on-farm grass productivity while feeding cows a high-quality, high-forage diet and precise dietary P levels offered dual benefits: 1) improved farm profitability from reduced purchases of dietary protein and P supplements, and 2) decreased runoff P losses from reduced P-levels in applied manure. Moreover, alternatives such as growing additional small grains on marginal lands and increasing milk production levels demonstrated great potential in increasing farm profitability. Overall, it is crucial that conservation measures such as no-till and cover cropping be implemented on new or existing corn lands as these areas often pose the highest threat for P losses through runoff. Although alternatives that would likely provide the largest net profit were evaluated one at a time to better quantify their individual impacts, combinations of these strategies, such as no-till corn plus a minimum-till cover crop, are recommended whenever feasible.

The potential of agricultural practices to increase C storage in cropped soils: an assessment for France

NASA Astrophysics Data System (ADS)

Chenu, Claire; Angers, Denis; Métay, Aurélie; Colnenne, Caroline; Klumpp, Katja; Bamière, Laure; Pardon, Lenaic; Pellerin, Sylvain

2014-05-01

Though large progress has been achieved in the last decades, net GHG emissions from the agricultural sector are still more poorly quantified than in other sectors. In this study, we examined i) technical mitigation options likely to store carbon in agricultural soils, ii) their potential of additional C storage per unit surface area and iii) applicable areas in mainland France. We considered only agricultural practices being technically feasible by farmers and involving no major change in either production systems or production levels. Moreover, only currently available techniques with validated efficiencies and presenting no major negative environmental impacts were taken into account. Four measures were expected to store additional C in agricultural soils: - Reducing tillage: either a switch to continuous direct seeding, direct seeding with occasional tillage once every five years, or continuous superficial (<15 cm) tillage. - Introducing cover crops in cropping systems: sown between two cash crops on arable farms, in orchards and vineyards (permanent or temporary cover cropping) . - Expanding agroforestry systems; planting of tree lines in cultivated fields and grasslands, and hedges around the field edges. - Increasing the life time of temporary sown grasslands: increase of life time to 5 years. The recent literature was reviewed in order to determine long term (>20yrs) C storage rates (MgC ha-1 y-1,) of cropping systems with and without the proposed practice. Then we analysed the conditions for potential application, in terms of feasibility, acceptance, limitation of yield losses and of other GHG emissions. According to the literature, additional C storage rates were 0.15 (0-0.3) MgC ha-1 y-1 for continuous direct seeding, 0.10 (0-0.2) MgC ha-1 y-1for occasional tillage one year in five, and 0.0 MgC ha-1 y-1 for superficial tillage. Cover crops were estimated to store 0.24 (0.13-0.37) MgC ha-1 y-1 between cash crops and 0.49 (0.23-0.72) MgC ha-1 y-1 when associated with vineyards. Hedges (i.e 60 m ha-1) stored 0.15 (0.05-0.26) Mg C ha-1 y-1. Very few estimates were available for temperate agroforestry system, and we proposed a value of 1.01 (0.11-1.36) Mg C ha-1 y-1for C stored in soil and in the tree biomass for systems comprising 30-50 trees ha-1. Increasing the life time of temporary sown grassland increased C stocls by 0.11 (0.07-0.22) Mg C ha-1 y-1. In general, practices with increased C inputs to soil through additional plant biomass (agroforestry, hedges and cover crops) resulted in higher additional C storage rates, while the reduction of soil organic matter mineralisation through reduced tillage seemed less effective. When applied to the French agricultural sector, excluding areas with soils with major technical constraints or negative environmental consequences (e.g. poorly aerated soils with high N2O emissions), the measures considered here allowed to increase French soil C stocks by 0 to more than 1 Tg C y-1. However, our estimates are associated with high uncertainties, due to the high variability in soil C storage associated with pedo-climatic conditions and cropping systems, and on the very few studies available for some practices such as agroforestry under temperate conditions.

Assessing the regional impacts of increased energy maize cultivation on farmland birds.

PubMed

Brandt, Karoline; Glemnitz, Michael

2014-02-01

The increasing cultivation of energy crops in Germany substantially affects the habitat function of agricultural landscapes. Precise ex ante evaluations regarding the impacts of this cultivation on farmland bird populations are rare. The objective of this paper was to implement a methodology to assess the regional impacts of increasing energy maize cultivation on the habitat quality of agricultural lands for farmland birds. We selected five farmland bird indicator species with varying habitat demands. Using a crop suitability modelling approach, we analysed the availability of potential habitat areas according to different land use scenarios for a real landscape in Northeast Germany. The model was based on crop architecture, cultivation period, and landscape preconditions. Our results showed that the habitat suitability of different crops varied between bird species, and scenario calculations revealed an increase and a decrease in the size of the potential breeding and feeding habitats, respectively. The effects observed in scenario 1 (increased energy maize by 15%) were not reproduced in all cases in scenario 2 (increased energy maize by 30%). Spatial aggregation of energy maize resulted in a negative effect for some species. Changes in the composition of the farmland bird communities, the negative effects on farmland bird species limited in distribution and spread and the relevance of the type of agricultural land use being replaced by energy crops are also discussed. In conclusion, we suggest a trade-off between biodiversity and energy targets by identifying biodiversity-friendly energy cropping systems.

Development of an Optical Device to Investigatechlorophyll Content of Tomato Leaves

NASA Astrophysics Data System (ADS)

Cui, Di; Li, Minzan; Li, Xiuhua

Chlorophyll content is an important indication for evaluating crop growth status and predicting crop yield. The NDVI (Normalized Difference Vegetation Index) is commonly used as an indicator in practical crop healthy monitoring. Hence, a spectroscopy-based device for indirectly measuring crop growth conditions in terms of NDVI is developed. This device consists of four channels: two are designed to measure the intensity of the sunlight and the other two are used to measure the reflected light from the crop canopy at the same time. An electronic control unit was designed to control the sensing and data recording processes, as well as to calculate the NDVI based on the sensed data. The measurable two wavelengths are 610 nm and 1220 nm. A series validation tests, comparing the measurement result against spectroradiometer readings, are conducted to evaluate the performance of the device. Leaf samples are collected to measure chlorophyll contents in laboratory. The correlation coefficient between the NDVI readings from the developed device and the chlorophyll content data measured by the UV-VIS Spectrophotometer reaches 0.81, which shows that the device can be used in practical crop management.

Crop Row Detection in Maize Fields Inspired on the Human Visual Perception

PubMed Central

Romeo, J.; Pajares, G.; Montalvo, M.; Guerrero, J. M.; Guijarro, M.; Ribeiro, A.

2012-01-01

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection. PMID:22623899

Heavy Metal Contamination of Vegetables Irrigated by Urban Stormwater: A Matter of Time?

PubMed Central

Tom, Minna; Fletcher, Tim D.; McCarthy, David T.

2014-01-01

Urban stormwater is a crucial resource at a time when climate change and population growth threaten freshwater supplies; but there are health risks from contaminants, such as toxic metals. It is vitally important to understand how to use this resource safely and responsibly. Our study investigated the extent of metal contamination in vegetable crops irrigated with stormwater under short- and long-term conditions. We created artificially aged gardens by adding metal-contaminated sediment to soil, simulating accumulation of metals in the soil from irrigation with raw stormwater over zero, five and ten years. Our crops - French bean (Phaseolus vulgaris), kale (Brassica oleracea var. acephala), and beetroot (Beta vulgaris) - were irrigated twice a week for 11 weeks, with either synthetic stormwater or potable water. They were then tested for concentrations of Cd, Cr, Pb, Cu and Zn. An accumulation of Pb was the most marked sign of contamination, with six of nine French bean and seven of nine beetroot leaf samples breaching Australia's existing guidelines. Metal concentration in a crop tended to increase with the effective age of the garden; but importantly, its rate of increase did not match the rate of increase in the soil. Our study also highlighted differences in sensitivity between different crop types. French bean demonstrated the highest levels of uptake, while kale displayed restrictive behaviour. Our study makes it clear: irrigation with stormwater is indeed feasible, as long as appropriate crops are selected and media are frequently turned over. We have also shown that an understanding of such risks yields meaningful information on appropriate safeguards. A holistic approach is needed - to account for all routes to toxic metal exposure, including especially Pb. A major outcome of our study is critical information for minimising health risks from stormwater irrigation of crops. PMID:25426946

Heavy metal contamination of vegetables irrigated by urban stormwater: a matter of time?

PubMed

Tom, Minna; Fletcher, Tim D; McCarthy, David T

2014-01-01

Urban stormwater is a crucial resource at a time when climate change and population growth threaten freshwater supplies; but there are health risks from contaminants, such as toxic metals. It is vitally important to understand how to use this resource safely and responsibly. Our study investigated the extent of metal contamination in vegetable crops irrigated with stormwater under short- and long-term conditions. We created artificially aged gardens by adding metal-contaminated sediment to soil, simulating accumulation of metals in the soil from irrigation with raw stormwater over zero, five and ten years. Our crops--French bean (Phaseolus vulgaris), kale (Brassica oleracea var. acephala), and beetroot (Beta vulgaris)--were irrigated twice a week for 11 weeks, with either synthetic stormwater or potable water. They were then tested for concentrations of Cd, Cr, Pb, Cu and Zn. An accumulation of Pb was the most marked sign of contamination, with six of nine French bean and seven of nine beetroot leaf samples breaching Australia's existing guidelines. Metal concentration in a crop tended to increase with the effective age of the garden; but importantly, its rate of increase did not match the rate of increase in the soil. Our study also highlighted differences in sensitivity between different crop types. French bean demonstrated the highest levels of uptake, while kale displayed restrictive behaviour. Our study makes it clear: irrigation with stormwater is indeed feasible, as long as appropriate crops are selected and media are frequently turned over. We have also shown that an understanding of such risks yields meaningful information on appropriate safeguards. A holistic approach is needed--to account for all routes to toxic metal exposure, including especially Pb. A major outcome of our study is critical information for minimising health risks from stormwater irrigation of crops.

Photosystem II Subunit S overexpression increases the efficiency of water use in a field-grown crop.

PubMed

Głowacka, Katarzyna; Kromdijk, Johannes; Kucera, Katherine; Xie, Jiayang; Cavanagh, Amanda P; Leonelli, Lauriebeth; Leakey, Andrew D B; Ort, Donald R; Niyogi, Krishna K; Long, Stephen P

2018-03-06

Insufficient water availability for crop production is a mounting barrier to achieving the 70% increase in food production that will be needed by 2050. One solution is to develop crops that require less water per unit mass of production. Water vapor transpires from leaves through stomata, which also facilitate the influx of CO 2 during photosynthetic assimilation. Here, we hypothesize that Photosystem II Subunit S (PsbS) expression affects a chloroplast-derived signal for stomatal opening in response to light, which can be used to improve water-use efficiency. Transgenic tobacco plants with a range of PsbS expression, from undetectable to 3.7 times wild-type are generated. Plants with increased PsbS expression show less stomatal opening in response to light, resulting in a 25% reduction in water loss per CO 2 assimilated under field conditions. Since the role of PsbS is universal across higher plants, this manipulation should be effective across all crops.

Projecting Future Land Use Changes in West Africa Driven by Climate and Socioeconomic Factors: Uncertainties and Implications for Adaptation

NASA Astrophysics Data System (ADS)

Wang, G.; Ahmed, K. F.; You, L.

2015-12-01

Land use changes constitute an important regional climate change forcing in West Africa, a region of strong land-atmosphere coupling. At the same time, climate change can be an important driver for land use, although its importance relative to the impact of socio-economic factors may vary significant from region to region. This study compares the contributions of climate change and socioeconomic development to potential future changes of agricultural land use in West Africa and examines various sources of uncertainty using a land use projection model (LandPro) that accounts for the impact of socioeconomic drivers on the demand side and the impact of climate-induced crop yield changes on the supply side. Future crop yield changes were simulated by a process-based crop model driven with future climate projections from a regional climate model, and future changes of food demand is projected using a model for policy analysis of agricultural commodities and trade. The impact of human decision-making on land use was explicitly considered through multiple "what-if" scenarios to examine the range of uncertainties in projecting future land use. Without agricultural intensification, the climate-induced decrease of crop yield together with increase of food demand are found to cause a significant increase in agricultural land use at the expense of forest and grassland by the mid-century, and the resulting land use land cover changes are found to feed back to the regional climate in a way that exacerbates the negative impact of climate on crop yield. Analysis of results from multiple decision-making scenarios suggests that human adaptation characterized by science-informed decision making to minimize land use could be very effective in many parts of the region.

Weed control changes and genetically modified herbicide tolerant crops in the USA 1996–2012

PubMed Central

Brookes, Graham

2014-01-01

Crops that have been genetically modified (GM) to be tolerant to herbicides have been widely grown in the USA since 1996. The rapid and widespread adoption of this technology reflects the important economic and environmental benefits that farmers have derived from its use (equal to $21.7 billion additional farm income and a 225 million kg reduction in herbicide active ingredient use 1996–2012). During this time, weed control practices in these crops relative to the ‘conventional alternative’ have evolved to reflect experience of using the technology, the challenges that have arisen and the increasing focus in recent years on developing sustainable production systems. This paper examines the evidence on the changing nature of herbicides used with these crops and in particular how farmers addressed the challenge of weed resistance. The evidence shows that use of the technology has resulted in a net reduction in both the amount of herbicide used and the associated environmental impact, as measured by the EIQ indicator when compared to what can reasonably be expected if the area planted to GM HT crops reverted to conventional production methods. It also facilitated many farmers being able to derive the economic and environmental benefits associated with switching from a plough-based to a no tillage or conservation tillage production system. In terms of herbicide use, the technology has also contributed to a change the profile of herbicides used. A broad range of, mostly selective herbicides has been replaced by one or 2 broad-spectrum herbicides (mostly glyphosate) used in conjunction with one or 2 other (complementary) herbicides. Since the mid-2000s, the average amount of herbicide applied and the associated environmental load, as measured by the EIQ indicator, have increased on both GM HT and conventional crops. A primary reason for these changes has been increasing incidence of weed species developing populations resistant to herbicides and increased awareness of the consequences of relying on a single or very limited number of herbicides for weed control. As a result, growers of GM HT crops have become much more proactive and diversified in their weed management programs in line with weed scientist recommendations and now include other herbicides (with different and complementary modes of action) in combination with glyphosate, even where instances of weed resistance to glyphosate have not been found. The willingness to proactively diversity weed management systems in the GM HT crops is also influenced by a desire to maintain effective weed control and hence continue to enjoy the benefits of no tillage and conservation tillage. Nevertheless, despite the increase in herbicide use in recent years, the use of GM HT technology continues to deliver significant economic and environmental gains to US farmers. PMID:25523177

Weed control changes and genetically modified herbicide tolerant crops in the USA 1996-2012.

PubMed

Brookes, Graham

2014-01-01

Crops that have been genetically modified (GM) to be tolerant to herbicides have been widely grown in the USA since 1996. The rapid and widespread adoption of this technology reflects the important economic and environmental benefits that farmers have derived from its use (equal to $21.7 billion additional farm income and a 225 million kg reduction in herbicide active ingredient use 1996-2012). During this time, weed control practices in these crops relative to the 'conventional alternative' have evolved to reflect experience of using the technology, the challenges that have arisen and the increasing focus in recent years on developing sustainable production systems. This paper examines the evidence on the changing nature of herbicides used with these crops and in particular how farmers addressed the challenge of weed resistance. The evidence shows that use of the technology has resulted in a net reduction in both the amount of herbicide used and the associated environmental impact, as measured by the EIQ indicator when compared to what can reasonably be expected if the area planted to GM HT crops reverted to conventional production methods. It also facilitated many farmers being able to derive the economic and environmental benefits associated with switching from a plough-based to a no tillage or conservation tillage production system. In terms of herbicide use, the technology has also contributed to a change the profile of herbicides used. A broad range of, mostly selective herbicides has been replaced by one or 2 broad-spectrum herbicides (mostly glyphosate) used in conjunction with one or 2 other (complementary) herbicides. Since the mid-2000s, the average amount of herbicide applied and the associated environmental load, as measured by the EIQ indicator, have increased on both GM HT and conventional crops. A primary reason for these changes has been increasing incidence of weed species developing populations resistant to herbicides and increased awareness of the consequences of relying on a single or very limited number of herbicides for weed control. As a result, growers of GM HT crops have become much more proactive and diversified in their weed management programs in line with weed scientist recommendations and now include other herbicides (with different and complementary modes of action) in combination with glyphosate, even where instances of weed resistance to glyphosate have not been found. The willingness to proactively diversity weed management systems in the GM HT crops is also influenced by a desire to maintain effective weed control and hence continue to enjoy the benefits of no tillage and conservation tillage. Nevertheless, despite the increase in herbicide use in recent years, the use of GM HT technology continues to deliver significant economic and environmental gains to US farmers.

Cover Crop Species and Management Influence Predatory Arthropods and Predation in an Organically Managed, Reduced-Tillage Cropping System.

PubMed

Rivers, Ariel N; Mullen, Christina A; Barbercheck, Mary E

2018-04-05

Agricultural practices affect arthropod communities and, therefore, have the potential to influence the activities of arthropods. We evaluated the effect of cover crop species and termination timing on the activity of ground-dwelling predatory arthropods in a corn-soybean-wheat rotation in transition to organic production in Pennsylvania, United States. We compared two cover crop treatments: 1) hairy vetch (Vicia villosa Roth) planted together with triticale (×Triticosecale Wittmack) after wheat harvest, and 2) cereal rye (Secale cereale Linnaeus) planted after corn harvest. We terminated the cover crops in the spring with a roller-crimper on three dates (early, middle, and late) based on cover crop phenology and standard practices for cash crop planting in our area. We characterized the ground-dwelling arthropod community using pitfall traps and assessed relative predation using sentinel assays with live greater waxworm larvae (Galleria mellonella Fabricius). The activity density of predatory arthropods was significantly higher in the hairy vetch and triticale treatments than in cereal rye treatments. Hairy vetch and triticale favored the predator groups Araneae, Opiliones, Staphylinidae, and Carabidae. Specific taxa were associated with cover crop condition (e.g., live or dead) and termination dates. Certain variables were positively or negatively associated with the relative predation on sentinel prey, depending on cover crop treatment and stage, including the presence of predatory arthropods and various habitat measurements. Our results suggest that management of a cover crop by roller-crimper at specific times in the growing season affects predator activity density and community composition. Terminating cover crops with a roller-crimper can conserve generalist predators.

Using remote sensing to calculate plant available nitrogen needed by crops on swine factory farm sprayfields in North Carolina

NASA Astrophysics Data System (ADS)

Christenson, Elizabeth; Serre, Marc

2015-10-01

North Carolina (NC) is the second largest producer of hogs in the United States with Duplin county, NC having the densest population of hogs in the world. In NC, liquid swine manure is generally stored in open-air lagoons and sprayed onto sprayfields with sprinkler systems to be used as fertilizer for crops. Swine factory farms, termed concentrated animal feeding operations (CAFOs), are regulated by the Department of Environment and Natural Resources (DENR) based on nutrient management plans (NMPs) having balanced plant available nitrogen (PAN). The estimated PAN in liquid manure being sprayed must be less than the estimated PAN needed crops during irrigation. Estimates for PAN needed by crops are dependent on crop and soil types. Objectives of this research were to develop a new, time-efficient method to identify PAN needed by crops on Duplin county sprayfields for years 2010-2014. Using remote sensing data instead of NMP data to identify PAN needed by crops allowed calendar year identification of which crops were grown on sprayfields instead of a five-year range of values. Although permitted data have more detailed crop information than remotely sensed data, identification of PAN needed by crops using remotely sensed data is more time efficient, internally consistent, easily publically accessible, and has the ability to identify annual changes in PAN on sprayfields. Once PAN needed by crops is known, remote sensing can be used to quantify PAN at other spatial scales, such as sub-watershed levels, and can be used to inform targeted water quality monitoring of swine CAFOs.

Tillage and Composting Strategies to Maximize Potentially Mineralizable Nitrogen in Maize-based Cropping Systems

USDA-ARS?s Scientific Manuscript database

Cereal crop yields vary drastically between developed and developing nations. In developing nations, a lack of synthetic nitrogen (N) fertilizer often limits yields. Low-cost soil management strategies that increase biologically available soil organic matter can reduce farmer reliance on synthetic N...

Analyzing the genomes of wild and cultivated beets

USDA-ARS?s Scientific Manuscript database

Sugar beet is an important crop plant that accounts for roughly 25% of the world's sugar production per year. We have previously shown that sugar beet has a quite narrow genetic base, presumably due to a domestication bottleneck. To increase the crop ´s stress tolerance, the introduction of desirabl...

Investment risk in bioenergy crops

DOE PAGES

Skevas, Theodoros; Swinton, Scott M.; Tanner, Sophia; ...

2015-11-18

Here, perennial, cellulosic bioenergy crops represent a risky investment. The potential for adoption of these crops depends not only on mean net returns, but also on the associated probability distributions and on the risk preferences of farmers. Using 6-year observed crop yield data from highly productive and marginally productive sites in the southern Great Lakes region and assuming risk neutrality, we calculate expected breakeven biomass yields and prices compared to corn ( Zea mays L.) as a benchmark. Next we develop Monte Carlo budget simulations based on stochastic crop prices and yields. The crop yield simulations decompose yield risk intomore » three components: crop establishment survival, time to maturity, and mature yield variability. Results reveal that corn with harvest of grain and 38% of stover (as cellulosic bioenergy feedstock) is both the most profitable and the least risky investment option. It dominates all perennial systems considered across a wide range of farmer risk preferences. Although not currently attractive for profit-oriented farmers who are risk neutral or risk averse, perennial bioenergy crops.« less

Investment risk in bioenergy crops

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skevas, Theodoros; Swinton, Scott M.; Tanner, Sophia

Here, perennial, cellulosic bioenergy crops represent a risky investment. The potential for adoption of these crops depends not only on mean net returns, but also on the associated probability distributions and on the risk preferences of farmers. Using 6-year observed crop yield data from highly productive and marginally productive sites in the southern Great Lakes region and assuming risk neutrality, we calculate expected breakeven biomass yields and prices compared to corn ( Zea mays L.) as a benchmark. Next we develop Monte Carlo budget simulations based on stochastic crop prices and yields. The crop yield simulations decompose yield risk intomore » three components: crop establishment survival, time to maturity, and mature yield variability. Results reveal that corn with harvest of grain and 38% of stover (as cellulosic bioenergy feedstock) is both the most profitable and the least risky investment option. It dominates all perennial systems considered across a wide range of farmer risk preferences. Although not currently attractive for profit-oriented farmers who are risk neutral or risk averse, perennial bioenergy crops.« less

Consensus, uncertainties and challenges for perennial bioenergy crops and land use.

PubMed

Whitaker, Jeanette; Field, John L; Bernacchi, Carl J; Cerri, Carlos E P; Ceulemans, Reinhart; Davies, Christian A; DeLucia, Evan H; Donnison, Iain S; McCalmont, Jon P; Paustian, Keith; Rowe, Rebecca L; Smith, Pete; Thornley, Patricia; McNamara, Niall P

2018-03-01

Perennial bioenergy crops have significant potential to reduce greenhouse gas (GHG) emissions and contribute to climate change mitigation by substituting for fossil fuels; yet delivering significant GHG savings will require substantial land-use change, globally. Over the last decade, research has delivered improved understanding of the environmental benefits and risks of this transition to perennial bioenergy crops, addressing concerns that the impacts of land conversion to perennial bioenergy crops could result in increased rather than decreased GHG emissions. For policymakers to assess the most cost-effective and sustainable options for deployment and climate change mitigation, synthesis of these studies is needed to support evidence-based decision making. In 2015, a workshop was convened with researchers, policymakers and industry/business representatives from the UK, EU and internationally. Outcomes from global research on bioenergy land-use change were compared to identify areas of consensus, key uncertainties, and research priorities. Here, we discuss the strength of evidence for and against six consensus statements summarising the effects of land-use change to perennial bioenergy crops on the cycling of carbon, nitrogen and water, in the context of the whole life-cycle of bioenergy production. Our analysis suggests that the direct impacts of dedicated perennial bioenergy crops on soil carbon and nitrous oxide are increasingly well understood and are often consistent with significant life cycle GHG mitigation from bioenergy relative to conventional energy sources. We conclude that the GHG balance of perennial bioenergy crop cultivation will often be favourable, with maximum GHG savings achieved where crops are grown on soils with low carbon stocks and conservative nutrient application, accruing additional environmental benefits such as improved water quality. The analysis reported here demonstrates there is a mature and increasingly comprehensive evidence base on the environmental benefits and risks of bioenergy cultivation which can support the development of a sustainable bioenergy industry.

Estimating Field Scale Crop Evapotranspiration using Landsat and MODIS Satellite Observations

NASA Astrophysics Data System (ADS)

Wong, A.; Jin, Y.; Snyder, R. L.; Daniele, Z.; Gao, F.

2016-12-01

Irrigation accounts for 80% of human freshwater consumption, and most of it return to the atmosphere through Evapotranspiration (ET). Given the challenges of already-stressed water resources and ground water regulation in California, a cost-effective, timely, and consistent spatial estimate of crop ET, from the farm to watershed level, is becoming increasingly important. The Priestley-Taylor (PT) approach, calibrated with field data and driven by satellite observations, shows great promise for accurate ET estimates across diverse ecosystems. We here aim to improve the robustness of the PT approach in agricultural lands, to enable growers and farm managers to tailor irrigation management based on in-field spatial variability and in-season variation. We optimized the PT coefficients for each crop type with available ET measurements from eddy covariance towers and/or surface renewal stations at six crop fields (Alfalfa, Almond, Citrus, Corn, Pistachio and Rice) in California. Good agreement was found between satellite-based estimates and field measurements of net radiation, with a RMSE of less than 36 W m-2. The crop type specific optimization performed well, with a RMSE of 30 W m-2 and a correlation of 0.81 for predicted daily latent heat flux. The calibrated algorithm was used to estimate ET at 30 m resolution over the Sacramento-San Joaquin Delta region for 2015 water year. It captures well the seasonal dynamics and spatial distribution of ET in Sacramento-San Joaquin Delta. A continuous monitoring of the dynamics and spatial heterogeneity of canopy and consumptive water use at a field scale, will help the growers to be well prepared and informed to adaptively manage water, canopy, and grove density to maximize the yield with the least amount of water.

"Development of an interactive crop growth web service architecture to review and forecast agricultural sustainability"

NASA Astrophysics Data System (ADS)

Seamon, E.; Gessler, P. E.; Flathers, E.; Walden, V. P.

2014-12-01

As climate change and weather variability raise issues regarding agricultural production, agricultural sustainability has become an increasingly important component for farmland management (Fisher, 2005, Akinci, 2013). Yet with changes in soil quality, agricultural practices, weather, topography, land use, and hydrology - accurately modeling such agricultural outcomes has proven difficult (Gassman et al, 2007, Williams et al, 1995). This study examined agricultural sustainability and soil health over a heterogeneous multi-watershed area within the Inland Pacific Northwest of the United States (IPNW) - as part of a five year, USDA funded effort to explore the sustainability of cereal production systems (Regional Approaches to Climate Change for Pacific Northwest Agriculture - award #2011-68002-30191). In particular, crop growth and soil erosion were simulated across a spectrum of variables and time periods - using the CropSyst crop growth model (Stockle et al, 2002) and the Water Erosion Protection Project Model (WEPP - Flanagan and Livingston, 1995), respectively. A preliminary range of historical scenarios were run, using a high-resolution, 4km gridded dataset of surface meteorological variables from 1979-2010 (Abatzoglou, 2012). In addition, Coupled Model Inter-comparison Project (CMIP5) global climate model (GCM) outputs were used as input to run crop growth model and erosion future scenarios (Abatzoglou and Brown, 2011). To facilitate our integrated data analysis efforts, an agricultural sustainability web service architecture (THREDDS/Java/Python based) is under development, to allow for the programmatic uploading, sharing and processing of variable input data, running model simulations, as well as downloading and visualizing output results. The results of this study will assist in better understanding agricultural sustainability and erosion relationships in the IPNW, as well as provide a tangible server-based tool for use by researchers and farmers - for both small scale field examination, or more regionalized scenarios.

Current perspectives on genetically modified crops and detection methods.

PubMed

Kamle, Madhu; Kumar, Pradeep; Patra, Jayanta Kumar; Bajpai, Vivek K

2017-07-01

Genetically modified (GM) crops are the fastest adopted commodities in the agribiotech industry. This market penetration should provide a sustainable basis for ensuring food supply for growing global populations. The successful completion of two decades of commercial GM crop production (1996-2015) is underscored by the increasing rate of adoption of genetic engineering technology by farmers worldwide. With the advent of introduction of multiple traits stacked together in GM crops for combined herbicide tolerance, insect resistance, drought tolerance or disease resistance, the requirement of reliable and sensitive detection methods for tracing and labeling genetically modified organisms in the food/feed chain has become increasingly important. In addition, several countries have established threshold levels for GM content which trigger legally binding labeling schemes. The labeling of GM crops is mandatory in many countries (such as China, EU, Russia, Australia, New Zealand, Brazil, Israel, Saudi Arabia, Korea, Chile, Philippines, Indonesia, Thailand), whereas in Canada, Hong Kong, USA, South Africa, and Argentina voluntary labeling schemes operate. The rapid adoption of GM crops has increased controversies, and mitigating these issues pertaining to the implementation of effective regulatory measures for the detection of GM crops is essential. DNA-based detection methods have been successfully employed, while the whole genome sequencing using next-generation sequencing (NGS) technologies provides an advanced means for detecting genetically modified organisms and foods/feeds in GM crops. This review article describes the current status of GM crop commercialization and discusses the benefits and shortcomings of common and advanced detection systems for GMs in foods and animal feeds.

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

PubMed Central

Hawkins, Ed; Fricker, Thomas E; Challinor, Andrew J; Ferro, Christopher A T; Kit Ho, Chun; Osborne, Tom M

2013-01-01

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target. PMID:23504849

IMPROVING BIOMASS LOGISTICS COST WITHIN AGRONOMIC SUSTAINABILITY CONSTRAINTS AND BIOMASS QUALITY TARGETS

DOE Office of Scientific and Technical Information (OSTI.GOV)

J. Richard Hess; Kevin L. Kenney; Christopher T. Wright

Equipment manufacturers have made rapid improvements in biomass harvesting and handling equipment. These improvements have increased transportation and handling efficiencies due to higher biomass densities and reduced losses. Improvements in grinder efficiencies and capacity have reduced biomass grinding costs. Biomass collection efficiencies (the ratio of biomass collected to the amount available in the field) as high as 75% for crop residues and greater than 90% for perennial energy crops have also been demonstrated. However, as collection rates increase, the fraction of entrained soil in the biomass increases, and high biomass residue removal rates can violate agronomic sustainability limits. Advancements inmore » quantifying multi-factor sustainability limits to increase removal rate as guided by sustainable residue removal plans, and mitigating soil contamination through targeted removal rates based on soil type and residue type/fraction is allowing the use of new high efficiency harvesting equipment and methods. As another consideration, single pass harvesting and other technologies that improve harvesting costs cause biomass storage moisture management challenges, which challenges are further perturbed by annual variability in biomass moisture content. Monitoring, sampling, simulation, and analysis provide basis for moisture, time, and quality relationships in storage, which has allowed the development of moisture tolerant storage systems and best management processes that combine moisture content and time to accommodate baled storage of wet material based upon “shelf-life.” The key to improving biomass supply logistics costs has been developing the associated agronomic sustainability and biomass quality technologies and processes that allow the implementation of equipment engineering solutions.« less

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

Greenhouse irrigation control system design based on ZigBee and fuzzy PID technology

NASA Astrophysics Data System (ADS)

Zhou, Bing; Yang, Qiliang; Liu, Kenan; Li, Peiqing; Zhang, Jing; Wang, Qijian

In order to achieve the water demand information accurately detect of the greenhouse crop and its precision irrigation automatic control, this article has designed a set of the irrigated control system based on ZigBee and fuzzy PID technology, which composed by the soil water potential sensor, CC2530F256 wireless microprocessor, IAR Embedded Workbench software development platform. And the time of Irrigation as the output .while the amount of soil water potential and crop growth cycle as the input. The article depended on Greenhouse-grown Jatropha to verify the object, the results show that the system can irrigate timely and appropriately according to the soil water potential and water demend of the different stages of Jatropha growth , which basically meet the design requirements. Therefore, the system has broad application prospects in the amount of greenhouse crop of fine control irrigation.

Crop classification and mapping based on Sentinel missions data in cloud environment

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.; Kussul, N.; Shelestov, A.; Vasiliev, V.

2017-12-01

Availability of high resolution satellite imagery (Sentinel-1/2/3, Landsat) over large territories opens new opportunities in agricultural monitoring. In particular, it becomes feasible to solve crop classification and crop mapping task at country and regional scale using time series of heterogenous satellite imagery. But in this case, we face with the problem of Big Data. Dealing with time series of high resolution (10 m) multispectral imagery we need to download huge volumes of data and then process them. The solution is to move "processing chain" closer to data itself to drastically shorten time for data transfer. One more advantage of such approach is the possibility to parallelize data processing workflow and efficiently implement machine learning algorithms. This could be done with cloud platform where Sentinel imagery are stored. In this study, we investigate usability and efficiency of two different cloud platforms Amazon and Google for crop classification and crop mapping problems. Two pilot areas were investigated - Ukraine and England. Google provides user friendly environment Google Earth Engine for Earth observation applications with a lot of data processing and machine learning tools already deployed. At the same time with Amazon one gets much more flexibility in implementation of his own workflow. Detailed analysis of pros and cons will be done in the presentation.

[Comparison of potential yield and resource utilization efficiency of main food crops in three provinces of Northeast China under climate change].

PubMed

Wang, Xiao-yu; Yang, Xiao-guang; Sun, Shuang; Xie, Wen-juan

2015-10-01

Based on the daily data of 65 meteorological stations from 1961 to 2010 and the crop phenology data in the potential cultivation zones of thermophilic and chimonophilous crops in Northeast China, the crop potential yields were calculated through step-by-step correction method. The spatio-temporal distribution of the crop potential yields at different levels was analyzed. And then we quantified the limitations of temperature and precipitation on the crop potential yields and compared the differences in the climatic resource utilization efficiency. The results showed that the thermal potential yields of six crops (including maize, rice, spring wheat, sorghum, millet and soybean) during the period 1961-2010 deceased from west to east. The climatic potential yields of the five crops (spring wheat not included) were higher in the south than in the north. The potential yield loss rate due to temperature limitations of the six crops presented a spatial distribution pattern and was higher in the east than in the west. Among the six main crops, the yield potential loss rate due to temperature limitation of the soybean was the highest (51%), and those of the other crops fluctuated within the range of 33%-41%. The potential yield loss rate due to water limitation had an obvious regional difference, and was high in Songnen Plain and Changbai Mountains. The potential yield loss rate of spring wheat was the highest (50%), and those of the other four rainfed crops fluctuated within the range of 8%-10%. The solar energy utilization efficiency of the six main crops ranged from 0.9% to 2.7%, in the order of maize> sorghum>rice>millet>spring wheat>soybean. The precipitation utilization efficiency of the maize, sorghum, spring wheat, millet and soybean under rainfed conditions ranged from 8 to 35 kg . hm-2 . mm-1, in the order of maize>sorghum>spring wheat>millet>soybean. In those areas with lower efficiency of solar energy utilization and precipitation utilization, such as Changbai Mountains and the south of Lesser Khingan Mountains, measures could be taken to increase the efficiency of resource utilization such as rational close-planting, selection of droughtresistant varieties, proper and timely fertilization, farming for soil water storage, optimization of crop layout and so on.

Responses of Crop Water Use Efficiency to Climate Change and Agronomic Measures in the Semiarid Area of Northern China

PubMed Central

Zhang, Jingting; Ren, Wei; An, Pingli; Pan, Zhihua; Wang, Liwei; Dong, Zhiqiang; He, Di; Yang, Jia; Pan, Shufen; Tian, Hanqin

2015-01-01

It has long been concerned how crop water use efficiency (WUE) responds to climate change. Most of existing researches have emphasized the impact of single climate factor but have paid less attention to the effect of developed agronomic measures on crop WUE. Based on the long-term field observations/experiments data, we investigated the changing responses of crop WUE to climate variables (temperature and precipitation) and agronomic practices (fertilization and cropping patterns) in the semi-arid area of northern China (SAC) during two periods, 1983–1999 and 2000–2010 (drier and warmer). Our results suggest that crop WUE was an intrinsical system sensitive to climate change and agronomic measures. Crops tend to reach the maximum WUE (WUEmax) in warm-dry environment while reach the stable minimum WUE (WUEmin) in warm-wet environment, with a difference between WUEmax and WUEmin ranging from 29.0%-55.5%. Changes in temperature and precipitation in the past three decades jointly enhanced crop WUE by 8.1%-30.6%. Elevated fertilizer and rotation cropping would increase crop WUE by 5.6–11.0% and 19.5–92.9%, respectively. These results indicate crop has the resilience by adjusting WUE, which is not only able to respond to subsequent periods of favorable water balance but also to tolerate the drought stress, and reasonable agronomic practices could enhance this resilience. However, this capacity would break down under impact of climate changes and unconscionable agronomic practices (e.g. excessive N/P/K fertilizer or traditional continuous cropping). Based on the findings in this study, a conceptual crop WUE model is constructed to indicate the threshold of crop resilience, which could help the farmer develop appropriate strategies in adapting the adverse impacts of climate warming. PMID:26336098

Responses of Crop Water Use Efficiency to Climate Change and Agronomic Measures in the Semiarid Area of Northern China.

PubMed

Zhang, Jingting; Ren, Wei; An, Pingli; Pan, Zhihua; Wang, Liwei; Dong, Zhiqiang; He, Di; Yang, Jia; Pan, Shufen; Tian, Hanqin

2015-01-01

It has long been concerned how crop water use efficiency (WUE) responds to climate change. Most of existing researches have emphasized the impact of single climate factor but have paid less attention to the effect of developed agronomic measures on crop WUE. Based on the long-term field observations/experiments data, we investigated the changing responses of crop WUE to climate variables (temperature and precipitation) and agronomic practices (fertilization and cropping patterns) in the semi-arid area of northern China (SAC) during two periods, 1983-1999 and 2000-2010 (drier and warmer). Our results suggest that crop WUE was an intrinsical system sensitive to climate change and agronomic measures. Crops tend to reach the maximum WUE (WUEmax) in warm-dry environment while reach the stable minimum WUE (WUEmin) in warm-wet environment, with a difference between WUEmax and WUEmin ranging from 29.0%-55.5%. Changes in temperature and precipitation in the past three decades jointly enhanced crop WUE by 8.1%-30.6%. Elevated fertilizer and rotation cropping would increase crop WUE by 5.6-11.0% and 19.5-92.9%, respectively. These results indicate crop has the resilience by adjusting WUE, which is not only able to respond to subsequent periods of favorable water balance but also to tolerate the drought stress, and reasonable agronomic practices could enhance this resilience. However, this capacity would break down under impact of climate changes and unconscionable agronomic practices (e.g. excessive N/P/K fertilizer or traditional continuous cropping). Based on the findings in this study, a conceptual crop WUE model is constructed to indicate the threshold of crop resilience, which could help the farmer develop appropriate strategies in adapting the adverse impacts of climate warming.

Adapt-N: A Cloud-Based Computational Tool for Crop Nitrogen Management that Improves Production and Environmental Outcomes

NASA Astrophysics Data System (ADS)

van Es, Harold; Sela, Shai; Marjerison, Rebecca; Melkonian, Jeff

2016-04-01

Maize production accounts for the largest share of crop land area in the US and is the largest consumer of nitrogen (N) fertilizers, while also having low N use efficiency. Routine application of N fertilizer has led to well-documented environmental problems and social costs. Adapt-N is a computational tool that combines soil, crop and management information with near-real-time weather data to estimate optimum N application rates for maize. Its cloud-based implementation allows for tracking and timely management of the dynamic gains and losses of N in cropping systems. This presentation will provide an overview of the tool and its implementation of farms. We also evaluated Adapt-N tool during five growing seasons (2011-to-2015) using a large dataset of both side-by-side (SBS) strip trials and multi-N rate experiments. The SBS trials consisted of 115 on-farm strip trials in Iowa and New York, each trial including yield results from replicated field-scale plots involving two sidedress N rate treatments: Adapt-N-estimated and Grower-selected (conventional). The Adapt-N rates were on average 53 and 30 kg ha-1 lower than Grower rates for NY and IA, respectively (-34% overall), with no statistically significant difference in yields. On average, Adapt-N rates increased grower profits by 63.9 ha-1 and resulted in an Adapt-N estimated decrease of 28 kg ha-1 (38%) in environmental N losses. A second set of strip trials involved multiple N-rate experiments in Wisconsin, Indiana, Ohio and NY, which allowed for the comparison of Adapt-N and conventional static recommendations to an Economic Optimum N Rate (determined through response model fitting). These trials demonstrated that Adapt-N can achieve the same profitability with greatly reduced average N inputs of 20 lbs N/ac for the Midwest and 65 lbs N/ac for the Northeast, resulting in significantly lower environmental losses. In conclusion, Adapt-N recommendations resulted in both increased growers profits and decreased environmental N losses by accounting for variable site and weather conditions.

Impacts of Past Land Use Changes on Water Resources: An Analog for Assessing Effects of Proposed Bioenergy Crops

NASA Astrophysics Data System (ADS)

Scanlon, B. R.; Schilling, K.; Young, M.; Duncan, I. J.; Gerbens-Leenes, P.

2011-12-01

Interest is increasing in renewable energy sources, including bioenergy. However, potential impacts of bioenergy crops on water resources need to be better understood before large scale expansion occurs. This study evaluates the potential for using past land use change impacts on water resources as an analog for assessing future bioenergy crop effects. Impacts were assessed for two cases and methods: (1) changes from perennial to annual crops in the Midwest U.S. using stream hydrograph separation; and (2) changes from perennial grasses and shrubs to annual crops in the Southwest U.S. using unsaturated zone and groundwater data. Results from the Midwest show that expanding the soybean production area by 80,000 km2 increased stream flow by 32%, based on data from Keokuk station in the Upper Mississippi River Basin. Using these relationships, further expansion of annual corn production for biofuels by 10 - 50% would increase streamflow by up to 40%, with related increases in nitrate, phosphate, and sediment pollutant transport to the Gulf of Mexico. The changes in water partitioning are attributed to reducing evapotranspiration, increasing recharge and baseflow discharge to streams. Similar results were found in the southwestern US, where changes from native perennial grasses and shrubs to annual crops increased recharge from ~0.0 to 24 mm/yr, raising water tables by up to 7 m in some regions and flushing accumulated salts into underlying aquifers in the southern High Plains. The changes in water partitioning are related to changes in rooting depth from deep rooted native vegetation to shallow rooted crops and growing season length. Further expansion of annual bioenergy crops, such as changes from Conservation Reserve Program to corn in the Midwest, will continue the trajectory of reducing ET, thereby increasing recharge and baseflow to streams and nutrient export. We hypothesize that changing bioenergy crops from annual crops to perennial grasses, such as switchgrass and Miscanthus, will reverse these changes in water partitioning, increasing ET, and decreasing recharge, baseflow and nutrient transport to streams. These changes occur primarily because of the deeper roots and longer growing season of the grasses, capturing water percolating downward. These projected changes in water resources are supported by high water footprints for perennial grasses relative to annual crops globally. While reducing pollution from nutrient loading is an obvious benefit, reducing recharge may have negative ramifications for groundwater and surface water resources. Our research shows that there are water quantity and quality consequences of cultivating various bioenergy crops that should be considered before large scale expansion occurs. Data from past land use changes provide valuable information that can be used as a guide to evaluate potential impacts of future land use changes related to bioenergy crops.

How do current irrigation practices perform? Evaluation of different irrigation scheduling approaches based on experiements and crop model simulations

NASA Astrophysics Data System (ADS)

Seidel, Sabine J.; Werisch, Stefan; Barfus, Klemens; Wagner, Michael; Schütze, Niels; Laber, Hermann

2014-05-01

The increasing worldwide water scarcity, costs and negative off-site effects of irrigation are leading to the necessity of developing methods of irrigation that increase water productivity. Various approaches are available for irrigation scheduling. Traditionally schedules are calculated based on soil water balance (SWB) calculations using some measure of reference evaporation and empirical crop coeffcients. These crop-specific coefficients are provided by the FAO but are also available for different regions (e.g. Germany). The approach is simple but there are several inaccuracies due to simplifications and limitations such as poor transferability. Crop growth models - which simulate the main physiological plant processes through a set of assumptions and calibration parameter - are widely used to support decision making, but also for yield gap or scenario analyses. One major advantage of mechanistic models compared to empirical approaches is their spatial and temporal transferability. Irrigation scheduling can also be based on measurements of soil water tension which is closely related to plant stress. Advantages of precise and easy measurements are able to be automated but face difficulties of finding the place where to probe especially in heterogenous soils. In this study, a two-year field experiment was used to extensively evaluate the three mentioned irrigation scheduling approaches regarding their efficiency on irrigation water application with the aim to promote better agronomic practices in irrigated horticulture. To evaluate the tested irrigation scheduling approaches, an extensive plant and soil water data collection was used to precisely calibrate the mechanistic crop model Daisy. The experiment was conducted with white cabbage (Brassica oleracea L.) on a sandy loamy field in 2012/13 near Dresden, Germany. Hereby, three irrigation scheduling approaches were tested: (i) two schedules were estimated based on SWB calculations using different crop coefficients, and (ii) one treatment was automatically drip irrigated using tensiometers (irrigation of 15 mm at a soil tension of -250 hPa at 30 cm soil depth). In treatment (iii), the irrigation schedule was estimated (using the same critera as in the tension-based treatment) applying the model Daisy partially calibrated against data of 2012. Moreover, one control treatment was minimally irrigated. Measured yield was highest for the tension-based treatment with a low irrigation water input (8.5 DM t/ha, 120 mm). Both SWB treatments showed lower yields and higher irrigation water input (both 8.3 DM t/ha, 306 and 410 mm). The simulation model based treatment yielded lower (7.5 DM t/ha, 106 mm) mainly due to drought stress caused by inaccurate simulation of the soil water dynamics and thus an overestimation of the soil moisture. The evaluation using the calibrated model estimated heavy deep percolation under both SWB treatments. Targeting the challenge to increase water productivity, soil water tension-based irrigation should be favoured. Irrigation scheduling based on SWB calculation requires accurate estimates of crop coefficients. A robust calibration of mechanistic crop models implies a high effort and can be recommended to farmers only to some extent but enables comprehensive crop growth and site analyses.

Breeding implications of drought stress under future climate for upland rice in Brazil.

PubMed

Ramirez-Villegas, Julian; Heinemann, Alexandre B; Pereira de Castro, Adriano; Breseghello, Flávio; Navarro-Racines, Carlos; Li, Tao; Rebolledo, Maria C; Challinor, Andrew J

2018-05-01

Rice is the most important food crop in the developing world. For rice production systems to address the challenges of increasing demand and climate change, potential and on-farm yield increases must be increased. Breeding is one of the main strategies toward such aim. Here, we hypothesize that climatic and atmospheric changes for the upland rice growing period in central Brazil are likely to alter environment groupings and drought stress patterns by 2050, leading to changing breeding targets during the 21st century. As a result of changes in drought stress frequency and intensity, we found reductions in productivity in the range of 200-600 kg/ha (up to 20%) and reductions in yield stability throughout virtually the entire upland rice growing area (except for the southeast). In the face of these changes, our crop simulation analysis suggests that the current strategy of the breeding program, which aims at achieving wide adaptation, should be adjusted. Based on the results for current and future climates, a weighted selection strategy for the three environmental groups that characterize the region is suggested. For the highly favorable environment (HFE, 36%-41% growing area, depending on RCP), selection should be done under both stress-free and terminal stress conditions; for the favorable environment (FE, 27%-40%), selection should aim at testing under reproductive and terminal stress, and for the least favorable environment (LFE, 23%-27%), selection should be conducted for response to reproductive stress only and for the joint occurrence of reproductive and terminal stress. Even though there are differences in timing, it is noteworthy that stress levels are similar across environments, with 40%-60% of crop water demand unsatisfied. Efficient crop improvement targeted toward adaptive traits for drought tolerance will enhance upland rice crop system resilience under climate change. © 2018 John Wiley & Sons Ltd.

Designing bioenergy crop buffers to mitigate nitrous oxide emissions and water quality impacts from agriculture

NASA Astrophysics Data System (ADS)

Gopalakrishnan, G.; Negri, C. M.

2010-12-01

There is a strong societal need to evaluate and understand the environmental aspects of bioenergy production, especially due to the significant increases in production mandated by many countries, including the United States. Bioenergy is a land-based renewable resource and increases in production are likely to result in large-scale conversion of land from current uses to bioenergy crop production; potentially causing increases in the prices of food, land and agricultural commodities as well as disruption of ecosystems. Current research on the environmental sustainability of bioenergy has largely focused on the potential of bioenergy crops to sequester carbon and mitigate greenhouse gas (GHG) emissions and possible impacts on water quality and quantity. A key assumption in these studies is that bioenergy crops will be grown in a manner similar to current agricultural crops such as corn and hence would affect the environment similarly. This study presents a systems approach where the agricultural, energy and environmental sectors are considered as components of a single system, and bioenergy crops are used to design multi-functional agricultural landscapes that meet society’s requirements for food, energy and environmental protection. We evaluate the production of bioenergy crop buffers on marginal land and using degraded water and discuss the potential for growing cellulosic bioenergy crops such as miscanthus and switchgrass in optimized systems such that (1) marginal land is brought into productive use; (2) impaired water is used to boost yields (3); clean freshwater is left for other uses that require higher water quality; and (4) feedstock diversification is achieved that helps ecological sustainability, biodiversity, and economic opportunities for farmers. The process-based biogeochemical model DNDC was used to simulate crop yield, nitrous oxide production and nitrate concentrations in groundwater when bioenergy crops were grown in buffer strips adjacent to corn fields. The bioenergy crops used in this study were miscanthus, switchgrass and native prairie grasses. Results indicated that growing bioenergy crops in buffer strips mitigated nutrient runoff and reduced nitrate concentrations in groundwater to below EPA’s mandated drinking water limit (10 mg/l). Additionally, nitrous oxide emissions in these systems were reduced by 50-90% when compared to corn fields without the bioenergy buffer strips. While all the bioenergy crop buffers had significant positive environmental benefits, switchgrass performed the best with respect to minimizing nutrient runoff and nitrous oxide emissions. The findings of this research have important implications with respect to land management for agriculture and bioenergy.

Cynara cardunculus suitability as energetic crop in the south east of Spain using compost as amendment

NASA Astrophysics Data System (ADS)

Lag, A.; Gómez, I.; Navarro, J.; Córdoba, P.; Bartual, J.

2009-04-01

Global warming demands urgent actions to reduce problems derivated from it. In this sense, fossil fuels should be replaced gradually with renewable energy sources, like energetic crops, to decrease or at least maintain CO2 levels in the atmosphere. For example, net carbon emissions from generation of a unit of bioenergy are 10 to 20 times lower than emissions from fossil fuel based generation. Compared with fossil fuels, the use of lignocellulosic feedstocks has greenhouse gas reduction potential and highly positive net energy returns because of low input demand and high yields per unit land area. In addition, conversion of degradated agricultural soils to perennial crops can improve soil quality by increasing C sequestration due to their perenniality, high biomass production, and deep root systems. For all these reasons, the aim of this study is to ascertain Cynara cardunculus sp suitability as energetic crop in the south-east of Spain, using compost as organic amendment. Five compost treatments were applied to the soil: 0 (D1), 20 (D2), 40 (D3), 60 (D4) and 80 (D5) t of compost/ha. The experiment lasted 5 months, sampling 3 times (January; April and June). Twelve Cynara Cardunculus plants were placed in each plot (4x7 m); half of them were collected at the end of the experiment. Treated sewage water was used to irrigate the crop. Organic carbon in soil and above ground biomass were studied. Dry weight yield production was between 494 (D4) to 740 kg/ha (D3). Considering that 45 to 50 % of plant dry weight matter could be assumed as carbon, carbon sequestration range from 0.8 to 1.2 t of CO2/ha for a short period of 5 months. Soil Organic carbon levels, at the end of the experiment, increased in each compost treatment compared with control value as follow: 16% (D2); 33% (D3); 43% (D4) and 73% (D5). The results show that Cynara cardunculus sp could be used as energetic crop in the south east of Spain, as it was suggested by the European Environmental Agency. However, further studies are needed with longer test time to set production potential of biomass, organic matter evolution and nature, carbon sequestration balance and compost influence in these properties. Acknowledgements: The author gratefully acknowledges the Spanish Ministry of Innovation and Science for a research fellowship (AP2007-01641); the "Estación Agraria Experimental de Elche" and "Instituto Valenciano de Investigaciones Agrarias" for their collaboration.

Sorghum - An alternative energy crop for marginal lands and reclamation sites

NASA Astrophysics Data System (ADS)

Lukas, Stefan; Theiß, Markus; Jäkel, Kerstin

2017-04-01

The production of biogas and the associated cultivation of energy crops are still of great importance. Considering increasing restrictions for the cultivation of standard biogas crop maize regarding an environmentally friendly production of biomass, a wider range of energy crops is needed. The cultivation of sorghum can contribute to this. As maize, sorghum is a C4-plant and offers a high biomass yield potential. Originated in the semi-arid tropics, sorghum is well adapted to warm and dry climate and particularly noted for its drought tolerance compared to maize. It also makes few demands on soil quality and shows a good capability of nutrient acquisition. Therefore, particularly on marginal areas and reclamation sites with low soil nutrient and water content sorghum can contribute to secure crop yield and income of farmers. The applied research project aims at and reflects on the establishment of sorghum as a profitable and ecological friendly cropping alternative to maize, especially in the face of probable climate change with increasing risks for agriculture. For this purpose, site differentiated growing and cultivar trials with a standardized planting design as well as several practical on-farm field experiments were conducted. The agronomical and economic results will lead to scientifically based procedures and standards for agricultural practice with respect to cultivation methods (drilling, pest-management, fertilization), cropping sequence and technique, cropping period or position in crop rotation. Even by now there is a promising feedback from the agricultural practice linked with an increasing demand for information. Moreover, the specific cropping area is increasing continuously. Therefore, the leading signs for the establishment of sorghum as profitable alternative to maize biogas production are positive. Sorghum cultures perform best as main crops in the warm D locations in the middle and East German dry areas. Here, the contribution margin differences between maize and sorghum were the least pronounced due to the poorer performance of maize under these site conditions. Furthermore, the comparatively lower land-lease rates in these regions allowed for positive equity capital formation also in sorghum crops.

Comparison between wavelet transform and moving average as filter method of MODIS imagery to recognize paddy cropping pattern in West Java

NASA Astrophysics Data System (ADS)

Dwi Nugroho, Kreshna; Pebrianto, Singgih; Arif Fatoni, Muhammad; Fatikhunnada, Alvin; Liyantono; Setiawan, Yudi

2017-01-01

Information on the area and spatial distribution of paddy field are needed to support sustainable agricultural and food security program. Mapping or distribution of cropping pattern paddy field is important to obtain sustainability paddy field area. It can be done by direct observation and remote sensing method. This paper discusses remote sensing for paddy field monitoring based on MODIS time series data. In time series MODIS data, difficult to direct classified of data, because of temporal noise. Therefore wavelet transform and moving average are needed as filter methods. The Objective of this study is to recognize paddy cropping pattern with wavelet transform and moving average in West Java using MODIS imagery (MOD13Q1) from 2001 to 2015 then compared between both of methods. The result showed the spatial distribution almost have the same cropping pattern. The accuracy of wavelet transform (75.5%) is higher than moving average (70.5%). Both methods showed that the majority of the cropping pattern in West Java have pattern paddy-fallow-paddy-fallow with various time planting. The difference of the planting schedule was occurs caused by the availability of irrigation water.

Observation and Modelling of Soil Water Content Towards Improved Performance Indicators of Large Irrigation Schemes

NASA Astrophysics Data System (ADS)

Labbassi, Kamal; Akdim, Nadia; Alfieri, Silvia Maria; Menenti, Massimo

2014-05-01

Irrigation performance may be evaluated for different objectives such as equity, adequacy, or effectiveness. We are using two performance indicators: IP2 measures the consistency of the allocation of the irrigation water with gross Crop Water requirements, while IP3 measures the effectiveness of irrigation by evaluating the increase in crop transpiration between the case of no irrigation and the case of different levels of irrigation. To evaluate IP3 we need to calculate the soil water balance for the two cases. We have developed a system based on the hydrological model SWAP (Soil Water atmosphere Plant) to calculate spatial and temporal patterns of crop transpiration T(x, y, t) and of the vertical distribution of soil water content θ(x, y, z, t). On one hand, in the absence of ground measurement of soil water content to validate and evaluate the precision of the estimated one, a possibility would be to use satellite retrievals of top soil water content, such as the data to be provided by SMAP. On the other hand, to calculate IP3 we need root zone rather than top soil water content. In principle, we could use the model SWAP to establish a relationship between the top soil and root zone water content. Such relationship could be a simple empirical one or a data assimilation procedure. In our study area (Doukkala- Morocco) we have assessed the consistency of the water allocation with the actual irrigated area and crop water requirements (CWR) by using a combination of multispectral satellite image time series (i,e RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season). To obtain IP2 (x, y, t) we need to determine ETc (x, y, t). We have applied two (semi)empirical approaches: the first one is the Kc-NDVI method, based on the correlation between the Near Difference Vegetation Index (NDVI) and the value of crop coefficient (kc); the second one is the analytical approach based on the direct application of Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (hc). The validation of spatial results using the dual crop coefficient approach (kcb) showed that the satellite-based estimates of ETc corresponded well with ground-based ETc i.e, R²=0.75 and RMSE=0.79 versus R²=0.73 and RMSE=0.89 for respectively kc-NDVI and analytical approach. To monitor IP3 (x, y, t) with the SWAP model we mapped soil hydrological properties combining soil maps with grain size analysis of a number of samples, and agricultural crops using multi-temporal classification of NDVI time series. The assessment of irrigation performance in term of adequacy between requirement and allocation showed that CWR are much larger than water supply for entire area, this mismatch is improved in the beginning of the growing season by means of Irrigation water requirement (IWR) and even more using the net irrigation water requirement (NIWR) estimated using SWAP model. We expect that the availability of SMAP data products will significantly improve the reliability and temporal sampling of our indicators.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

The Impact of Changing Snowmelt Timing on Non-Irrigated Crop Yield in Idaho

NASA Astrophysics Data System (ADS)

Murray, E. M.; Cobourn, K.; Flores, A. N.; Pierce, J. L.; Kunkel, M. L.

2013-12-01

The impacts of climate change on water resources have implications for both agricultural production and grower welfare. Many mountainous regions in the western U.S. rely on snowmelt as the dominant surface water source, and in Idaho, reconstructions of spring snowmelt timing have demonstrated a trend toward earlier, more variable snowmelt dates within the past 20 years. This earlier date and increased variability in snowmelt timing have serious implications for agriculture, but there is considerable uncertainty about how agricultural impacts vary by region, crop-type, and practices like irrigation vs. dryland farming. Establishing the relationship between snowmelt timing and agricultural yield is important for understanding how changes in large-scale climatic indices (like snowmelt date) may be associated with changes in agricultural yield. This is particularly important where local practitioner behavior is influenced by historically observed relationships between these climate indices and yield. In addition, a better understanding of the influence of changes in snowmelt on non-irrigated crop yield may be extrapolated to better understand how climate change may alter biomass production in non-managed ecosystems. To investigate the impact of snowmelt date on non-irrigated crop yield, we developed a multiple linear regression model to predict historical wheat and barley yield in several Idaho counties as a function of snowmelt date, climate variables (precipitation and growing degree-days), and spatial differences between counties. The relationship between snowmelt timing and non-irrigated crop yield at the county level is strong in many of the models, but differs in magnitude and direction for the two different crops. Results show interesting spatial patterns of variability in the correlation between snowmelt timing and crop yield. In four southern counties that border the Snake River Plain and one county bordering Oregon, non-irrigated wheat and/or barley yield are significantly lower in years with early snowmelt timing, on average (P < 0.10). In contrast, in northern Idaho, barley yield is significantly higher in years with early snowmelt timing. Overall, this statistical modeling exercise indicates that the trend toward earlier snowmelt date may positively impact non-irrigated crop yield in some regions of Idaho, while negatively impacting yield in other areas. Additional research is necessary to identify spatial controls on the variable relationship between snowmelt timing and yield. Regional variability in the response of crops to changes in snowmelt timing may indicate that external factors (e.g. higher amounts of summer rain in northern vs. southern Idaho) may play an important role in crop yield. This study indicates that targeted regional analysis is necessary to determine the influence of climate change on agriculture, as local variability can cause the same forcing to produce opposite results.

Operationalizing crop monitoring system for informed decision making related to food security in Nepal

NASA Astrophysics Data System (ADS)

Qamer, F. M.; Shah, S. N. Pd.; Murthy, M. S. R.; Baidar, T.; Dhonju, K.; Hari, B. G.

2014-11-01

In Nepal, two thirds of the total population depend on agriculture for their livelihoods and more than one third of Gross Domestic Product (GDP) comes from the agriculture sector. However, effective agriculture production across the country remains a serious challenge due to various factors, such as a high degree of spatial and temporal climate variability, irrigated and rain-fed agriculture systems, farmers' fragile social and economic fabric, and unique mountain practices. ICIMOD through SERVIR-Himalaya initiative with collaboration of Ministry of Agricultural Development (MoAD) is working on developing a comprehensive crop monitoring system which aims to provide timely information on crop growth and drought development conditions. This system analyzes historical climate and crop conditions patterns and compares this data with the current growing season to provide timely assessment of crop growth. Using remote sensing data for vegetation indices, temperature and rainfall, the system generated anomaly maps are inferred to predict the increase or shortfall in production. Comparisons can be made both spatially and in graphs and figures at district and Village Developmental Committee (VDC) levels. Timely information on possible anomaly in crop production is later used by the institutions like Ministry of Agricultural Development, Nepal and World Food Programme, Nepal to trigger appropriate management response. Future potential includes integrating data on agricultural inputs, socioeconomics, demographics, and transportation to holistically assess food security in the region served by SERVIR-Himalaya.

Historical development of crop-related water footprints and inter-regional virtual water flows within China

NASA Astrophysics Data System (ADS)

Zhuo, La; Mekonnen, Mesfin M.; Hoekstra, Arjen Y.

2015-04-01

China is facing water-related challenges, including an uneven distribution of water resources, both temporally and spatially, and an increasing competition over the limited water resources among different sectors. This issue has been widely researched and was finally included into the National Plan 2011 (the 2011 No. 1 Document by the State Council of China). However, there is still lack of information on how population growth and rapid urbanization have affected the water resources in China over the last decades. The current study aims at investigating (i) the intra-annual variation of green and blue water footprints (WFs) of crop production in China over the period 1978-2009 at a spatial resolution of 5 by 5 arc-minute; (ii) the yearly virtual water (VW) balances of 31 provinces within China, related water savings for the country, as well as the VW flows among eight economic regions resulting from inter-regional crop trade over the same period; and (iii) the development of the WF related to crop consumption by Chinese consumers. Results show that, over the period 1978-2009, the total WF related to crop production within China increased by only 4%), but regional changes were significant. From the 1980s to the 2000s, the shift of the cropping centre from the South to the North resulted in an increase of about 16% in the blue WF and 19% in the green WF in the North and a reduction of the blue and green WF in the South by 11% and 3%, respectively. China as a whole was a net virtual water importer related to crop trade, thus saving domestic water resources. China's inter-regional crop trade generated a blue water 'loss' annually by transferring crops from provinces with relatively low crop water productivity to provinces with relatively high productivity. Over the decades, the original VW flow from the South coastal region to the Northeast was reversed. Rice was the all-time dominant crop in the inter-regional VW flows (accounting for 34% in 2009), followed by wheat and maize. The WF of the South related to crop consumption increased by 17% (~61 billion m3) while the increase in the North was 8% (~23 billion m3) from the 1980s to the 2000s. The national average annual WF per capita of crop consumption reduced by 8% (from 606 m3cap-1y-1 to 559 m3cap-1y-1) from the 1980s to the 2000s owing to the reduction in the WF per unit mass of a crop. However, because of the movement of the major agricultural areas to the North, the WF per capita in Inner Mongolia, for example, was increased (by 4% from the 1980s to the 2000s) due to the higher WF per unit mass of a crop locally, but with an increased level of self-reliance in crop consumption. Accounting for the different production and consumption characteristics across provinces is crucial for China's water governance.

Deforestation impacts on soil organic carbon stocks in the Semiarid Chaco Region, Argentina.

PubMed

Villarino, Sebastián Horacio; Studdert, Guillermo Alberto; Baldassini, Pablo; Cendoya, María Gabriela; Ciuffoli, Lucía; Mastrángelo, Matias; Piñeiro, Gervasio

2017-01-01

Land use change affects soil organic carbon (SOC) and generates CO 2 emissions. Moreover, SOC depletion entails degradation of soil functions that support ecosystem services. Large areas covered by dry forests have been cleared in the Semiarid Chaco Region of Argentina for cropping expansion. However, deforestation impacts on the SOC stock and its distribution in the soil profile have been scarcely reported. We assessed these impacts based on the analysis of field data along a time-since-deforestation-for-cropping chronosequence, and remote sensing indices. Soil organic C was determined up to 100cm depth and physically fractionated into mineral associated organic carbon (MAOC) and particulate organic C (POC). Models describing vertical distribution of SOC were fitted. Total SOC, POC and MAOC stocks decreased markedly with increasing cropping age. Particulate organic C was the most sensitive fraction to cultivation. After 10yr of cropping SOC loss was around 30%, with greater POC loss (near 60%) and smaller MAOC loss (near 15%), at 0-30cm depth. Similar relative SOC losses were observed in deeper soil layers (30-60 and 60-100cm). Deforestation and subsequent cropping also modified SOC vertical distribution. Soil organic C loss was negatively associated with the proportion of maize in the rotation and total crop biomass inputs, but positively associated with the proportion of soybean in the rotation. Without effective land use polices, deforestation and agricultural expansion can lead to rapid soil degradation and reductions in the provision of important ecosystem services. Copyright © 2016 Elsevier B.V. All rights reserved.

Increasing homogeneity in global food supplies and the implications for food security

PubMed Central

Khoury, Colin K.; Bjorkman, Anne D.; Dempewolf, Hannes; Ramirez-Villegas, Julian; Guarino, Luigi; Jarvis, Andy; Rieseberg, Loren H.; Struik, Paul C.

2014-01-01

The narrowing of diversity in crop species contributing to the world’s food supplies has been considered a potential threat to food security. However, changes in this diversity have not been quantified globally. We assess trends over the past 50 y in the richness, abundance, and composition of crop species in national food supplies worldwide. Over this period, national per capita food supplies expanded in total quantities of food calories, protein, fat, and weight, with increased proportions of those quantities sourcing from energy-dense foods. At the same time the number of measured crop commodities contributing to national food supplies increased, the relative contribution of these commodities within these supplies became more even, and the dominance of the most significant commodities decreased. As a consequence, national food supplies worldwide became more similar in composition, correlated particularly with an increased supply of a number of globally important cereal and oil crops, and a decline of other cereal, oil, and starchy root species. The increase in homogeneity worldwide portends the establishment of a global standard food supply, which is relatively species-rich in regard to measured crops at the national level, but species-poor globally. These changes in food supplies heighten interdependence among countries in regard to availability and access to these food sources and the genetic resources supporting their production, and give further urgency to nutrition development priorities aimed at bolstering food security. PMID:24591623

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

NASA Astrophysics Data System (ADS)

Xiong, Jun; Thenkabail, Prasad S.; Gumma, Murali K.; Teluguntla, Pardhasaradhi; Poehnelt, Justin; Congalton, Russell G.; Yadav, Kamini; Thau, David

2017-04-01

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world's population, but only about 6% of world's irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

Land Surface Phenologies of the Northern Great Plains: Possible Futures Arising From Land and Climate Change

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Wimberly, M. C.; Senay, G.; Wang, A.; Chang, J.; Wright, C. R.; Hansen, M. C.

2008-12-01

Land cover change across the Northern Great Plains of North America over the past three decades has been driven by changes in agricultural management (conservation tillage; irrigation), government incentives (Conservation Reserve Program; subsidies to grain-based ethanol), crop varieties (cold-hardy soybean), and market dynamics (increasing world demand). Climate change across the Northern Great Plains over the past three decades has been evident in trends toward earlier warmth in the spring and a longer frost-free season. Together these land and climate changes induce shifts in local and regional land surface phenologies (LSPs). Any significant shift in LSP may correspond to a significant shift in evapotranspiration, with consequences for regional hydrometeorology. We explored possible future scenarios involving land use and climate change in six steps. First, we defined the nominal draw areas of current and future biorefineries in North Dakota, South Dakota, Nebraska, Minnesota, and Iowa and masked those land cover types within the draw areas that were unlikely to change to agricultural use (open water, settlements, forests, etc.). Second, we estimated the proportion of corn and soybean remaining within the masked draw areas using MODIS-derived crop maps. Third, in each draw area, we modified LSPs to simulate crop changes for a control and two treatment scenarios. In the control, we used LSP profiles identified from MODIS Collection 5 NBAR data. In one treatment, we increased the proportion of tallgrass LSPs in the draw areas to represent widespread cultivation of a perennial cellulosic crop, like switchgrass. In a second treatment, we increased the proportion of corn LSPs in the draw areas to represent increased corn cultivation. Fourth, we characterized the seasonal progression of the thermal regime associated with the LSP profiles using MODIS Land Surface Temperature (LST) products. Fifth, we modeled the LSP profile as a quadratic function of accumulated growing degree-days based on the LST time series. Sixth, we used representative IPCC AR4 mid-century projections to force the quadratic models and produce possible future LSPs. The resulting shifts in potential peak vegetation to earlier dates indicate potential seasonal shifts in evapotranspiration.

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

USGS Publications Warehouse

Gumma, Murali Krishna; Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Rao, Mahesh N.; Mohammed, Irshad A.; Whitbread, Anthony M.

2016-01-01

The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea (Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary (kharif) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second (rabi) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

Impacts of projected mid-century temperatures on thermal regimes for select specialty and fieldcrops common to the southwestern U.S.

NASA Astrophysics Data System (ADS)

Elias, E.; Lopez-Brody, N.; Dialesandro, J.; Steele, C. M.; Rango, A.

2015-12-01

The impacts of projected temperature increases in agricultural ecosystems are complex, varyingby region, cropping system, crop growth stage and humidity. We analyze the impacts of mid-century temperature increases on crops grown in five southwestern states: Arizona, California,New Mexico, Nevada and Utah. Here we present a spatial impact assessment of commonsouthwestern specialty (grapes, almonds and tomatoes) and field (alfalfa, cotton and corn)crops. This analysis includes three main components: development of empirical temperaturethresholds for each crop, classification of predicted future climate conditions according to thesethresholds, and mapping the probable impacts of these climatic changes on each crop. We use30m spatial resolution 2012 crop distribution and seasonal minimum and maximumtemperature normals (1970 to 2000) to define the current thermal envelopes for each crop.These represent the temperature range for each season where 95% of each crop is presentlygrown. Seasonal period change analysis of mid-century temperatures changes downscaled from20 CMIP5 models (RCP8.5) estimate future temperatures. Change detection maps representareas predicted to become more or less suitable, or remain unchanged. Based upon mid-centurytemperature changes, total regional suitable area declined for all crops except cotton, whichincreased by 20%. For each crop there are locations which change to and from optimal thermalenvelope conditions. More than 80% of the acres currently growing tomatoes and almonds willshift outside the present 95% thermal range. Fewer acres currently growing alfalfa (14%) andcotton (20%) will shift outside the present 95% thermal range by midcentury. Crops outsidepresent thermal envelopes by midcentury may adapt, possibly aided by adaptation technologiessuch as misters or shade structures, to the new temperature regime or growers may elect togrow alternate crops better suited to future thermal envelopes.

Land Use and Land Cover Change Modeling Using Remote Sensing and Soft Computing Approach to Assess Sugarcane Expansion Impacts in Tropical Agriculture

NASA Astrophysics Data System (ADS)

Vicente, L. E.; Koga-Vicente, A.; Friedel, M. J.; Victoria, D.; Zullo, J., Jr.; Gomes, D.; Bayma-Silva, G.

2014-12-01

Agriculture is related with land-use/cover changes (LUCC) over large areas and, in recent years, increase in demand of ethanol fuel has been influence in expansion of areas occupied with corn and sugar cane, raw material for ethanol production. Nevertheless, there´s a concern regarding the impacts on food security, such as, decrease in areas planted with food crops. Considering that the LUCC is highly dynamic, the use of Remote Sensing is a tool for monitoring changes quickly and precisely in order to provide information for agricultural planning. In this work, Remote Sensing techniques were used to monitor the LUCC occurred in municipalities of São Paulo state- Brazil related with sugarcane crops expansion in order to (i) evaluate and quantify the previous land cover in areas of sugarcane crop expansion, and (ii) provide information to elaborate a future land cover scenario based on Self Organizing Map (SOM) approach. The land cover classification procedure was based on Landsat 5 TM images, obtained from the Global Land Survey. The Landsat images were then segmented into homogeneous objects, with represent areas on the ground with similar spatial and spectral characteristics. These objects are related to the distinct land cover types that occur in each municipality. The segmentation procedure resulted in polygons over the three time periods along twenty years (1990-2010). The land cover for each object was visually identified, based on its shape, texture and spectral characteristics. Land cover types considered were: sugarcane plantations, pasture lands, natural cover, forest plantation, permanent crop, short cycle crop, water bodies and urban areas. SOM technique was used to estimate the values for the future land cover scenarios for the selected municipalities, using the information of land change provided by the remote sensing and data from official sources.

Dry spell trend analysis in Kenya and the Murray Darling Basin using daily rainfall

NASA Astrophysics Data System (ADS)

Muita, R. R.; van Ogtrop, F. F.; Vervoort, R. W.

2012-04-01

Important agricultural areas in Kenya and the Murray Darling Basin (MDB) in Australia are largely semi-arid to arid. Persistent dry periods and timing of dry spells directly impact the availability of soil moisture and hence crop production in these regions. Most studies focus on the analysis of dry spell lengths at an annual scale. However, timing and length of dry spells at finer temporal scales is more beneficial for cropping when considering a trade-off between the time scale and the ability to analyse dry spell length. The aim of this study was to analyse the interannual and intra annual variations in dry spell lengths in the regions to inform crop management. This study analysed monthly dry spells based on daily rainfall for 1961-2010 on a limited dataset of 13 locations in Kenya and 17 locations in the MDB. This dataset was the most consistent across both regions and future analysis will incorporate more stations and longer time periods where available. Dry spell lengths were analysed by month and year and trends in monthly and annual dry spell lengths were analysed using Generalised Linear Models (GLM) and the Mann Kendall test (MK). Overall, monthly dryspell lengths are right skewed with higher frequency of shorter dryspells (3-25 days). In Kenya, significant increases in mean dry spell lengths (p≤0.02) are observed in inland arid-to semi humid locations but this temporal trend appears to decrease in highland and the coastal regions. Analysis of the MDB stations suggests changes in seasonality. For example, spatial trends suggest a North-South increase in dry spell length in summer (December - February), but a shortening after February. Generally, the GLM and MK results are similar in the two regions but the MK test tends to give higher values of positive slope coefficients and lower values for negative coefficients compared to GLM. This may limit the ability of finding the best estimates for model coefficients. Previous studies in Australia and Kenya have relied on continuous climatic indices based on global climate models and stochastic processes resulting in limited and mixed results. For agronomical purposes, our results show that direct assessment of dry spells lengths from daily rainfall also indicates changes in dry spells trends in Kenya and the MDB and that such an analysis is easy to use and requires limited assumptions. This initial analysis identifies significant increasing trends in the dry spell lengths in some areas and periods in Kenya and the MDB. This has major implications for crop production in these regions and it is recommended that this information be incorporated in the regions' management decisions. KEY WORDS: monthly dry spell length; Generalized Linear Models; Mann -Kendall test; month; Kenya, Murray Darling Basin (MDB).

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Effects on aquatic and human health due to large scale bioenergy crop expansion.

PubMed

Love, Bradley J; Einheuser, Matthew D; Nejadhashemi, A Pouyan

2011-08-01

In this study, the environmental impacts of large scale bioenergy crops were evaluated using the Soil and Water Assessment Tool (SWAT). Daily pesticide concentration data for a study area consisting of four large watersheds located in Michigan (totaling 53,358 km²) was estimated over a six year period (2000-2005). Model outputs for atrazine, bromoxynil, glyphosate, metolachlor, pendimethalin, sethoxydim, triflualin, and 2,4-D model output were used to predict the possible long-term implications that large-scale bioenergy crop expansion may have on the bluegill (Lepomis macrochirus) and humans. Threshold toxicity levels were obtained for the bluegill and for human consumption for all pesticides being evaluated through an extensive literature review. Model output was compared to each toxicity level for the suggested exposure time (96-hour for bluegill and 24-hour for humans). The results suggest that traditional intensive row crops such as canola, corn and sorghum may negatively impact aquatic life, and in most cases affect the safe drinking water availability. The continuous corn rotation, the most representative rotation for current agricultural practices for a starch-based ethanol economy, delivers the highest concentrations of glyphosate to the stream. In addition, continuous canola contributed to a concentration of 1.11 ppm of trifluralin, a highly toxic herbicide, which is 8.7 times the 96-hour ecotoxicity of bluegills and 21 times the safe drinking water level. Also during the period of study, continuous corn resulted in the impairment of 541,152 km of stream. However, there is promise with second-generation lignocellulosic bioenergy crops such as switchgrass, which resulted in a 171,667 km reduction in total stream length that exceeds the human threshold criteria, as compared to the base scenario. Results of this study may be useful in determining the suitability of bioenergy crop rotations and aid in decision making regarding the adaptation of large-scale bioenergy cropping systems. Published by Elsevier B.V.

Reducing N2O and NO emissions while sustaining crop productivity in a Chinese vegetable-cereal double cropping system.

PubMed

Yao, Zhisheng; Yan, Guangxuan; Zheng, Xunhua; Wang, Rui; Liu, Chunyan; Butterbach-Bahl, Klaus

2017-12-01

High nitrogen (N) inputs in Chinese vegetable and cereal productions played key roles in increasing crop yields. However, emissions of the potent greenhouse gas nitrous oxide (N 2 O) and atmospheric pollutant nitric oxide (NO) increased too. For lowering the environmental costs of crop production, it is essential to optimize N strategies to maintain high crop productivity, while reducing the associated N losses. We performed a 2 year-round field study regarding the effect of different combinations of poultry manure and chemical N fertilizers on crop yields, N use efficiency (NUE) and N 2 O and NO fluxes from a Welsh onion-winter wheat system in the North China Plain. Annual N 2 O and NO emissions averaged 1.14-3.82 kg N ha -1 yr -1 (or 5.54-13.06 g N kg -1 N uptake) and 0.57-1.87 kg N ha -1 yr -1 (or 2.78-6.38 g N kg -1 N uptake) over all treatments, respectively. Both N 2 O and NO emissions increased linearly with increasing total N inputs, and the mean annual direct emission factors (EF d ) were 0.39% for N 2 O and 0.19% for NO. Interestingly, the EF d for chemical N fertilizers (N 2 O: 0.42-0.48%; NO: 0.07-0.11%) was significantly lower than for manure N (N 2 O: 1.35%; NO: 0.76%). Besides, a negative power relationship between yield-scaled N 2 O, NO or N 2 O + NO emissions and NUE was observed, suggesting that improving NUE in crop production is crucial for increasing crop yields while decreasing nitrogenous gas release. Compared to the current farmers' fertilization rate, alternative practices with reduced chemical N fertilizers increased NUE and decreased annual N 2 O + NO emissions substantially, while crop yields remained unaffected. As a result, annual yield-scaled N 2 O + NO emissions were reduced by > 20%. Our study shows that a reduction of current application rates of chemical N fertilizers by 30-50% does not affect crop productivity, while at the same time N 2 O and NO emissions would be reduced significantly. Copyright © 2017 Elsevier Ltd. All rights reserved.

Abiotic stress QTL in lettuce crop–wild hybrids: comparing greenhouse and field experiments

PubMed Central

Hartman, Yorike; Hooftman, Danny A P; Uwimana, Brigitte; Schranz, M Eric; van de Wiel, Clemens C M; Smulders, Marinus J M; Visser, Richard G F; Michelmore, Richard W; van Tienderen, Peter H

2014-01-01

The development of stress-tolerant crops is an increasingly important goal of current crop breeding. A higher abiotic stress tolerance could increase the probability of introgression of genes from crops to wild relatives. This is particularly relevant to the discussion on the risks of new GM crops that may be engineered to increase abiotic stress resistance. We investigated abiotic stress QTL in greenhouse and field experiments in which we subjected recombinant inbred lines from a cross between cultivated Lactuca sativa cv. Salinas and its wild relative L. serriola to drought, low nutrients, salt stress, and aboveground competition. Aboveground biomass at the end of the rosette stage was used as a proxy for the performance of plants under a particular stress. We detected a mosaic of abiotic stress QTL over the entire genome with little overlap between QTL from different stresses. The two QTL clusters that were identified reflected general growth rather than specific stress responses and colocated with clusters found in earlier studies for leaf shape and flowering time. Genetic correlations across treatments were often higher among different stress treatments within the same experiment (greenhouse or field), than among the same type of stress applied in different experiments. Moreover, the effects of the field stress treatments were more correlated with those of the greenhouse competition treatments than to those of the other greenhouse stress experiments, suggesting that competition rather than abiotic stress is a major factor in the field. In conclusion, the introgression risk of stress tolerance (trans-)genes under field conditions cannot easily be predicted based on genomic background selection patterns from controlled QTL experiments in greenhouses, especially field data will be needed to assess potential (negative) ecological effects of introgression of these transgenes into wild relatives. PMID:25360276

Nature-based agricultural solutions: Scaling perennial grains across Africa.

PubMed

Peter, Brad G; Mungai, Leah M; Messina, Joseph P; Snapp, Sieglinde S

2017-11-01

Modern plant breeding tends to focus on maximizing yield, with one of the most ubiquitous implementations being shorter-duration crop varieties. It is indisputable that these breeding efforts have resulted in greater yields in ideal circumstances; however, many farmed locations across Africa suffer from one or more conditions that limit the efficacy of modern short-duration hybrids. In view of global change and increased necessity for intensification, perennial grains and long-duration varieties offer a nature-based solution for improving farm productivity and smallholder livelihoods in suboptimal agricultural areas. Specific conditions where perennial grains should be considered include locations where biophysical and social constraints reduce agricultural system efficiency, and where conditions are optimal for crop growth. Using a time-series of remotely-sensed data, we locate the marginal agricultural lands of Africa, identifying suboptimal temperature and precipitation conditions for the dominant crop, i.e., maize, as well as optimal climate conditions for two perennial grains, pigeonpea and sorghum. We propose that perennial grains offer a lower impact, sustainable nature-based solution to this subset of climatic drivers of marginality. Using spatial analytic methods and satellite-derived climate information, we demonstrate the scalability of perennial pigeonpea and sorghum across Africa. As a nature-based solution, we argue that perennial grains offer smallholder farmers of marginal lands a sustainable solution for enhancing resilience and minimizing risk in confronting global change, while mitigating social and edaphic drivers of low and variable production. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A Portable Farmland Information Collection System with Multiple Sensors.

PubMed

Zhang, Jianfeng; Hu, Jinyang; Huang, Lvwen; Zhang, Zhiyong; Ma, Yimian

2016-10-22

Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture-efficient use of agricultural resources, and improving the crop yields and quality-some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops.

A Portable Farmland Information Collection System with Multiple Sensors

PubMed Central

Zhang, Jianfeng; Hu, Jinyang; Huang, Lvwen; Zhang, Zhiyong; Ma, Yimian

2016-01-01

Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture—efficient use of agricultural resources, and improving the crop yields and quality—some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops. PMID:27782076

Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

NASA Astrophysics Data System (ADS)

Yeo, I.-Y.; Lee, S.; Sadeghi, A. M.; Beeson, P. C.; Hively, W. D.; McCarty, G. W.; Lang, M. W.

2014-12-01

Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay watershed (CBW), which is located in the mid-Atlantic US, winter cover crop use has been emphasized, and federal and state cost-share programs are available to farmers to subsidize the cost of cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops to improve water quality at the watershed scale (~ 50 km2) and to identify critical source areas of high nitrate export. A physically based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data to simulate hydrological processes and agricultural nutrient cycling over the period of 1990-2000. To accurately simulate winter cover crop biomass in relation to growing conditions, a new approach was developed to further calibrate plant growth parameters that control the leaf area development curve using multitemporal satellite-based measurements of species-specific winter cover crop performance. Multiple SWAT scenarios were developed to obtain baseline information on nitrate loading without winter cover crops and to investigate how nitrate loading could change under different winter cover crop planting scenarios, including different species, planting dates, and implementation areas. The simulation results indicate that winter cover crops have a negligible impact on the water budget but significantly reduce nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading from agricultural lands was approximately 14 kg ha-1, but decreased to 4.6-10.1 kg ha-1 with cover crops resulting in a reduction rate of 27-67% at the watershed scale. Rye was the most effective species, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of cover crops (~ 30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~ 2 kg ha-1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of cover crop implementation. Agricultural fields with well-drained soils and those that were more frequently used to grow corn had a higher potential for nitrate leaching and export to the waterways. This study supports the effective implementation of cover crop programs, in part by helping to target critical pollution source areas for cover crop implementation.

Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil.

PubMed

Battisti, R; Sentelhas, P C; Boote, K J

2018-05-01

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO 2 ] (380, 480, 580, 680, and 780 ppm), rainfall (- 30, - 15, 0, + 15, and + 30%), and solar radiation (- 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha -1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from - 15 to + 15%, whereas [CO 2 ] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO 2 .

An epidemiological model for externally sourced vector-borne viruses applied to Bean yellow mosaic virus in lupin crops in a Mediterranean-type environment.

PubMed

Maling, T; Diggle, A J; Thackray, D J; Siddique, K H M; Jones, R A C

2008-12-01

A hybrid mechanistic/statistical model was developed to predict vector activity and epidemics of vector-borne viruses spreading from external virus sources to an adjacent crop. The pathosystem tested was Bean yellow mosaic virus (BYMV) spreading from annually self-regenerating, legume-based pastures to adjacent crops of narrow-leafed lupin (Lupinus angustifolius) in the winter-spring growing season in a region with a Mediterranean-type environment where the virus persists over summer within dormant seed of annual clovers. The model uses a combination of daily rainfall and mean temperature during late summer and early fall to drive aphid population increase, migration of aphids from pasture to lupin crops, and the spread of BYMV. The model predicted time of arrival of aphid vectors and resulting BYMV spread successfully for seven of eight datasets from 2 years of field observations at four sites representing different rainfall and geographic zones of the southwestern Australian grainbelt. Sensitivity analysis was performed to determine the relative importance of the main parameters that describe the pathosystem. The hybrid mechanistic/statistical approach used created a flexible analytical tool for vector-mediated plant pathosystems that made useful predictions even when field data were not available for some components of the system.

Banana orchard inventory using IRS LISS sensors

NASA Astrophysics Data System (ADS)

Nishant, Nilay; Upadhayay, Gargi; Vyas, S. P.; Manjunath, K. R.

2016-04-01

Banana is one of the major crops of India with increasing export potential. It is important to estimate the production and acreage of the crop. Thus, the present study was carried out to evolve a suitable methodology for estimating banana acreage. Area estimation methodology was devised around the fact that unlike other crops, the time of plantation of banana is different for different farmers as per their local practices or conditions. Thus in order to capture the peak signatures, biowindow of 6 months was considered, its NDVI pattern studied and the optimum two months were considered when banana could be distinguished from other competing crops. The final area of banana for the particular growing cycle was computed by integrating the areas of these two months using LISS III data with spatial resolution of 23m. Estimated banana acreage in the three districts were 11857Ha, 15202ha and 11373Ha for Bharuch, Anand and Vadodara respectively with corresponding accuracy of 91.8%, 90% and 88.16%. Study further compared the use of LISS IV data of 5.8m spatial resolution for estimation of banana using object based as well as per-pixel classification and the results were compared with statistical reports for both the approaches. In the current paper we depict the various methodologies to accurately estimate the banana acreage.

Remote sensing-based Information for crop monitoring: contribution of SAR and Moderate resolution optical data on Asian rice production

NASA Astrophysics Data System (ADS)

Boschetti, Mirco; Holectz, Francesco; Manfron, Giacinto; Collivignarelli, Francesco; Nelson, Andrew

2013-04-01

Updated information on crop typology and status are strongly required to support suitable action to better manage agriculture production and reduce food insecurity. In this field, remote sensing has been demonstrated to be a suitable tool to monitor crop condition however rarely the tested system became really operative. The ones today available, such as the European Commission MARS, are mainly based on the analysis of NDVI time series and required ancillary external information like crop mask to interpret the seasonal signal. This condition is not always guarantied worldwide reducing the potentiality of the remote sensing monitoring. Moreover in tropical countries cloud contamination strongly reduce the possibility of using optical remote sensing data for crop monitoring. In this framework we focused our analysis on the rice production monitoring in Asian tropical area. Rice is in fact the staple food for half of the world population (FAO 2004), in Asia almost 90% of the world's rice is produced and consumed and Rice and poverty often coincide. In this contest the production of reliable rice production information is of extreme interest. We tried to address two important issue in terms of required geospatial information for crop monitoring: rice crop detection (rice map) and seasonal dynamics analysis (phenology). We use both SAR and Optical data in order to exploit the potential complementarity of this system. Multi-temporal ASAR Wide Swath data are in fact the best option to deal with cloud contamination. SAR can easily penetrate the clouds providing information on the surface target. Temporal analysis of archive ASAR data allowed to derived accurate map, at 100m spatial resolution, of permanent rice cultivated areas. On the other and high frequency revisiting optical data, in this case MODIS, have been used to extract seasonal information for the year under analysis. MOD09A1 Surface Reflectance 8-Day L3 Global 500m have been exploited to derive time series of Vegetation Index. A temporal smoothing procedure based on Savitzky-Golay polynomial filter function was applied to the original 8-day composite VI data (EVI and NDVI) in order to eliminate spurious data which affect the time series and to produce an interpolated VI temporal profile. Finally within the area previously identify as rice by SAR analysis phenological estimation have been conducted. Crop growth minima and maxima, respectively indicator of rice transplanting and heading, have been identify from the derivative analysis time series. This procedure was tested in Bangladesh for the year 2011. Results showed that the combined use of both data typology represents the more suitable multisource framework to provide reliable information on rice crop growth. Preliminary maps analysis reveals how SAR rice detection was in agreement with local information and phenology extracted by MODIS data provided spatially distributed data comparable with local knowledge of crop calendar.

An algorithm for deciding the number of clusters and validating using simulated data with application to exploring crop population structure

USDA-ARS?s Scientific Manuscript database

A first step in exploring population structure in crop plants and other organisms is to define the number of subpopulations that exist for a given data set. The genetic marker data sets being generated have become increasingly large over time and commonly are the high-dimension, low sample size (HDL...

Comparison of Uncalibrated Rgbvi with Spectrometer-Based Ndvi Derived from Uav Sensing Systems on Field Scale

NASA Astrophysics Data System (ADS)

Bareth, G.; Bolten, A.; Gnyp, M. L.; Reusch, S.; Jasper, J.

2016-06-01

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers' field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.

Optimization on Paddy Crops in Central Java (with Solver, SVD on Least Square and ACO (Ant Colony Algorithm))

NASA Astrophysics Data System (ADS)

Parhusip, H. A.; Trihandaru, S.; Susanto, B.; Prasetyo, S. Y. J.; Agus, Y. H.; Simanjuntak, B. H.

2017-03-01

Several algorithms and objective functions on paddy crops have been studied to get optimal paddy crops in Central Java based on the data given from Surakarta and Boyolali. The algorithms are linear solver, least square and Ant Colony Algorithms (ACO) to develop optimization procedures on paddy crops modelled with Modified GSTAR (Generalized Space-Time Autoregressive) and nonlinear models where the nonlinear models are quadratic and power functions. The studied data contain paddy crops from Surakarta and Boyolali determining the best period of planting in the year 1992-2012 for Surakarta where 3 periods for planting are known and the optimal amount of paddy crops in Boyolali in the year 2008-2013. Having these analyses may guide the local agriculture government to give a decision on rice sustainability in its region. The best period for planting in Surakarta is observed, i.e. the best period is in September-December based on the data 1992-2012 by considering the planting area, the cropping area, and the paddy crops are the most important factors to be taken into account. As a result, we can refer the paddy crops in this best period (about 60.4 thousand tons per year) as the optimal results in 1992-2012 where the used objective function is quadratic. According to the research, the optimal paddy crops in Boyolali about 280 thousand tons per year where the studied factors are the amount of rainfalls, the harvested area and the paddy crops in 2008-2013. In this case, linear and power functions are studied to be the objective functions. Compared to all studied algorithms, the linear solver is still recommended to be an optimization tool for a local agriculture government to predict paddy crops in future.

Simulating crop growth with Expert-N-GECROS under different site conditions in Southwest Germany

NASA Astrophysics Data System (ADS)

Poyda, Arne; Ingwersen, Joachim; Demyan, Scott; Gayler, Sebastian; Streck, Thilo

2016-04-01

When feedbacks between the land surface and the atmosphere are investigated by Atmosphere-Land surface-Crop-Models (ALCM) it is fundamental to accurately simulate crop growth dynamics as plants directly influence the energy partitioning at the plant-atmosphere interface. To study both the response and the effect of intensive agricultural crop production systems on regional climate change in Southwest Germany, the crop growth model GECROS (YIN & VAN LAAR, 2005) was calibrated based on multi-year field data from typical crop rotations in the Kraichgau and Swabian Alb regions. Additionally, the SOC (soil organic carbon) model DAISY (MÜLLER et al., 1998) was implemented in the Expert-N model tool (ENGEL & PRIESACK, 1993) and combined with GECROS. The model was calibrated based on a set of plant (BBCH, LAI, plant height, aboveground biomass, N content of biomass) and weather data for the years 2010 - 2013 and validated with the data of 2014. As GECROS adjusts the root-shoot partitioning in response to external conditions (water, nitrogen, CO2), it is suitable to simulate crop growth dynamics under changing climate conditions and potentially more frequent stress situations. As C and N pools and turnover rates in soil as well as preceding crop effects were expected to considerably influence crop growth, the model was run in a multi-year, dynamic way. Crop residues and soil mineral N (nitrate, ammonium) available for the subsequent crop were accounted for. The model simulates growth dynamics of winter wheat, winter rape, silage maize and summer barley at the Kraichgau and Swabian Alb sites well. The Expert-N-GECROS model is currently parameterized for crops with potentially increasing shares in future crop rotations. First results will be shown.

The release of genetically modified crops into the environment. Part II. Overview of ecological risk assessment.

PubMed

Conner, Anthony J; Glare, Travis R; Nap, Jan-Peter

2003-01-01

Despite numerous future promises, there is a multitude of concerns about the impact of GM crops on the environment. Key issues in the environmental assessment of GM crops are putative invasiveness, vertical or horizontal gene flow, other ecological impacts, effects on biodiversity and the impact of presence of GM material in other products. These are all highly interdisciplinary and complex issues. A crucial component for a proper assessment is defining the appropriate baseline for comparison and decision. For GM crops, the best and most appropriately defined reference point is the impact of plants developed by traditional breeding. The latter is an integral and accepted part of agriculture. In many instances, the putative impacts identified for GM crops are very similar to the impacts of new cultivars derived from traditional breeding. When assessing GM crops relative to existing cultivars, the increased knowledge base underpinning the development of GM crops will provide greater confidence in the assurances plant science can give on the risks of releasing such crops.

Northwest Ohio crop yield benefits of water capture and subirrigation based on future climate change projections

USDA-ARS?s Scientific Manuscript database

Climate change projections for the Midwest U.S. indicate increased growing season crop water deficits in the future that will adversely impact the sustainability of agricultural production. Systems that capture water on site for later subirrigation use have potential as a climate adaptation strateg...

Gaussian processes-based predictive models to estimate reference ET from alternative meteorological data sources for irrigation scheduling

USDA-ARS?s Scientific Manuscript database

Accurate estimates of daily crop evapotranspiration (ET) are needed for efficient irrigation management, especially in arid and semi-arid irrigated regions where crop water demand exceeds rainfall. The impact of inaccurate ET estimates can be tremendous in both irrigation cost and the increased dema...

Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

NASA Astrophysics Data System (ADS)

Jiao, Xianfeng; Kovacs, John M.; Shang, Jiali; McNairn, Heather; Walters, Dan; Ma, Baoluo; Geng, Xiaoyuan

2014-10-01

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude-Pottier and Freeman-Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude-Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman-Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.

The application of genomics and bioinformatics to accelerate crop improvement in a changing climate.

PubMed

Batley, Jacqueline; Edwards, David

2016-04-01

The changing climate and growing global population will increase pressure on our ability to produce sufficient food. The breeding of novel crops and the adaptation of current crops to the new environment are required to ensure continued food production. Advances in genomics offer the potential to accelerate the genomics based breeding of crop plants. However, relating genomic data to climate related agronomic traits for use in breeding remains a huge challenge, and one which will require coordination of diverse skills and expertise. Bioinformatics, when combined with genomics has the potential to help maintain food security in the face of climate change through the accelerated production of climate ready crops. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Robotic Platform for Corn Seedling Morphological Traits Characterization

PubMed Central

Lu, Hang; Tang, Lie; Whitham, Steven A.; Mei, Yu

2017-01-01

Crop breeding plays an important role in modern agriculture, improving plant performance, and increasing yield. Identifying the genes that are responsible for beneficial traits greatly facilitates plant breeding efforts for increasing crop production. However, associating genes and their functions with agronomic traits requires researchers to observe, measure, record, and analyze phenotypes of large numbers of plants, a repetitive and error-prone job if performed manually. An automated seedling phenotyping system aimed at replacing manual measurement, reducing sampling time, and increasing the allowable work time is thus highly valuable. Toward this goal, we developed an automated corn seedling phenotyping platform based on a time-of-flight of light (ToF) camera and an industrial robot arm. A ToF camera is mounted on the end effector of the robot arm. The arm positions the ToF camera at different viewpoints for acquiring 3D point cloud data. A camera-to-arm transformation matrix was calculated using a hand-eye calibration procedure and applied to transfer different viewpoints into an arm-based coordinate frame. Point cloud data filters were developed to remove the noise in the background and in the merged seedling point clouds. A 3D-to-2D projection and an x-axis pixel density distribution method were used to segment the stem and leaves. Finally, separated leaves were fitted with 3D curves for morphological traits characterization. This platform was tested on a sample of 60 corn plants at their early growth stages with between two to five leaves. The error ratios of the stem height and leave length measurements are 13.7% and 13.1%, respectively, demonstrating the feasibility of this robotic system for automated corn seedling phenotyping. PMID:28895892

A Robotic Platform for Corn Seedling Morphological Traits Characterization.

PubMed

Lu, Hang; Tang, Lie; Whitham, Steven A; Mei, Yu

2017-09-12

Crop breeding plays an important role in modern agriculture, improving plant performance, and increasing yield. Identifying the genes that are responsible for beneficial traits greatly facilitates plant breeding efforts for increasing crop production. However, associating genes and their functions with agronomic traits requires researchers to observe, measure, record, and analyze phenotypes of large numbers of plants, a repetitive and error-prone job if performed manually. An automated seedling phenotyping system aimed at replacing manual measurement, reducing sampling time, and increasing the allowable work time is thus highly valuable. Toward this goal, we developed an automated corn seedling phenotyping platform based on a time-of-flight of light (ToF) camera and an industrial robot arm. A ToF camera is mounted on the end effector of the robot arm. The arm positions the ToF camera at different viewpoints for acquiring 3D point cloud data. A camera-to-arm transformation matrix was calculated using a hand-eye calibration procedure and applied to transfer different viewpoints into an arm-based coordinate frame. Point cloud data filters were developed to remove the noise in the background and in the merged seedling point clouds. A 3D-to-2D projection and an x -axis pixel density distribution method were used to segment the stem and leaves. Finally, separated leaves were fitted with 3D curves for morphological traits characterization. This platform was tested on a sample of 60 corn plants at their early growth stages with between two to five leaves. The error ratios of the stem height and leave length measurements are 13.7% and 13.1%, respectively, demonstrating the feasibility of this robotic system for automated corn seedling phenotyping.

Limited Impact of a Fall-Seeded, Spring-Terminated Rye Cover Crop on Beneficial Arthropods.

PubMed

Dunbar, Mike W; Gassmann, Aaron J; O'Neal, Matthew E

2017-04-01

Cover crops are beneficial to agroecosystems because they decrease soil erosion and nutrient loss while increasing within-field plant diversity. Greater plant diversity within cropping systems can positively affect beneficial arthropod communities. We hypothesized that increasing plant diversity within annually rotated corn and soybean with the addition of a rye cover crop would positively affect the beneficial ground and canopy-dwelling communities compared with rotated corn and soybean grown without a cover crop. From 2011 through 2013, arthropod communities were measured at two locations in Iowa four times throughout each growing season. Pitfall traps were used to sample ground-dwelling arthropods within the corn and soybean plots and sweep nets were used to measure the beneficial arthropods in soybean canopies. Beneficial arthropods captured were identified to either class, order, or family. In both corn and soybean, community composition and total community activity density and abundance did not differ between plots that included the rye cover crop and plots without the rye cover crop. Most taxa did not significantly respond to the presence of the rye cover crop when analyzed individually, with the exceptions of Carabidae and Gryllidae sampled from soybean pitfall traps. Activity density of Carabidae was significantly greater in soybean plots that included a rye cover crop, while activity density of Gryllidae was significantly reduced in plots with the rye cover crop. Although a rye cover crop may be agronomically beneficial, there may be only limited effects on beneficial arthropods when added within an annual rotation of corn and soybean. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset

PubMed Central

Donald, Margaret R.; Mengersen, Kerrie L.; Young, Rick R.

2015-01-01

While a variety of statistical models now exist for the spatio-temporal analysis of two-dimensional (surface) data collected over time, there are few published examples of analogous models for the spatial analysis of data taken over four dimensions: latitude, longitude, height or depth, and time. When taking account of the autocorrelation of data within and between dimensions, the notion of closeness often differs for each of the dimensions. Here, we consider a number of approaches to the analysis of such a dataset, which arises from an agricultural experiment exploring the impact of different cropping systems on soil moisture. The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors. In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm). Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping. PMID:26513746

The support of local farming communities and crop production resilience to climate change through the cultivation of giant reed (Arundo donax sp.). An Italian case study.

NASA Astrophysics Data System (ADS)

Bonfante, Antonello; Impagliazzo, Adriana; Fiorentino, Nunzio; Langella, Giuliano; Mori, Mauro; Fagnano, Massimo

2017-04-01

In literature on climate change, the bioenergy crops are well known for their ability to reduce greenhouse gases emission and increase the soil carbon stock. Nevertheless, in several countries they are considered in competition with food crops, representing a problem for facing the current and future "Food Security" issue related to climate change and population increase. At same time, in order to sustain local farming communities and crop production, mitigation actions at farm scale are identified to face climate change. However, in some cases the specific actions required by the pedo-climatic conditions, are not always economically sustainable by farmers. In this contest, the energy crops with high environmental adaptability and high productive performances, as the giant reed (Arundo donax spp.), cultivated in the areas not suitable for food crop (marginal areas) may represent an opportunity to increase the farmers' incomes, through the direct sale of above ground biomass (AGB) (as raw material for energy or green chemistry) or by means of the obtained biofuel or biogas. Moreover, the bioenergy crops, with low-input cropping systems (i.e. without irrigation and fertilized with compost from organic residues) are considered the most efficient crops for Greenhouse Gases reduction. In fact, they have a direct effect on processes affecting the CC at global scale: i) preserving and improving the soil carbon stock, ii) reducing the soil tillage and preserving the soil erosion, iii) allowing the conservation of fossil fuel resources with a reduction of CO2 emission in the atmosphere. Finally, their high environmental adaptability allows a riparian vegetation use (in the soils near water drains or rivers) with an important effect on the interception of nutrients as nitrogen and phosphorus that, if leached, have a high environmental impact on watercourses (e.g. eutrophication). Thus the correct use of this crops allows to respond to 3 of 17 Sustainable Development Goals (SDG) of United Nations: (i) SDG 2 on food security and sustainable agriculture, (ii) SDG 7 on reliable, sustainable and modern energy and (iii) SDG 13 on action to combat climate change and its impacts. Therefore, in order to support the resilience of local farming communities and food production, a mitigation action to face climate change can be bases on the assessment of the possible increase of farmers' incomes derived by the cultivation of bioenergy crops in their marginal areas. On these premises, we have evaluated the giant reed responses in the marginal areas of an agricultural district of southern Italy (Destra Sele) and evaluated the expected farmers' income in a near future (2021-2050) through a simulation model application. In order to realize this applicative and pro-active approach to farmer support, the normalized water productivity index of giant reed has been determined, through the use of agro-hydrological model SWAP, calibrated and validated on two years of long term field experiment on giant reed, realized within of study area. Keywords: Climate Change; SWAP; giant reed;water productivity (WP); Sustainable Development Goals (SDG)

Modelling climate change impact: A case of bambara groundnut (Vigna subterranea)

NASA Astrophysics Data System (ADS)

Mabhaudhi, Tafadzwanashe; Chibarabada, Tendai Polite; Chimonyo, Vimbayi Grace Petrova; Modi, Albert Thembinkosi

2018-06-01

Climate change projections for southern Africa indicate low and erratic rainfall as well as increasing frequency and intensity of rainfall extremes such as drought. The 2015/16 drought devastated large parts of southern Africa and highlighted the need for drought tolerant crops. Bambara groundnut is an African indigenous crop, commonly cultivated in southern Africa, with a higher potential for drought tolerance compared to other staple legumes. AquaCrop model was used to evaluate the impacts of climate change on yield, water use (ET) and water productivity (WP) of bambara groundnut using climate change data representative of the past (1961-1991), present (1995-2025), mid-century (2030-2060) and late century (2065-2095) obtained from five global circulation models (GCMs). The carbon dioxide (CO2) file selected was for the A2 scenario. The model was run at a sub-catchment level. Model simulations showed that yield and WP of bambara groundnut will increase over time. The mean values of yield at the different time scales across the GCMs showed that yield of bambara groundnut increased by ∼9% from the past to the present, will increase by ∼15% from the present to mid-century and will increase by 6% from mid-to late-century. The simulated results of ET showed seasonal ranges of 703-796 mm. Of this, 45% was lost to soil evaporation, suggesting the need for developing bambara groundnut varieties with faster establishment and high canopy cover. Model simulations showed an increase in WP by ∼13% from the past to present and ∼15% from the present to mid-century and ∼11% from mid-century to late century. While the results of these simulations are preliminary, they confirm the view that bambara groundnut is a potential future crop suitable for cultivation in marginal agricultural production areas. Future research should focus on crop improvement to improve current yield of bambara groundnut.

Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators

NASA Astrophysics Data System (ADS)

Krause, Andreas; Pugh, Thomas A. M.; Bayer, Anita D.; Doelman, Jonathan C.; Humpenöder, Florian; Anthoni, Peter; Olin, Stefan; Bodirsky, Benjamin L.; Popp, Alexander; Stehfest, Elke; Arneth, Almut

2017-11-01

Land management for carbon storage is discussed as being indispensable for climate change mitigation because of its large potential to remove carbon dioxide from the atmosphere, and to avoid further emissions from deforestation. However, the acceptance and feasibility of land-based mitigation projects depends on potential side effects on other important ecosystem functions and their services. Here, we use projections of future land use and land cover for different land-based mitigation options from two land-use models (IMAGE and MAgPIE) and evaluate their effects with a global dynamic vegetation model (LPJ-GUESS). In the land-use models, carbon removal was achieved either via growth of bioenergy crops combined with carbon capture and storage, via avoided deforestation and afforestation, or via a combination of both. We compare these scenarios to a reference scenario without land-based mitigation and analyse the LPJ-GUESS simulations with the aim of assessing synergies and trade-offs across a range of ecosystem service indicators: carbon storage, surface albedo, evapotranspiration, water runoff, crop production, nitrogen loss, and emissions of biogenic volatile organic compounds. In our mitigation simulations cumulative carbon storage by year 2099 ranged between 55 and 89 GtC. Other ecosystem service indicators were influenced heterogeneously both positively and negatively, with large variability across regions and land-use scenarios. Avoided deforestation and afforestation led to an increase in evapotranspiration and enhanced emissions of biogenic volatile organic compounds, and to a decrease in albedo, runoff, and nitrogen loss. Crop production could also decrease in the afforestation scenarios as a result of reduced crop area, especially for MAgPIE land-use patterns, if assumed increases in crop yields cannot be realized. Bioenergy-based climate change mitigation was projected to affect less area globally than in the forest expansion scenarios, and resulted in less pronounced changes in most ecosystem service indicators than forest-based mitigation, but included a possible decrease in nitrogen loss, crop production, and biogenic volatile organic compounds emissions.

Impacts of elevated atmospheric CO2 on nutrient content of important food crops

NASA Astrophysics Data System (ADS)

Dietterich, Lee H.; Zanobetti, Antonella; Kloog, Itai; Huybers, Peter; Leakey, Andrew D. B.; Bloom, Arnold J.; Carlisle, Eli; Fernando, Nimesha; Fitzgerald, Glenn; Hasegawa, Toshihiro; Holbrook, N. Michele; Nelson, Randall L.; Norton, Robert; Ottman, Michael J.; Raboy, Victor; Sakai, Hidemitsu; Sartor, Karla A.; Schwartz, Joel; Seneweera, Saman; Usui, Yasuhiro; Yoshinaga, Satoshi; Myers, Samuel S.

2015-07-01

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Impacts of elevated atmospheric CO₂ on nutrient content of important food crops.

PubMed

Dietterich, Lee H; Zanobetti, Antonella; Kloog, Itai; Huybers, Peter; Leakey, Andrew D B; Bloom, Arnold J; Carlisle, Eli; Fernando, Nimesha; Fitzgerald, Glenn; Hasegawa, Toshihiro; Holbrook, N Michele; Nelson, Randall L; Norton, Robert; Ottman, Michael J; Raboy, Victor; Sakai, Hidemitsu; Sartor, Karla A; Schwartz, Joel; Seneweera, Saman; Usui, Yasuhiro; Yoshinaga, Satoshi; Myers, Samuel S

2015-01-01

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Impacts of elevated atmospheric CO2 on nutrient content of important food crops

PubMed Central

Dietterich, Lee H.; Zanobetti, Antonella; Kloog, Itai; Huybers, Peter; Leakey, Andrew D. B.; Bloom, Arnold J.; Carlisle, Eli; Fernando, Nimesha; Fitzgerald, Glenn; Hasegawa, Toshihiro; Holbrook, N. Michele; Nelson, Randall L.; Norton, Robert; Ottman, Michael J.; Raboy, Victor; Sakai, Hidemitsu; Sartor, Karla A.; Schwartz, Joel; Seneweera, Saman; Usui, Yasuhiro; Yoshinaga, Satoshi; Myers, Samuel S.

2015-01-01

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients. PMID:26217490

Modeling the status, trends, and impacts of wild bee abundance in the United States

PubMed Central

Koh, Insu; Lonsdorf, Eric V.; Williams, Neal M.; Brittain, Claire; Isaacs, Rufus; Gibbs, Jason; Ricketts, Taylor H.

2016-01-01

Wild bees are highly valuable pollinators. Along with managed honey bees, they provide a critical ecosystem service by ensuring stable pollination to agriculture and wild plant communities. Increasing concern about the welfare of both wild and managed pollinators, however, has prompted recent calls for national evaluation and action. Here, for the first time to our knowledge, we assess the status and trends of wild bees and their potential impacts on pollination services across the coterminous United States. We use a spatial habitat model, national land-cover data, and carefully quantified expert knowledge to estimate wild bee abundance and associated uncertainty. Between 2008 and 2013, modeled bee abundance declined across 23% of US land area. This decline was generally associated with conversion of natural habitats to row crops. We identify 139 counties where low bee abundances correspond to large areas of pollinator-dependent crops. These areas of mismatch between supply (wild bee abundance) and demand (cultivated area) for pollination comprise 39% of the pollinator-dependent crop area in the United States. Further, we find that the crops most highly dependent on pollinators tend to experience more severe mismatches between declining supply and increasing demand. These trends, should they continue, may increase costs for US farmers and may even destabilize crop production over time. National assessments such as this can help focus both scientific and political efforts to understand and sustain wild bees. As new information becomes available, repeated assessments can update findings, revise priorities, and track progress toward sustainable management of our nation’s pollinators. PMID:26699460

Modeling the status, trends, and impacts of wild bee abundance in the United States.

PubMed

Koh, Insu; Lonsdorf, Eric V; Williams, Neal M; Brittain, Claire; Isaacs, Rufus; Gibbs, Jason; Ricketts, Taylor H

2016-01-05

Wild bees are highly valuable pollinators. Along with managed honey bees, they provide a critical ecosystem service by ensuring stable pollination to agriculture and wild plant communities. Increasing concern about the welfare of both wild and managed pollinators, however, has prompted recent calls for national evaluation and action. Here, for the first time to our knowledge, we assess the status and trends of wild bees and their potential impacts on pollination services across the coterminous United States. We use a spatial habitat model, national land-cover data, and carefully quantified expert knowledge to estimate wild bee abundance and associated uncertainty. Between 2008 and 2013, modeled bee abundance declined across 23% of US land area. This decline was generally associated with conversion of natural habitats to row crops. We identify 139 counties where low bee abundances correspond to large areas of pollinator-dependent crops. These areas of mismatch between supply (wild bee abundance) and demand (cultivated area) for pollination comprise 39% of the pollinator-dependent crop area in the United States. Further, we find that the crops most highly dependent on pollinators tend to experience more severe mismatches between declining supply and increasing demand. These trends, should they continue, may increase costs for US farmers and may even destabilize crop production over time. National assessments such as this can help focus both scientific and political efforts to understand and sustain wild bees. As new information becomes available, repeated assessments can update findings, revise priorities, and track progress toward sustainable management of our nation's pollinators.

The cultivation of energy crops for biogas production and the application of digestates are characterized by high variability of CO2 exchange and soil organic C stock changes

NASA Astrophysics Data System (ADS)

Augustin, Juergen; Fiedler, Sebastian; Heintze, Gawan; Rohwer, Marcus; Prescher, Anne-Katrin; Pohl, Madlen; Jurisch, Nicole; Hagemann, Ulrike

2017-04-01

In Germany, agricultural production accounts for approx. 15% of total anthropogenic greenhouse gas emissions. The cultivation of energy crops is thus considered an important option to reduce the climate impact and maintain or increase soil organic carbon (SOC) stocks. In particular, this applies to the continuously expanding cultivation of energy crops for biogas production and the associated use of residues from anaerobic digestion (digestates) as organic fertilizer. To date, there is only limited and contradicting evidence on the impacts of this management practice on the CO2 exchange as well as the change of SOC stocks. We will present results from a 4-year field study at 5 sites in Germany using identical methods to investigate the interacting effects of i) 3 N-fertilizer treatments including calcium ammonium nitrate and digestates and ii) a crop rotation of 7 energy crops like maize, sorghum, triticale, and wheat on net ecosystem CO2 exchange (NEE) and the change of SOC stocks. We used the manual chamber approach for measuring NEE as the difference between gross primary production and ecosystem respiration. The determination of SOC stock changes was based on a C budget approach, which includes the cumulated annual NEE, the C export by harvest, and the C import by application of anaerobic digestates. The CO2 exchange and the change of SOC stocks were influenced by multiple factors like crop, site, fertilization, and climate, as well as their complex interactions. A large proportion of the variability of the CO2 exchange can be attributed to interannual climatic variability. Productive crops like maize and sorghum generally feature the most intensive CO2 exchange, while less productive crops can compensate for this by means of longer cultivation times. Regardless of the extreme variability, pronounced and partly significant differences of NEE and C budgets between sites were observed. On average, SOC stocks declined over a full crop rotation, but with highly variable positive and negative C budgets. This indicates that, in most cases, neither the selected crops nor the application of anaerobic digestates were sufficient to compensate for SOC losses. Apparently, the potential of anaerobic digestates to maintain or increase SOC stocks is considerably smaller than expected. If continuous decreases of SOC stocks due to energy crop cultivation are to be avoided, additional studies on the optimization of crop rotations (selection of plants with high C input), and digestate fertilization (type of digestate, amount and application technique) are required. A continuously improved version of the methodology used in this study promises faster and more precise results than classic long-term field trials.

Eight years of Conservation Agriculture-based cropping systems research in Eastern Africa to conserve soil and water and mitigate effects of climate change

NASA Astrophysics Data System (ADS)

Araya, Tesfay; Nyssen, Jan; Govaerts, Bram; Lanckriet, Sil; Baudron, Frédéric; Deckers, Jozef; Cornelis, Wim

2014-05-01

In Ethiopia, repeated plowing, complete removal of crop residues at harvest, aftermath grazing of crop fields and occurrence of repeated droughts have reduced the biomass return to the soil and aggravated cropland degradation. Conservation Agriculture (CA)-based resource conserving cropping systems may reduce runoff and soil erosion, and improve soil quality, thereby increasing crop productivity. Thus, a long-term tillage experiment has been carried out (2005 to 2012) on a Vertisol to quantify - among others - changes in runoff and soil loss for two local tillage practices, modified to integrate CA principles in semi-arid northern Ethiopia. The experimental layout was a randomized complete block design with three replications on permanent plots of 5 m by 19 m. The tillage treatments were (i) derdero+ (DER+) with a furrow and permanent raised bed planting system, ploughed only once at planting by refreshing the furrow from 2005 to 2012 and 30% standing crop residue retention, (ii) terwah+ (TER+) with furrows made at 1.5 m interval, plowed once at planting, 30% standing crop residue retention and fresh broad beds, and (iii) conventional tillage (CT) with a minimum of three plain tillage operations and complete removal of crop residues. All the plowing and reshaping of the furrows was done using the local ard plough mahresha and wheat, teff, barley and grass pea were grown. Glyphosate was sprayed starting from the third year onwards (2007) at 2 l ha-1 before planting to control pre-emergent weeds in CA plots. Runoff and soil loss were measured daily. Soil water content was monitored every 6 days. Significantly different (p<0.05) runoff coefficients averaged over 8 years were 14, 20 and 27% for DER+, TER+ and CT, respectively. Mean soil losses were 4 t ha-1 y-1 in DER+, 13 in TER+ and 18 in CT. Soil water storage during the growing season was constantly higher in CA-based systems compared with CT. A period of at least three years of cropping was required before improvements in crop yield became significant. Further, modeling of the sediment budgets shows that total soil loss due to sheet and rill erosion in cropland, when CA would be practiced at large scale in a 180 ha catchment, would reduce to 581 t y-1, instead of 1109 t y-1 under the current farmer practice. Using NASA/GISS Model II precipitation projections of IPCC scenario A1FI, CA is estimated to reduce soil loss and runoff and mitigate the effect of increased rainfall due to climate change. For smallholder farmers in semi-arid agro-ecosystems, CA-based systems constitute a field rainwater and soil conservation improvement strategy that enhances crop and economic productivity and reduces siltation of reservoirs, especially under changing climate. The reduction in draught power requirement would enable a reduction in oxen density and crop residue demand for livestock feed, which would encourage smallholder farmers to increase biomass return to the soil. Adoption of CA-based systems in the study area requires further work to improve smallholder farmers' awareness on benefits, to guarantee high standards during implementation and to design appropriate weed management strategies.

A center for commercial development of space: Real-time satellite mapping. Remote sensing-based agricultural information expert system

NASA Technical Reports Server (NTRS)

Hadipriono, Fabian C.; Diaz, Carlos F.; Merritt, Earl S.

1989-01-01

The research project results in a powerful yet user friendly CROPCAST expert system for use by a client to determine the crop yield production of a certain crop field. The study is based on the facts that heuristic assessment and decision making in agriculture are significant and dominate much of agribusiness. Transfer of the expert knowledge concerning remote sensing based crop yield production into a specific expert system is the key program in this study. A knowledge base consisting of a root frame, CROP-YIELD-FORECAST, and four subframes, namely, SATELLITE, PLANT-PHYSIOLOGY, GROUND, and MODEL were developed to accommodate the production rules obtained from the domain expert. The expert system shell Personal Consultant Plus version 4.0. was used for this purpose. An external geographic program was integrated to the system. This project is the first part of a completely built expert system. The study reveals that much effort was given to the development of the rules. Such effort is inevitable if workable, efficient, and accurate rules are desired. Furthermore, abundant help statements and graphics were included. Internal and external display routines add to the visual capability of the system. The work results in a useful tool for the client for making decisions on crop yield production.

Historical and simulated ecosystem carbon dynamics in Ghana: Land use, management, and climate

USGS Publications Warehouse

Tan, Z.; Tieszen, L.L.; Tachie-Obeng, E.; Liu, S.; Dieye, A.M.

2009-01-01

We used the General Ensemble biogeochemical Modeling System (GEMS) to simulate responses of natural and managed ecosystems to changes in land use and land cover, management, and climate for a forest/savanna transitional zone in central Ghana. Model results show that deforestation for crop production during the 20th century resulted in a substantial reduction in ecosystem carbon (C) stock from 135.4 Mg C ha−1 in 1900 to 77.0 Mg C ha−1 in 2000, and in soil organic C stock within the top 20 cm of soil from 26.6 Mg C ha−1 to 21.2 Mg C ha−1. If no land use change takes place from 2000 through 2100, low and high climate change scenarios (increase in temperature and decrease in precipitation over time) will result in losses of soil organic C stock by 16% and 20%, respectively. A low nitrogen (N) fertilization rate is the principal constraint on current crop production. An increase in N fertilization under the low climate change scenario would lead to an increase in the average crop yield by 21% with 30 kg N ha−1 and by 42% with 60 kg N ha−1 (varying with crop species), accordingly, the average soil C stock would decrease by 2% and increase by 17%, in all cropping systems by 2100. The results suggest that a reasonable N fertilization rate is critical to achieve food security and agricultural sustainability in the study area through the 21st century. Adaptation strategies for climate change in this study area require national plans to support policies and practices that provide adequate N fertilizers to sustain soil C and crop yields and to consider high temperature tolerant crop species if these temperature projections are exceeded.

Historical and simulated ecosystem carbon dynamics in Ghana: land use, management, and climate

NASA Astrophysics Data System (ADS)

Tan, Z.; Tieszen, L. L.; Tachie-Obeng, E.; Liu, S.; Dieye, A. M.

2009-01-01

We used the General Ensemble biogeochemical Modeling System (GEMS) to simulate responses of natural and managed ecosystems to changes in land use and land cover, management, and climate for a forest/savanna transitional zone in central Ghana. Model results show that deforestation for crop production during the 20th century resulted in a substantial reduction in ecosystem carbon (C) stock from 135.4 Mg C ha-1 in 1900 to 77.0 Mg C ha-1 in 2000, and in soil organic C stock within the top 20 cm of soil from 26.6 Mg C ha-1 to 21.2 Mg C ha-1. If no land use change takes place from 2000 through 2100, low and high climate change scenarios (increase in temperature and decrease in precipitation over time) will result in losses of soil organic C stock by 16% and 20%, respectively. A low nitrogen (N) fertilization rate is the principal constraint on current crop production. An increase in N fertilization under the low climate change scenario would lead to an increase in the average crop yield by 21% with 30 kg N ha-1 and by 42% with 60 kg N ha-1 (varying with crop species), accordingly, the average soil C stock would decrease by 2% and increase by 17%, in all cropping systems by 2100. The results suggest that a reasonable N fertilization rate is critical to achieve food security and agricultural sustainability in the study area through the 21st century. Adaptation strategies for climate change in this study area require national plans to support policies and practices that provide adequate N fertilizers to sustain soil C and crop yields and to consider high temperature tolerant crop species if these temperature projections are exceeded.

A numerical study of the effect of irrigation on land-atmosphere interactions in a spring wheat cropland in India using a coupled atmosphere-crop growth dynamics model

NASA Astrophysics Data System (ADS)

Kumari, S.; Sharma, P.; Srivastava, A.; Rastogi, D.; Sehgal, V. K.; Dhakar, R.; Roy, S. B.

2017-12-01

Vegetation dynamics and surface meteorology are tightly coupled through the exchange of momentum, moisture and heat between the land surface and the atmosphere. In this study, we use a recently developed coupled atmosphere-crop growth dynamics model to study these exchanges and their effects in a spring wheat cropland in northern India. In particular, we investigate the role of irrigation in controlling crop growth rates, surface meteorology, and sensible and latent heat fluxes. The model is developed by implementing a crop growth module based on the Simple and Universal Crop growth Simulator (SUCROS) model in the Weather Research Forecasting (WRF) mesoscale atmospheric model. The crop module calculates photosynthesis rates, carbon assimilation, and biomass partitioning as a function of environmental factors and crop development stage. The leaf area index (LAI) and root depth calculated by the crop module is then fed to the Noah-MP land module of WRF to calculate land-atmosphere fluxes. The crop model is calibrated using data from an experimental spring wheat crop site in the Indian Agriculture Research Institute. The coupled model is capable of simulating the observed spring wheat phenology. Irrigation is simulated by changing the soil moisture levels from 50% - 100% of field capacity. Results show that the yield first increases with increasing soil moisture and then starts decreasing as we further increase the soil moisture. Yield attains its maximum value with soil moisture at the level of 60% water of FC. At this level, high LAI values lead to a decrease in the Bowen Ratio because more energy is transferred to the atmosphere as latent heat rather than sensible heat resulting in a cooling effect on near-surface air temperatures. Apart from improving simulation of land-atmosphere interactions, this coupled modeling approach can form the basis for the seamless crop yield and seasonal scale weather outlook prediction system.

Effects of crop species richness on pest-natural enemy systems based on an experimental model system using a microlandscape.

PubMed

Zhao, ZiHua; Shi, PeiJian; Men, XingYuan; Ouyang, Fang; Ge, Feng

2013-08-01

The relationship between crop richness and predator-prey interactions as they relate to pest-natural enemy systems is a very important topic in ecology and greatly affects biological control services. The effects of crop arrangement on predator-prey interactions have received much attention as the basis for pest population management. To explore the internal mechanisms and factors driving the relationship between crop richness and pest population management, we designed an experimental model system of a microlandscape that included 50 plots and five treatments. Each treatment had 10 repetitions in each year from 2007 to 2010. The results showed that the biomass of pests and their natural enemies increased with increasing crop biomass and decreased with decreasing crop biomass; however, the effects of plant biomass on the pest and natural enemy biomass were not significant. The relationship between adjacent trophic levels was significant (such as pests and their natural enemies or crops and pests), whereas non-adjacent trophic levels (crops and natural enemies) did not significantly interact with each other. The ratio of natural enemy/pest biomass was the highest in the areas of four crop species that had the best biological control service. Having either low or high crop species richness did not enhance the pest population management service and lead to loss of biological control. Although the resource concentration hypothesis was not well supported by our results, high crop species richness could suppress the pest population, indicating that crop species richness could enhance biological control services. These results could be applied in habitat management aimed at biological control, provide the theoretical basis for agricultural landscape design, and also suggest new methods for integrated pest management.

Current warming will reduce yields unless maize breeding and seed systems adapt immediately

NASA Astrophysics Data System (ADS)

Challinor, A. J.; Koehler, A.-K.; Ramirez-Villegas, J.; Whitfield, S.; Das, B.

2016-10-01

The development of crop varieties that are better suited to new climatic conditions is vital for future food production. Increases in mean temperature accelerate crop development, resulting in shorter crop durations and reduced time to accumulate biomass and yield. The process of breeding, delivery and adoption (BDA) of new maize varieties can take up to 30 years. Here, we assess for the first time the implications of warming during the BDA process by using five bias-corrected global climate models and four representative concentration pathways with realistic scenarios of maize BDA times in Africa. The results show that the projected difference in temperature between the start and end of the maize BDA cycle results in shorter crop durations that are outside current variability. Both adaptation and mitigation can reduce duration loss. In particular, climate projections have the potential to provide target elevated temperatures for breeding. Whilst options for reducing BDA time are highly context dependent, common threads include improved recording and sharing of data across regions for the whole BDA cycle, streamlining of regulation, and capacity building. Finally, we show that the results have implications for maize across the tropics, where similar shortening of duration is projected.

The Crop Risk Zones Monitoring System for resilience to drought in the Sahel

NASA Astrophysics Data System (ADS)

Vignaroli, Patrizio; Rocchi, Leandro; De Filippis, Tiziana; Tarchiani, Vieri; Bacci, Maurizio; Toscano, Piero; Pasqui, Massimiliano; Rapisardi, Elena

2016-04-01

Food security is still one of the major concerns that Sahelian populations have to face. In the Sahel, agriculture is primarily based on rainfed crops and it is often structurally inadequate to manage the climatic variability. The predominantly rainfed cropping system of Sahel region is dependent on season quality on a year-to-year basis, and susceptible to weather extremes of droughts and extreme temperatures. Low water-storage capacity and high dependence on rainfed agriculture leave the agriculture sector even more vulnerable to climate risks. Crop yields may suffer significantly with either a late onset or early cessation of the rainy season, as well as with a high frequency of damaging dry spells. Early rains at the beginning of the season are frequently followed by dry spells which may last a week or longer. As the amount of water stored in the soil at this time of the year is negligible, early planted crops can suffer water shortage stresses during a prolonged dry spell. Therefore, the choice of the sowing date is of fundamental importance for farmers. The ability to estimate effectively the onset of the season and potentially dangerous dry spells becomes therefore vital for planning rainfed agriculture practices aiming to minimize risks and maximize yields. In this context, advices to farmers are key drivers for prevention allowing a better adaptation of traditional crop calendar to climatic variability. In the Sahel, particularly in CILSS (Permanent Interstates Committee for Drought Control in the Sahel) countries, national Early Warning System (EWS) for food security are underpinned by Multidisciplinary Working Groups (MWGs) lead by National Meteorological Services (NMS). The EWSs are mainly based on tools and models utilizing numeric forecasts and satellite data to outlook and monitor the growing season. This approach is focused on the early identification of risks and on the production of information within the prescribed time period for decision-making. Since the '90s, analysis tools and models based on meteorological satellites data have been developed within different regional and national initiatives to allow near-real-time monitoring of the cropping season. The software was in general stand-alone applications, transferred to MWGs without continuous user support and updates. Currently MWGs in the Sahel do not have any working operational tool for drought risk identification and forecast, because such tools are by now obsolete from the IT perspective. The challenge and the objective of this work is to provide to MWGs and local end-users an open access/source Crop Risk Zones Monitoring System (CRZ-MS) supporting decision making for drought risk reduction and resilience improvement. A first prototype has been developed for Niger and Mali NMSs, based on a coherent Open Source web-based infrastructure to treat all input and output data in a interoperable, platform-independent and uniform way. The System architecture and functions are based on a agro-meteorological model, running in two different modes: 1) diagnostic mode for the drought monitoring during the agro-pastoral campaign allowing MWGs to identify agricultural drought risk areas in order to support decision making at local and national level in agricultural drought management. This early warning information also represents an input for estimating the nutritional food insecurity, for the identification of potentially vulnerable populations and assessing food crises risks by National EWSs put in place by CILSS with EU, FAO and WFP. 2) predictive mode for "advisory-support" activities to the farmers by the Agricultural Extension Services, in order to implement the most appropriate strategies for minimizing drought risk on crops (i.e. identification of the optimal period of sowing, choice of varieties based on the expected length of the growing season, adoption of suitable cultural practices for soil water management) and to build farmers resilience. To increase the accessibility of appropriate and targeted drought risk information, it is essential to move from generic information to specific advises for end-users at different decision-making levels, bridging the gap between available technology and local users' needs. Thus, advices to farmers are a fundamental component of prevention allowing a better country's preparedness to cope with weather variability.

Altered cropping pattern and cultural continuation with declined prosperity following abrupt and extreme arid event at ~4,200 yrs BP: Evidence from an Indus archaeological site Khirsara, Gujarat, western India

PubMed Central

Pokharia, Anil K.; Sharma, Shalini; Bajpai, Sunil; Nath, Jitendra; Kumaran, R. N.; Negi, Bipin Chandra

2017-01-01

Archaeological sites hold important clues to complex climate-human relationships of the past. Human settlements in the peripheral zone of Indus culture (Gujarat, western India) are of considerable importance in the assessment of past monsoon-human-subsistence-culture relationships and their survival thresholds against climatic stress exerted by abrupt changes. During the mature phase of Harappan culture between ~4,600–3,900yrsBP, the ~4,100±100yrsBP time slice is widely recognized as one of the major, abrupt arid-events imprinted innumerous well-dated palaeo records. However, the veracity of this dry event has not been established from any archaeological site representing the Indus (Harappan) culture, and issues concerning timing, changes in subsistence pattern, and the likely causes of eventual abandonment (collapse) continue to be debated. Here we show a significant change in crop-pattern (from barley-wheat based agriculture to ‘drought-resistant’ millet-based crops) at ~4,200 yrs BP, based on abundant macrobotanical remains and C isotopes of soil organic matter (δ13CSOM) in an archaeological site at Khirsara, in the Gujarat state of western India. The crop-change appears to be intentional and was likely used as an adaptation measure in response to deteriorated monsoonal conditions. The ceramic and architectural remains of the site indicate that habitation survived and continued after the ~4,200yrsBP dry climatic phase, but with declined economic prosperity. Switching to millet-based crops initially helped inhabitants to avoid immediate collapse due to climatic stresses, but continued aridity and altered cropping pattern led to a decline in prosperity levels of inhabitants and eventual abandonment of the site at the end of the mature Harappan phase. PMID:28985232

Enhancing Soil Productivity Using a Multi-Crop Rotation and Beef Cattle Grazing

NASA Astrophysics Data System (ADS)

Şentürklü, Songül; Landblom, Douglas; Cihacek, Larry; Brevik, Eric

2016-04-01

Agricultural production systems that include complimentary plant, soil and animal interaction contribute to sustainability. In sustainable livestock systems integrated with crop production, the soil resource is impacted positively. The goal of this research was to maximize beef cattle and crop economic yield, while improving the soil resource by increasing soil organic matter (SOM) and subsequently seasonal soil nitrogen fertility over a 5-year period (2011-2015). Each experimental crop field used in the study was 1.74 ha. Small-seeded crops were planted using a JD 1590 No-Till drill. Corn (C) and sunflowers (SF) were planted using a JD 7000 No-Till planter. The cropping sequence used in the study was SF, hard red spring wheat (HRSW), fall seeded winter triticale-hairy vetch (T-HV), spring harvested for hay/mid-June seeded 7-species cover crop (CC; SF, Everleaf Oat, Flex Winter Pea, HV, Winfred Forage Rape, Ethiopian Cabbage, Hunter Leaf Turnip), C (85-day var.), and field pea-barley intercrop (PBY). The HRSW and SF were harvested as cash crops and the PBY, C, and CC were harvested by grazing cattle. In the system, yearling beef steers grazed PBY and unharvested C before feedlot entry, and after weaning, gestating cows grazed CC. Seasonal soil nitrogen fertility was measured at 0-15, 15-30, and 30-61 cm depths approximately every two weeks from June to October, 2014. The regression illustrating the relationship between SOM and average seasonal available mineral nitrogen shows that for each percentage increase in SOM there is a corresponding N increase of 1.47 kg/ha. Nitrogen fertilizer applications for the 5-year period of the study were variable; however, the overall trend was for reduced fertilizer requirement as SOM increased. At the same time, grain, oilseed, and annual forage crop yields increased year over year (2011-2015) except for the 2014 crop year, when above average precipitation delayed seeding and early frost killed the C and SF crops prematurely. Crop yields were as follows for the 5 crop years in the study (2011-2015): (1) CC was 0.25, 10.5, 8.03, 1.53, and 7.22t/ha, (2) C silage was 4.08, 9.04, 9.91, 8.65, and 14.4 t/ha, (3) C grain was 1.04, 3.81, 6.09, 3.11, and 5.1 t/ha, (4) SF was 1.10, 1.96, 2.42, 1.31, and 2.29 t/ha, (5) PBY forage was 0.0, 7.68, 11.2, 9.3, and 8.72 t/ha. When cattle grazed annual forage crops (C, PBY, and CC), animal manure and trampling contributed to the overall improvement of soil fertility. These data suggest that the combined effect of a multi-crop rotation that includes animal grazing enhances soil fertility and subsequently crop yields, and animal production for a sustainable integrated agricultural system.

Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

2014-12-01

The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are associated to derive food production estimates. Based on trends analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. CropWatch bulletin can be downloaded from the CropWatch website at http://www.cropwatch.com.cn.

Carbon dioxide and methane fluxes from legumes based rotations under conventional and organic practices

NASA Astrophysics Data System (ADS)

Sánchez-Navarro, Virginia; Zornoza, Raúl; Faz, Ángel; Fernández, Juan A.

2017-04-01

In this study we assessed the effect of two different rotations based on winter (faba bean) or summer (cowpea) legumes on the direct emissions of CO2 and CH4. Faba bean was rotated with the summer melon crop (Cucumis melo) while cowpea was rotated with the winter broccoli crop (Brassica oleracea). We also assessed if different legume cultivars and management practices (conventional and organic) significantly influenced gas emissions. The study was randomly designed in blocks with four replications, in plots of 10 m2, during two complete cycles. Gas samples were taken in different times (0, 30 and 60 minutes) once a week using the static gas chamber technique for each crop. Results showed that cumulative CO2 emissions in broccoli decreased after the rotation with both cowpea cultivars under conventional management practices. Faba bean cultivars and management practices had no influence on cumulative CO2 emissions in melon crop. Cumulative CH4 emissions in broccoli crop were lowest after the rotation with Grey-eyed pea than Black-eyed pea cultivar, under both management practices. However, faba bean cultivars and management practices had no influence on cumulative CH4 emissions in melon crop. Cumulative CH4 emissions in melon crop were highest than in the rest of crops. Cowpea cultivar and management practice influenced cumulative CH4 and CO2 emissions of broccoli crop, respectively. Faba bean cultivar and management practice had no effect on cumulative CH4 and CO2 emissions of melon crop. Acknowledgements: This research was financed by the FP7 European Project Eurolegume (FP7-KBBE-613781).

Effects of stored feed cropping systems and farm size on the profitability of Maine organic dairy farm simulations.

PubMed

Hoshide, A K; Halloran, J M; Kersbergen, R J; Griffin, T S; DeFauw, S L; LaGasse, B J; Jain, S

2011-11-01

United States organic dairy production has increased to meet the growing demand for organic milk. Despite higher prices received for milk, organic dairy farmers have come under increasing financial stress due to increases in concentrated feed prices over the past few years, which can make up one-third of variable costs. Market demand for milk has also leveled in the last year, resulting in some downward pressure on prices paid to dairy farmers. Organic dairy farmers in the Northeast United States have experimented with growing different forage and grain crops to maximize on-farm production of protein and energy to improve profitability. Three representative organic feed systems were simulated using the integrated farm system model for farms with 30, 120, and 220 milk cows. Increasing intensity of equipment use was represented by organic dairy farms growing only perennial sod (low) to those with corn-based forage systems, which purchase supplemental grain (medium) or which produce and feed soybeans (high). The relative profitability of these 3 organic feed systems was strongly dependent on dairy farm size. From results, we suggest smaller organic dairy farms can be more profitable with perennial sod-based rather than corn-based forage systems due to lower fixed costs from using only equipment associated with perennial forage harvest and storage. The largest farm size was more profitable using a corn-based system due to greater economies of scale for growing soybeans, corn grain, winter cereals, and corn silages. At an intermediate farm size of 120 cows, corn-based forage systems were more profitable if perennial sod was not harvested at optimum quality, corn was grown on better soils, or if milk yield was 10% higher. Delayed harvest decreased the protein and energy content of perennial sod crops, requiring more purchased grain to balance the ration and resulting in lower profits. Corn-based systems were less affected by lower perennial forage quality, as corn silage is part of the forage base. Growing on better soils increased corn yields more than perennial forage yields. Large corn-based organic dairy farms that produced and fed soybeans minimized off-farm grain purchases and were the most profitable among large farms. Although perennial sod-based systems purchased more grain, these organic systems were more profitable under timely forage harvest, decreased soil quality, and relatively lower purchased energy prices and higher protein supplement prices. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Effects of ecological and conventional agricultural intensification practices on maize yields in sub-Saharan Africa under potential climate change

NASA Astrophysics Data System (ADS)

Folberth, Christian; Yang, Hong; Gaiser, Thomas; Liu, Junguo; Wang, Xiuying; Williams, Jimmy; Schulin, Rainer

2014-04-01

Much of Africa is among the world’s regions with lowest yields in staple food crops, and climate change is expected to make it more difficult to catch up in crop production in particular in the long run. Various agronomic measures have been proposed for lifting agricultural production in Africa and to adapt it to climate change. Here, we present a projection of potential climate change impacts on maize yields under different intensification options in Sub-Saharan Africa (SSA) using an agronomic model, GIS-based EPIC (GEPIC). Fallow and nutrient management options taken into account are (a) conventional intensification with high mineral N supply and a bare fallow, (b) moderate mineral N supply and cowpea rotation, and (c) moderate mineral N supply and rotation with a fast growing N fixing tree Sesbania sesban. The simulations suggest that until the 2040s rotation with Sesbania will lead to an increase in yields due to increasing N supply besides improving water infiltration and soils’ water holding capacity. Intensive cultivation with a bare fallow or an herbaceous crop like cowpea in the rotation is predicted to result in lower yields and increased soil erosion during the same time span. However, yields are projected to decrease in all management scenarios towards the end of the century, should temperature increase beyond critical thresholds. The results suggest that the effect of eco-intensification as a sole means of adapting agriculture to climate change is limited in Sub-Saharan Africa. Highly adverse temperatures would rather have to be faced by improved heat tolerant cultivars, while strongly adverse decreases in precipitation would have to be faced by expanding irrigation where feasible. While the evaluation of changes in agro-environmental variables like soil organic carbon, erosion, and soil humidity hints that these are major factors influencing climate change resilience of the field crop, no direct relationship between these factors, crop yields, and changes in climate variables could be identified. This will need further detailed studies at the field and regional scale.

Water savings from reduced alfalfa cropping in California's Upper San Joaquin Valley

NASA Astrophysics Data System (ADS)

Singh, K. K.; Gray, J.

2017-12-01

Water and food and forage security are inextricably linked. In fact, 90% of global freshwater is consumed for food production. Food demand increases as populations grow and diets change, making water increasingly scarce. This tension is particularly acute, contentious, and popularly appreciated in California's Central Valley, which is one of the most important non-grain cropping areas in the United States. While the water-intensive production of tree nuts like almonds and pistachios has received the most popular attention, it is California's nation-leading alfalfa production that consumes the most water. Alfalfa, the "Queen of Forages" is the preferred feedstock for California's prodigious dairy industry. It is grown year-round, and single fields can be harvested more than four times a year; a practice which can require in excess of 1.5 m of irrigation water. Given the water scarcity in the region, the production of alfalfa is under increasing scrutiny with respect to long-term sustainability. However, the potential water savings associated with alternative crops, and various levels of alfalfa replacement have not been quantified. Here, we address that knowledge gap by simulating the ecohydrology of the Upper San Joaquin's cropping system under various scenarios of alfalfa crop replacement with crops of comparable economic value. Specifically, we use the SWAT model to evaluate the water savings that would be realized at 33%, 66%, and 100% alfalfa replacement with economically comparable, but more water efficient crops such as tomatoes. Our results provide an important quantification of the potential water savings under alternative cropping systems that, importantly, also addresses the economic concerns of farmers. Results like these provide critical guidance to farmers and land/water decision makers as they plan for a more sustainable and productive agricultural future.

Estimating the Impact and Spillover Effect of Climate Change on Crop Yield in Northern Ghana.

NASA Astrophysics Data System (ADS)

Botchway, E.

2016-12-01

In tropical regions of the world human-induced climate change is likely to impact negatively on crop yields. To investigate the impact of climate change and its spillover effect on mean and variance of crop yields in northern Ghana, the Just and Pope stochastic production function and the Spatial Durbin model were adopted. Surprisingly, the results suggest that both precipitation and average temperature have positive effects on mean crop yield during the wet season. Wet season average temperature has a significant spillover effect in the region, whereas precipitation during the wet season has only one significant spillover effect on maize yield. Wet season precipitation does not have a strong significant effect on crop yield despite the rainfed nature of agriculture in the region. Thus, even if there are losers and winners as a result of future climate change at the regional level, future crop yield would largely depend on future technological development in agriculture, which may improve yields over time despite the changing climate. We argue, therefore, that technical improvement in farm management such as improved seeds and fertilizers, conservation tillage and better pest control, may have a more significant role in increasing observed crop productivity levels over time. So investigating the relative importance of non-climatic factors on crop yield may shed more light on where appropriate interventions can help in improving crop yields. Climate change, also, needs to be urgently assessed at the level of the household, so that poor and vulnerable people dependent on agriculture can be appropriately targeted in research and development activities whose object is poverty alleviation.

A decision support system (GesCoN) for managing fertigation in open field vegetable crops. Part I-methodological approach and description of the software.

PubMed

Elia, Antonio; Conversa, Giulia

2015-01-01

Reduced water availability and environmental pollution caused by nitrogen (N) losses have increased the need for rational management of irrigation and N fertilization in horticultural systems. Decision support systems (DSS) could be powerful tools to assist farmers to improve irrigation and N fertilization efficiency. Currently, fertilization by drip irrigation system (fertigation) is used for many vegetable crops around the world. The paper illustrates the theoretical basis, the methodological approach and the structure of a DSS called GesCoN for fertigation management in open field vegetable crops. The DSS is based on daily water and N balance, considering the water lost by evapotranspiration (ET) and the N content in the aerial part of the crop (N uptake) as subtraction and the availability of water and N in the wet soil volume most effected by roots as the positive part. For the water balance, reference ET can be estimated using the Penman-Monteith (PM) or the Priestley-Taylor and Hargreaves models, specifically calibrated under local conditions. Both single or dual Kc approach can be used to calculate crop ET. Rain runoff and deep percolation are considered to calculate the effective rainfall. The soil volume most affected by the roots, the wet soil under emitters and their interactions are modeled. Crop growth is modeled by a non-linear logistic function on the basis of thermal time, but the model takes into account thermal and water stresses and allows an in-season calibration through a dynamic adaptation of the growth rate to the specific genetic and environmental conditions. N crop demand is related to DM accumulation by the N critical curve. N mineralization from soil organic matter is daily estimated. The DSS helps users to evaluate the daily amount of water and N fertilizer that has to be applied in order to fulfill the water and N-crop requirements to achieve the maximum potential yield, while reducing the risk of nitrate outflows.

Effects of adjusting cropping systems on utilization efficiency of climatic resources in Northeast China under future climate scenarios

NASA Astrophysics Data System (ADS)

Guo, Jianping; Zhao, Junfang; Xu, Yanhong; Chu, Zheng; Mu, Jia; Zhao, Qian

Quantitatively evaluating the effects of adjusting cropping systems on the utilization efficiency of climatic resources under climate change is an important task for assessing food security in China. To understand these effects, we used daily climate variables obtained from the regional climate model RegCM3 from 1981 to 2100 under the A1B scenario and crop observations from 53 agro-meteorological experimental stations from 1981 to 2010 in Northeast China. Three one-grade zones of cropping systems were divided by heat, water, topography and crop-type, including the semi-arid areas of the northeast and northwest (III), the one crop area of warm-cool plants in semi-humid plain or hilly regions of the northeast (IV), and the two crop area in irrigated farmland in the Huanghuaihai Plain (VI). An agro-ecological zone model was used to calculate climatic potential productivities. The effects of adjusting cropping systems on climate resource utilization in Northeast China under the A1B scenario were assessed. The results indicated that from 1981 to 2100 in the III, IV and VI areas, the planting boundaries of different cropping systems in Northeast China obviously shifted toward the north and the east based on comprehensively considering the heat and precipitation resources. However, due to high temperature stress, the climatic potential productivity of spring maize was reduced in the future. Therefore, adjusting the cropping system is an effective way to improve the climatic potential productivity and climate resource utilization. Replacing the one crop in one year model (spring maize) by the two crops in one year model (winter wheat and summer maize) significantly increased the total climatic potential productivity and average utilization efficiencies. During the periods of 2011-2040, 2041-2070 and 2071-2100, the average total climatic potential productivities of winter wheat and summer maize increased by 9.36%, 11.88% and 12.13% compared to that of spring maize, respectively. Additionally, compared with spring maize, the average utilization efficiencies of thermal resources of winter wheat and summer maize dramatically increased by 9.2%, 12.1% and 12.0%, respectively. The increases in the average utilization efficiencies of precipitation resources of winter wheat and summer maize were 1.78 kg hm-2 mm-1, 2.07 kg hm-2 mm-1 and 1.92 kg hm-2 mm-1 during 2011-2040, 2041-2070 and 2071-2100, respectively. Our findings highlight that adjusting cropping systems can dominantly contribute to utilization efficiency increases of agricultural climatic resources in Northeast China in the future.

Evaluating the synchronicity in yield variations of staple crops at global scale

NASA Astrophysics Data System (ADS)

Yokozawa, M.

2014-12-01

Reflecting the recent globalization trend in world commodity market, several major production countries are producing large amount of staple crops, especially, maize and soybean. Thus, simultaneous crop failure (abrupt reduction in crop yield, lean year) due to extreme weather and/or climate change could lead to unstable food supply. This study try to examine the synchronicity in yield variations of staple crops at global scale. We use a gridded crop yields database, which includes the historical year-to-year changes in staple crop yields with a spatial resolution of 1.125 degree in latitude/longitude during a period of 1982-2006 (Iizumi et al. 2013). It has been constructed based on the agriculture statistics issued by local administrative bureaus in each country. For the regions being lack of data, an interpolation was conducted to obtain the values referring to the NPP estimates from satellite data as well as FAO country yield. For each time series of the target crop yield, we firstly applied a local kernel regression to represent the long-term trend component. Next, the deviations of yearly yield from the long-term trend component were defined as ΔY(i, y) in year y at grid i. Then, the correlation of deviation between grids i and j in year y is defined as Cij(y) = ΔY(i, y) ΔY(j, y). In addition, Pij = <ΔY(i, y) ΔY(j, y)> represents the time-averaged correlation of deviation between grids i and j. Bracket <...> means the time average operation over 25 years (1982-2006). As the results, figures show the time changes in the number of grid pairs, in which both the deviation are negative. It represent the time changes in ratio of the grid pairs where both crop yields synchronically decreased to the total grid pairs. The years denoted by arrows in the figures indicate the case that all the ratios of three country pairs (i.e. China-USA, USA-Brazil and Brazil-China) are relatively larger (>0.6 for soybean and >0.5 for maize). This suggests that the reductions in crop yield occurred synchronically in three countries in these years, which are the simultaneous lean years (as of lower yield compared to that of long-term trend).

Bats and birds increase crop yield in tropical agroforestry landscapes.

PubMed

Maas, Bea; Clough, Yann; Tscharntke, Teja

2013-12-01

Human welfare is significantly linked to ecosystem services such as the suppression of pest insects by birds and bats. However, effects of biocontrol services on tropical cash crop yield are still largely unknown. For the first time, we manipulated the access of birds and bats in an exclosure experiment (day, night and full exclosures compared to open controls in Indonesian cacao agroforestry) and quantified the arthropod communities, the fruit development and the final yield over a long time period (15 months). We found that bat and bird exclusion increased insect herbivore abundance, despite the concurrent release of mesopredators such as ants and spiders, and negatively affected fruit development, with final crop yield decreasing by 31% across local (shade cover) and landscape (distance to primary forest) gradients. Our results highlight the tremendous economic impact of common insectivorous birds and bats, which need to become an essential part of sustainable landscape management. © 2013 John Wiley & Sons Ltd/CNRS.

[Composition and stability of soil aggregates in hedgerow-crop slope land].

PubMed

Pu, Yu-Lin; Lin, Chao-Wen; Xie, De-Ti; Wei, Chao-Fu; Ni, Jiu-Pai

2013-01-01

Based on a long-term experiment of using hedgerow to control soil and water loss, this paper studied the composition and stability of soil aggregates in a hedgerow-crop slope land. Compared with those under routine contour cropping, the contents of > 0.25 mm soil mechanical-stable and water-stable aggregates under the complex mode hedgerow-crop increased significantly by 13.3%-16.1% and 37.8% -55.6%, respectively. Under the complex mode, the contents of > 0.25 mm soil water-stable aggregates on each slope position increased obviously, and the status of > 0.25 mm soil water-stable aggregates being relatively rich at low slope and poor at top slope was improved. Planting hedgerow could significantly increase the mean mass diameter and geometric mean diameter of soil aggregates, decrease the fractal dimension of soil aggregates and the destruction rate of > 0.25 mm soil aggregates, and thus, increase the stability and erosion-resistance of soil aggregates in slope cropland. No significant effects of slope and hedgerow types were observed on the composition, stability and distribution of soil aggregates.

Model-based coefficient method for calculation of N leaching from agricultural fields applied to small catchments and the effects of leaching reducing measures

NASA Astrophysics Data System (ADS)

Kyllmar, K.; Mårtensson, K.; Johnsson, H.

2005-03-01

A method to calculate N leaching from arable fields using model-calculated N leaching coefficients (NLCs) was developed. Using the process-based modelling system SOILNDB, leaching of N was simulated for four leaching regions in southern Sweden with 20-year climate series and a large number of randomised crop sequences based on regional agricultural statistics. To obtain N leaching coefficients, mean values of annual N leaching were calculated for each combination of main crop, following crop and fertilisation regime for each leaching region and soil type. The field-NLC method developed could be useful for following up water quality goals in e.g. small monitoring catchments, since it allows normal leaching from actual crop rotations and fertilisation to be determined regardless of the weather. The method was tested using field data from nine small intensively monitored agricultural catchments. The agreement between calculated field N leaching and measured N transport in catchment stream outlets, 19-47 and 8-38 kg ha -1 yr -1, respectively, was satisfactory in most catchments when contributions from land uses other than arable land and uncertainties in groundwater flows were considered. The possibility of calculating effects of crop combinations (crop and following crop) is of considerable value since changes in crop rotation constitute a large potential for reducing N leaching. When the effect of a number of potential measures to reduce N leaching (i.e. applying manure in spring instead of autumn; postponing ploughing-in of ley and green fallow in autumn; undersowing a catch crop in cereals and oilseeds; and increasing the area of catch crops by substituting winter cereals and winter oilseeds with corresponding spring crops) was calculated for the arable fields in the catchments using field-NLCs, N leaching was reduced by between 34 and 54% for the separate catchments when the best possible effect on the entire potential area was assumed.

Analysis of Numerical Weather Predictions of Reference Evapotranspiration and Precipitation

NASA Astrophysics Data System (ADS)

Bughici, Theodor; Lazarovitch, Naftali; Fredj, Erick; Tas, Eran

2017-04-01

This study attempts to improve the forecast skill of the evapotranspiration (ET0) and Precipitation for the purpose of crop irrigation management over Israel using the Weather Research and Forecasting (WRF) Model. Optimized crop irrigation, in term of timing and quantities, decreases water and agrochemicals demand. Crop water demands depend on evapotranspiration and precipitation. The common method for computing reference evapotranspiration, for agricultural needs, ET0, is according to the FAO Penman-Monteith equation. The weather variables required for ET0 calculation (air temperature, relative humidity, wind speed and solar irradiance) are estimated by the WRF model. The WRF Model with two-way interacting domains at horizontal resolutions of 27, 9 and 3 km is used in the study. The model prediction was performed in an hourly time resolution and a 3 km spatial resolution, with forecast lead-time of up to four days. The WRF prediction of these variables have been compared against measurements from 29 meteorological stations across Israel for the year 2013. The studied area is small but with strong climatic gradient, diverse topography and variety of synoptic conditions. The forecast skill that was used for forecast validation takes into account the prediction bias, mean absolute error and root mean squared error. The forecast skill of the variables was almost robust to lead time, except for precipitation. The forecast skill was tested across stations with respect to topography and geographic location and for all stations with respect to seasonality and synoptic weather system determined by employing a semi-objective synoptic systems classification to the forecasted days. It was noticeable that forecast skill of some of the variables was deteriorated by seasonality and topography. However, larger impacts in the ET0 skill scores on the forecasted day are achieved by a synoptic based forecast. These results set the basis for increasing the robustness of ET0 to synoptic effects and for more precise crop irrigation over Israel.

African crop yield reductions due to increasingly unbalanced Nitrogen and Phosphorus consumption

NASA Astrophysics Data System (ADS)

van der Velde, Marijn; Folberth, Christian; Balkovič, Juraj; Ciais, Philippe; Fritz, Steffen; Janssens, Ivan A.; Obersteiner, Michael; See, Linda; Skalský, Rastislav; Xiong, Wei; Peñuealas, Josep

2014-05-01

The impact of soil nutrient depletion on crop production has been known for decades, but robust assessments of the impact of increasingly unbalanced nitrogen (N) and phosphorus (P) application rates on crop production are lacking. Here, we use crop response functions based on 741 FAO maize crop trials and EPIC crop modeling across Africa to examine maize yield deficits resulting from unbalanced N:P applications under low, medium, and high input scenarios, for past (1975), current, and future N:P mass ratios of respectively, 1:0.29, 1:0.15, and 1:0.05. At low N inputs (10 kg/ha), current yield deficits amount to 10% but will increase up to 27% under the assumed future N:P ratio, while at medium N inputs (50 kg N/ha), future yield losses could amount to over 40%. The EPIC crop model was then used to simulate maize yields across Africa. The model results showed relative median future yield reductions at low N inputs of 40%, and 50% at medium and high inputs, albeit with large spatial variability. Dominant low-quality soils such as Ferralsols, which are strongly adsorbing P, and Arenosols with a low nutrient retention capacity, are associated with a strong yield decline, although Arenosols show very variable crop yield losses at low inputs. Optimal N:P ratios, i.e. those where the lowest amount of applied P produces the highest yield (given N input) where calculated with EPIC to be as low as 1:0.5. Finally, we estimated the additional P required given current N inputs, and given N inputs that would allow Africa to close yield gaps (ca. 70%). At current N inputs, P consumption would have to increase 2.3-fold to be optimal, and to increase 11.7-fold to close yield gaps. The P demand to overcome these yield deficits would provide a significant additional pressure on current global extraction of P resources.

Mining Halophytes for Plant Growth-Promoting Halotolerant Bacteria to Enhance the Salinity Tolerance of Non-halophytic Crops

PubMed Central

Etesami, Hassan; Beattie, Gwyn A.

2018-01-01

Salinity stress is one of the major abiotic stresses limiting crop production in arid and semi-arid regions. Interest is increasing in the application of PGPRs (plant growth promoting rhizobacteria) to ameliorate stresses such as salinity stress in crop production. The identification of salt-tolerant, or halophilic, PGPRs has the potential to promote saline soil-based agriculture. Halophytes are a useful reservoir of halotolerant bacteria with plant growth-promoting capabilities. Here, we review recent studies on the use of halophilic PGPRs to stimulate plant growth and increase the tolerance of non-halophytic crops to salinity. These studies illustrate that halophilic PGPRs from the rhizosphere of halophytic species can be effective bio-inoculants for promoting the production of non-halophytic species in saline soils. These studies support the viability of bioinoculation with halophilic PGPRs as a strategy for the sustainable enhancement of non-halophytic crop growth. The potential of this strategy is discussed within the context of ensuring sustainable food production for a world with an increasing population and continuing climate change. We also explore future research needs for using halotolerant PGPRs under salinity stress. PMID:29472908

Possible pathways and tensions in the food and water nexus

NASA Astrophysics Data System (ADS)

Grafton, R. Quentin; Williams, John; Jiang, Qiang

2017-05-01

"Bottom-up" field-based, crop-hydrological models are used to estimate food production and irrigation water extractions under multiple scenarios of water and nitrogen use and crop yield increases from 2010 to 2050 for 19 countries. The results show: (1) a food deficit before 2050 under a worst case climate change scenario in terms of annual crop yield improvement; (2) substantial water deficits, as a result of irrigation, for major food-producing countries that will prevent these nations from meeting their domestic food requirements in the absence of investments in water infrastructure or food imports; and (3) a plateau in terms of crop food production associated with increased water extractions given no further increase in the current area of irrigated agriculture. Possible pathways to respond to the tensions in the food-water nexus are evaluated and include: (1) higher water productivity; (2) food trade; (3) improvements in both crop yield and "sustainable" total factor productivity; (4) greater investment in water infrastructure; and (5) integrative policies and decision processes. Without a combination of some, or all, of these possible pathways, appropriately adapted to bio-physical and socio-economic circumstances, the world faces grave risks in food and water security out to 2050.

Farmer response to climatic and agricultural market drivers: characteristic time scales and sensitivities

NASA Astrophysics Data System (ADS)

Wurster, P. M.; Maneta, M. P.; Vicente-Serrano, S. M.; Beguería, S.; Silverman, N. L.; Holden, Z.

2017-12-01

Agriculture in the intermountain western United States is dominated by extensive farming and ranching, mostly reliant on rainfed crops and therefore very exposed to precipitation shortfalls. It is also poorly diversified, dominated by five or six major grain crops, which makes it vulnerable to changes in agricultural markets. The economy of the region is very reliant on this type of agriculture, making the entire economy vulnerable to climatic and market fluctuations. Western agriculture is also of significant importance for national food security. Resource managers in the region are increasingly concerned with the impacts that more frequent and severe droughts, or the collapse of crop prices, may have on producers and food production. Effective resource management requires an understanding not only of the regional impact of adverse climatic and market events, but also of which geographic areas are most vulnerable, and why. Unfortunately, few studies exist that look into how farmers in different geographic areas respond to climate and market drivers. In this study we analyze the influence of precipitation and crop price anomalies on crop production, and map the characteristic time scale of these anomalies that correlate best with production anomalies for the 56 counties of Montana, U.S.A. We conduct this analysis using the standardized precipitation index (SPI), and defining a standardized crop value index (SCVI) and a standardized crop production index (SCPI). We use 38 years of data to calculate precipitation anomalies at monthly time scales and annual data to calculate crop price and production anomalies. The standardization of the indices allows for straightforward comparison of the relative influence of climatic and market fluctuations on production anomalies. We apply our methodology to winter wheat, spring durum wheat, barley, alfalfa, and beets which are the most valuable crops produced in the state. Results from this study show that precipitation anomalies accumulated between 3 and 8 months in spring are most explanatory of production anomalies, but that crop price anomalies interact with climatic factors. This study will provide agricultural producers and water managers with valuable information regarding the resiliency of key crops in the state and of Montana's rural economy.

Modeling Pollinator Community Response to Contrasting Bioenergy Scenarios

PubMed Central

Bennett, Ashley B.; Meehan, Timothy D.; Gratton, Claudio; Isaacs, Rufus

2014-01-01

In the United States, policy initiatives aimed at increasing sources of renewable energy are advancing bioenergy production, especially in the Midwest region, where agricultural landscapes dominate. While policy directives are focused on renewable fuel production, biodiversity and ecosystem services will be impacted by the land-use changes required to meet production targets. Using data from field observations, we developed empirical models for predicting abundance, diversity, and community composition of flower-visiting bees based on land cover. We used these models to explore how bees might respond under two contrasting bioenergy scenarios: annual bioenergy crop production and perennial grassland bioenergy production. In the two scenarios, 600,000 ha of marginal annual crop land or marginal grassland were converted to perennial grassland or annual row crop bioenergy production, respectively. Model projections indicate that expansion of annual bioenergy crop production at this scale will reduce bee abundance by 0 to 71%, and bee diversity by 0 to 28%, depending on location. In contrast, converting annual crops on marginal soil to perennial grasslands could increase bee abundance from 0 to 600% and increase bee diversity between 0 and 53%. Our analysis of bee community composition suggested a similar pattern, with bee communities becoming less diverse under annual bioenergy crop production, whereas bee composition transitioned towards a more diverse community dominated by wild bees under perennial bioenergy crop production. Models, like those employed here, suggest that bioenergy policies have important consequences for pollinator conservation. PMID:25365559

Remediation of degraded arable steppe soils in Moldova using vetch as green manure

NASA Astrophysics Data System (ADS)

Wiesmeier, M.; Lungu, M.; Hübner, R.; Cerbari, V.

2015-01-01

In the Republic of Moldova, non-sustainable arable farming led to severe degradation and erosion of fertile steppe soils (Chernozems). As a result, the Chernozems lost about 40% of their initial amounts of soil organic carbon (SOC). Aim of this study was to remediate degraded arable soils and promote carbon sequestration by implementation of cover cropping and green manuring in Moldova. Thereby, the suitability of the legume hairy vetch (Vicia sativa) as cover crop under the dry, continental climate of Moldova was examined. At two experimental sites, the effect of cover cropping on chemical and physical soil properties as well as on yields of subsequent main crops was determined. The results showed a significant increase of SOC after incorporation of hairy vetch due to a high above- and belowground biomass production that was related with a high input of carbon and nitrogen. A calculation of SOC stocks based on equivalent soil masses revealed a sequestration of around 3 t C ha-1 yr-1 as a result of hairy vetch cover cropping. The buildup of SOC was associated with an improvement of the soil structure as indicated by a distinct decrease of bulk density and a relative increase of macroaggregates at the expense of microaggregates and clods. As a result, yields of subsequent main crops increased by around 20%. Our results indicated that hairy vetch is a promising cover crop to remediate degraded steppe soils, control soil erosion and sequestrate substantial amounts of atmospheric C in arable soils of Moldova.