Distribution Development for STORM Ingestion Input Parameters

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

Fulton, John

The Sandia-developed Transport of Radioactive Materials (STORM) code suite is used as part of the Radioisotope Power System Launch Safety (RPSLS) program to perform statistical modeling of the consequences due to release of radioactive material given a launch accident. As part of this modeling, STORM samples input parameters from probability distributions with some parameters treated as constants. This report described the work done to convert four of these constant inputs (Consumption Rate, Average Crop Yield, Cropland to Landuse Database Ratio, and Crop Uptake Factor) to sampled values. Consumption rate changed from a constant value of 557.68 kg / yr tomore » a normal distribution with a mean of 102.96 kg / yr and a standard deviation of 2.65 kg / yr. Meanwhile, Average Crop Yield changed from a constant value of 3.783 kg edible / m 2 to a normal distribution with a mean of 3.23 kg edible / m 2 and a standard deviation of 0.442 kg edible / m 2 . The Cropland to Landuse Database ratio changed from a constant value of 0.0996 (9.96%) to a normal distribution with a mean value of 0.0312 (3.12%) and a standard deviation of 0.00292 (0.29%). Finally the crop uptake factor changed from a constant value of 6.37e -4 (Bq crop /kg)/(Bq soil /kg) to a lognormal distribution with a geometric mean value of 3.38e -4 (Bq crop /kg)/(Bq soil /kg) and a standard deviation value of 3.33 (Bq crop /kg)/(Bq soil /kg)« less

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys

PubMed Central

Yao, Rongjiang; Yang, Jingsong; Wu, Danhua; Xie, Wenping; Gao, Peng; Jin, Wenhui

2016-01-01

Reliable and real-time information on soil and crop properties is important for the development of management practices in accordance with the requirements of a specific soil and crop within individual field units. This is particularly the case in salt-affected agricultural landscape where managing the spatial variability of soil salinity is essential to minimize salinization and maximize crop output. The primary objectives were to use linear mixed-effects model for soil salinity and crop yield calibration with horizontal and vertical electromagnetic induction (EMI) measurements as ancillary data, to characterize the spatial distribution of soil salinity and crop yield and to verify the accuracy of spatial estimation. Horizontal and vertical EMI (type EM38) measurements at 252 locations were made during each survey, and root zone soil samples and crop samples at 64 sampling sites were collected. This work was periodically conducted on eight dates from June 2012 to May 2013 in a coastal salt-affected mud farmland. Multiple linear regression (MLR) and restricted maximum likelihood (REML) were applied to calibrate root zone soil salinity (ECe) and crop annual output (CAO) using ancillary data, and spatial distribution of soil ECe and CAO was generated using digital soil mapping (DSM) and the precision of spatial estimation was examined using the collected meteorological and groundwater data. Results indicated that a reduced model with EMh as a predictor was satisfactory for root zone ECe calibration, whereas a full model with both EMh and EMv as predictors met the requirement of CAO calibration. The obtained distribution maps of ECe showed consistency with those of EMI measurements at the corresponding time, and the spatial distribution of CAO generated from ancillary data showed agreement with that derived from raw crop data. Statistics of jackknifing procedure confirmed that the spatial estimation of ECe and CAO exhibited reliability and high accuracy. A general increasing trend of ECe was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone ECe coincided with those of daily rainfall, water table and groundwater data. Long-range EMI surveys and data collection are needed to capture the spatial and temporal variability of soil and crop parameters. Such results allowed us to conclude that, cost-effective and efficient EMI surveys, as one part of multi-source data for DSM, could be successfully used to characterize the spatial variability of soil salinity, to monitor the spatial and temporal dynamics of soil salinity, and to spatially estimate potential crop yield. PMID:27203697

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys.

PubMed

Yao, Rongjiang; Yang, Jingsong; Wu, Danhua; Xie, Wenping; Gao, Peng; Jin, Wenhui

2016-01-01

Reliable and real-time information on soil and crop properties is important for the development of management practices in accordance with the requirements of a specific soil and crop within individual field units. This is particularly the case in salt-affected agricultural landscape where managing the spatial variability of soil salinity is essential to minimize salinization and maximize crop output. The primary objectives were to use linear mixed-effects model for soil salinity and crop yield calibration with horizontal and vertical electromagnetic induction (EMI) measurements as ancillary data, to characterize the spatial distribution of soil salinity and crop yield and to verify the accuracy of spatial estimation. Horizontal and vertical EMI (type EM38) measurements at 252 locations were made during each survey, and root zone soil samples and crop samples at 64 sampling sites were collected. This work was periodically conducted on eight dates from June 2012 to May 2013 in a coastal salt-affected mud farmland. Multiple linear regression (MLR) and restricted maximum likelihood (REML) were applied to calibrate root zone soil salinity (ECe) and crop annual output (CAO) using ancillary data, and spatial distribution of soil ECe and CAO was generated using digital soil mapping (DSM) and the precision of spatial estimation was examined using the collected meteorological and groundwater data. Results indicated that a reduced model with EMh as a predictor was satisfactory for root zone ECe calibration, whereas a full model with both EMh and EMv as predictors met the requirement of CAO calibration. The obtained distribution maps of ECe showed consistency with those of EMI measurements at the corresponding time, and the spatial distribution of CAO generated from ancillary data showed agreement with that derived from raw crop data. Statistics of jackknifing procedure confirmed that the spatial estimation of ECe and CAO exhibited reliability and high accuracy. A general increasing trend of ECe was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone ECe coincided with those of daily rainfall, water table and groundwater data. Long-range EMI surveys and data collection are needed to capture the spatial and temporal variability of soil and crop parameters. Such results allowed us to conclude that, cost-effective and efficient EMI surveys, as one part of multi-source data for DSM, could be successfully used to characterize the spatial variability of soil salinity, to monitor the spatial and temporal dynamics of soil salinity, and to spatially estimate potential crop yield.

Optimizing cropland cover for stable food production in Sub-Saharan Africa using simulated yield and Modern Portfolio Theory

NASA Astrophysics Data System (ADS)

Bodin, P.; Olin, S.; Pugh, T. A. M.; Arneth, A.

2014-12-01

Food security can be defined as stable access to food of good nutritional quality. In Sub Saharan Africa access to food is strongly linked to local food production and the capacity to generate enough calories to sustain the local population. Therefore it is important in these regions to generate not only sufficiently high yields but also to reduce interannual variability in food production. Traditionally, climate impact simulation studies have focused on factors that underlie maximum productivity ignoring the variability in yield. By using Modern Portfolio Theory, a method stemming from economics, we here calculate optimum current and future crop selection that maintain current yield while minimizing variance, vs. maintaining variance while maximizing yield. Based on simulated yield using the LPJ-GUESS dynamic vegetation model, the results show that current cropland distribution for many crops is close to these optimum distributions. Even so, the optimizations displayed substantial potential to either increase food production and/or to decrease its variance regionally. Our approach can also be seen as a method to create future scenarios for the sown areas of crops in regions where local food production is important for food security.

Water savings of redistributing global crop production

NASA Astrophysics Data System (ADS)

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

2016-04-01

Human demand for crop production is expected to increase substantially in the coming decades as a result of population growth, richer diets and biofuel use. For food production to keep pace, unprecedented amounts of resources - water, fertilizers, energy - will be required. This has led to calls for 'sustainable intensification' in which yields are increased on existing croplands while seeking to minimize impacts on water and other agricultural resources. Recent studies have quantified aspects of this, showing that there is a large potential to improve crop yields and increase harvest frequencies to better meet human demand. Though promising, both solutions would necessitate large additional inputs of water and fertilizer in order to be achieved under current technologies. However, the question of whether the current distribution of crops is, in fact, the best for realizing maximized production has not been considered to date. To this end, we ask: Is it possible to minimize water demand by simply growing crops where soil and climate conditions are best suited? Here we use maps of agro-ecological suitability - a measure of physical and chemical soil fertility - for 15 major food crops to identify differences between current crop distributions and where they can most suitably be planted. By redistributing crops across currently cultivated lands, we determine what distribution of crops would maintain current calorie production and agricultural value while minimizing the water demand of crop production. In doing this, our study provides a novel tool for policy makers and managers to integrate food security, environmental sustainability, and rural livelihoods by improving the use of freshwater resources without compromising crop calorie production or rural livelihoods.

Calorie increase and water savings of redistributing global crop production

NASA Astrophysics Data System (ADS)

Davis, K. F.; Seveso, A.; Rulli, M. C.; D'Odorico, P.

2015-12-01

Human demand for crop production is expected to increase substantially in the coming decades as a result of population growth, richer diets and biofuel use. In order for food production to keep pace, unprecedented amounts of resources - water, fertilizers, energy - will be required. This has led to calls for 'sustainable intensification' in which yields are increased on existing croplands while seeking to minimize impacts on water and other agricultural resources. Recent studies have quantified aspects of this, showing that there is a large potential to improve crop yields and increase harvest frequencies to better meet human demand. Though promising, both solutions would necessitate large additional inputs of water and fertilizer in order to be achieved under current technologies. However, the question of whether the current distribution of crops is, in fact, the best for realizing maximized production has not been considered to date. To this end, we ask: Is it possible to increase calorie production and minimize water demand by simply growing crops where soil and climate conditions are best suited? Here we use maps of agro-ecological suitability - a measure of physical and chemical soil fertility - for 15 major food crops to identify differences between current crop distributions and where they can most suitably be planted. By redistributing crops across currently cultivated lands, we determine the potential improvement in calorie production as well as the associated change in water demand. We also consider what distribution of crops would maintain current calorie production while minimizing crop water demand. In doing all of this, our study provides a novel tool for improving crop calorie production without necessarily increasing resource demands.

Tree growth and management in Ugandan agroforestry systems: effects of root pruning on tree growth and crop yield.

PubMed

Wajja-Musukwe, Tellie-Nelson; Wilson, Julia; Sprent, Janet I; Ong, Chin K; Deans, J Douglas; Okorio, John

2008-02-01

Tree root pruning is a potential tool for managing belowground competition when trees and crops are grown together in agroforestry systems. We investigated the effects of tree root pruning on shoot growth and root distribution of Alnus acuminata (H.B. & K.), Casuarina equisetifolia L., Grevillea robusta A. Cunn. ex R. Br., Maesopsis eminii Engl. and Markhamia lutea (Benth.) K. Schum. and on yield of adjacent crops in sub-humid Uganda. The trees were 3 years old at the commencement of the study, and most species were competing strongly with crops. Tree roots were pruned 41 months after planting by cutting and back-filling a trench to a depth of 0.3 m, at a distance of 0.3 m from the trees, on one side of the tree row. The trench was reopened and roots recut at 50 and 62 months after planting. We assessed the effects on tree growth and root distribution over a 3 year period, and crop yield after the third root pruning at 62 months. Overall, root pruning had only a slight effect on aboveground tree growth: height growth was unaffected and diameter growth was reduced by only 4%. A substantial amount of root regrowth was observed by 11 months after pruning. Tree species varied in the number and distribution of roots, and C. equisetifolia and M. lutea had considerably more roots per unit of trunk volume than the other species, especially in the surface soil layers. Casuarina equisetifolia and M. eminii were the tree species most competitive with crops and G. robusta and M. lutea the least competitive. Crop yield data provided strong evidence of the redistribution of root activity following root pruning, with competition increasing on the unpruned side of tree rows. Thus, one-sided root pruning will be useful in only a few circumstances.

Application of future remote sensing systems to irrigation

NASA Technical Reports Server (NTRS)

Miller, L. D.

1982-01-01

Area estimates of irrigated crops and knowledge of crop type are required for modeling water consumption to assist farmers, rangers, and agricultural consultants in scheduling irrigation for distributed management of crop yields. Information on canopy physiology and soil moisture status on a spatial basis is potentially available from remote sensors, so the questions to be addressed relate to: (1) timing (data frequency, instantaneous and integrated measurement); and scheduling (widely distributed spatial demands); (2) spatial resolution; (3) radiometric and geometric accuracy and geoencoding; and (4) information/data distribution. This latter should be overnight, with no central storage, onsite capture, and low cost.

Characterizing spatial and temporal variability of crop yield caused by climate and irrigation in the North China Plain

NASA Astrophysics Data System (ADS)

Chen, Chao; Baethgen, Walter E.; Wang, Enli; Yu, Qiang

2011-12-01

Grain yields of wheat and maize were obtained from national statistics and simulated with an agricultural system model to investigate the effects of historical climate variability and irrigation on crop yield in the North China Plain (NCP). Both observed and simulated yields showed large temporal and spatial variability due to variations in climate and irrigation supply. Wheat yield under full irrigation (FI) was 8 t ha-1 or higher in 80% of seasons in the north, it ranged from 7 to 10 t ha-1 in 90% of seasons in central NCP, and less than 9 t ha-1 in 85% of seasons in the south. Reduced irrigation resulted in increased crop yield variability. Wheat yield under supplemental irrigation, i.e., to meet only 50% of irrigation water requirement [supplemental irrigation (SI)] ranged from 2.7 to 8.8 t ha-1 with the maximum frequency of seasons having the range of 4-6 t ha-1 in the north, 4-7 t ha-1 in central NCP, and 5-8 t ha-1 in the south. Wheat yield under no irrigation (NI) was lower than 1 t ha-1 in about 50% of seasons. Considering the NCP as a whole, simulated maize yield under FI ranged from 3.9 to 11.8 t ha-1 with similar frequency distribution in the range of 6-11.8 t ha-1 with the interval of 2 t ha-1. It ranged from 0 to 11.8 t ha-1, uniformly distributed into the range of 4-10 t ha-1 under SI, and NI. The results give an insight into the levels of regional crop production affected by climate and water management strategies.

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.

Soybean yield in relation to distance from the Itaipu reservoir

NASA Astrophysics Data System (ADS)

de Faria, Rogério Teixeira; Junior, Ruy Casão; Werner, Simone Silmara; Junior, Luiz Antônio Zanão; Hoogenboom, Gerrit

2016-07-01

Crops close to small water bodies may exhibit changes in yield if the water mass causes significant changes in the microclimate of areas near the reservoir shoreline. The scientific literature describes this effect as occurring gradually, with higher intensity in the sites near the shoreline and decreasing intensity with distance from the reservoir. Experiments with two soybean cultivars were conducted during four crop seasons to evaluate soybean yield in relation to distance from the Itaipu reservoir and determine the effect of air temperature and water availability on soybean crop yield. Fifteen experimental sites were distributed in three transects perpendicular to the Itaipu reservoir, covering an area at approximately 10 km from the shoreline. The yield gradient between the site closest to the reservoir and the sites farther away in each transect did not show a consistent trend, but varied as a function of distance, crop season, and cultivar. This finding indicates that the Itaipu reservoir does not affect the yield of soybean plants grown within approximately 10 km from the shoreline. In addition, the variation in yield among the experimental sites was not attributed to thermal conditions because the temperature was similar within transects. However, the crop water availability was responsible for higher differences in yield among the neighboring experimental sites related to water stress caused by spatial variability in rainfall, especially during the soybean reproductive period in January and February.

Adverse weather impacts on arable cropping systems

NASA Astrophysics Data System (ADS)

Gobin, Anne

2016-04-01

Damages due to extreme or adverse weather strongly depend on crop type, crop stage, soil conditions and management. The impact is largest during the sensitive periods of the farming calendar, and requires a modelling approach to capture the interactions between the crop, its environment and the occurrence of the meteorological event. The hypothesis is that extreme and adverse weather events can be quantified and subsequently incorporated in current crop models. Since crop development is driven by thermal time and photoperiod, a regional crop model was used 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. Risk profiles and associated return levels were obtained by fitting generalized extreme value distributions to block maxima for air humidity, water balance and temperature variables. The risk profiles were subsequently confronted with yields and yield losses for the major arable crops in Belgium, notably winter wheat, winter barley, winter oilseed rape, sugar beet, potato and maize at the field (farm records) to regional scale (statistics). The average daily vapour pressure deficit (VPD) and reference evapotranspiration (ET0) during the growing season is significantly lower (p < 0.001) and has a higher variability before 1988 than after 1988. Distribution patterns of VPD and ET0 have relevant impacts on crop yields. The response to rising temperatures depends on the crop's capability to condition its microenvironment. Crops short of water close their stomata, lose their evaporative cooling potential and ultimately become susceptible to heat stress. Effects of heat stress therefore have to be combined with moisture availability such as the precipitation deficit or the soil water balance. Risks of combined heat and moisture deficit stress appear during the summer. These risks are subsequently related to crop damage. The methodology of defining meteorological risks and subsequently relating the risk to the cropping calendar will be demonstrated for major arable crops in Belgium. Physically based crop models assist in understanding the links between adverse weather events, sensitive crop stages and crop damage. Financial support was obtained from Belspo under research contract SD/RI/03A.

Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change

DOE PAGES

Mistry, Malcolm N.; Wing, Ian Sue; De Cian, Enrica

2017-07-10

Global gridded crop models (GGCMs) are the workhorse of assessments of the agricultural impacts of climate change. Yet the changes in crop yields projected by different models in response to the same meteorological forcing can differ substantially. Through an inter-method comparison, we provide a first glimpse into the origins and implications of this divergence—both among GGCMs and between GGCMs and historical observations. We examine yields of rainfed maize, wheat, and soybeans simulated by six GGCMs as part of the Inter-Sectoral Impact Model Intercomparison Project-Fast Track (ISIMIP-FT) exercise, comparing 1981–2004 hindcast yields over the coterminous United States (US) against US Departmentmore » of Agriculture (USDA) time series for about 1000 counties. Leveraging the empirical climate change impacts literature, we estimate reduced-form econometric models of crop yield responses to temperature and precipitation exposures for both GGCMs and observations. We find that up to 60% of the variance in both simulated and observed yields is attributable to weather variation. A majority of the GGCMs have difficulty reproducing the observed distribution of percentage yield anomalies, and exhibit aggregate responses that show yields to be more weather-sensitive than in the observational record over the predominant range of temperature and precipitation conditions. In conclusion, this disparity is largely attributable to heterogeneity in GGCMs' responses, as opposed to uncertainty in historical weather forcings, and is responsible for widely divergent impacts of climate on future crop yields.« less

Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change

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

Mistry, Malcolm N.; Wing, Ian Sue; De Cian, Enrica

Global gridded crop models (GGCMs) are the workhorse of assessments of the agricultural impacts of climate change. Yet the changes in crop yields projected by different models in response to the same meteorological forcing can differ substantially. Through an inter-method comparison, we provide a first glimpse into the origins and implications of this divergence—both among GGCMs and between GGCMs and historical observations. We examine yields of rainfed maize, wheat, and soybeans simulated by six GGCMs as part of the Inter-Sectoral Impact Model Intercomparison Project-Fast Track (ISIMIP-FT) exercise, comparing 1981–2004 hindcast yields over the coterminous United States (US) against US Departmentmore » of Agriculture (USDA) time series for about 1000 counties. Leveraging the empirical climate change impacts literature, we estimate reduced-form econometric models of crop yield responses to temperature and precipitation exposures for both GGCMs and observations. We find that up to 60% of the variance in both simulated and observed yields is attributable to weather variation. A majority of the GGCMs have difficulty reproducing the observed distribution of percentage yield anomalies, and exhibit aggregate responses that show yields to be more weather-sensitive than in the observational record over the predominant range of temperature and precipitation conditions. In conclusion, this disparity is largely attributable to heterogeneity in GGCMs' responses, as opposed to uncertainty in historical weather forcings, and is responsible for widely divergent impacts of climate on future crop yields.« less

Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change

NASA Astrophysics Data System (ADS)

Mistry, Malcolm N.; Wing, Ian Sue; De Cian, Enrica

2017-07-01

Global gridded crop models (GGCMs) are the workhorse of assessments of the agricultural impacts of climate change. Yet the changes in crop yields projected by different models in response to the same meteorological forcing can differ substantially. Through an inter-method comparison, we provide a first glimpse into the origins and implications of this divergence—both among GGCMs and between GGCMs and historical observations. We examine yields of rainfed maize, wheat, and soybeans simulated by six GGCMs as part of the Inter-Sectoral Impact Model Intercomparison Project-Fast Track (ISIMIP-FT) exercise, comparing 1981-2004 hindcast yields over the coterminous United States (US) against US Department of Agriculture (USDA) time series for about 1000 counties. Leveraging the empirical climate change impacts literature, we estimate reduced-form econometric models of crop yield responses to temperature and precipitation exposures for both GGCMs and observations. We find that up to 60% of the variance in both simulated and observed yields is attributable to weather variation. A majority of the GGCMs have difficulty reproducing the observed distribution of percentage yield anomalies, and exhibit aggregate responses that show yields to be more weather-sensitive than in the observational record over the predominant range of temperature and precipitation conditions. This disparity is largely attributable to heterogeneity in GGCMs’ responses, as opposed to uncertainty in historical weather forcings, and is responsible for widely divergent impacts of climate on future crop yields.

Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania

PubMed Central

Tumbo, S. D.; Kihupi, N. I.; Rwehumbiza, Filbert B.

2017-01-01

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly. PMID:28536708

Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania.

PubMed

Msongaleli, Barnabas M; Tumbo, S D; Kihupi, N I; Rwehumbiza, Filbert B

2017-01-01

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly.

Evaluation of Precipitation Indices for Global Crop Modeling and Definition of Drought Response Function to Yields

NASA Astrophysics Data System (ADS)

Kaneko, D.

2017-12-01

Climate change initiates abnormal meteorological disasters. Drought causes climate instability, thus producing poor harvests because of low rates of photosynthesis and sterile pollination. This research evaluates drought indices regarding precipitation and includes this data in global geophysical crop models that concern with evaporation, stomata opening, advection-effects from sea surface temperature anomalies, photosynthesis, carbon partitioning, crop yields, and crop production. Standard precipitation index (SPI) is a useful tool because of related variable not used in the stomata model. However, SPI is not an adequate tool for drought in irrigated fields. Contrary to expectations, the global comparisons of spatial characteristics between stomata opening/evapotranspiration and SPI for monitoring continental crop extremes produced serious defects and obvious differences between evapotranspiration and the small stomata-opening phenomena. The reason for this is that SPI does not include surface air temperature in its analysis. The Penman equation (Epen) describes potential evaporation better than SPI for recent hot droughts caused by climate change. However, the distribution of precipitation is a necessary condition for crop monitoring because it affirms the trend of the dry results computed by crop models. Consequently, the author uses global precipitation data observed by microwave passive sensors on TRMM and GCOM-W satellites. This remote sensing data conveniently supplies spatial distributions of global and seasonal precipitation. The author has designed a model to measure the effects of drought on crop yield and the degree of stomata closure related to the photosynthesis rate. To determine yield effects, the drought injury function is defined by integrating stomata closure during the two seasons from flowering to pollination. The stomata, defined by ratio between Epen and Eac, reflect the effects of drought and irrigation. Stomata-closure model includes the factors of soil moisture or irrigation effects inside the actual evapotranspiration computed using a complimentary model. The evaluation of precipitation indices provides necessary but not sufficient conditions for drought. They supply reference information for the trend/accuracy of an injury response function.

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.

Biophysical and Economic Uncertainty in the Analysis of Poverty Impacts of Climate Change

NASA Astrophysics Data System (ADS)

Hertel, T. W.; Lobell, D. B.; Verma, M.

2011-12-01

This paper seeks to understand the main sources of uncertainty in assessing the impacts of climate change on agricultural output, international trade, and poverty. We incorporate biophysical uncertainty by sampling from a distribution of global climate model predictions for temperature and precipitation for 2050. The implications of these realizations for crop yields around the globe are estimated using the recently published statistical crop yield functions provided by Lobell, Schlenker and Costa-Roberts (2011). By comparing these yields to those predicted under current climate, we obtain the likely change in crop yields owing to climate change. The economic uncertainty in our analysis relates to the response of the global economic system to these biophysical shocks. We use a modified version of the GTAP model to elicit the impact of the biophysical shocks on global patterns of production, consumption, trade and poverty. Uncertainty in these responses is reflected in the econometrically estimated parameters governing the responsiveness of international trade, consumption, production (and hence the intensive margin of supply response), and factor supplies (which govern the extensive margin of supply response). We sample from the distributions of these parameters as specified by Hertel et al. (2007) and Keeney and Hertel (2009). We find that, even though it is difficult to predict where in the world agricultural crops will be favorably affected by climate change, the responses of economic variables, including output and exports can be far more robust (Table 1). This is due to the fact that supply and demand decisions depend on relative prices, and relative prices depend on productivity changes relative to other crops in a given region, or relative to similar crops in other parts of the world. We also find that uncertainty in poverty impacts of climate change appears to be almost entirely driven by biophysical uncertainty.

Increasing Efficiency of Water Use in Agriculture through Management of Soil Water Repellency to Optimize Soil and Water Productivity

NASA Astrophysics Data System (ADS)

Moore, Demie; Kostka, Stan; McMillan, Mica; Gadd, Nick

2010-05-01

Water's ability to infiltrate and disperse in soils, and soil's ability to receive, transport, retain, filter and release water are important factors in the efficient use of water in agriculture. Deteriorating soil conditions, including development of soil water repellency, negatively impact hydrological processes and, consequently, the efficiency of rainfall and irrigation. Soil water repellency is increasingly being identified in diverse soils and cropping systems. Recently research has been conducted on the use of novel soil surfactants (co-formulations of alkyl polyglycoside and block copolymer surfactants) to avoid or overcome soil water repellency and enhance water distribution in soils. Results indicate that this is an effective and affordable approach to maintaining or restoring soil and water productivity in irrigated cropping systems. Results from studies conducted in Australia and the United States to determine how this technology modifies soil hydrological behavior and crop yields will be presented. A range of soils and various crops, including potatoes, corn, apples and grapes, were included. Several rates were compared to controls for effect on soil moisture levels, soil water distribution, and crop yield. An economic analysis was also conducted in some trials. Treatments improved rootzone water status, significantly increased crop yield and quality, and in some cases allowed significant reductions in water requirements. Where assessed, a positive economic return was generated. This technology holds promise as a strategy for increasing efficiency of water use in agriculture.

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.

Negative impacts of climate change on cereal yields: statistical evidence from France

NASA Astrophysics Data System (ADS)

Gammans, Matthew; Mérel, Pierre; Ortiz-Bobea, Ariel

2017-05-01

In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.

Crop water production functions for grain sorghum and winter wheat

USDA-ARS?s Scientific Manuscript database

Productivity of water-limited cropping systems can be reduced by untimely distribution of water as well as cold and heat stress. The objective was to develop relationships among weather parameters, water use, and grain productivity to produce functions forecasting grain yields of grain sorghum and w...

Crop water production functions of grain sorghum and winter wheat in Kansas and Texas

USDA-ARS?s Scientific Manuscript database

Productivity of water-limited cropping systems can be reduced by untimely distribution of water as well as cold and heat stress. Our study objective was to develop relationships among weather variables, water use, and grain productivity to produce production functions for forecasting grain yields of...

Using remote sensing and soil physical properties for predicting the spatial distribution of cotton lint yield

USDA-ARS?s Scientific Manuscript database

Timely reflectance data from cotton (Gossypium hirsutum L.) production fields provide a useful tool for crop health assessment and site-specific crop management decisions. This field study investigated the relationships among site-specific normalized difference vegetation index (NDVI), soil physical...

[Effects of tillage practices on root spatial distribution and yield of spring wheat and pea in the dry land farming areas of central Gansu, China].

PubMed

Zhang, Ming Jun; Li, Ling Ling; Xie, Jun Hong; Peng, Zheng Kai; Ren, Jin Hu

2017-12-01

A field experiment was conducted to explore the mechanism of cultivation measures in affecting crop yield by investigating root distribution in spring wheat-pea rotation based on a long-term conservation tillage practices in a farming region of Gansu. The results showed that with the develo-pment of growth period, the total root length, root surface area of spring wheat and pea showed a consistent trend of increase after initial decrease and reached the maximum at flowering stage. Higher root distribution was found in the 0-10 cm soil layer at seedling and 10-30 cm soil layer at flowering and maturity stages in spring wheat, while in the field pea, higher root distribution was found in the 0-10 cm soil layer at seedling and maturity, and in the 10-30 cm soil layer at flowering stages. No tillage with straw mulching and plastic mulching increased the root length and root surface area. Compared with conventional tillage in spring wheat and field pea, root length increased by 35.9% to 92.6%, and root surface area increased by 43.2% to 162.4%, respectively. No tillage with straw mulching and plastic mulching optimized spring wheat and pea root system distribution, compared with conventional tillage, increased spring wheat and field pea root length and root surface area ratio at 0-10 cm depths at the seedling stage, the root distribution at deeper depths increased significantly at flowering and maturity stages, and no tillage with straw mulching increased root length and root surface area ratio by 3.3% and 9.7% respectively, in 30-80 cm soil layer at the flowering stage. The total root length, root surface area and yield had significantly positive correlation for spring wheat in each growth period, and the total root length and pea yield also had significant positive correlation. No tillage with straw mulching and plastic mulching boosted yield of spring wheat and pea by 23.4%-38.7% compared with the conventional tillage, and the water use efficiency was increased by 13.7%-28.5%. It was concluded that no-till farming and straw mulching (plastic) could increase crop root length and root surface area, optimize the spatial distribution of roots in the soil, enhance crop root layer absorption ability, so as to improve crop yield and water utilization.

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.

A hybrid framework for assessing maize drought vulnerability in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Kamali, B.; Abbaspour, K. C.; Wehrli, B.; Yang, H.

2017-12-01

Drought has devastating impacts on crop yields. Quantifying drought vulnerability is the first step to better design of mitigation policies. The vulnerability of crop yield to drought has been assessed with different methods, however they lack a standardized base to measure its components and a procedure that facilitates spatial and temporal comparisons. This study attempts to quantify maize drought vulnerability through linking the Drought Exposure Index (DEI) to the Crop Failure Index (CFI). DEI and CFI were defined by fitting probability distribution functions to precipitation and maize yield respectively. To acquire crop drought vulnerability index (CDVI), DEI and CFI were combined in a hybrid framework which classifies CDVI with the same base as DEI and CFI. The analysis were implemented on Sub-Saharan African countries using maize yield simulated with the Environmental Policy Integrated Climate (EPIC) model at 0.5° resolution. The model was coupled with the Sequential Uncertainty Fitting algorithm for calibration at country level. Our results show that Central Africa and those Western African countries located below the Sahelian strip receive higher amount of precipitation, but experience high crop failure. Therefore, they are identified as more vulnerable regions compared to countries such as South Africa, Tanzania, and Kenya. We concluded that our hybrid approach complements information on crop drought vulnerability quantification and can be applied to different regions and scales.

Impacts of multiple global environmental changes on African crop yield and water use efficiency: Implications to food and water security

NASA Astrophysics Data System (ADS)

Pan, S.; Yang, J.; Zhang, J.; Xu, R.; Dangal, S. R. S.; Zhang, B.; Tian, H.

2016-12-01

Africa is one of the most vulnerable regions in the world to climate change and climate variability. Much concern has been raised about the impacts of climate and other environmental factors on water resource and food security through the climate-water-food nexus. Understanding the responses of crop yield and water use efficiency to environmental changes is particularly important because Africa is well known for widespread poverty, slow economic growth and agricultural systems particularly sensitive to frequent and persistent droughts. However, the lack of integrated understanding has limited our ability to quantify and predict the potential of Africa's agricultural sustainability and freshwater supply, and to better manage the system for meeting an increasing food demand in a way that is socially and environmentally or ecologically sustainable. By using the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed the spatial and temporal patterns of crop yield, evapotranspiration (ET) and water use efficiency across entire Africa in the past 35 years (1980-2015) and the rest of the 21st century (2016-2099). Our preliminary results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion (about 50%), elevated atmospheric CO2 concentration, and nitrogen deposition. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Climate extremes especially droughts and heat wave have largely reduced crop yield in the most vulnerable regions. Our results indicate that N fertilizer could be a major driver to improve food security in Africa. Future climate warming could reduce crop yield and shift cropland distribution. Our study further suggests that improving water use efficiency through land management practices including the increased uses of fertilizers and irrigation will be the key for reducing the loss of crop yield in a warming climate and extreme weather.

Decoupling the deep: crop rotations, fertilization and soil physico-chemical properties down the profile

NASA Astrophysics Data System (ADS)

Hobley, Eleanor; Honermeier, Bernd; Don, Axel; Amelung, Wulf; Kögel-Knabner, Ingrid

2017-04-01

Crop fertilization provides vital plant nutrients (e.g. NPK) to ensure yield security but is also associated with negative environmental impacts. In particular, inorganic, mineral nitrogen (Nmin) fertilization leads to emissions during its energy intensive production as well as Nmin leaching to receiving waters. Incorporating legumes into crop rotations can provide organic N to the soil and subsequent crops, reducing the need for mineral N fertilizer and its negative environmental impacts. An added bonus is the potential to enhance soil organic carbon stocks, thereby reducing atmospheric CO2 concentrations. In this study we assessed the effects of legumes in rotation and fertilization regimes on the depth distribution - down to 1 m - of total soil nitrogen (Ntot), soil organic carbon (SOC) as well as isotopic composition (δ13C, δ15N), electrical conductivity and bulk density as well as agricultural yields at a long-term field experiment in Gießen, Germany. Fertilization had significant but small impacts on the soil chemical environment, most particularly the salt content of the soil, with PK fertilization increasing electrical conductivity throughout the soil profile. Similarly, fertilization resulted in a small reduction of soil pH throughout the soil profile. N fertilization, in particular, significantly increased yields, whereas PK fertilizer had only marginal yield effects, indicating that these systems are N limited. This N limitation was confirmed by significant yield benefits with leguminous crops in rotation, even in combination with mineral N fertilizer. The soil was physically and chemically influenced by the choice of crop rotation. Adding clover as a green mulch crop once every 4 years resulted in an enrichment of total N and SOC at the surface compared with fava beans and maize, but only in combination with PK fertilization. In contrast, fava beans and to a lesser extent maize in rotation lowered bulk densities in the subsoil compared with clover. This resulted in a reduction of N density at depth, which was not mirrored in C densities, indicating that fava beans decouple C and N cycles in the deep soil profile. We then tested whether these effects are a result of plant (i.e. enhanced rooting depth associated with lowered subsoil bulk density) or microbial (i.e. N-cycling and denitrification processes) activities, by investigating the isotopic signatures of C and N down the profile. Our results indicate that the selection of crop rotation influences soil C and N cycling and depth distribution. Although mineral N fertilizer has significant benefits for yield, the choice of crop rotation has a greater influence on soil C and N cycling and specifically the addition of leguminous plants into rotation can provide additional yield benefits and stability. Incorporating legumes into crop rotations affects soil physical and chemical properties and decouples C and N cycles in the deep soil profile, indicating different nutrient and water cycling processes in the deep soil profile.

Topographic controls on soil nutrient variations in a Silvopasture system

USDA-ARS?s Scientific Manuscript database

Topography plays a crucial role in the spatial distribution of nutrients in soils because of its influence on the flow and (re)distribution of water and energy in a landscape. Information on the spatial pattern of soil nutrient distribution would benefit management decisions to maximize crop yield a...

Spatial estimation from remotely sensed data via empirical Bayes models

NASA Technical Reports Server (NTRS)

Hill, J. R.; Hinkley, D. V.; Kostal, H.; Morris, C. N.

1984-01-01

Multichannel satellite image data, available as LANDSAT imagery, are recorded as a multivariate time series (four channels, multiple passovers) in two spatial dimensions. The application of parametric empirical Bayes theory to classification of, and estimating the probability of, each crop type at each of a large number of pixels is considered. This theory involves both the probability distribution of imagery data, conditional on crop types, and the prior spatial distribution of crop types. For the latter Markov models indexed by estimable parameters are used. A broad outline of the general theory reveals several questions for further research. Some detailed results are given for the special case of two crop types when only a line transect is analyzed. Finally, the estimation of an underlying continuous process on the lattice is discussed which would be applicable to such quantities as crop yield.

Multiyear high-resolution carbon exchange over European croplands from the integration of observed crop yields into CarbonTracker Europe

NASA Astrophysics Data System (ADS)

Combe, Marie; Vilà-Guerau de Arellano, Jordi; de Wit, Allard; Peters, Wouter

2016-04-01

Carbon exchange over croplands plays an important role in the European carbon cycle over daily-to-seasonal time scales. Not only do crops occupy one fourth of the European land area, but their photosynthesis and respiration are large and affect CO2 mole fractions at nearly every atmospheric CO2 monitoring site. A better description of this crop carbon exchange in our CarbonTracker Europe data assimilation system - which currently treats crops as unmanaged grasslands - could strongly improve its ability to constrain terrestrial carbon fluxes. Available long-term observations of crop yield, harvest, and cultivated area allow such improvements, when combined with the new crop-modeling framework we present. This framework can model the carbon fluxes of 10 major European crops at high spatial and temporal resolution, on a 12x12 km grid and 3-hourly time-step. The development of this framework is threefold: firstly, we optimize crop growth using the process-based WOrld FOod STudies (WOFOST) agricultural crop growth model. Simulated yields are downscaled to match regional crop yield observations from the Statistical Office of the European Union (EUROSTAT) by estimating a yearly regional parameter for each crop species: the yield gap factor. This step allows us to better represent crop phenology, to reproduce the observed multiannual European crop yields, and to construct realistic time series of the crop carbon fluxes (gross primary production, GPP, and autotrophic respiration, Raut) on a fine spatial and temporal resolution. Secondly, we combine these GPP and Raut fluxes with a simple soil respiration model to obtain the total ecosystem respiration (TER) and net ecosystem exchange (NEE). And thirdly, we represent the horizontal transport of carbon that follows crop harvest and its back-respiration into the atmosphere during harvest consumption. We distribute this carbon using observations of the density of human and ruminant populations from EUROSTAT. We assess the model's ability to represent the seasonal GPP, TER and NEE fluxes using observations at 6 European FluxNet winter wheat and grain maize sites and compare it with the fluxes of the current terrestrial carbon cycle model of CarbonTracker Europe: the Simple Biosphere - Carnegie-Ames-Stanford Approach (SiBCASA) model. We find that the new model framework provides a detailed, realistic, and strongly observation-driven estimate of carbon exchange over European croplands. Its products will be made available to the scientific community through the ICOS Carbon Portal, and serve as a new cropland component in CarbonTracker Europe flux estimates.

Detecting the Spatio-temporal Distribution of Soil Salinity and Its Relationship to Crop Growth in a Large-scale Arid Irrigation District Based on Sampling Experiment and Remote Sensing

NASA Astrophysics Data System (ADS)

Ren, D.; Huang, G., Sr.; Xu, X.; Huang, Q., Sr.; Xiong, Y.

2016-12-01

Soil salinity analysis on a regional scale is of great significance for protecting agriculture production and maintaining eco-environmental health in arid and semi-arid irrigated areas. In this study, the Hetao Irrigation District (Hetao) in Inner Mongolia Autonomous Region, with suffering long-term soil salinization problems, was selected as the case study area. Field sampling experiments and investigations related to soil salt contents, crop growth and yields were carried out across the whole area, during April to August in 2015. Soil salinity characteristics in space and time were systematically analyzed for Hetao as well as the corresponding impacts on crops. Remotely sensed map of soil salinity distribution for surface soil was also derived based on the Landsat OLI data with a 30 m resolution. The results elaborated the temporal and spatial dynamics of soil salinity and the relationships with irrigation, groundwater depth and crop water consumption in Hetao. In addition, the strong spatial variability of salinization was clearly presented by the remotely sensed map of soil salinity. Further, the relationship between soil salinity and crop growth was analyzed, and then the impact degrees of soil salinization on cropping pattern, leaf area index, plant height and crop yield were preliminarily revealed. Overall, this study can provide very useful information for salinization control and guide the future agricultural production and soil-water management for the arid irrigation districts analogous to Hetao.

Photosynthesis, light use efficiency, and yield of reduced-chlorophyll soybean mutants in field conditions

USDA-ARS?s Scientific Manuscript database

Reducing chlorophyll (chl) content may improve the conversion efficiency of absorbed radiation into biomass (ec) and therefore yield in dense monoculture crops by improving light penetration and distribution within the canopy. Modeling suggests that reducing chl content may also reduce leaf temperat...

Patterns of zone management uncertainty in cotton using tarnished plant bug distributions, NDVI, soil EC, yield and thermal imagery

USDA-ARS?s Scientific Manuscript database

Management zones for various crops have been delineated using NDVI (Normalized Difference Vegetation Index), apparent bulk soil electrical conductivity (ECa - Veris), and yield data; however, estimations of uncertainty for these data layers are equally important considerations. The objective of this...

Impacts of plastic film mulching on crop yields, soil water, nitrate, and organic carbon in Northwestern China: A meta-analysis.

PubMed

Ma, Dedi; Chen, Lei; Qu, Hongchao; Wang, Yilin; Misselbrook, Tom; Jiang, Rui

2018-04-01

In order to increase crop yield in semi-arid and arid areas, plastic film mulching (PFM) is widely used in Northwestern China. To date, many studies have addressed the effects of PFM on soil physical and biochemical properties in rain-fed agriculture in Northwestern China, but the findings of different studies are often contradictory. Therefore, a comprehensive review of the impacts of PFM on soil water content, soil nutrients and food production is needed. We compiled the results of 1278 observations to evaluate the overall effects of PFM on soil water content, the distribution of nitrate and soil organic carbon, and crop yield in rain-fed agriculture in Northwestern China. Our results showed that PFM increased soil moisture and nitrate concentration in topsoils (0-20 cm) by 12.9% and 28.2%, respectively, but slightly decreased (1.8%) soil organic carbon (SOC) content in the 0-10 cm soil layer. PFM significantly increased grain yields by 43.1%, with greatest effect in spring maize (79.4%). When related to cumulative precipitation during the crop growing season, yield increase from PFM was greatest (72.8%) at 200-300 mm, which was attributed to the large increase for spring maize and potato, implying that crop zoning would be beneficial for PFM in this region. When related to N application rate, crop yields benefited most from PFM (80.2%) at 200-300 kg/ha. A cost-benefit analysis indicated that PFM increased economic return by an average of 29.5%, with the best improvement for spring maize (71.1%) and no increase for spring wheat. In conclusion, PFM can significantly increase crop yield and economic return (especially for spring maize) in rain-fed agriculture areas of Northwestern China. Crop zoning is recommended for PFM to achieve the largest economic benefit. However, full account needs to be taken of the environmental impacts relating to N loss, SOC depletion and film pollution to evaluate the sustainability of PFM systems and further research is required to quantify and mitigate these impacts.

Feeding nine billion: the challenge to sustainable crop production.

PubMed

Gregory, Peter J; George, Timothy S

2011-11-01

In the recent past there was a widespread working assumption in many countries that problems of food production had been solved, and that food security was largely a matter of distribution and access to be achieved principally by open markets. The events of 2008 challenged these assumptions, and made public a much wider debate about the costs of current food production practices to the environment and whether these could be sustained. As in the past 50 years, it is anticipated that future increases in crop production will be achieved largely by increasing yields per unit area rather than by increasing the area of cropped land. However, as yields have increased, so the ratio of photosynthetic energy captured to energy expended in crop production has decreased. This poses a considerable challenge: how to increase yield while simultaneously reducing energy consumption (allied to greenhouse gas emissions) and utilizing resources such as water and phosphate more efficiently. Given the timeframe in which the increased production has to be realized, most of the increase will need to come from crop genotypes that are being bred now, together with known agronomic and management practices that are currently under-developed.

EnviroAtlas - Cultivated biological nitrogen fixation in agricultural lands by 12-digit HUC in the Conterminous United States, 2006

EPA Pesticide Factsheets

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not

Morphological plasticity of root growth under mild water stress increases water use efficiency without reducing yield in maize

NASA Astrophysics Data System (ADS)

Cai, Qian; Zhang, Yulong; Sun, Zhanxiang; Zheng, Jiaming; Bai, Wei; Zhang, Yue; Liu, Yang; Feng, Liangshan; Feng, Chen; Zhang, Zhe; Yang, Ning; Evers, Jochem B.; Zhang, Lizhen

2017-08-01

A large yield gap exists in rain-fed maize (Zea mays L.) production in semi-arid regions, mainly caused by frequent droughts halfway through the crop-growing period due to uneven distribution of rainfall. It is questionable whether irrigation systems are economically required in such a region since the total amount of rainfall does generally meet crop requirements. This study aimed to quantitatively determine the effects of water stress from jointing to grain filling on root and shoot growth and the consequences for maize grain yield, above- and below-ground dry matter, water uptake (WU) and water use efficiency (WUE). Pot experiments were conducted in 2014 and 2015 with a mobile rain shelter to achieve conditions of no, mild or severe water stress. Maize yield was not affected by mild water stress over 2 years, while severe stress reduced yield by 56 %. Both water stress levels decreased root biomass slightly but shoot biomass substantially. Mild water stress decreased root length but increased root diameter, resulting in no effect on root surface area. Due to the morphological plasticity in root growth and the increase in root / shoot ratio, WU under water stress was decreased, and overall WUE for both above-ground dry matter and grain yield increased. Our results demonstrate that an irrigation system might be not economically and ecologically necessary because the frequently occurring mild water stress did not reduce crop yield much. The study helps us to understand crop responses to water stress during a critical water-sensitive period (middle of the crop-growing season) and to mitigate drought risk in dry-land agriculture.

Biomass and nitrogen-use efficiency of grain sorghum (Sorghum bicolor L.) with nitrogen and supplemental irrigation in Coastal Plain Region, USA

USDA-ARS?s Scientific Manuscript database

Poor rainfall distribution and soil conditions such as high soil strength, low water holding capacity of soils and poor soil fertility in the humid Coastal Plain region may affect production of grain crops. Nitrogen insufficiency and water stress can both reduce crop yield, but little information is...

Crop damage and livestock depredation by wildlife: a case study from Nanda Devi Biosphere Reserve, India.

PubMed

Rao, K S; Maikhuri, R K; Nautiyal, S; Saxena, K G

2002-11-01

The success of conserving biological resources in any Biosphere Reserve or protected area depends on the extent of support and positive attitudes and perceptions of local people have towards such establishments. Ignoring the dependence of the local people for their subsistence needs on resources of such areas leads to conflicts between protected area managers and the local inhabitants. Crop yield losses and livestock depredation were serious problems observed in most buffer zone villages of Nanda Devi Biosphere Reserve. In the present study 10 villages situated in the buffer zone of Nanada Devi Biosphere Reserve (1612 km2 area) in Chamoli district of Uttaranchal, India were studied during 1996-97 using a questionnaire survey of each household (419 = households; 2253 = total population in 1991; 273 ha = cultivated area). Estimates of crop yield losses were made using paired plots technique in four representative villages for each crop species. The magnitude of crop yield losses varied significantly with the distance of agricultural field from forest boundary. The total crop yield losses were high for wheat and potato in all the villages. The spatial distribution of total crop yield losses in any village indicated that they were highest in the area near to forest and least in the area near to village for all crops. Losses from areas near to forest contributed to more than 50% of total losses for each crop in all villages. However, in Lata, Peng and Tolma villages, the losses are high for kidney bean and chemmi (local variety of kidney bean) which varied between 18.5% to 30% of total losses in those villages. Potato alone represents 43.6% of total crop yield loss due to wildlife in Dronagiri village in monetary terms. Among the crops, the monetary value of yield losses are least for amaranth and highest for kidney bean. The projected total value of crop yield losses due to wildlife damage for buffer zone villages located in Garhwal Himalaya is about Rs. 538,620 (US$ 15,389). Besides food grains, horticultural crops i.e. apple, also suffered maximum damage. Major wildlife agents responsible for crop damage were wild boar, bear, porcupine, monkey, musk deer and partridge (chokor). Monkey and wild boar alone accounted for about 50% to 60% of total crop damage in the study villages. Goat and sheep are the major livestock killed by leopard. The total value of livestock losses at prevailing market rates is about Rs. 1,024,520 (US$ 29,272) in the study villages. Due to existing conservation policies and laxity in implementation of preventive measures, the problems for local inhabitants are increasing. Potential solutions discussed emphasize the need to undertake suitable and appropriate protective measures to minimize the crop losses. Change in cropping and crop composition, particularly cultivation of medicinal plants (high value low volume crops), were also suggested. Besides, fair and quick disbursement of compensation for crop loss and livestock killing need to be adopted. Local people of the buffer zone area already have a negative attitude towards park/reserve establishment due to socio-political changes inducing major economic losses and this attitude may lead to clashes and confrontations if proper ameliorative measures are not taken immediately.

Advancing the climate data driven crop-modeling studies in the dry areas of Northern Syria and Lebanon: an important first step for assessing impact of future climate.

PubMed

Dixit, Prakash N; Telleria, Roberto

2015-04-01

Inter-annual and seasonal variability in climatic parameters, most importantly rainfall, have potential to cause climate-induced risk in long-term crop production. Short-term field studies do not capture the full nature of such risk and the extent to which modifications to crop, soil and water management recommendations may be made to mitigate the extent of such risk. Crop modeling studies driven by long-term daily weather data can predict the impact of climate-induced risk on crop growth and yield however, the availability of long-term daily weather data can present serious constraints to the use of crop models. To tackle this constraint, two weather generators namely, LARS-WG and MarkSim, were evaluated in order to assess their capabilities of reproducing frequency distributions, means, variances, dry spell and wet chains of observed daily precipitation, maximum and minimum temperature, and solar radiation for the eight locations across cropping areas of Northern Syria and Lebanon. Further, the application of generated long-term daily weather data, with both weather generators, in simulating barley growth and yield was also evaluated. We found that overall LARS-WG performed better than MarkSim in generating daily weather parameters and in 50 years continuous simulation of barley growth and yield. Our findings suggest that LARS-WG does not necessarily require long-term e.g., >30 years observed weather data for calibration as generated results proved to be satisfactory with >10 years of observed data except in area with higher altitude. Evaluating these weather generators and the ability of generated weather data to perform long-term simulation of crop growth and yield is an important first step to assess the impact of future climate on yields, and to identify promising technologies to make agricultural systems more resilient in the given region. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Climate Change and Projected Impacts in Agriculture: an Example on Mediterranean Crops

NASA Astrophysics Data System (ADS)

Ferrise, R.; Moriondo, M.; Bindi, M.

2009-04-01

Recently, the availability of multi-model ensemble prediction methods has permitted the assignment of likelihoods to future climate projections. This allowed moving from the scenario-based approach to the risk-based approach in assessing the effects of climate change, thus providing more useful information for decision-makers that, as reported by Schneider (2001), need probability estimates to assess the seriousness of the projected impacts. The probabilistic approach to evaluate crop response to climate change mainly consists in applying an impact model (such as crop growth model) to a very large number of climate projections so to provide a probabilistic distribution of the variable selected to evaluate the impact. By comparing the outputs of the multi-simulation with a critical threshold (such as minimum yield below which it is not admissible to fall), it is possible to evaluate the risk related to future climate conditions. Unfortunately, such an approach is a time-consuming process due to the large number of model runs needed for such a procedure. An alternative method relies on the set up of impact response surfaces (RS) with respect to key climatic variables on which a probabilistic representation of projected changes in the same climatic variables may be overlaid (Fronzek et al. 2008). This approach was exploited within the ENSEMBLES EU Project aiming at assessing climate change impact on typical Mediterranean crops. This work presents the results of the project with a particular concerning about the assessment of risk, of durum wheat (T. turgidum L. subsp. durum (Desf.) Husn) and grapevine (Vitis vinifera L.) yield falling below fixed thresholds, using probabilistic information about future climate. Methodology The simple mechanistic crop growth models, SIRIUS Quality (Jamieson et al., 1998) and VITE-model (Bindi et al., 1997a,b), were selected to respectively simulate durum wheat and grapevine yields in present and future scenarios. SIRIUS Quality is a wheat simulation model that calculates biomass production from photosynthetically active radiation and grain growth from simple partition rules. VITE-model is a model that uses a simplified mechanistic approach based on the accumulated degree days, the radiation use efficiency and the fruit biomass index to simulate the main processes regulating grapevine development, growth and yield. The selected crop growth models were adopted to create yield RSs of both crops over the suitable cultivated area in the Mediterranean Basin. Yield RSs were calculated performing a scenario sensitivity analysis by altering the baseline climate with respect to temperature and precipitation changes. The baseline climate consisted of 30 years (1975-2005) of daily minimum and maximum temperatures, rainfall and global radiation. Meteorological data were extracted from the MARS JRC Archive and are referred to a grid with a spatial resolution of 50 Km x 50 Km covering the whole European area. The sensitivity analysis was performed for precipitation changes (from -40% to 20%) and temperature changes (from 0°C to +8°C), uniformly applied across all the year. To take in account for the effect of rising CO2, the yield RSs for future periods, were produced considering CO2 air concentration level according to the A1B SRES emission scenario. For each rainfall and temperature combination the average yield over the 30-years period was calculated. The probabilistic distribution of future yields was estimated by applying a bilinear interpolative method to overlap, onto the RSs, the data from perturbed physics experiment of Hadley Centre for future scenarios (joint distribution of annual temperature and rainfall changes). Critical thresholds of impact were determined by calculating, for each grid cell, the distribution of the 30-years average yield according to the joint distribution data for present period (1990-2010) and selecting the values that correspond to the 20th percentile of the cumulative distribution. Finally, future yields were compared with yield threshold to assess the risk of yield shortfall that, in each time period, was defined as the percentage of projected yields that not overcome the selected threshold. Results Maps of durum wheat and grapevine low productivity risk were generated for the next century over the Mediterranean Basin. For durum wheat, with the exception of Portugal and Southern Spain, in the next 30 years risk of low crop productivity shows an overall reduction, due to the fertilizing effect of CO2 increase that counterbalances for the negative impact of rising temperature and reducing rainfall. Thereafter, these latter negative effects become greater and the risk progressively increases starting from lower latitudes. Maximum risk was estimated in 2060 when strong reductions in yield were accounted all over the study area. The smaller reductions in risk, estimated for the end of the next century, may be explained by the greater uncertainty in climate projections. South Portugal, South Spain and Peloponnesus resulted the most vulnerable areas showing increase in risk probability up to 50%, while risk in Galicia, Slovenia, Croatia and central-southern France always resulted lower then present time. As regard grapevine, in the great part of the case study area, the yield seems to have beneficial effect from future climate change. In Central-Western Europe and at lower latitudes the projected yields never fall below the risk threshold, indicating a prevailing effect of CO2 fertilisation. By the other hand, Central-Northern Italy and North of Greece result the most vulnerable areas. In these regions the likelihood of reduced yields quickly rises and remains very high (>50%) until the end of the century, denoting a greater negative effect of temperature and rainfall. Conclusions From these results it may be argued that the impact of future climate change on crop yields is the resultant of the contrasting effects of changes in temperature and precipitation, CO2 increase and uncertainty in climate projections. The intensity of these effects is very site and crop dependent and may vary with time, differently affecting the assessment of risk. As a consequence, the patterns of risk of low crop productivity will change depending on which of these effects will prevail. References Bindi M. et al., 1997a "A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). I. Model description". Vitis 36:67-71 Bindi M. et al., 1997b "A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). II. Model validation". Vitis 36:73-76 Carter T. et al., 2006 "". Fronzek S. et al 2008 "Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia". Climatic Change (submitted) Jamieson et al., 1998 "Sirius: a mechanistic model of wheat response to environmental variation". Eur. J. Agron. 8:161-179. Schneider S. 2001 "What is ‘dangerous' climate change?". Nature 411:17-19

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

PubMed Central

Baron, Christian; Sultan, Benjamin; Balme, Maud; Sarr, Benoit; Traore, Seydou; Lebel, Thierry; Janicot, Serge; Dingkuhn, Michael

2005-01-01

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level. PMID:16433096

Challenges in breeding for yield increase for drought.

PubMed

Sinclair, Thomas R

2011-06-01

Crop genetic improvement for environmental stress at the molecular and physiological level is very complex and challenging. Unlike the example of the current major commercial transgenic crops for which biotic stress tolerance is based on chemicals alien to plants, the complex, redundant and homeostatic molecular and physiological systems existing in plants must be altered for drought tolerance improvement. Sophisticated tools must be developed to monitor phenotype expression at the crop level to characterize variation among genotypes across a range of environments. Once stress-tolerant cultivars are developed, regional probability distributions describing yield response across years will be necessary. This information can then aid in identifying environmental conditions for positive and negative responses to genetic modification to guide farmer selection of stress-tolerant cultivars. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

NASA Technical Reports Server (NTRS)

Glotter, Michael J.; Ruane, Alex C.; Moyer, Elisabeth J.; Elliott, Joshua W.

2015-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

Evaluating the sensitivity of agricultural model performance to different climate inputs

PubMed Central

Glotter, Michael J.; Moyer, Elisabeth J.; Ruane, Alex C.; Elliott, Joshua W.

2017-01-01

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled to observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections, but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely-used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources – reanalysis, reanalysis bias-corrected with observed climate, and a control dataset – and compared to observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by un-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. However, some issues persist for all choices of climate inputs: crop yields appear oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. PMID:29097985

Commercial Crop Yields Reveal Strengths and Weaknesses for Organic Agriculture in the United States.

PubMed

Kniss, Andrew R; Savage, Steven D; Jabbour, Randa

2016-01-01

Land area devoted to organic agriculture has increased steadily over the last 20 years in the United States, and elsewhere around the world. A primary criticism of organic agriculture is lower yield compared to non-organic systems. Previous analyses documenting the yield deficiency in organic production have relied mostly on data generated under experimental conditions, but these studies do not necessarily reflect the full range of innovation or practical limitations that are part of commercial agriculture. The analysis we present here offers a new perspective, based on organic yield data collected from over 10,000 organic farmers representing nearly 800,000 hectares of organic farmland. We used publicly available data from the United States Department of Agriculture to estimate yield differences between organic and conventional production methods for the 2014 production year. Similar to previous work, organic crop yields in our analysis were lower than conventional crop yields for most crops. Averaged across all crops, organic yield averaged 67% of conventional yield [corrected]. However, several crops had no significant difference in yields between organic and conventional production, and organic yields surpassed conventional yields for some hay crops. The organic to conventional yield ratio varied widely among crops, and in some cases, among locations within a crop. For soybean (Glycine max) and potato (Solanum tuberosum), organic yield was more similar to conventional yield in states where conventional yield was greatest. The opposite trend was observed for barley (Hordeum vulgare), wheat (Triticum aestevum), and hay crops, however, suggesting the geographical yield potential has an inconsistent effect on the organic yield gap.

Commercial Crop Yields Reveal Strengths and Weaknesses for Organic Agriculture in the United States

PubMed Central

Savage, Steven D.; Jabbour, Randa

2016-01-01

Land area devoted to organic agriculture has increased steadily over the last 20 years in the United States, and elsewhere around the world. A primary criticism of organic agriculture is lower yield compared to non-organic systems. Previous analyses documenting the yield deficiency in organic production have relied mostly on data generated under experimental conditions, but these studies do not necessarily reflect the full range of innovation or practical limitations that are part of commercial agriculture. The analysis we present here offers a new perspective, based on organic yield data collected from over 10,000 organic farmers representing nearly 800,000 hectares of organic farmland. We used publicly available data from the United States Department of Agriculture to estimate yield differences between organic and conventional production methods for the 2014 production year. Similar to previous work, organic crop yields in our analysis were lower than conventional crop yields for most crops. Averaged across all crops, organic yield averaged 80% of conventional yield. However, several crops had no significant difference in yields between organic and conventional production, and organic yields surpassed conventional yields for some hay crops. The organic to conventional yield ratio varied widely among crops, and in some cases, among locations within a crop. For soybean (Glycine max) and potato (Solanum tuberosum), organic yield was more similar to conventional yield in states where conventional yield was greatest. The opposite trend was observed for barley (Hordeum vulgare), wheat (Triticum aestevum), and hay crops, however, suggesting the geographical yield potential has an inconsistent effect on the organic yield gap. PMID:27552217

Impacts of soil and groundwater salinization on tree crop performance in post-tsunami Aceh Barat, Indonesia

NASA Astrophysics Data System (ADS)

Marohn, C.; Distel, A.; Dercon, G.; Wahyunto; Tomlinson, R.; Noordwijk, M. v.; Cadisch, G.

2012-09-01

The Indian Ocean tsunami of December 2004 had far reaching consequences for agriculture in Aceh province, Indonesia, and particularly in Aceh Barat district, 150 km from the seaquake epicentre. In this study, the spatial distribution and temporal dynamics of soil and groundwater salinity and their impact on tree crops were monitored in Aceh Barat from 2006 to 2008. On 48 sampling points along ten transects, covering 40 km of coastline, soil and groundwater salinity were measured and related to mortality and yield depression of the locally most important tree crops. Given a yearly rainfall of over 3000 mm, initial groundwater salinity declined rapidly from over 10 to less than 2 mS cm-1 within two years. On the other hand, seasonal dynamics of the groundwater table in combination with intrusion of saline water into the groundwater body led to recurring elevated salinity, sufficient to affect crops. Tree mortality and yield depression in the flooded area varied considerably between tree species. Damage to coconut (65% trees damaged) was related to tsunami run-up height, while rubber (50% trees damaged) was mainly affected by groundwater salinity. Coconut yields (-35% in average) were constrained by groundwater Ca2+ and Mg2+, while rubber yields (-65% on average) were related to groundwater chloride, pH and soil sodium. These findings have implications on planting deep-rooted tree crops as growth will be constrained by ongoing oscillations of the groundwater table and salinity.

Modelling supply and demand of bioenergy from short rotation coppice and Miscanthus in the UK.

PubMed

Bauen, A W; Dunnett, A J; Richter, G M; Dailey, A G; Aylott, M; Casella, E; Taylor, G

2010-11-01

Biomass from lignocellulosic energy crops can contribute to primary energy supply in the short term in heat and electricity applications and in the longer term in transport fuel applications. This paper estimates the optimal feedstock allocation of herbaceous and woody lignocellulosic energy crops for England and Wales based on empirical productivity models. Yield maps for Miscanthus, willow and poplar, constrained by climatic, soil and land use factors, are used to estimate the potential resource. An energy crop supply-cost curve is estimated based on the resource distribution and associated production costs. The spatial resource model is then used to inform the supply of biomass to geographically distributed demand centres, with co-firing plants used as an illustration. Finally, the potential contribution of energy crops to UK primary energy and renewable energy targets is discussed. Copyright 2010 Elsevier Ltd. All rights reserved.

Vertical farming increases lettuce yield per unit area compared to conventional horizontal hydroponics.

PubMed

Touliatos, Dionysios; Dodd, Ian C; McAinsh, Martin

2016-08-01

Vertical farming systems (VFS) have been proposed as an engineering solution to increase productivity per unit area of cultivated land by extending crop production into the vertical dimension. To test whether this approach presents a viable alternative to horizontal crop production systems, a VFS (where plants were grown in upright cylindrical columns) was compared against a conventional horizontal hydroponic system (HHS) using lettuce ( Lactuca sativa L . cv. "Little Gem") as a model crop. Both systems had similar root zone volume and planting density. Half-strength Hoagland's solution was applied to plants grown in perlite in an indoor controlled environment room, with metal halide lamps providing artificial lighting. Light distribution (photosynthetic photon flux density, PPFD) and yield (shoot fresh weight) within each system were assessed. Although PPFD and shoot fresh weight decreased significantly in the VFS from top to base, the VFS produced more crop per unit of growing floor area when compared with the HHS. Our results clearly demonstrate that VFS presents an attractive alternative to horizontal hydroponic growth systems and suggest that further increases in yield could be achieved by incorporating artificial lighting in the VFS.

Environmental limitation mapping of potential biomass resources across the conterminous United States

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

Daly, Christopher; Halbleib, Michael D.; Hannaway, David B.

Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long-term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstockmore » Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM-ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long-term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low-temperature response, summer high-temperature response, and soil pH, salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.« less

Environmental limitation mapping of potential biomass resources across the conterminous United States

DOE PAGES

Daly, Christopher; Halbleib, Michael D.; Hannaway, David B.; ...

2017-12-22

Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long-term production potentials in the United states. Such information is a starting point for planners and economic modelers, and there is a need for this spatial information to be developed in a consistent manner for a variety of crops, so that their production potentials can be intercompared to support crop selection decisions. As part of the Sun Grant Regional Feedstockmore » Partnership (RFP), an approach to mapping these potential biomass resources was developed to take advantage of the informational synergy realized when bringing together coordinated field trials, close interaction with expert agronomists, and spatial modeling into a single, collaborative effort. A modeling and mapping system called PRISM-ELM was designed to answer a basic question: How do climate and soil characteristics affect the spatial distribution and long-term production patterns of a given crop? This empirical/mechanistic/biogeographical hybrid model employs a limiting factor approach, where productivity is determined by the most limiting of the factors addressed in submodels that simulate water balance, winter low-temperature response, summer high-temperature response, and soil pH, salinity, and drainage. Yield maps are developed through linear regressions relating soil and climate attributes to reported yield data. The model was parameterized and validated using grain yield data for winter wheat and maize, which served as benchmarks for parameterizing the model for upland and lowland switchgrass, CRP grasses, Miscanthus, biomass sorghum, energycane, willow, and poplar. The resulting maps served as potential production inputs to analyses comparing the viability of biomass crops under various economic scenarios. The modeling and parameterization framework can be expanded to include other biomass crops.« less

Climate change and drought effects on rural income distribution in the Mediterranean: a case study for Spain

NASA Astrophysics Data System (ADS)

Quiroga, S.; Suárez, C.

2015-07-01

This paper examines the effects of climate change and drought on agricultural outputs in Spanish rural areas. By now the effects of drought as a response to climate change or policy restrictions have been analyzed through response functions considering direct effects on crop productivity and incomes. These changes also affect incomes distribution in the region and therefore modify the social structure. Here we consider this complementary indirect effect on social distribution of incomes which is essential in the long term. We estimate crop production functions for a range of Mediterranean crops in Spain and we use a decomposition of inequalities measure to estimate the impact of climate change and drought on yield disparities. This social aspect is important for climate change policies since it can be determinant for the public acceptance of certain adaptation measures in a context of drought. We provide the empirical estimations for the marginal effects of the two considered impacts: farms' income average and social income distribution. In our estimates we consider crop productivity response to both bio-physical and socio-economic aspects to analyze long term implications on both competitiveness and social disparities. We find disparities in the adaptation priorities depending on the crop and the region analyzed.

Temporal variability of green and blue water footprint worldwide

NASA Astrophysics Data System (ADS)

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

2016-04-01

Water footprint assessment is becoming widely used in the scientific literature and it is proving useful in a number of multidisciplinary contexts. Given this increasing popularity, measures of green and blue water footprint (or virtual water content, VWC) require evaluations of uncertainty and variability to quantify the reliability of proposed analyses. As of today, no studies are known to assess the temporal variability of crop VWC at the global scale; the present contribution aims at filling this gap. We use a global high-resolution distributed model to compute the VWC of staple crops (wheat and maize), basing on the soil water balance, forced by hydroclimatic imputs, and on the total crop evapotranspiration in multiple growing seasons. Crop actual yield is estimated using country-based yield data, adjusted to account for spatial variability, allowing for the analysis of the different role played by climatic and management factors in the definition of crop yield. The model is then run using hydroclimatic data, i.e., precipitation and potential evapotranspiration, for the period 1961-2013 as taken from the CRU database (CRU TS v. 3.23) and using the corresponding country-based yield data from FAOSTAT. Results provide the time series of total evapotranspiration, actual yield and VWC, with separation between green and blue VWC, and the overall volume of water used for crop production, both at the cell scale (5x5 arc-min) and aggregated at the country scale. Preliminary results indicate that total (green+blue) VWC is, in general, weekly dependent on hydroclimatic forcings if water for irrigation is unlimited, because irrigated agriculture allows to compensate temporary water shortage. Conversely, most part of the VWC variability is found to be determined by the temporal evolution of crop yield. At the country scale, the total water used by countries for agricultural production has seen a limited change in time, but the marked increase in the water-use efficiency expressed by VWC has determined an increase of production. Such increase has helped to meet the increasing global food demand in the past 50 years.

Assessment of physical and chemical indicators of sandy soil quality for sustainable crop production

NASA Astrophysics Data System (ADS)

Lipiec, Jerzy; Usowicz, Boguslaw

2017-04-01

Sandy soils are used in agriculture in many regions of the world. The share of sandy soils in Poland is about 55%. The aim of this study was to assess spatial variability of soil physical and chemical properties affecting soil quality and crop yields in the scale of field (40 x 600 m) during three years of different weather conditions. The experimental field was located on the post glacial and acidified sandy deposits of low productivity (Szaniawy, Podlasie Region, Poland). Physical soil quality indicators included: content of sand, silt, clay and water, bulk density and those chemical: organic carbon, cation exchange capacity, acidity (pH). Measurements of the most soil properties were done at spring and summer each year in topsoil and subsoil layer in 150 points. Crop yields were evaluated in places close to measuring points of the soil properties. Basic statistics including mean, standard deviation, skewness, kurtosis minimal, maximal and correlations between the soil properties and crop yields were calculated. Analysis of spatial dependence and distribution for each property was performed using geostatistical methods. Mathematical functions were fitted to the experimentally derived semivariograms that were used for mapping the soil properties and crop yield by kriging. The results showed that the largest variations had clay content (CV 67%) and the lowest: sand content (5%). The crop yield was most negatively correlated with sand content and most positively with soil water content and cation exchange capacity. In general the exponential semivariogram models fairly good matched to empirical data. The range of semivariogram models of the measured indicators varied from 14 m to 250 m indicate high and moderate spatial variability. The values of the nugget-to-sill+nugget ratios showed that most of the soil properties and crop yields exhibited strong and moderate spatial dependency. The kriging maps allowed identification of low yielding sub-field areas that correspond with low soil organic carbon and cation exchange capacity and high content of sand. These areas are considered as management zones to improve crop productivity and soil properties responsible for soil quality and functions. We conclude that soil organic carbon, cation exchange capacity and pH should be included as indicators of soil quality in sandy soils. The study was funded by HORIZON 2020, European Commission, Programme H2020-SFS-2015-2: Soil Care for profitable and sustainable crop production in Europe, project No. 677407 (SoilCare, 2016-2021).

Increasing crop diversity mitigates weather variations and improves yield stability.

PubMed

Gaudin, Amélie C M; Tolhurst, Tor N; Ker, Alan P; Janovicek, Ken; Tortora, Cristina; Martin, Ralph C; Deen, William

2015-01-01

Cropping sequence diversification provides a systems approach to reduce yield variations and improve resilience to multiple environmental stresses. Yield advantages of more diverse crop rotations and their synergistic effects with reduced tillage are well documented, but few studies have quantified the impact of these management practices on yields and their stability when soil moisture is limiting or in excess. Using yield and weather data obtained from a 31-year long term rotation and tillage trial in Ontario, we tested whether crop rotation diversity is associated with greater yield stability when abnormal weather conditions occur. We used parametric and non-parametric approaches to quantify the impact of rotation diversity (monocrop, 2-crops, 3-crops without or with one or two legume cover crops) and tillage (conventional or reduced tillage) on yield probabilities and the benefits of crop diversity under different soil moisture and temperature scenarios. Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations. Introducing small grains into short corn-soybean rotation was enough to provide substantial benefits on long-term soybean yields and their stability while the effects on corn were mostly associated with the temporal niche provided by small grains for underseeded red clover or alfalfa. Crop diversification strategies increased the probability of harnessing favorable growing conditions while decreasing the risk of crop failure. In hot and dry years, diversification of corn-soybean rotations and reduced tillage increased yield by 7% and 22% for corn and soybean respectively. Given the additional advantages associated with cropping system diversification, such a strategy provides a more comprehensive approach to lowering yield variability and improving the resilience of cropping systems to multiple environmental stresses. This could help to sustain future yield levels in challenging production environments.

Closing yield gaps: perils and possibilities for biodiversity conservation.

PubMed

Phalan, Ben; Green, Rhys; Balmford, Andrew

2014-04-05

Increasing agricultural productivity to 'close yield gaps' creates both perils and possibilities for biodiversity conservation. Yield increases often have negative impacts on species within farmland, but at the same time could potentially make it more feasible to minimize further cropland expansion into natural habitats. We combine global data on yield gaps, projected future production of maize, rice and wheat, the distributions of birds and their estimated sensitivity to changes in crop yields to map where it might be most beneficial for bird conservation to close yield gaps as part of a land-sparing strategy, and where doing so might be most damaging. Closing yield gaps to attainable levels to meet projected demand in 2050 could potentially help spare an area equivalent to that of the Indian subcontinent. Increasing yields this much on existing farmland would inevitably reduce its biodiversity, and therefore we advocate efforts both to constrain further increases in global food demand, and to identify the least harmful ways of increasing yields. The land-sparing potential of closing yield gaps will not be realized without specific mechanisms to link yield increases to habitat protection (and restoration), and therefore we suggest that conservationists, farmers, crop scientists and policy-makers collaborate to explore promising mechanisms.

Climate change and drought effects on rural income distribution in the Mediterranean: a case study for Spain

NASA Astrophysics Data System (ADS)

Quiroga, Sonia; Suárez, Cristina

2016-06-01

This paper examines the effects of climate change and drought on agricultural incomes in Spanish rural areas. Present research has focused on the effects of these extreme climatological events through response functions, considering effects on crop productivity and average incomes. Among the impacts of droughts, we focused on potential effects on income distribution. The study of the effects on abnormally dry periods is therefore needed in order to perform an analysis of diverse social aspects in the long term. We estimate crop production functions for a range of Mediterranean crops in Spain and we use a measure of the decomposition of inequality to estimate the impact of climate change and drought on yield disparities. Certain adaptation measures may require a better understanding of risks by the public to achieve general acceptance. We provide empirical estimations for the marginal effects of the two impacts considered: farms' average income and income distribution. Our estimates consider crop production response to both biophysical and socio-economic aspects to analyse long-term implications on competitiveness and disparities. As for the results, we find disparities in the adaptation priorities depending on the crop and the region analysed.

Evaluation of Projected Agricultural Climate Risk over the Contiguous US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2017-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural production. Additionally, climate extremes have recently highlighted the vulnerability of our agricultural system to climate variability. This study seeks to fill two important gaps in current knowledge: how does the widespread response of irrigated crops differ from rainfed and how can we best account for uncertainty in yield responses. We developed a stochastic approach to evaluate climate risk quantitatively to better understand the historical impacts of climate change and estimate the future impacts it may bring about to agricultural system. Our model consists of Bayesian regression, distribution fitting, and Monte Carlo simulation to simulate rainfed and irrigated crop yields at the US county level. The model was fit using historical data for 1970-2010 and was then applied over different climate regions in the contiguous US using the CMIP5 climate projections. The relative importance of many major growing season climate indices, such as consecutive dry days without rainfall or heavy precipitation, was evaluated to determine what climate indices play a role in affecting future crop yields. The statistical modeling framework also evaluated the impact of irrigation by using county-level irrigated and rainfed yields separately. Furthermore, the projected years with negative yield anomalies were specifically evaluated in terms of magnitude, trend and potential climate drivers. This framework provides estimates of the agricultural climate risk for the 21st century that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. The results of this study can contribute to decision making about crop choice and water use in an uncertain future climate.

Simulation of targeted pollutant-mitigation-strategies to reduce nitrate and sediment hotspots in agricultural watershed.

PubMed

Teshager, Awoke Dagnew; Gassman, Philip W; Secchi, Silvia; Schoof, Justin T

2017-12-31

About 50% of U.S. water pollution problems are caused by non-point source (NPS) pollution, primarily sediment and nutrients from agricultural areas, despite the widespread implementation of agricultural Best Management Practices (BMPs). However, the effectiveness of implementation strategies and type of BMPs at watershed scale are still not well understood. In this study, the Soil and Water Assessment Tool (SWAT) ecohydrological model was used to assess the effectiveness of pollutant mitigation strategies in the Raccoon River watershed (RRW) in west-central Iowa, USA. We analyzed fourteen management scenarios based on systematic combinations of five strategies: fertilizer/manure management, changing row-crop land to perennial grass, vegetative filter strips, cover crops and shallower tile drainage systems, specifically aimed at reducing nitrate and total suspended sediment yields from hotspot areas in the RRW. Moreover, we assessed implications of climate change on management practices, and the impacts of management practices on water availability, row crop yield, and total agricultural production. Our results indicate that sufficient reduction of nitrate load may require either implementation of multiple management practices (38.5% with current setup) or conversion of extensive areas into perennial grass (up to 49.7%) to meet and maintain the drinking water standard. However, climate change may undermine the effectiveness of management practices, especially late in the 21st century, cutting the reduction by up to 65% for nitrate and more for sediment loads. Further, though our approach is targeted, it resulted in a slight decrease (~5%) in watershed average crop yield and hence an overall reduction in total crop production, mainly due to the conversion of row-crop lands to perennial grass. Such yield reductions could be quite spatially heterogeneously distributed (0 to 40%). Copyright © 2017 Elsevier B.V. All rights reserved.

Global Agriculture Yields and Conflict under Future Climate

NASA Astrophysics Data System (ADS)

Rising, J.; Cane, M. A.

2013-12-01

Aspects of climate have been shown to correlate significantly with conflict. We investigate a possible pathway for these effects through changes in agriculture yields, as predicted by field crop models (FAO's AquaCrop and DSSAT). Using satellite and station weather data, and surveyed data for soil and management, we simulate major crop yields across all countries between 1961 and 2008, and compare these to FAO and USDA reported yields. Correlations vary by country and by crop, from approximately .8 to -.5. Some of this range in crop model performance is explained by crop varieties, data quality, and other natural, economic, and political features. We also quantify the ability of AquaCrop and DSSAT to simulate yields under past cycles of ENSO as a proxy for their performance under changes in climate. We then describe two statistical models which relate crop yields to conflict events from the UCDP/PRIO Armed Conflict dataset. The first relates several preceding years of predicted yields of the major grain in each country to any conflict involving that country. The second uses the GREG ethnic group maps to identify differences in predicted yields between neighboring regions. By using variation in predicted yields to explain conflict, rather than actual yields, we can identify the exogenous effects of weather on conflict. Finally, we apply precipitation and temperature time-series under IPCC's A1B scenario to the statistical models. This allows us to estimate the scale of the impact of future yields on future conflict. Centroids of the major growing regions for each country's primary crop, based on USDA FAS consumption. Correlations between simulated yields and reported yields, for AquaCrop and DSSAT, under the assumption that no irrigation, fertilization, or pest control is used. Reported yields are the average of FAO yields and USDA FAS yields, where both are available.

Impact of crop rotation and soil amendments on long-term no-tilled soybean yields

USDA-ARS?s Scientific Manuscript database

Continuous cropping systems without cover crops are perceived as unsustainable for long-term yield and soil health. To test this, cropping sequence and cover crop effects on soybean (Glycine max L.) yields were assessed. Main effects were 10 cropping sequences of soybean, corn (Zea mays L.), and co...

Tradeoffs between water requirements and yield stability in annual vs. perennial crops

NASA Astrophysics Data System (ADS)

Vico, Giulia; Brunsell, Nathaniel A.

2018-02-01

Population growth and changes in climate and diets will likely further increase the pressure on agriculture and water resources globally. Currently, staple crops are obtained from annuals plants. A shift towards perennial crops may enhance many ecosystem services, but at the cost of higher water requirements and lower yields. It is still unclear when the advantages of perennial crops overcome their disadvantages and perennial crops are thus a sustainable solution. Here we combine a probabilistic description of the soil water balance and crop development with an extensive dataset of traits of congeneric annuals and perennials to identify the conditions for which perennial crops are more viable than annual ones with reference to yield, yield stability, and effective use of water. We show that the larger and more developed roots of perennial crops allow a better exploitation of soil water resources and a reduction of yield variability with respect to annual species, but their yields remain lower when considering grain crops. Furthermore, perennial crops have higher and more variable irrigation requirements and lower water productivity. These results are important to understand the potential consequences for yield, its stability, and water resource use of a shift from annual to perennial crops and, more generally, if perennial crops may be more resilient than annual crops in the face of climatic fluctuations.

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

DOE PAGES

Blanc, Élodie

2017-01-26

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

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

Blanc, Élodie

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

Yield and Economic Responses of Peanut to Crop Rotation Sequence

USDA-ARS?s Scientific Manuscript database

Proper crop rotation is essential to maintaining high peanut yield and quality. However, the economic considerations of maintaining or altering crop rotation sequences must incorporate the commodity prices, production costs, and yield responses of all crops in, or potentially in, the crop rotation ...

Modelling impacts of second generation bioenergy production on Ecosystem Services in Europe

NASA Astrophysics Data System (ADS)

Henner, D. N.; Smith, P.; Davies, C.; McNamara, N. P.

2016-12-01

Bioenergy crops are an important source of renewable energy and likely to play a major role in transitioning to a lower CO2 energy system. There is, however, uncertainty about the impacts of the growth of bioenergy crops on broader sustainability encompassed by ecosystem services, further enhanced by ongoing climate change. The goal of this project is to develop a comprehensive model that covers ecosystem services at a continental scale including biodiversity and pollination, water and air security, erosion control and soil security, GHG emissions, soil C and cultural services like tourism value. The technical distribution potential and likely yield of second generation energy crops, such as Miscanthus, Short Rotation Coppice (SRC; willow and poplar) was modelled using ECOSSE, DayCent, SalixFor and MiscanFor models. In addition, methods like water footprint tools, tourism value maps and ecosystem valuation tools and models are utilised. We will present results for synergies and trade-offs between land use change and ecosystem services, impact on food security and land management. Further, we will show modelled yield maps for different cultivars of Miscanthus, willow and poplar in Europe and constraint/opportunity maps based on projected yield and other factors e.g. total economic value, technical potential, current land use, climate change and trade-offs and synergies. It will be essential to include multiple ecosystem services when assessing the potential for bioenergy production/expansion that does not impact other land uses or provisioning services. Considering that the soil GHG balance is dominated by change in soil organic carbon (SOC) and the difference among Miscanthus and SRC is largely determined by yield, an important target for management of perennial energy crops is to achieve the best possible yield using the most appropriate energy crop and cultivar for the local situation. This research could inform future policy decisions on bioenergy crops in Europe.

A probabilistic model framework for evaluating year-to-year variation in crop productivity

NASA Astrophysics Data System (ADS)

Yokozawa, M.; Iizumi, T.; Tao, F.

2008-12-01

Most models describing the relation between crop productivity and weather condition have so far been focused on mean changes of crop yield. For keeping stable food supply against abnormal weather as well as climate change, evaluating the year-to-year variations in crop productivity rather than the mean changes is more essential. We here propose a new framework of probabilistic model based on Bayesian inference and Monte Carlo simulation. As an example, we firstly introduce a model on paddy rice production in Japan. It is called PRYSBI (Process- based Regional rice Yield Simulator with Bayesian Inference; Iizumi et al., 2008). The model structure is the same as that of SIMRIW, which was developed and used widely in Japan. The model includes three sub- models describing phenological development, biomass accumulation and maturing of rice crop. These processes are formulated to include response nature of rice plant to weather condition. This model inherently was developed to predict rice growth and yield at plot paddy scale. We applied it to evaluate the large scale rice production with keeping the same model structure. Alternatively, we assumed the parameters as stochastic variables. In order to let the model catch up actual yield at larger scale, model parameters were determined based on agricultural statistical data of each prefecture of Japan together with weather data averaged over the region. The posterior probability distribution functions (PDFs) of parameters included in the model were obtained using Bayesian inference. The MCMC (Markov Chain Monte Carlo) algorithm was conducted to numerically solve the Bayesian theorem. For evaluating the year-to-year changes in rice growth/yield under this framework, we firstly iterate simulations with set of parameter values sampled from the estimated posterior PDF of each parameter and then take the ensemble mean weighted with the posterior PDFs. We will also present another example for maize productivity in China. The framework proposed here provides us information on uncertainties, possibilities and limitations on future improvements in crop model as well.

Influence of management history and landscape variables on soil organic carbon and soil redistribution

USGS Publications Warehouse

Venteris, E.R.; McCarty, G.W.; Ritchie, J.C.; Gish, T.

2004-01-01

Controlled studies to investigate the interaction between crop growth, soil properties, hydrology, and management practices are common in agronomy. These sites (much as with real world farmland) often have complex management histories and topographic variability that must be considered. In 1993 an interdisiplinary study was started for a 20-ha site in Beltsville, MD. Soil cores (271) were collected in 1999 in a 30-m grid (with 5-m nesting) and analyzed as part of the site characterization. Soil organic carbon (SOC) and 137Cesium (137Cs) were measured. Analysis of aerial photography from 1992 and of farm management records revealed that part of the site had been maintained as a swine pasture and the other portion as cropped land. Soil properties, particularly soil redistribution and SOC, show large differences in mean values between the two areas. Mass C is 0.8 kg m -2 greater in the pasture area than in the cropped portion. The pasture area is primarily a deposition site, whereas the crop area is dominated by erosion. Management influence is suggested, but topographic variability confounds interpretation. Soil organic carbon is spatially structured, with a regionalized variable of 120 m. 137Cs activity lacks spatial structure, suggesting disturbance of the profile by animal activity and past structures such as swine shelters and roads. Neither SOC nor 137Cs were strongly correlated to terrain parameters, crop yields, or a seasonal soil moisture index predicted from crop yields. SOC and 137Cs were weakly correlated (r2 ???0.2, F-test P-value 0.001), suggesting that soil transport controls, in part, SOC distribution. The study illustrates the importance of past site history when interpreting the landscape distribution of soil properties, especially those strongly influenced by human activity. Confounding variables, complex soil hydrology, and incomplete documentation of land use history make definitive interpretations of the processes behind the spatial distributions difficult. Such complexity may limit the accuracy of scaling approaches to mapping SOC and soil redistribution.

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.

Increasing Crop Diversity Mitigates Weather Variations and Improves Yield Stability

PubMed Central

Gaudin, Amélie C. M.; Tolhurst, Tor N.; Ker, Alan P.; Janovicek, Ken; Tortora, Cristina; Martin, Ralph C.; Deen, William

2015-01-01

Cropping sequence diversification provides a systems approach to reduce yield variations and improve resilience to multiple environmental stresses. Yield advantages of more diverse crop rotations and their synergistic effects with reduced tillage are well documented, but few studies have quantified the impact of these management practices on yields and their stability when soil moisture is limiting or in excess. Using yield and weather data obtained from a 31-year long term rotation and tillage trial in Ontario, we tested whether crop rotation diversity is associated with greater yield stability when abnormal weather conditions occur. We used parametric and non-parametric approaches to quantify the impact of rotation diversity (monocrop, 2-crops, 3-crops without or with one or two legume cover crops) and tillage (conventional or reduced tillage) on yield probabilities and the benefits of crop diversity under different soil moisture and temperature scenarios. Although the magnitude of rotation benefits varied with crops, weather patterns and tillage, yield stability significantly increased when corn and soybean were integrated into more diverse rotations. Introducing small grains into short corn-soybean rotation was enough to provide substantial benefits on long-term soybean yields and their stability while the effects on corn were mostly associated with the temporal niche provided by small grains for underseeded red clover or alfalfa. Crop diversification strategies increased the probability of harnessing favorable growing conditions while decreasing the risk of crop failure. In hot and dry years, diversification of corn-soybean rotations and reduced tillage increased yield by 7% and 22% for corn and soybean respectively. Given the additional advantages associated with cropping system diversification, such a strategy provides a more comprehensive approach to lowering yield variability and improving the resilience of cropping systems to multiple environmental stresses. This could help to sustain future yield levels in challenging production environments. PMID:25658914

Crop yield responses to a hardwood biochar across varied soils and climate conditions

USDA-ARS?s Scientific Manuscript database

Biochars applied to soil for crop yield improvements have produced mixed results. The assorted crop yield responses may be linked to employing biochars with diverse chemical and physical characteristics. To clarify if biochars can improve crop yields, it may be prudent to evaluate one biochar type...

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) ...

Modeling olive-crop forecasting in Tunisia

NASA Astrophysics Data System (ADS)

Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

2017-05-01

Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

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 reduced nitrogen inputs on crop yield and nitrogen use efficiency in a long-term maize-soybean relay strip intercropping system.

PubMed

Chen, Ping; Du, Qing; Liu, Xiaoming; Zhou, Li; Hussain, Sajad; Lei, Lu; Song, Chun; Wang, Xiaochun; Liu, Weiguo; Yang, Feng; Shu, Kai; Liu, Jiang; Du, Junbo; Yang, Wenyu; Yong, Taiwen

2017-01-01

The blind pursuit of high yields via increased fertilizer inputs increases the environmental costs. Relay intercropping has advantages for yield, but a strategy for N management is urgently required to decrease N inputs without yield loss in maize-soybean relay intercropping systems (IMS). Experiments were conducted with three levels of N and three planting patterns, and dry matter accumulation, nitrogen uptake, nitrogen use efficiency (NUE), competition ratio (CR), system productivity index (SPI), land equivalent ratio (LER), and crop root distribution were investigated. Our results showed that the CR of soybean was greater than 1, and that the change in root distribution in space and time resulted in an interspecific facilitation in IMS. The maximum yield of maize under monoculture maize (MM) occurred with conventional nitrogen (CN), whereas under IMS, the maximum yield occurred with reduced nitrogen (RN). The yield of monoculture soybean (MS) and of soybean in IMS both reached a maximum under RN. The LER of IMS varied from 1.85 to 2.36, and the SPI peaked under RN. Additionally, the NUE of IMS increased by 103.7% under RN compared with that under CN. In conclusion, the separation of the root ecological niche contributed to a positive interspecific facilitation, which increased the land productivity. Thus, maize-soybean relay intercropping with reduced N input provides a very useful approach to increase land productivity and avert environmental pollution.

Effects of different on-farm management on yield and water use efficiency of Potato crop cultivated in semiarid environments under subsurface drip irrigation

NASA Astrophysics Data System (ADS)

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

2016-04-01

In Tunisia the amount of water for irrigated agriculture is higher than about 80% of the total resource.The increasing population and the rising food demand, associated to the negative effects of climate change,make it crucial to adopt strategies aiming to improve water use efficiency (WUE). Moreover, the absence of an effective public policy for water management amplifies the imbalance between water supply and its demand. Despite improved irrigation technologies can enhance the efficiency of water distribution systems, to achieve environmental goals it is also necessaryto identify on-farm management strategies accounting for actual crop water requirement. The main objective of the paper was to assess the effects of different on-farm managementstrategies (irrigation scheduling and planting date) on yield and water use efficiency of Potato crop (Solanumtuberosum L.) irrigated with a subsurface drip system, under the semi-arid climate of central Tunisia. Experiments were carried out during three growing seasons (2012, 2014 and 2015) at the High Agronomic Institute of ChottMariem in Sousse, by considering different planting dates and irrigation depths, the latter scheduled according to the climate observed during the season. All the considered treatments received the same pesticide and fertilizer management. Experiments evidenced that the climatic variability characterizing the examined seasons (photoperiod, solar radiation and average temperature) affects considerably the crop phenological stages, and the late sowing shortens the crop cycle.It has also been demonstrated that Leaf Area Index (LAI) and crop yield resulted relatively higher for those treatments receiving larger amounts of seasonal water. Crop yield varied between 16.3 t/ha and 39.1 t/ha, with a trend linearly related to the ratio between the seasonal amount of water supplied (Irrigation, I and Precipitation, P) and the maximum crop evapotranspiration (ETm). The maximum crop yield was in particular obtained for a value of this ratio equal to 1.45. Moreover, when increasing the seasonal pluviometric deficit (P-ETm) and therefore the irrigation depth (I), standard deviations of crop yield tended to decrease, as a consequence ofthe more uniform soil water content in the root zone. In terms of agronomic water use efficiency (AWUE),differences among the investigated treatments varied in a quite narrow range,due to thecombined effects of seasonal precipitation and atmospheric water demand on irrigation depths and crop yield.On the other hand, when considering irrigation water use efficiency (IWUE), more relevant differences between treatments were observed,being the higher values of IWUEgenerally associated to the lower irrigation depths. However, to define the best irrigation management strategy it is necessary, from one side, to consider the availability of water and from the other, to perform aneconomic analysis accounting for the cost of water and the related benefits achievable by the farmer.

A Remote Sensing-Derived Corn Yield Assessment Model

NASA Astrophysics Data System (ADS)

Shrestha, Ranjay Man

Agricultural studies and food security have become critical research topics due to continuous growth in human population and simultaneous shrinkage in agricultural land. In spite of modern technological advancements to improve agricultural productivity, more studies on crop yield assessments and food productivities are still necessary to fulfill the constantly increasing food demands. Besides human activities, natural disasters such as flood and drought, along with rapid climate changes, also inflect an adverse effect on food productivities. Understanding the impact of these disasters on crop yield and making early impact estimations could help planning for any national or international food crisis. Similarly, the United States Department of Agriculture (USDA) Risk Management Agency (RMA) insurance management utilizes appropriately estimated crop yield and damage assessment information to sustain farmers' practice through timely and proper compensations. Through County Agricultural Production Survey (CAPS), the USDA National Agricultural Statistical Service (NASS) uses traditional methods of field interviews and farmer-reported survey data to perform annual crop condition monitoring and production estimations at the regional and state levels. As these manual approaches of yield estimations are highly inefficient and produce very limited samples to represent the entire area, NASS requires supplemental spatial data that provides continuous and timely information on crop production and annual yield. Compared to traditional methods, remote sensing data and products offer wider spatial extent, more accurate location information, higher temporal resolution and data distribution, and lower data cost--thus providing a complementary option for estimation of crop yield information. Remote sensing derived vegetation indices such as Normalized Difference Vegetation Index (NDVI) provide measurable statistics of potential crop growth based on the spectral reflectance and could be further associated with the actual yield. Utilizing satellite remote sensing products, such as daily NDVI derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m pixel size, the crop yield estimation can be performed at a very fine spatial resolution. Therefore, this study examined the potential of these daily NDVI products within agricultural studies and crop yield assessments. In this study, a regression-based approach was proposed to estimate the annual corn yield through changes in MODIS daily NDVI time series. The relationship between daily NDVI and corn yield was well defined and established, and as changes in corn phenology and yield were directly reflected by the changes in NDVI within the growing season, these two entities were combined to develop a relational model. The model was trained using 15 years (2000-2014) of historical NDVI and county-level corn yield data for four major corn producing states: Kansas, Nebraska, Iowa, and Indiana, representing four climatic regions as South, West North Central, East North Central, and Central, respectively, within the U.S. Corn Belt area. The model's goodness of fit was well defined with a high coefficient of determination (R2>0.81). Similarly, using 2015 yield data for validation, 92% of average accuracy signified the performance of the model in estimating corn yield at county level. Besides providing the county-level corn yield estimations, the derived model was also accurate enough to estimate the yield at finer spatial resolution (field level). The model's assessment accuracy was evaluated using the randomly selected field level corn yield within the study area for 2014, 2015, and 2016. A total of over 120 plot level corn yield were used for validation, and the overall average accuracy was 87%, which statistically justified the model's capability to estimate plot-level corn yield. Additionally, the proposed model was applied to the impact estimation by examining the changes in corn yield due to flood events during the growing season. Using a 2011 Missouri River flood event as a case study, field-level flood impact map on corn yield throughout the flooded regions was produced and an overall agreement of over 82.2% was achieved when compared with the reference impact map. The future research direction of this dissertation research would be to examine other major crops outside the Corn Belt region of the U.S.

Mapping Human-Dominated Landscapes: the Distribution and Yield of Major Crops of the World

NASA Astrophysics Data System (ADS)

Monfreda, C.; Ramankutty, N.; Foley, J. A.

2005-12-01

Croplands cover 18 million km2, an area the size of South America, and provide ecosystem goods and services essential to human well-being. Most global land-cover classifications group the diversity of croplands into a single or very few categories, thereby excluding critical information to answer key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information on land-use practices is even more limited. The relative lack of information about agricultural landscapes results partly from difficulties in using satellite data to identify individual crop types and land-use practices at a global scale. We address limitations common to remote-sensing classifications by distributing national, state, and county level statistics across a recently updated global dataset of cropland cover at 5 minute resolution. The resulting datasets depict the fractional harvested area and yield of twenty distinct crop types: maize, wheat, rice, sorghum, millet, barley, oats, soybeans, sunflower, rapeseed/canola, pulses, groundnuts/peanuts, oil palm, cassava, potatoes, sugar cane, sugar beets, tobacco, coffee, and cotton. These datasets represent the state of agriculture circa the year 2000 and will be made available for applications in ecological analysis, modeling, visualization, and education.

Assessment of climate change impact on yield of major crops in the Banas River Basin, India.

PubMed

Dubey, Swatantra Kumar; Sharma, Devesh

2018-09-01

Crop growth models like AquaCrop are useful in understanding the impact of climate change on crop production considering the various projections from global circulation models and regional climate models. The present study aims to assess the climate change impact on yield of major crops in the Banas River Basin i.e., wheat, barley and maize. Banas basin is part of the semi-arid region of Rajasthan state in India. AquaCrop model is used to calculate the yield of all the three crops for a historical period of 30years (1981-2010) and then compared with observed yield data. Root Mean Square Error (RMSE) values are calculated to assess the model accuracy in prediction of yield. Further, the calibrated model is used to predict the possible impacts of climate change and CO 2 concentration on crop yield using CORDEX-SA climate projections of three driving climate models (CNRM-CM5, CCSM4 and MPI-ESM-LR) for two different scenarios (RCP4.5 and RCP8.5) for the future period 2021-2050. RMSE values of simulated yield with respect to observed yield of wheat, barley and maize are 11.99, 16.15 and 19.13, respectively. It is predicted that crop yield of all three crops will increase under the climate change conditions for future period (2021-2050). Copyright © 2018 Elsevier B.V. All rights reserved.

Development of estimation method for crop yield using MODIS satellite imagery data and process-based model for corn and soybean in US Corn-Belt region

NASA Astrophysics Data System (ADS)

Lee, J.; Kang, S.; Jang, K.; Ko, J.; Hong, S.

2012-12-01

Crop productivity is associated with the food security and hence, several models have been developed to estimate crop yield by combining remote sensing data with carbon cycle processes. In present study, we attempted to estimate crop GPP and NPP using algorithm based on the LUE model and a simplified respiration model. The state of Iowa and Illinois was chosen as the study site for estimating the crop yield for a period covering the 5 years (2006-2010), as it is the main Corn-Belt area in US. Present study focuses on developing crop-specific parameters for corn and soybean to estimate crop productivity and yield mapping using satellite remote sensing data. We utilized a 10 km spatial resolution daily meteorological data from WRF to provide cloudy-day meteorological variables but in clear-say days, MODIS-based meteorological data were utilized to estimate daily GPP, NPP, and biomass. County-level statistics on yield, area harvested, and productions were used to test model predicted crop yield. The estimated input meteorological variables from MODIS and WRF showed with good agreements with the ground observations from 6 Ameriflux tower sites in 2006. For examples, correlation coefficients ranged from 0.93 to 0.98 for Tmin and Tavg ; from 0.68 to 0.85 for daytime mean VPD; from 0.85 to 0.96 for daily shortwave radiation, respectively. We developed county-specific crop conversion coefficient, i.e. ratio of yield to biomass on 260 DOY and then, validated the estimated county-level crop yield with the statistical yield data. The estimated corn and soybean yields at the county level ranged from 671 gm-2 y-1 to 1393 gm-2 y-1 and from 213 gm-2 y-1 to 421 gm-2 y-1, respectively. The county-specific yield estimation mostly showed errors less than 10%. Furthermore, we estimated crop yields at the state level which were validated against the statistics data and showed errors less than 1%. Further analysis for crop conversion coefficient was conducted for 200 DOY and 280 DOY. For the case of 280 DOY, Crop yield estimation showed better accuracy for soybean at county level. Though the case of 200 DOY resulted in less accuracy (i.e. 20% mean bias), it provides a useful tool for early forecasting of crop yield. We improved the spatial accuracy of estimated crop yield at county level by developing county-specific crop conversion coefficient. Our results indicate that the aboveground crop biomass can be estimated successfully with the simple LUE and respiration models combined with MODIS data and then, county-specific conversion coefficient can be different with each other across different counties. Hence, applying region-specific conversion coefficient is necessary to estimate crop yield with better accuracy.

The Space-Time Variation of Global Crop Yields, Detecting Simultaneous Outliers and Identifying the Teleconnections with Climatic Patterns

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Pal, I.; Khanbilvardi, R.

2017-12-01

An understanding of the climate factors that influence the space-time variability of crop yields is important for food security purposes and can help us predict global food availability. In this study, we address how the crop yield trends of countries globally were related to each other during the last several decades and the main climatic variables that triggered high/low crop yields simultaneously across the world. Robust Principal Component Analysis (rPCA) is used to identify the primary modes of variation in wheat, maize, sorghum, rice, soybeans, and barley yields. Relations between these modes of variability and important climatic variables, especially anomalous sea surface temperature (SSTa), are examined from 1964 to 2010. rPCA is also used to identify simultaneous outliers in each year, i.e. systematic high/low crop yields across the globe. The results demonstrated spatiotemporal patterns of these crop yields and the climate-related events that caused them as well as the connection of outliers with weather extremes. We find that among climatic variables, SST has had the most impact on creating simultaneous crop yields variability and yield outliers in many countries. An understanding of this phenomenon can benefit global crop trade networks.

Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...

Closing yield gaps: perils and possibilities for biodiversity conservation

PubMed Central

Phalan, Ben; Green, Rhys; Balmford, Andrew

2014-01-01

Increasing agricultural productivity to ‘close yield gaps’ creates both perils and possibilities for biodiversity conservation. Yield increases often have negative impacts on species within farmland, but at the same time could potentially make it more feasible to minimize further cropland expansion into natural habitats. We combine global data on yield gaps, projected future production of maize, rice and wheat, the distributions of birds and their estimated sensitivity to changes in crop yields to map where it might be most beneficial for bird conservation to close yield gaps as part of a land-sparing strategy, and where doing so might be most damaging. Closing yield gaps to attainable levels to meet projected demand in 2050 could potentially help spare an area equivalent to that of the Indian subcontinent. Increasing yields this much on existing farmland would inevitably reduce its biodiversity, and therefore we advocate efforts both to constrain further increases in global food demand, and to identify the least harmful ways of increasing yields. The land-sparing potential of closing yield gaps will not be realized without specific mechanisms to link yield increases to habitat protection (and restoration), and therefore we suggest that conservationists, farmers, crop scientists and policy-makers collaborate to explore promising mechanisms. PMID:24535392

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.

Progress and Challenges in Predicting Crop Responses to Atmospheric [CO2

NASA Astrophysics Data System (ADS)

Kent, J.; Paustian, K.

2017-12-01

Increasing atmospheric [CO2] directly accelerates photosynthesis in C3 crops, and indirectly promotes yields by reducing stomatal conductance and associated water losses in C3 and C4 crops. Several decades of experiments have exposed crops to eCO2 in greenhouses and other enclosures and observed yield increases on the order of 33%. FACE systems were developed in the early 1990s to better replicate open-field growing conditions. Some authors contend that FACE results indicate lower crop yield responses than enclosure studies, while others maintain no significant difference or attribute differences to various methodological factors. The crop CO2 response processes in many crop models were developed using results from enclosure experiments. This work tested the ability of one such model, DayCent, to reproduce crop responses to CO2 enrichment from several FACE experiments. DayCent performed well at simulating yield and transpiration responses in C4 crops, but significantly overestimated yield responses in C3 crops. After adjustment of CO2-response parameters, DayCent was able to reproduce mean yield responses for specific crops. However, crop yield responses from FACE experiments vary widely across years and sites, and likely reflect complex interactions between conditions such as weather, soils, cultivars, and biotic stressors. Further experimental work is needed to identify the secondary variables that explain this variability so that models can more reliably forecast crop yields under climate change. Likewise, CO2 impacts on crop outcomes such as belowground biomass allocation and grain N content have implications for agricultural C fluxes and human nutrition, respectively, but are poorly understood and thus difficult to simulate with confidence.

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

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

NASA Astrophysics Data System (ADS)

Blanc, Elodie; Caron, Justin; Fant, Charles; Monier, Erwan

2017-08-01

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO2 fertilization effect compared to an unconstrained GHG emission scenario.

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields.

PubMed

Blanc, Elodie; Caron, Justin; Fant, Charles; Monier, Erwan

2017-08-01

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.

Assessing gaps in irrigated agricultural productivity through satellite earth observations-A case study of the Fergana Valley, Central Asia

NASA Astrophysics Data System (ADS)

Löw, Fabian; Biradar, Chandrashekhar; Fliemann, Elisabeth; Lamers, John P. A.; Conrad, Christopher

2017-07-01

Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.

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.

Environmental risk assessments for transgenic crops producing output trait enzymes

PubMed Central

Tuttle, Ann; Shore, Scott; Stone, Terry

2009-01-01

The environmental risks from cultivating crops producing output trait enzymes can be rigorously assessed by testing conservative risk hypotheses of no harm to endpoints such as the abundance of wildlife, crop yield and the rate of degradation of crop residues in soil. These hypotheses can be tested with data from many sources, including evaluations of the agronomic performance and nutritional quality of the crop made during product development, and information from the scientific literature on the mode-of-action, taxonomic distribution and environmental fate of the enzyme. Few, if any, specific ecotoxicology or environmental fate studies are needed. The effective use of existing data means that regulatory decision-making, to which an environmental risk assessment provides essential information, is not unnecessarily complicated by evaluation of large amounts of new data that provide negligible improvement in the characterization of risk, and that may delay environmental benefits offered by transgenic crops containing output trait enzymes. PMID:19924556

Impacts of El Nino Southern Oscillation on the Global Yields of Major Crops

NASA Technical Reports Server (NTRS)

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

2014-01-01

The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Nino/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Nino likely improves the global-mean soybean yield by 2.15.4 but appears to change the yields of maize, rice and wheat by -4.3 to +0.8. The global-mean yields of all four crops during La Nina years tend to be below normal (-4.5 to 0.0).Our findings highlight the importance of ENSO to global crop production.

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.

Reduction of CMIP5 models bias using Cumulative Distribution Function transform and impact on crops yields simulations across West Africa.

NASA Astrophysics Data System (ADS)

Moise Famien, Adjoua; Defrance, Dimitri; Sultan, Benjamin; Janicot, Serge; Vrac, Mathieu

2017-04-01

Different CMIP exercises show that the simulations of the future/current temperature and precipitation are complex with a high uncertainty degree. For example, the African monsoon system is not correctly simulated and most of the CMIP5 models underestimate the precipitation. Therefore, Global Climate Models (GCMs) show significant systematic biases that require bias correction before it can be used in impacts studies. Several methods of bias corrections have been developed for several years and are increasingly using more complex statistical methods. The aims of this work is to show the interest of the CDFt (Cumulative Distribution Function transfom (Michelangeli et al.,2009)) method to reduce the data bias from 29 CMIP5 GCMs over Africa and to assess the impact of bias corrected data on crop yields prediction by the end of the 21st century. In this work, we apply the CDFt to daily data covering the period from 1950 to 2099 (Historical and RCP8.5) and we correct the climate variables (temperature, precipitation, solar radiation, wind) by the use of the new daily database from the EU project WATer and global CHange (WATCH) available from 1979 to 2013 as reference data. The performance of the method is assessed in several cases. First, data are corrected based on different calibrations periods and are compared, on one hand, with observations to estimate the sensitivity of the method to the calibration period and, on other hand, with another bias-correction method used in the ISIMIP project. We find that, whatever the calibration period used, CDFt corrects well the mean state of variables and preserves their trend, as well as daily rainfall occurrence and intensity distributions. However, some differences appear when compared to the outputs obtained with the method used in ISIMIP and show that the quality of the correction is strongly related to the reference data. Secondly, we validate the bias correction method with the agronomic simulations (SARRA-H model (Kouressy et al., 2008)) by comparison with FAO crops yields estimations over West Africa. Impact simulations show that crop model is sensitive to input data. They show also decreasing in crop yields by the end of this century. Michelangeli, P. A., Vrac, M., & Loukos, H. (2009). Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36(11). Kouressy M, Dingkuhn M, Vaksmann M and Heinemann A B 2008: Adaptation to diverse semi-arid environments of sorghum genotypes having different plant type and sensitivity to photoperiod. Agric. Forest Meteorol., http://dx.doi.org/10.1016/j.agrformet.2007.09.009

The impact of sea surface temperature on winter wheat in Iberian Peninsula

NASA Astrophysics Data System (ADS)

Capa-Morocho, Mirian; Rodríguez-Fonseca, Belen; Ruiz-Ramos, Margarita

2016-04-01

Climate variability is the main driver of changes in crops yield, especially for rainfed production systems. This is also the case of Iberian Peninsula (IP) (Capa-Morocho et al., 2014), where wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. Previous works have shown that large-scale oceanic patterns have a significant impact on precipitation over IP (Rodriguez-Fonseca and de Castro, 2002; Rodríguez-Fonseca et al., 2006). The existence of some predictability of precipitation has encouraged us to analyze the possible predictability of the wheat yield in the IP using sea surface temperature (SST) anomalies as predictor. For this purpose, a crop model site specific calibrated for the Northeast of IP and several reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that wheat yield anomalies are associated with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) SST. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures and precipitation experienced by the crop during flowering and grain filling. Results from this study could have important implications for predictability issues in agricultural planning and management, such as insurance coverage, changes in sowing dates and choice of species and varieties.

Improving the Agronomy of Alyssum murale for Extensive Phytomining: A Five-Year Field Study.

PubMed

Bani, Aida; Echevarria, Guillaume; Sulçe, Sulejman; Morel, Jean Louis

2015-01-01

Large ultramafic areas exist in Albania, which could be suitable for phytomining with native Alyssum murale. We undertook a five-year field experiment on an ultramafic Vertisol, aimed at optimizing a low-cost Ni-phytoextraction crop of A. murale which is adapted to the Balkans. The following aspects were studied on 18-m2 plots in natural conditions: the effect of (i) plant phenology and element distribution, (ii) plant nutrition and fertilization, (iii) plant cover and weed control and (iv), planting technique (natural cover vs. sown crop). The optimal harvest time was set at the mid-flowering stage when Ni concentration and biomass yield were highest. The application of N, P, and K fertilizers, and especially a split 100-kg ha(-1) N application, increased the density of A. murale against all other species. It significantly increased shoot yield, without reducing Ni concentration. In natural stands, the control of graminaceous weeds required the use of an anti-monocots herbicide. However, after the optimization of fertilization and harvest time, weed control procured little benefit. Finally, cropping sown A. murale was more efficient than enhancing native stands and gave higher biomass and phytoextraction yields; biomass yields progressively improved from 0.3 to 9.0 t ha(-1) and phytoextracted Ni increased from 1.7 to 105 kg ha(-1).

Diverse rotations and poultry litter improves soybean yield

USDA-ARS?s Scientific Manuscript database

Continuous cropping systems without rotations or cover crops are perceived as unsustainable for long-term yield and soil health. Continuous systems, defined as continually producing a crop on the same parcel of land for more than three years, is thought to reduce yields. Given that crop rotations a...

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

DOE PAGES

Blanc, Elodie; Caron, Justin; Fant, Charles; ...

2017-06-27

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climatemore » change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.« less

Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields

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

Blanc, Elodie; Caron, Justin; Fant, Charles

While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climatemore » change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water-stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.« less

Root-knot nematode management in double-cropped plasticulture vegetables.

PubMed

Desaeger, J A; Csinos, A S

2006-03-01

Combination treatments of chisel-injected fumigants (methyl bromide, 1,3-D, metam sodium, and chloropicrin) on a first crop, followed by drip-applied fumigants (metam sodium and 1,3-D +/- chloropicrin) on a second crop, with and without oxamyl drip applications were evaluated for control of Meloidogyne incognita in three different tests (2002 to 2004) in Tifton, GA. First crops were eggplant or tomato, and second crops were cantaloupe, squash, or jalapeno pepper. Double-cropped vegetables suffered much greater root-knot nematode (RKN) pressure than first crops, and almost-total yield loss occurred when second crops received no nematicide treatment. On a first crop of eggplant, all fumigants provided good nematode control and average yield increases of 10% to 15 %. On second crops, higher application rates and fumigant combinations (metam sodium and 1,3-D +/- chloropicrin) improved RKN control and increased yields on average by 20% to 35 % compared to the nonfumigated control. Oxamyl increased yields of the first crop in 2003 on average by 10% to 15% but had no effect in 2004 when RKN failed to establish itself. On double-cropped squash in 2003, oxamyl following fumigation provided significant additional reduction in nematode infection and increased squash yields on average by 30% to 75%.

Landscape-Scale water balance of cotton fields

USDA-ARS?s Scientific Manuscript database

Information on the temporal and spatial distribution of the components of the water balance of a production field is necessary to manage agronomic inputs. Furthermore, factors that determine crop yield require knowledge of the energy, water, nutrient and carbon balance and their interaction. The in...

Portfolio optimization for seed selection in diverse weather scenarios.

PubMed

Marko, Oskar; Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

2017-01-01

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

Portfolio optimization for seed selection in diverse weather scenarios

PubMed Central

Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

2017-01-01

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017. PMID:28863173

Linking Field and Satellite Observations to Reveal Differences in Single vs. Double-Cropped Soybean Yields in Central Brazil

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.

2016-12-01

Soy-corn double cropping (DC) has been widely adopted in Central Brazil alongside single cropped (SC) soybean production. DC involves different cropping calendars, soy varieties, and may be associated with different crop yield patterns and volatility than SC. Study of the performance of the region's agriculture in a changing climate depends on tracking differences in the productivity of SC vs. DC, but has been limited by crop yield data that conflate the two systems. We predicted SC and DC yields across Central Brazil, drawing on field observations and remotely sensed data. We first modeled field yield estimates as a function of remotely sensed DC status and vegetation index (VI) metrics, and other management and biophysical factors. We then used the statistical model estimated to predict SC and DC soybean yields at each 500 m2 grid cell of Central Brazil for harvest years 2001 - 2015. The yield estimation model was constructed using 1) a repeated cross-sectional survey of soybean yields and management factors for years 2007-2015, 2) a custom agricultural land cover classification dataset which assimilates earlier datasets for the region, and 3) 500m 8-day MODIS image composites used to calculate the wide dynamic range vegetation index (WDRVI) and derivative metrics such as area under the curve for WDRVI values in critical crop development periods. A statistical yield estimation model which primarily entails WDRVI metrics, DC status, and spatial fixed effects was developed on a subset of the yield dataset. Model validation was conducted by predicting previously withheld yield records, and then assessing error and goodness-of-fit for predicted values with metrics including root mean squared error (RMSE), mean squared error (MSE), and R2. We found a statistical yield estimation model which incorporates WDRVI and DC status to be way to estimate crop yields over the region. Statistical properties of the resulting gridded yield dataset may be valuable for understanding linkages between crop yields, farm management factors, and climate.

Quasi 3D modelling of water flow in the sandy soil

NASA Astrophysics Data System (ADS)

Rezaei, Meisam; Seuntjens, Piet; Joris, Ingeborg; Boënne, Wesley; De Pue, Jan; Cornelis, Wim

2016-04-01

Monitoring and modeling tools may improve irrigation strategies in precision agriculture. Spatial interpolation is required for analyzing the effects of soil hydraulic parameters, soil layer thickness and groundwater level on irrigation management using hydrological models at field scale. We used non-invasive soil sensor, a crop growth (LINGRA-N) and a soil hydrological model (Hydrus-1D) to predict soil-water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. In the first step, the sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models at one soil column. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. In the second step, to predict Ks over the whole field, the spatial distributions of Ks and its relationship between co-located soil ECa measured by a DUALEM-21S sensor were investigated. Measured groundwater levels and soil layer thickness were interpolated using ordinary point kriging (OK) to a 0.5 by 0.5 m in aim of digital elevation maps. In the third step, a quasi 3D modelling approach was conducted using interpolated data as input hydraulic parameter, geometric information and boundary conditions in the integrated model. In addition, three different irrigation scenarios namely current, no irrigation and optimized irrigations were carried out to find out the most efficient irrigation regime. In this approach, detailed field scale maps of soil water stress, water storage and crop yield were produced at each specific time interval to evaluate the best and most efficient distribution of water using standard gun sprinkler irrigation. The results show that the effect of the position of the groundwater level was dominant in soil-water content prediction and associated water stress. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity Ks in a two-layered soil. Results demonstrated the large spatial variability of Ks (CV = 86.21%). A significant negative correlation was found between ln Ks and ECa (r = 0.83; P≤0.01). This site-specific relation between ln Ks and ECa was used to predict Ks for the whole field after validation using an independent dataset of measured Ks. Result showed that this approach can accurately determine the field scale irrigation requirements, taking into account variations in boundary conditions and spatial variations of model parameters across the field. We found that uniform distribution of water using standard gun sprinkler irrigation is not an efficient approach since at locations with shallow groundwater, the amount of water applied will be excessive as compared to the crop requirements, while in locations with a deeper groundwater table, the crop irrigation requirements will not be met during crop water stress. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to ~25% irrigation water as compared to the current irrigation regime. This resulted in a yield increase of ~7%, simulated by the crop growth model.

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.

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.

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 ...

A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability.

PubMed

Williams, Alwyn; Jordan, Nicholas R; Smith, Richard G; Hunter, Mitchell C; Kammerer, Melanie; Kane, Daniel A; Koide, Roger T; Davis, Adam S

2018-05-31

Climate models predict increasing weather variability, with negative consequences for crop production. Conservation agriculture (CA) may enhance climate resilience by generating certain soil improvements. However, the rate at which these improvements accrue is unclear, and some evidence suggests CA can lower yields relative to conventional systems unless all three CA elements are implemented: reduced tillage, sustained soil cover, and crop rotational diversity. These cost-benefit issues are important considerations for potential adopters of CA. Given that CA can be implemented across a wide variety of regions and cropping systems, more detailed and mechanistic understanding is required on whether and how regionally-adapted CA can improve soil properties while minimizing potential negative crop yield impacts. Across four US states, we assessed short-term impacts of regionally-adapted CA systems on soil properties and explored linkages with maize and soybean yield stability. Structural equation modeling revealed increases in soil organic matter generated by cover cropping increased soil cation exchange capacity, which improved soybean yield stability. Cover cropping also enhanced maize minimum yield potential. Our results demonstrate individual CA elements can deliver rapid improvements in soil properties associated with crop yield stability, suggesting that regionally-adapted CA may play an important role in developing high-yielding, climate-resilient agricultural systems.

Integrated crop management practices for maximizing grain yield of double-season rice crop.

PubMed

Wang, Depeng; Huang, Jianliang; Nie, Lixiao; Wang, Fei; Ling, Xiaoxia; Cui, Kehui; Li, Yong; Peng, Shaobing

2017-01-12

Information on maximum grain yield and its attributes are limited for double-season rice crop grown under the subtropical environment. This study was conducted to examine key characteristics associated with high yielding double-season rice crop through a comparison between an integrated crop management (ICM) and farmers' practice (FP). Field experiments were conducted in the early and late seasons in the subtropical environment of Wuxue County, Hubei Province, China in 2013 and 2014. On average, grain yield in ICM was 13.5% higher than that in FP. A maximum grain yield of 9.40 and 10.53 t ha -1 was achieved under ICM in the early- and late-season rice, respectively. Yield improvement of double-season rice with ICM was achieved with the combined effects of increased plant density and optimized nutrient management. Yield gain of ICM resulted from a combination of increases in sink size due to more panicle number per unit area and biomass production, further supported by the increased leaf area index, leaf area duration, radiation use efficiency, crop growth rate, and total nitrogen uptake compared with FP. Further enhancement in the yield potential of double-season rice should focus on increasing crop growth rate and biomass production through improved and integrated crop management practices.

Contribution of insect pollinators to crop yield and quality varies with agricultural intensification

PubMed Central

Potts, Simon G.; Steffan-Dewenter, Ingolf; Vaissière, Bernard E.; Woyciechowski, Michal; Krewenka, Kristin M.; Tscheulin, Thomas; Roberts, Stuart P.M.; Szentgyörgyi, Hajnalka; Westphal, Catrin; Bommarco, Riccardo

2014-01-01

Background. Up to 75% of crop species benefit at least to some degree from animal pollination for fruit or seed set and yield. However, basic information on the level of pollinator dependence and pollinator contribution to yield is lacking for many crops. Even less is known about how insect pollination affects crop quality. Given that habitat loss and agricultural intensification are known to decrease pollinator richness and abundance, there is a need to assess the consequences for different components of crop production. Methods. We used pollination exclusion on flowers or inflorescences on a whole plant basis to assess the contribution of insect pollination to crop yield and quality in four flowering crops (spring oilseed rape, field bean, strawberry, and buckwheat) located in four regions of Europe. For each crop, we recorded abundance and species richness of flower visiting insects in ten fields located along a gradient from simple to heterogeneous landscapes. Results. Insect pollination enhanced average crop yield between 18 and 71% depending on the crop. Yield quality was also enhanced in most crops. For instance, oilseed rape had higher oil and lower chlorophyll contents when adequately pollinated, the proportion of empty seeds decreased in buckwheat, and strawberries’ commercial grade improved; however, we did not find higher nitrogen content in open pollinated field beans. Complex landscapes had a higher overall species richness of wild pollinators across crops, but visitation rates were only higher in complex landscapes for some crops. On the contrary, the overall yield was consistently enhanced by higher visitation rates, but not by higher pollinator richness. Discussion. For the four crops in this study, there is clear benefit delivered by pollinators on yield quantity and/or quality, but it is not maximized under current agricultural intensification. Honeybees, the most abundant pollinator, might partially compensate the loss of wild pollinators in some areas, but our results suggest the need of landscape-scale actions to enhance wild pollinator populations. PMID:24749007

Contribution of insect pollinators to crop yield and quality varies with agricultural intensification.

PubMed

Bartomeus, Ignasi; Potts, Simon G; Steffan-Dewenter, Ingolf; Vaissière, Bernard E; Woyciechowski, Michal; Krewenka, Kristin M; Tscheulin, Thomas; Roberts, Stuart P M; Szentgyörgyi, Hajnalka; Westphal, Catrin; Bommarco, Riccardo

2014-01-01

Background. Up to 75% of crop species benefit at least to some degree from animal pollination for fruit or seed set and yield. However, basic information on the level of pollinator dependence and pollinator contribution to yield is lacking for many crops. Even less is known about how insect pollination affects crop quality. Given that habitat loss and agricultural intensification are known to decrease pollinator richness and abundance, there is a need to assess the consequences for different components of crop production. Methods. We used pollination exclusion on flowers or inflorescences on a whole plant basis to assess the contribution of insect pollination to crop yield and quality in four flowering crops (spring oilseed rape, field bean, strawberry, and buckwheat) located in four regions of Europe. For each crop, we recorded abundance and species richness of flower visiting insects in ten fields located along a gradient from simple to heterogeneous landscapes. Results. Insect pollination enhanced average crop yield between 18 and 71% depending on the crop. Yield quality was also enhanced in most crops. For instance, oilseed rape had higher oil and lower chlorophyll contents when adequately pollinated, the proportion of empty seeds decreased in buckwheat, and strawberries' commercial grade improved; however, we did not find higher nitrogen content in open pollinated field beans. Complex landscapes had a higher overall species richness of wild pollinators across crops, but visitation rates were only higher in complex landscapes for some crops. On the contrary, the overall yield was consistently enhanced by higher visitation rates, but not by higher pollinator richness. Discussion. For the four crops in this study, there is clear benefit delivered by pollinators on yield quantity and/or quality, but it is not maximized under current agricultural intensification. Honeybees, the most abundant pollinator, might partially compensate the loss of wild pollinators in some areas, but our results suggest the need of landscape-scale actions to enhance wild pollinator populations.

Biomass production of 12 winter cereal cover crop cultivars and their effect on subsequent no-till corn yield

USDA-ARS?s Scientific Manuscript database

Cover crops can improve the sustainability and resilience of corn and soybean production systems. However, there have been isolated reports of corn yield reductions following winter rye cover crops. Although there are many possible causes of corn yield reductions following winter cereal cover crops,...

Long-term Tillage and Cropping Sequence Effect on Dryland Crop Yields and Carbon and Nitrogen Cycling

USDA-ARS?s Scientific Manuscript database

Improved management practices are needed to increase dryland crop yields and soil organic matter compared with conventional farming practices in the northern Great Plains. We evaluated the 21-yr effect of tillage and cropping sequence on dryland grain and biomass (stems + leaves) yields and N uptake...

Changes in crop yields and their variability at different levels of global warming

NASA Astrophysics Data System (ADS)

Ostberg, Sebastian; Schewe, Jacob; Childers, Katelin; Frieler, Katja

2018-05-01

An assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.

Parameter-induced uncertainty quantification of crop yields, soil N2O and CO2 emission for 8 arable sites across Europe using the LandscapeDNDC model

NASA Astrophysics Data System (ADS)

Santabarbara, Ignacio; Haas, Edwin; Kraus, David; Herrera, Saul; Klatt, Steffen; Kiese, Ralf

2014-05-01

When using biogeochemical models to estimate greenhouse gas emissions at site to regional/national levels, the assessment and quantification of the uncertainties of simulation results are of significant importance. The uncertainties in simulation results of process-based ecosystem models may result from uncertainties of the process parameters that describe the processes of the model, model structure inadequacy as well as uncertainties in the observations. Data for development and testing of uncertainty analisys were corp yield observations, measurements of soil fluxes of nitrous oxide (N2O) and carbon dioxide (CO2) from 8 arable sites across Europe. Using the process-based biogeochemical model LandscapeDNDC for simulating crop yields, N2O and CO2 emissions, our aim is to assess the simulation uncertainty by setting up a Bayesian framework based on Metropolis-Hastings algorithm. Using Gelman statistics convergence criteria and parallel computing techniques, enable multi Markov Chains to run independently in parallel and create a random walk to estimate the joint model parameter distribution. Through means distribution we limit the parameter space, get probabilities of parameter values and find the complex dependencies among them. With this parameter distribution that determines soil-atmosphere C and N exchange, we are able to obtain the parameter-induced uncertainty of simulation results and compare them with the measurements data.

New Insights on Plant Salt Tolerance Mechanisms and Their Potential Use for Breeding

PubMed Central

Hanin, Moez; Ebel, Chantal; Ngom, Mariama; Laplaze, Laurent; Masmoudi, Khaled

2016-01-01

Soil salinization is a major threat to agriculture in arid and semi-arid regions, where water scarcity and inadequate drainage of irrigated lands severely reduce crop yield. Salt accumulation inhibits plant growth and reduces the ability to uptake water and nutrients, leading to osmotic or water-deficit stress. Salt is also causing injury of the young photosynthetic leaves and acceleration of their senescence, as the Na+ cation is toxic when accumulating in cell cytosol resulting in ionic imbalance and toxicity of transpiring leaves. To cope with salt stress, plants have evolved mainly two types of tolerance mechanisms based on either limiting the entry of salt by the roots, or controlling its concentration and distribution. Understanding the overall control of Na+ accumulation and functional studies of genes involved in transport processes, will provide a new opportunity to improve the salinity tolerance of plants relevant to food security in arid regions. A better understanding of these tolerance mechanisms can be used to breed crops with improved yield performance under salinity stress. Moreover, associations of cultures with nitrogen-fixing bacteria and arbuscular mycorrhizal fungi could serve as an alternative and sustainable strategy to increase crop yields in salt-affected fields. PMID:27965692

Climatic Droughts and the Impacts on Crop Yields in Northern India during the Past Century

NASA Astrophysics Data System (ADS)

Ge, Y.; Cai, X.; Zhu, T.

2014-12-01

Drought has become an increasingly severe threat to water and food security recently. This study presents a novel method to calculate the return period of drought, considering drought as event characterized by expected drought inter-arrival time, duration, severity and peak intensity. Recently, Copula distribution, a multivariable probability distribution, is used to deal with strongly correlated variables in analyzing complex hydrologic phenomenon. This study assesses drought conditions in Northern India, including 8 sites, in the past century using Palmer Drought Severity Index (PDSI) from two latest datasets, Dai (2011, 2013) and Sheffield et al. (2012), which concluded conflicting results about global average drought trend. Our results include the change of the severity, intensity and duration of drought events during the past century and the impact of the drought condition on crop yields in the region. It is found that drought variables are highly correlated, thus copulas joint distribution enables the estimation of multi-variate return period. Based on Dai's dataset from 1900 to 2012, for a fixed drought return period the severity and duration is lower for the period before1955 in sites close to the Indus basin (site 1) or off the coast of the Indian Ocean (Bay of Bengal) (site 8), while they are higher for the period after 1955 in other inland sites (sites 3-7), (e.g., severity in Fig.1). Projections based on two models (IPCC AR4 and AR5) in Dai (2011, 2013) suggested less severity and shorter duration in longer-year drought (e.g., 100-year drought), but larger in shorter-year drought (e.g., 2-year drought). Drought could bring nonlinear responses and unexpected losses in agriculture system, thus prediction and management are essential. Therefore, in the years with extreme drought conditions, impact assessment of drought on crop yield of corn, barley, wheat and sorghum will be also conducted through correlating crop yields with drought conditions during corresponding growing seasons. A. Dai, J. Geophys. Res., 116, D12115 (2011).A. Dai, Nature Climate Change, 3, 52-58 (2013). J. Sheffield, E.F. Wood, M. L. Roderick, Nature, 491, 435-438 (2012) Fig. 1 Return period for severity from 1900 to 1954 (green), from 1955 to 2012 (red), and from 2013 to 2099 (black for AR4, blue for AR5), respectively for 8 sites.

Wildlife-friendly farming increases crop yield: evidence for ecological intensification.

PubMed

Pywell, Richard F; Heard, Matthew S; Woodcock, Ben A; Hinsley, Shelley; Ridding, Lucy; Nowakowski, Marek; Bullock, James M

2015-10-07

Ecological intensification has been promoted as a means to achieve environmentally sustainable increases in crop yields by enhancing ecosystem functions that regulate and support production. There is, however, little direct evidence of yield benefits from ecological intensification on commercial farms growing globally important foodstuffs (grains, oilseeds and pulses). We replicated two treatments removing 3 or 8% of land at the field edge from production to create wildlife habitat in 50-60 ha patches over a 900 ha commercial arable farm in central England, and compared these to a business as usual control (no land removed). In the control fields, crop yields were reduced by as much as 38% at the field edge. Habitat creation in these lower yielding areas led to increased yield in the cropped areas of the fields, and this positive effect became more pronounced over 6 years. As a consequence, yields at the field scale were maintained--and, indeed, enhanced for some crops--despite the loss of cropland for habitat creation. These results suggested that over a 5-year crop rotation, there would be no adverse impact on overall yield in terms of monetary value or nutritional energy. This study provides a clear demonstration that wildlife-friendly management which supports ecosystem services is compatible with, and can even increase, crop yields. © 2015 The Authors.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

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.

The Lower Sevier River Basin Crop Monitor and Forecast Decision Support System: Exploiting Landsat Imagery to Provide Continuous Information to Farmers and Water Managers

NASA Astrophysics Data System (ADS)

Torres-Rua, A. F.; Walker, W. R.; McKee, M.

2013-12-01

The last century has seen a large number of innovations in agriculture such as better policies for water control and management, upgraded water conveyance, irrigation, distribution, and monitoring systems, and better weather forecasting products. In spite of this, irrigation management and irrigation water deliveries by farmers/water managers is still based on factors like water share amounts, tradition, and past experience on irrigation. These factors are not necessarily related to the actual crop water use; they are followed because of the absence of related information provided in a timely manner at an affordable cost. Thus, it is necessary to develop means to deliver continuous and personalized information about crop water requirements to water users/managers at the field and irrigation system levels so managers at these levels can better quantify the required versus available water for irrigation during the irrigation season. This study presents a new decision support system (DSS) platform that addresses the absence of information on actual crop water requirements and crop performance by providing continuous updated farm-based crop water use along with other farm performance indicators such as crop yield and farm management to irrigators and water managers. This DSS exploits the periodicity of the Landsat Satellite Mission (8 to 16 days, depending on the period of interest) to provide remote monitoring at the individual field and irrigation system levels. The Landsat satellite images are converted into information about crop water use, yield performance and field management through application of state-of-the-art semi-physical and statistical algorithms that provide this information at a pixel basis that are ultimately aggregated to field and irrigation system levels. A version of the DSS has been implemented for the agricultural lands in the Lower Sevier River, Utah, and has been operational since the beginning of the 2013 irrigation season. The main goal of this DSS implementation is to provide continuous and personalized information to farmers and water managers regarding crops in fields and the irrigation delivery system throughout the irrigation season so that decisions related to agricultural water use can result in water savings while not diminishing crop yields.

Root-Knot Nematode Management in Double-Cropped Plasticulture Vegetables

PubMed Central

Desaeger, J. A.; Csinos, A. S.

2006-01-01

Combination treatments of chisel-injected fumigants (methyl bromide, 1,3-D, metam sodium, and chloropicrin) on a first crop, followed by drip-applied fumigants (metam sodium and 1,3-D ± chloropicrin) on a second crop, with and without oxamyl drip applications were evaluated for control of Meloidogyne incognita in three different tests (2002 to 2004) in Tifton, GA. First crops were eggplant or tomato, and second crops were cantaloupe, squash, or jalapeno pepper. Double-cropped vegetables suffered much greater root-knot nematode (RKN) pressure than first crops, and almost-total yield loss occurred when second crops received no nematicide treatment. On a first crop of eggplant, all fumigants provided good nematode control and average yield increases of 10% to 15 %. On second crops, higher application rates and fumigant combinations (metam sodium and 1,3-D ± chloropicrin) improved RKN control and increased yields on average by 20% to 35 % compared to the nonfumigated control. Oxamyl increased yields of the first crop in 2003 on average by 10% to 15% but had no effect in 2004 when RKN failed to establish itself. On double-cropped squash in 2003, oxamyl following fumigation provided significant additional reduction in nematode infection and increased squash yields on average by 30% to 75%. PMID:19259431

Climate variation explains a third of global crop yield variability

PubMed Central

Ray, Deepak K.; Gerber, James S.; MacDonald, Graham K.; West, Paul C.

2015-01-01

Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (~32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability. PMID:25609225

Response of double cropping suitability to climate change in the United States

NASA Astrophysics Data System (ADS)

Seifert, Christopher A.; Lobell, David B.

2015-02-01

In adapting US agriculture to the climate of the 21st century, a key unknown is whether cropping frequency may increase, helping to offset projected negative yield impacts in major production regions. Combining daily weather data and crop phenology models, we find that cultivated area in the US suited to dryland winter wheat-soybeans, the most common double crop (DC) system, increased by up to 28% from 1988 to 2012. Changes in the observed distribution of DC area over the same period agree well with this suitability increase, evidence consistent with climate change playing a role in recent DC expansion in phenologically constrained states. We then apply the model to projections of future climate under the RCP45 and RCP85 scenarios and estimate an additional 126-239% increase, respectively, in DC area. Sensitivity tests reveal that in most instances, increases in mean temperature are more important than delays in fall freeze in driving increased DC suitability. The results suggest that climate change will relieve phenological constraints on wheat-soy DC systems over much of the United States, though it should be recognized that impacts on corn and soybean yields in this region are expected to be negative and larger in magnitude than the 0.4-0.75% per decade benefits we estimate here for double cropping.

Quantifying the impacts of climatic trend and fluctuation on crop yields in northern China.

PubMed

Qiao, Jianmin; Yu, Deyong; Liu, Yupeng

2017-10-01

Climate change plays a critical role in crop yield variations, which has attracted a great deal of concern worldwide. However, the mechanisms of how climatic trend and fluctuations affect crop yields are not well understood and need to be further investigated. Thus, using the GIS-based Environmental Policy Integrated Climate (EPIC) model, we simulated the yields of major crops (i.e., wheat, maize, and rice) and evaluated the impacts of climatic factors on crop yields in the Agro-Pastoral Transitional Zone (APTZ) of northern China between 1980 and 2010. The partial least squares regression model was used to assess the contribution rates of climatic factors (i.e., precipitation, photosynthetically active radiation (PAR), minimum temperature (T min ), maximum temperature (T max )) to the variation of crop yields. The Breaks for Additive Season and Trend (BFAST) model was adopted to decompose the climate factors into trend and fluctuation components, and the relative contributions of climate trend and fluctuation were then evaluated. The results indicated that the contributions of climatic factors to yield variations of wheat, maize, and rice were 31.7, 37.7, and 23.1%, respectively. That is, climate change had larger impacts on maize than wheat and rice. More cultivated areas were significantly and positively correlated with precipitation than with other climatic factors due to the limited precipitation in the APTZ. Also, climatic trend component had positive impacts on crop yields in the whole region, whereas the climate fluctuation was associated mainly with the areas where the crop yields decreased. This study helps improve our understanding of the mechanisms of climate change impacts on crop yields, and provides useful scientific information for designing regional-scale strategies of adaptation to climate change.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Towards a Solid Foundation of Using Remotely Sensed Solar-Induced Chlorophyll Fluorescence for Crop Monitoring and Yield Forecast

NASA Astrophysics Data System (ADS)

Chen, Y.; Sun, Y.; You, L.; Liu, Y.

2017-12-01

The growing demand for food production due to population increase coupled with high vulnerability to volatile environmental changes poses a paramount challenge for mankind in the coming century. Real-time crop monitoring and yield forecasting must be a key part of any solution to this challenge as these activities provide vital information needed for effective and efficient crop management and for decision making. However, traditional methods of crop growth monitoring (e.g., remotely sensed vegetation indices) do not directly relate to the most important function of plants - photosynthesis and therefore crop yield. The recent advance in the satellite remote sensing of Solar-Induced chlorophyll Fluorescence (SIF), an integrative photosynthetic signal from molecular origin and a direct measure of plant functions holds great promise for real-time monitoring of crop growth conditions and forecasting yields. In this study, we use satellite measurements of SIF from both the Global Ozone Monitoring Experiment-2 (GOME-2) onboard MetOp-A and the Orbiting Carbon Observatory-2 (OCO-2) satellites to estimate crop yield using both process-based and statistical models. We find that SIF-based crop yield well correlates with the global yield product Spatial Production Allocation Model (SPAM) derived from ground surveys for all major crops including maize, soybean, wheat, sorghum, and rice. The potential and challenges of using upcoming SIF satellite missions for crop monitoring and prediction will also be discussed.

Differential Impacts of Climate Change on Crops and Agricultural Regions in India

NASA Astrophysics Data System (ADS)

Sharma, A. N.

2015-12-01

As India's farmers and policymakers consider potential adaptation strategies to climate change, some questions loom large: - Which climate variables best explain the variability of crop yields? - How does the vulnerability of crop yields to climate vary regionally? - How are these risks likely to change in the future? While process-based crop modelling has started to answer many of these questions, we believe statistical approaches can complement these in improving our understanding of climate vulnerabilities and appropriate responses. We use yield data collected over three decades for more than ten food crops grown in India along with a variety of statistical approaches to answer the above questions. The ability of climate variables to explain yield variation varies greatly by crop and season, which is expected. Equally important, the ability of models to predict crop yields as well as their coefficients varies greatly by district even for districts which are relatively close to each other and similar in their agricultural practices. We believe these results encourage caution and nuance when making projections about climate impacts on crop yields in the future. Most studies about climate impacts on crop yields focus on a handful of major food crops. By extending our analysis to all the crops with long-term district level data in India as well as two growing seasons we gain a more comprehensive picture. Our results indicate that there is a great deal of variability even at relatively small scales, and that this must be taken into account if projections are to be made useful to policymakers.

Detection of meteorological extreme effect on historical crop yield anomaly

NASA Astrophysics Data System (ADS)

Kim, W.; Iizumi, T.; Nishimori, M.

2017-12-01

Meteorological extremes of temperature and precipitation are a critical issue in the global climate change, and some studies investigating how the extreme changes in accordance with the climate change are continuously reported. However, it is rarely understandable that the extremes affect crop yield worldwide as heatwave, coolwave, drought, and flood, albeit some local or national reports are available. Therefore, we globally investigated the extremes effects on the variability of historical yield of maize, rice, soy, and wheat with a standardized index and a historical yield anomaly. For the regression analysis, the standardized index is annually aggregated in the consideration of a crop calendar, and the historical yield is detrended with 5-year moving average. Throughout this investigation, we found that the relationship between the aggregated standardized index and the historical yield anomaly shows not merely positive correlation but also negative correlation in all crops in the globe. Namely, the extremes cause decrease of crop yield as a matter of course, but increase in some regions contrastingly. These results help us to quantify the extremes effect on historical crop yield anomaly.

Sustainability analysis of bioenergy based land use change under climate change and variability

NASA Astrophysics Data System (ADS)

Raj, C.; Chaubey, I.; Brouder, S. M.; Bowling, L. C.; Cherkauer, K. A.; Frankenberger, J.; Goforth, R. R.; Gramig, B. M.; Volenec, J. J.

2014-12-01

Sustainability analyses of futuristic plausible land use and climate change scenarios are critical in making watershed-scale decisions for simultaneous improvement of food, energy and water management. Bioenergy production targets for the US are anticipated to impact farming practices through the introduction of fast growing and high yielding perennial grasses/trees, and use of crop residues as bioenergy feedstocks. These land use/land management changes raise concern over potential environmental impacts of bioenergy crop production scenarios, both in terms of water availability and water quality; impacts that may be exacerbated by climate variability and change. The objective of the study was to assess environmental, economic and biodiversity sustainability of plausible bioenergy scenarios for two watersheds in Midwest US under changing climate scenarios. The study considers fourteen sustainability indicators under nine climate change scenarios from World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3). The distributed hydrological model SWAT (Soil and Water Assessment Tool) was used to simulate perennial bioenergy crops such as Miscanthus and switchgrass, and corn stover removal at various removal rates and their impacts on hydrology and water quality. Species Distribution Models (SDMs) developed to evaluate stream fish response to hydrology and water quality changes associated with land use change were used to quantify biodiversity sustainability of various bioenergy scenarios. The watershed-scale sustainability analysis was done in the St. Joseph River watershed located in Indiana, Michigan, and Ohio; and the Wildcat Creek watershed, located in Indiana. The results indicate streamflow reduction at watershed outlet with increased evapotranspiration demands for high-yielding perennial grasses. Bioenergy crops in general improved in-stream water quality compared to conventional cropping systems (maize-soybean). Water quality benefits due to land use change were generally greater than the effects of climate change variability.

Effect of Nutrient Management Planning on Crop Yield, Nitrate Leaching and Sediment Loading in Thomas Brook Watershed

NASA Astrophysics Data System (ADS)

Amon-Armah, Frederick; Yiridoe, Emmanuel K.; Ahmad, Nafees H. M.; Hebb, Dale; Jamieson, Rob; Burton, David; Madani, Ali

2013-11-01

Government priorities on provincial Nutrient Management Planning (NMP) programs include improving the program effectiveness for environmental quality protection, and promoting more widespread adoption. Understanding the effect of NMP on both crop yield and key water-quality parameters in agricultural watersheds requires a comprehensive evaluation that takes into consideration important NMP attributes and location-specific farming conditions. This study applied the Soil and Water Assessment Tool (SWAT) to investigate the effects of crop and rotation sequence, tillage type, and nutrient N application rate on crop yield and the associated groundwater leaching and sediment loss. The SWAT model was applied to the Thomas Brook Watershed, located in the most intensively managed agricultural region of Nova Scotia, Canada. Cropping systems evaluated included seven fertilizer application rates and two tillage systems (i.e., conventional tillage and no-till). The analysis reflected cropping systems commonly managed by farmers in the Annapolis Valley region, including grain corn-based and potato-based cropping systems, and a vegetable-horticulture system. ANOVA models were developed and used to assess the effects of crop management choices on crop yield and two water-quality parameters (i.e., leaching and sediment loading). Results suggest that existing recommended N-fertilizer rate can be reduced by 10-25 %, for grain crop production, to significantly lower leaching ( P > 0.05) while optimizing the crop yield. The analysis identified the nutrient N rates in combination with specific crops and rotation systems that can be used to manage leaching while balancing impacts on crop yields within the watershed.

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

NASA Astrophysics Data System (ADS)

Souty, F.; Brunelle, T.; Dumas, P.; Dorin, B.; Ciais, P.; Crassous, R.; Müller, C.; Bondeau, A.

2012-02-01

Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i) a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii) a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii) a spatially explicit distribution of potential (maximal) crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL). The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. The land-use modelling approach described in this paper entails several advantages. Firstly, it makes it possible to explore interactions among different types of biomass demand for food and animal feed, in a consistent approach, including indirect effects on land-use change resulting from international trade. Secondly, yield variations induced by the possible expansion of croplands on less suitable marginal lands are modelled by using regional land area distributions of potential yields, and a calculated boundary between intensive and extensive production. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.

Large Area Crop Inventory Experiment (LACIE). Feasibility of assessing crop condition and yield from LANDSAT data

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. Yield modelling for crop production estimation derived a means of predicting the within-a-year yield and the year-to-year variability of yield over some fixed or randomly located unit of area. Preliminary studies indicated that the requirements for interpreting LANDSAT data for yield may be sufficiently similar to those of signature extension that it is feasible to investigate the automated estimation of production. The concept of an advanced yield model consisting of both spectral and meteorological components was endorsed. Rationale for using meteorological parameters originated from known between season and near harvest dynamics in crop environmental-condition-yield relationships.

Yield model development project implementation plan

NASA Technical Reports Server (NTRS)

Ambroziak, R. A.

1982-01-01

Tasks remaining to be completed are summarized for the following major project elements: (1) evaluation of crop yield models; (2) crop yield model research and development; (3) data acquisition processing, and storage; (4) related yield research: defining spectral and/or remote sensing data requirements; developing input for driving and testing crop growth/yield models; real time testing of wheat plant process models) and (5) project management and support.

SWAT ungauged: Hydrological budget and crop yield predictions in the Upper Mississippi River Basin

USDA-ARS?s Scientific Manuscript database

Physically based, distributed hydrologic models are increasingly used in assessments of water resources, best management practices, and climate and land use changes. Model performance evaluation in ungauged basins is an important research topic. In this study, we propose a framework for developing S...

78 FR 38285 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-26

...) whether the collection of information is necessary for the proper performance of the functions of the... category, acres and yields of irrigated and non-irrigated crops, quantity of water applied and method of... of water distribution systems, and number of irrigation wells and pumps. The primary purpose of FRIS...

Understanding the Impact of Extreme Temperature on Crop Production in Karnataka in India

NASA Astrophysics Data System (ADS)

Mahato, S.; Murari, K. K.; Jayaraman, T.

2017-12-01

The impact of extreme temperature on crop yield is seldom explored in work around climate change impact on agriculture. Further, these studies are restricted mainly to crops such as wheat and maize. Since different agro-climatic zones bear different crops and cropping patterns, it is important to explore the nature of the impact of changes in climate variables in agricultural systems under differential conditions. The study explores the effects of temperature rise on the major crops paddy, jowar, ragi and tur in the state of Karnataka of southern India. The choice of the unit of study to understand impact of climate variability on crop yields is largely restricted to availability of data for the unit. While, previous studies have dealt with this issue by replacing yield with NDVI at finer resolution, the use of an index in place of yield data has its limitations and may not reflect the true estimates. For this study, the unit considered is taluk, i.e. sub-district level. The crop yield for taluk is obtained between the year the 1995 to 2011 by aggregating point yield data from crop cutting experiments for each year across the taluks. The long term temperature data shows significantly increasing trend that ranges between 0.6 to 0.75 C across Karnataka. Further, the analysis suggests a warming trend in seasonal average temperature for Kharif and Rabi seasons across districts. The study also found that many districts exhibit the tendency of occurrence of extreme temperature days, which is of particular concern in terms of crop yield, since exposure of crops to extreme temperature has negative consequences for crop production and productivity. Using growing degree days GDD, extreme degree days EDD and total season rainfall as predictor variables, the fixed effect model shows that EDD is a more influential parameter as compared to GDD and rainfall. Also it has a statistically significant negative effect in most cases. Further, quantile regression was used to evaluate the robustness of the estimates of EDD in relation to crop yield. This showed the estimates to be robust across quantiles for most of the crops studied. Thus indicating a strong negative influence of exposure to extreme temperature on crop yield in the region.

Regional crop yield forecasting: a probabilistic approach

NASA Astrophysics Data System (ADS)

de Wit, A.; van Diepen, K.; Boogaard, H.

2009-04-01

Information on the outlook on yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies with a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, and for commodity traders. Process-based mechanistic crop models are an important tool for providing such information, because they can integrate the effect of crop management, weather and soil on crop growth. When properly integrated in a yield forecasting system, the aggregated model output can be used to predict crop yield and production at regional, national and continental scales. Nevertheless, given the scales at which these models operate, the results are subject to large uncertainties due to poorly known weather conditions and crop management. Current yield forecasting systems are generally deterministic in nature and provide no information about the uncertainty bounds on their output. To improve on this situation we present an ensemble-based approach where uncertainty bounds can be derived from the dispersion of results in the ensemble. The probabilistic information provided by this ensemble-based system can be used to quantify uncertainties (risk) on regional crop yield forecasts and can therefore be an important support to quantitative risk analysis in a decision making process.

The limits of crop productivity: validating theoretical estimates and determining the factors that limit crop yields in optimal environments

NASA Technical Reports Server (NTRS)

Bugbee, B.; Monje, O.

1992-01-01

Plant scientists have sought to maximize the yield of food crops since the beginning of agriculture. There are numerous reports of record food and biomass yields (per unit area) in all major crop plants, but many of the record yield reports are in error because they exceed the maximal theoretical rates of the component processes. In this article, we review the component processes that govern yield limits and describe how each process can be individually measured. This procedure has helped us validate theoretical estimates and determine what factors limit yields in optimal environments.

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Teng, William; Chiu, Long; Doraiswamy, Paul; Kempler, Steven; Liu, Zhong; Pham, Long; Rui, Hualan

2005-01-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

NASA Technical Reports Server (NTRS)

Van Bussel, Lenny G. J.; Ewert, Frank; Zhao, Gang; Hoffmann, Holger; Enders, Andreas; Wallach, Daniel; Asseng, Senthold; Baigorria, Guillermo A.; Basso, Bruno; Biernath, Christian;

Methane production through anaerobic digestion of various energy crops grown in sustainable crop rotations.

PubMed

Amon, Thomas; Amon, Barbara; Kryvoruchko, Vitaliy; Machmüller, Andrea; Hopfner-Sixt, Katharina; Bodiroza, Vitomir; Hrbek, Regina; Friedel, Jürgen; Pötsch, Erich; Wagentristl, Helmut; Schreiner, Matthias; Zollitsch, Werner

2007-12-01

Biogas production is of major importance for the sustainable use of agrarian biomass as renewable energy source. Economic biogas production depends on high biogas yields. The project aimed at optimising anaerobic digestion of energy crops. The following aspects were investigated: suitability of different crop species and varieties, optimum time of harvesting, specific methane yield and methane yield per hectare. The experiments covered 7 maize, 2 winter wheat, 2 triticale varieties, 1 winter rye, and 2 sunflower varieties and 6 variants with permanent grassland. In the course of the vegetation period, biomass yield and biomass composition were measured. Anaerobic digestion was carried out in eudiometer batch digesters. The highest methane yields of 7500-10200 m(N)(3)ha(-1) were achieved from maize varieties with FAO numbers (value for the maturity of the maize) of 300 to 600 harvested at "wax ripeness". Methane yields of cereals ranged from 3200 to 4500 m(N)(3)ha(-1). Cereals should be harvested at "grain in the milk stage" to "grain in the dough stage". With sunflowers, methane yields between 2600 and 4550 m(N)(3)ha(-1) were achieved. There were distinct differences between the investigated sunflower varieties. Alpine grassland can yield 2700-3500 m(N)(3)CH(4)ha(-1). The methane energy value model (MEVM) was developed for the different energy crops. It estimates the specific methane yield from the nutrient composition of the energy crops. Energy crops for biogas production need to be grown in sustainable crop rotations. The paper outlines possibilities for optimising methane yield from versatile crop rotations that integrate the production of food, feed, raw materials and energy. These integrated crop rotations are highly efficient and can provide up to 320 million t COE which is 96% of the total energy demand of the road traffic of the EU-25 (the 25 Member States of the European Union).

[Main interspecific competition and land productivity of fruit-crop intercropping in Loess Region of West Shauxi].

PubMed

Yun, Lei; Bi, Hua-Xing; Tian, Xiao-Ling; Cui, Zhe-Wei; Zhou, Hui-Zi; Gao, Lu-Bo; Liu, Li-Xia

2011-05-01

Taking the four typical fruit-crop intercropping models, i.e., walnut-peanut, walnut-soybean, apple-peanut, and apple-soybean, in the Loess Region of western Shanxi Province as the objects, this paper analyzed the crop (peanut and soybean) photosynthetic active radiation (PAR), net photosynthetic rate (P(n)), yield, and soil moisture content. Comparing with crop monoculture, fruit-crop intercropping decreased the crop PAR and P(n). The smaller the distance from tree rows, the smaller the crop PAR and P(n). There was a significantly positive correlation between the P(n) and crop yield, suggesting that illumination was one of the key factors affecting crop yield. From the whole trend, the 0-100 cm soil moisture content had no significant differences between walnut-crop intercropping systems and corresponding monoculture cropping systems, but had significant differences between apple-crop intercropping systems and corresponding monoculture cropping systems, indicating that the competition for soil moisture was more intense in apple-crop intercropping systems than in walnut-crop intercropping systems. Comparing with monoculture, fruit-crop intercropping increased the land use efficiency and economic benefit averagely by 70% and 14%, respectively, and walnut-crop intercropping was much better than apple-crop intercropping. To increase the crop yield in fruit-crop intercropping systems, the following strategies should be taken: strengthening the management of irrigation and fertilization, increasing the distances or setting root barriers between crop and tree rows, regularly and properly pruning, and planting shade-tolerant crops in intercropping.

Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications

NASA Astrophysics Data System (ADS)

Quiroga, S.; Fernández-Haddad, Z.; Iglesias, A.

2011-02-01

The increasing pressure on water systems in the Mediterranean enhances existing water conflicts and threatens water supply for agriculture. In this context, one of the main priorities for agricultural research and public policy is the adaptation of crop yields to water pressures. This paper focuses on the evaluation of hydrological risk and water policy implications for food production. Our methodological approach includes four steps. For the first step, we estimate the impacts of rainfall and irrigation water on crop yields. However, this study is not limited to general crop production functions since it also considers the linkages between those economic and biophysical aspects which may have an important effect on crop productivity. We use statistical models of yield response to address how hydrological variables affect the yield of the main Mediterranean crops in the Ebro river basin. In the second step, this study takes into consideration the effects of those interactions and analyzes gross value added sensitivity to crop production changes. We then use Montecarlo simulations to characterize crop yield risk to water variability. Finally we evaluate some policy scenarios with irrigated area adjustments that could cope in a context of increased water scarcity. A substantial decrease in irrigated land, of up to 30% of total, results in only moderate losses of crop productivity. The response is crop and region specific and may serve to prioritise adaptation strategies.

The Role of Climate Covariability on Crop Yields in the Conterminous United States

DOE PAGES

Leng, Guoyong; Zhang, Xuesong; Huang, Maoyi; ...

2016-09-12

The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here in this paper, we analyze county-level corn and soybean yields and observed climate for the period 1983–2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R,more » an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. Finally, the structure of the dominant climate factors did not change substantially over 1983–2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.« less

Yield and Economic Responses of Peanut to Crop Rotation Sequence

USDA-ARS?s Scientific Manuscript database

National Peanut Research Laboratory, Dawson, GA 39842. Proper crop rotation is essential to maintaining high peanut yield and quality. However, the economic considerations of maintaining or altering crop rotation sequences must incorporate the commodity prices, production costs, and yield responses...

Hyperspectral imagery for mapping crop yield for precision agriculture

USDA-ARS?s Scientific Manuscript database

Crop yield is perhaps the most important piece of information for crop management in precision agriculture. It integrates the effects of various spatial variables such as soil properties, topographic attributes, tillage, plant population, fertilization, irrigation, and pest infestations. A yield map...

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing.

PubMed

Duncan, John M A; Dash, Jadunandan; Atkinson, Peter M

2015-04-01

Remote sensing-derived wheat crop yield-climate models were developed to highlight the impact of temperature variation during thermo-sensitive periods (anthesis and grain-filling; TSP) of wheat crop development. Specific questions addressed are: can the impact of temperature variation occurring during the TSP on wheat crop yield be detected using remote sensing data and what is the impact? Do crop critical temperature thresholds during TSP exist in real world cropping landscapes? These questions are tested in one of the world's major wheat breadbaskets of Punjab and Haryana, north-west India. Warming average minimum temperatures during the TSP had a greater negative impact on wheat crop yield than warming maximum temperatures. Warming minimum and maximum temperatures during the TSP explain a greater amount of variation in wheat crop yield than average growing season temperature. In complex real world cereal croplands there was a variable yield response to critical temperature threshold exceedance, specifically a more pronounced negative impact on wheat yield with increased warming events above 35 °C. The negative impact of warming increases with a later start-of-season suggesting earlier sowing can reduce wheat crop exposure harmful temperatures. However, even earlier sown wheat experienced temperature-induced yield losses, which, when viewed in the context of projected warming up to 2100 indicates adaptive responses should focus on increasing wheat tolerance to heat. This study shows it is possible to capture the impacts of temperature variation during the TSP on wheat crop yield in real world cropping landscapes using remote sensing data; this has important implications for monitoring the impact of climate change, variation and heat extremes on wheat croplands. © 2014 John Wiley & Sons Ltd.

Cover crops support ecological intensification of arable cropping systems

NASA Astrophysics Data System (ADS)

Wittwer, Raphaël A.; Dorn, Brigitte; Jossi, Werner; van der Heijden, Marcel G. A.

2017-02-01

A major challenge for agriculture is to enhance productivity with minimum impact on the environment. Several studies indicate that cover crops could replace anthropogenic inputs and enhance crop productivity. However, so far, it is unclear if cover crop effects vary between different cropping systems, and direct comparisons among major arable production systems are rare. Here we compared the short-term effects of various cover crops on crop yield, nitrogen uptake, and weed infestation in four arable production systems (conventional cropping with intensive tillage and no-tillage; organic cropping with intensive tillage and reduced tillage). We hypothesized that cover cropping effects increase with decreasing management intensity. Our study demonstrated that cover crop effects on crop yield were highest in the organic system with reduced tillage (+24%), intermediate in the organic system with tillage (+13%) and in the conventional system with no tillage (+8%) and lowest in the conventional system with tillage (+2%). Our results indicate that cover crops are essential to maintaining a certain yield level when soil tillage intensity is reduced (e.g. under conservation agriculture), or when production is converted to organic agriculture. Thus, the inclusion of cover crops provides additional opportunities to increase the yield of lower intensity production systems and contribute to ecological intensification.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

PubMed Central

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C.

2014-01-01

Background and Aims Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Methods Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. Key Results To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se. Conclusions This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. PMID:24984712

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2018-06-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2017-12-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Future possible crop yield scenarios under multiple SSP and RCP scenarios.

NASA Astrophysics Data System (ADS)

Sakurai, G.; Yokozawa, M.; Nishimori, M.; Okada, M.

2016-12-01

Understanding the effect of future climate change on global crop yields is one of the most important tasks for global food security. Future crop yields would be influenced by climatic factors such as the changes of temperature, precipitation and atmospheric carbon dioxide concentration. On the other hand, the effect of the changes of agricultural technologies such as crop varieties, pesticide and fertilizer input on crop yields have large uncertainty. However, not much is available on the contribution ratio of each factor under the future climate change scenario. We estimated the future global yields of four major crops (maize, soybean, rice and wheat) under three Shared Socio Economic Pathways (SSPs) and four Representative Concentration Pathways (RCPs). For this purpose, firstly, we estimated a parameter of a process based model (PRYSBI2) using a Bayesian method for each 1.125 degree spatial grid. The model parameter is relevant to the agricultural technology (we call "technological parameter" here after). Then, we analyzed the relationship between the values of technological parameter and GDP values. We found that the estimated values of the technological parameter were positively correlated with the GDP. Using the estimated relationship, we predicted future crop yield during 2020 and 2100 under SSP1, SSP2 and SSP3 scenarios and RCP 2.6, 4.5, 6.0 and 8.5. The estimated crop yields were different among SSP scenarios. However, we found that the yield difference attributable to SSPs were smaller than those attributable to CO2 fertilization effects and climate change. Particularly, the estimated effect of the change of atmospheric carbon dioxide concentration on global yields was more than four times larger than that of GDP for C3 crops.

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production.

PubMed

Kukal, Meetpal S; Irmak, Suat

2018-02-22

Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.

Random Forests for Global and Regional Crop Yield Predictions.

PubMed

Jeong, Jig Han; Resop, Jonathan P; Mueller, Nathaniel D; Fleisher, David H; Yun, Kyungdahm; Butler, Ethan E; Timlin, Dennis J; Shim, Kyo-Moon; Gerber, James S; Reddy, Vangimalla R; Kim, Soo-Hyung

2016-01-01

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

2017-12-01

Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, p<0.05). The estimated crop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

Climate Variability and Yields of Major Staple Food Crops in Northern Ghana

NASA Astrophysics Data System (ADS)

Amikuzuno, J.

2012-12-01

Climate variability, the short-term fluctuations in average weather conditions, and agriculture affect each other. Climate variability affects the agroecological and growing conditions of crops and livestock, and is recently believed to be the greatest impediment to the realisation of the first Millennium Development Goal of reducing poverty and food insecurity in arid and semi-arid regions of developing countries. Conversely, agriculture is a major contributor to climate variability and change by emitting greenhouse gases and reducing the agroecology's potential for carbon sequestration. What however, is the empirical evidence of this inter-dependence of climate variability and agriculture in Sub-Sahara Africa? In this paper, we provide some insight into the long run relationship between inter-annual variations in temperature and rainfall, and annual yields of the most important staple food crops in Northern Ghana. Applying pooled panel data of rainfall, temperature and yields of the selected crops from 1976 to 2010 to cointegration and Granger causality models, there is cogent evidence of cointegration between seasonal, total rainfall and crop yields; and causality from rainfall to crop yields in the Sudano-Guinea Savannah and Guinea Savannah zones of Northern Ghana. This suggests that inter-annual yields of the crops have been influenced by the total mounts of rainfall in the planting season. Temperature variability over the study period is however stationary, and is suspected to have minimal effect if any on crop yields. Overall, the results confirm the appropriateness of our attempt in modelling long-term relationships between the climate and crop yield variables.

Remote-sensing-based rapid assessment of flood crop loss to support USDA flooding decision-making

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Yang, Z.; Hipple, J.; Shrestha, R.

2016-12-01

Floods often cause significant crop loss in the United States. Timely and objective assessment of flood-related crop loss is very important for crop monitoring and risk management in agricultural and disaster-related decision-making in USDA. Among all flood-related information, crop yield loss is particularly important. Decision on proper mitigation, relief, and monetary compensation relies on it. Currently USDA mostly relies on field surveys to obtain crop loss information and compensate farmers' loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. Recent studies have demonstrated that Earth observation (EO) data are useful in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. There are three stages of flood damage assessment, including rapid assessment, early recovery assessment, and in-depth assessment. EO-based flood assessment methods currently rely on the time-series of vegetation index to assess the yield loss. Such methods are suitable for in-depth assessment but are less suitable for rapid assessment since the after-flood vegetation index time series is not available. This presentation presents a new EO-based method for the rapid assessment of crop yield loss immediately after a flood event to support the USDA flood decision making. The method is based on the historic records of flood severity, flood duration, flood date, crop type, EO-based both before- and immediate-after-flood crop conditions, and corresponding crop yield loss. It hypotheses that a flood of same severity occurring at the same pheonological stage of a crop will cause the similar damage to the crop yield regardless the flood years. With this hypothesis, a regression-based rapid assessment algorithm can be developed by learning from historic records of flood events and corresponding crop yield loss. In this study, historic records of MODIS-based flood and vegetation products and USDA/NASS crop type and crop yield data are used to train the regression-based rapid assessment algorithm. Validation of the rapid assessment algorithm indicates it can predict the yield loss at 90% accuracy, which is accurate enough to support USDA on flood-related quick response and mitigation.

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

PubMed

Wang, Enli; Martre, Pierre; Zhao, Zhigan; Ewert, Frank; Maiorano, Andrea; Rötter, Reimund P; Kimball, Bruce A; Ottman, Michael J; Wall, Gerard W; White, Jeffrey W; Reynolds, Matthew P; Alderman, Phillip D; Aggarwal, Pramod K; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andrew J; De Sanctis, Giacomo; Doltra, Jordi; Fereres, Elias; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A; Izaurralde, Roberto C; Jabloun, Mohamed; Jones, Curtis D; Kersebaum, Kurt C; Koehler, Ann-Kristin; Liu, Leilei; Müller, Christoph; Naresh Kumar, Soora; Nendel, Claas; O'Leary, Garry; Olesen, Jørgen E; Palosuo, Taru; Priesack, Eckart; Eyshi Rezaei, Ehsan; Ripoche, Dominique; Ruane, Alex C; Semenov, Mikhail A; Shcherbak, Iurii; Stöckle, Claudio; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wallach, Daniel; Wang, Zhimin; Wolf, Joost; Zhu, Yan; Asseng, Senthold

2017-07-17

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 crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

NASA Technical Reports Server (NTRS)

Wang, Enli; Martre, Pierre; Zhao, Zhigan; Ewert, Frank; Maiorano, Andrea; Rotter, Reimund P.; Kimball, Bruce A.; Ottman, Michael J.; White, Jeffrey W.; Reynolds, Matthew P.;

Fusion of multi-source remote sensing data for agriculture monitoring tasks

NASA Astrophysics Data System (ADS)

Skakun, S.; Franch, B.; Vermote, E.; Roger, J. C.; Becker Reshef, I.; Justice, C. O.; Masek, J. G.; Murphy, E.

2016-12-01

Remote sensing data is essential source of information for enabling monitoring and quantification of crop state at global and regional scales. Crop mapping, state assessment, area estimation and yield forecasting are the main tasks that are being addressed within GEO-GLAM. Efficiency of agriculture monitoring can be improved when heterogeneous multi-source remote sensing datasets are integrated. Here, we present several case studies of utilizing MODIS, Landsat-8 and Sentinel-2 data along with meteorological data (growing degree days - GDD) for winter wheat yield forecasting, mapping and area estimation. Archived coarse spatial resolution data, such as MODIS, VIIRS and AVHRR, can provide daily global observations that coupled with statistical data on crop yield can enable the development of empirical models for timely yield forecasting at national level. With the availability of high-temporal and high spatial resolution Landsat-8 and Sentinel-2A imagery, course resolution empirical yield models can be downscaled to provide yield estimates at regional and field scale. In particular, we present the case study of downscaling the MODIS CMG based generalized winter wheat yield forecasting model to high spatial resolution data sets, namely harmonized Landsat-8 - Sentinel-2A surface reflectance product (HLS). Since the yield model requires corresponding in season crop masks, we propose an automatic approach to extract winter crop maps from MODIS NDVI and MERRA2 derived GDD using Gaussian mixture model (GMM). Validation for the state of Kansas (US) and Ukraine showed that the approach can yield accuracies > 90% without using reference (ground truth) data sets. Another application of yearly derived winter crop maps is their use for stratification purposes within area frame sampling for crop area estimation. In particular, one can simulate the dependence of error (coefficient of variation) on the number of samples and strata size. This approach was used for estimating the area of winter crops in Ukraine for 2013-2016. The GMM-GDD approach is further extended for HLS data to provide automatic winter crop mapping at 30 m resolution for crop yield model and area estimation. In case of persistent cloudiness, addition of Sentinel-1A synthetic aperture radar (SAR) images is explored for automatic winter crop mapping.

Projected Irrigation Requirement Under Climate Change in Korean Peninsula by Apply Global Hydrologic Model to Local Scale.

NASA Astrophysics Data System (ADS)

Yang, B.; Lee, D. K.

2016-12-01

Understanding spatial distribution of irrigation requirement is critically important for agricultural water management. However, many studies considering future agricultural water management in Korea assessed irrigation requirement on watershed or administrative district scale, but have not accounted the spatial distribution. Lumped hydrologic model has typically used in Korea for simulating watershed scale irrigation requirement, while distribution hydrologic model can simulate the spatial distribution grid by grid. To overcome this shortcoming, here we applied a grid base global hydrologic model (H08) into local scale to estimate spatial distribution under future irrigation requirement of Korean Peninsula. Korea is one of the world's most densely populated countries, with also high produce and demand of rice which requires higher soil moisture than other crops. Although, most of the precipitation concentrate in particular season and disagree with crop growth season. This precipitation character makes management of agricultural water which is approximately 60% of total water usage critical issue in Korea. Furthermore, under future climate change, the precipitation predicted to be more concentrated and necessary need change of future water management plan. In order to apply global hydrological model into local scale, we selected appropriate major crops under social and local climate condition in Korea to estimate cropping area and yield, and revised the cropping area map more accurately. As a result, future irrigation requirement estimation varies under each projection, however, slightly decreased in most case. The simulation reveals, evapotranspiration increase slightly while effective precipitation also increase to balance the irrigation requirement. This finding suggest practical guideline to decision makers for further agricultural water management plan including future development of water supply plan to resolve water scarcity.

A generalized approach to wheat yield forecasting using earth observations: Data considerations, application and relevance

NASA Astrophysics Data System (ADS)

Becker-Reshef, Inbal

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. The issue of food security has rapidly risen to the top of government agendas around the world as the recent lack of food access led to unprecedented food prices, hunger, poverty, and civil conflict. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production, for stabilizing food prices, developing effective agricultural policies, and for coordinating responses to regional food shortages. Earth Observations (EO) data offer a practical means for generating such information as they provide global, timely, cost-effective, and synoptic information on crop condition and distribution. Their utility for crop production forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions. Nevertheless it is widely acknowledged that EO data could be better utilized within the operational monitoring systems and thus there is a critical need for research focused on developing practical robust methods for agricultural monitoring. Within this context this dissertation focused on advancing EO-based methods for crop yield forecasting and on demonstrating the potential relevance for adopting EO-based crop forecasts for providing timely reliable agricultural intelligence. This thesis made contributions to this field by developing and testing a robust EO-based method for wheat production forecasting at state to national scales using available and easily accessible data. The model was developed in Kansas (KS) using coarse resolution normalized difference vegetation index (NDVI) time series data in conjunction with out-of-season wheat masks and was directly applied in Ukraine to assess its transferability. The model estimated yields within 7% in KS and 10% in Ukraine of final estimates 6 weeks prior to harvest. The relevance of adopting such methods to provide timely reliable information to crop commodity markets is demonstrated through a 2010 case study.

Changes in rainfed and irrigated crop yield response to climate in the western US

NASA Astrophysics Data System (ADS)

Li, X.; Troy, T. J.

2018-06-01

As the global population increases and the climate changes, ensuring a secure food supply is increasingly important. One strategy is irrigation, which allows for crops to be grown outside their optimal climate growing regions and which buffers against climate variability. Although irrigation is a positive climate adaptation mechanism for agriculture, it has a potentially negative effect on water resources as it can lead to groundwater depletion and diminished surface water supplies. This study quantifies how crop yields are affected by climate variability and extremes and the impact of irrigation on crop yield increases under various growing-season climate conditions. To do this, we use historical climate data and county-level rainfed and irrigated crop yields for maize, soybean, winter and spring wheat over the US to analyze the relationship between climate, crop yields, and irrigation. We find that there are optimal climates, specific to each crop, where irrigation provides a benefit and other conditions where irrigation proves to have marginal, if any, benefits. Furthermore, the relationship between crop yields and climate has changed over the last decades, with a changing sensitivity in the relationship of soybean and winter wheat yields to certain climate variables, like crop reference evapotranspiration. These two conclusions have important implications for agricultural and water resource system planning, as it implies there are more optimal climate conditions where irrigation is particularly productive and regions where irrigation should be reconsidered as there is not a significant agricultural benefit and the water could be used more productively.

The implication of irrigation in climate change impact assessment: a European-wide study.

PubMed

Zhao, Gang; Webber, Heidi; Hoffmann, Holger; Wolf, Joost; Siebert, Stefan; Ewert, Frank

2015-11-01

This study evaluates the impacts of projected climate change on irrigation requirements and yields of six crops (winter wheat, winter barley, rapeseed, grain maize, potato, and sugar beet) in Europe. Furthermore, the uncertainty deriving from consideration of irrigation, CO2 effects on crop growth and transpiration, and different climate change scenarios in climate change impact assessments is quantified. Net irrigation requirement (NIR) and yields of the six crops were simulated for a baseline (1982-2006) and three SRES scenarios (B1, B2 and A1B, 2040-2064) under rainfed and irrigated conditions, using a process-based crop model, SIMPLACE . We found that projected climate change decreased NIR of the three winter crops in northern Europe (up to 81 mm), but increased NIR of all the six crops in the Mediterranean regions (up to 182 mm yr(-1) ). Climate change increased yields of the three winter crops and sugar beet in middle and northern regions (up to 36%), but decreased their yields in Mediterranean countries (up to 81%). Consideration of CO2 effects can alter the direction of change in NIR for irrigated crops in the south and of yields for C3 crops in central and northern Europe. Constraining the model to rainfed conditions for spring crops led to a negative bias in simulating climate change impacts on yields (up to 44%), which was proportional to the irrigation ratio of the simulation unit. Impacts on NIR and yields were generally consistent across the three SRES scenarios for the majority of regions in Europe. We conclude that due to the magnitude of irrigation and CO2 effects, they should both be considered in the simulation of climate change impacts on crop production and water availability, particularly for crops and regions with a high proportion of irrigated crop area. © 2015 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.

The impact of Global Warming on global crop yields due to changes in pest pressure

NASA Astrophysics Data System (ADS)

Battisti, D. S.; Tewksbury, J. J.; Deutsch, C. A.

2011-12-01

A billion people currently lack reliable access to sufficient food and almost half of the calories feeding these people come from just three crops: rice, maize, wheat. Insect pests are among the largest factors affecting the yield of these three crops, but models assessing the effects of global warming on crops rarely consider changes in insect pest pressure on crop yields. We use well-established relationships between temperature and insect physiology to project climate-driven changes in pest pressure, defined as integrated population metabolism, for the three major crops. By the middle of this century, under most scenarios, insect pest pressure is projected to increase by more than 50% in temperate areas, while increases in tropical regions will be more modest. Yield relationships indicate that the largest increases in insect pest pressure are likely to occur in areas where yield is greatest, suggesting increased strain on global food markets.

Impacts of extreme heat and drought on crop yields in China: an assessment by using the DLEM-AG2 model

NASA Astrophysics Data System (ADS)

Zhang, J.; Yang, J.; Pan, S.; Tian, H.

2016-12-01

China is not only one of the major agricultural production countries with the largest population in the world, but it is also the most susceptible to climate change and extreme events. Much concern has been raised about how extreme climate has affected crop yield, which is crucial for China's food supply security. However, the quantitative assessment of extreme heat and drought impacts on crop yield in China has rarely been investigated. By using the Dynamic Land Ecosystem Model (DLEM-AG2), a highly integrated process-based ecosystem model with crop-specific simulation, here we quantified spatial and temporal patterns of extreme climatic heat and drought stress and their impacts on the yields of major food crops (rice, wheat, maize, and soybean) across China during 1981-2015, and further investigated the underlying mechanisms. Simulated results showed that extreme heat and drought stress significantly reduced national cereal production and increased the yield gaps between potential yield and rain-fed yield. The drought stress was the primary factor to reduce crop yields in the semi-arid and arid regions, and extreme heat stress slightly aggravated the yield loss. The yield gap between potential yield and rain-fed yield was larger at locations with lower precipitation. Our results suggest that a large exploitable yield gap in response to extreme climatic heat-drought stress offers an opportunity to increase productivity in China by optimizing agronomic practices, such as irrigation, fertilizer use, sowing density, and sowing date.

Crop yield response to increasing biochar rates

USDA-ARS?s Scientific Manuscript database

The benefit or detriment to crop yield from biochar application varies with biochar type/rate, soil, crop, or climate. The objective of this research was to identify yield response of cotton (Gossypium hirsutum L.), corn (Zea mayes L.), and peanut (Arachis hypogaea L.) to hardwood biochar applied at...

Simulating large-scale crop yield by using perturbed-parameter ensemble method

NASA Astrophysics Data System (ADS)

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

2010-12-01

Toshichika Iizumi, Masayuki Yokozawa, Gen Sakurai, Motoki Nishimori Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Japan Abstract One of concerning issues of food security under changing climate is to predict the inter-annual variation of crop production induced by climate extremes and modulated climate. To secure food supply for growing world population, methodology that can accurately predict crop yield on a large scale is needed. However, for developing a process-based large-scale crop model with a scale of general circulation models (GCMs), 100 km in latitude and longitude, researchers encounter the difficulties in spatial heterogeneity of available information on crop production such as cultivated cultivars and management. This study proposed an ensemble-based simulation method that uses a process-based crop model and systematic parameter perturbation procedure, taking maize in U.S., China, and Brazil as examples. The crop model was developed modifying the fundamental structure of the Soil and Water Assessment Tool (SWAT) to incorporate the effect of heat stress on yield. We called the new model PRYSBI: the Process-based Regional-scale Yield Simulator with Bayesian Inference. The posterior probability density function (PDF) of 17 parameters, which represents the crop- and grid-specific features of the crop and its uncertainty under given data, was estimated by the Bayesian inversion analysis. We then take 1500 ensemble members of simulated yield values based on the parameter sets sampled from the posterior PDF to describe yearly changes of the yield, i.e. perturbed-parameter ensemble method. The ensemble median for 27 years (1980-2006) was compared with the data aggregated from the county yield. On a country scale, the ensemble median of the simulated yield showed a good correspondence with the reported yield: the Pearson’s correlation coefficient is over 0.6 for all countries. In contrast, on a grid scale, the correspondence is still high in most grids regardless of the countries. However, the model showed comparatively low reproducibility in the slope areas, such as around the Rocky Mountains in South Dakota, around the Great Xing'anling Mountains in Heilongjiang, and around the Brazilian Plateau. As there is a wide-ranging local climate conditions in the complex terrain, such as the slope of mountain, the GCM grid-scale weather inputs is likely one of major sources of error. The results of this study highlight the benefits of the perturbed-parameter ensemble method in simulating crop yield on a GCM grid scale: (1) the posterior PDF of parameter could quantify the uncertainty of parameter value of the crop model associated with the local crop production aspects; (2) the method can explicitly account for the uncertainty of parameter value in the crop model simulations; (3) the method achieve a Monte Carlo approximation of probability of sub-grid scale yield, accounting for the nonlinear response of crop yield to weather and management; (4) the method is therefore appropriate to aggregate the simulated sub-grid scale yields to a grid-scale yield and it may be a reason for high performance of the model in capturing inter-annual variation of yield.

Differentiating Wheat Genotypes by Bayesian Hierarchical Nonlinear Mixed Modeling of Wheat Root Density.

PubMed

Wasson, Anton P; Chiu, Grace S; Zwart, Alexander B; Binns, Timothy R

2017-01-01

Ensuring future food security for a growing population while climate change and urban sprawl put pressure on agricultural land will require sustainable intensification of current farming practices. For the crop breeder this means producing higher crop yields with less resources due to greater environmental stresses. While easy gains in crop yield have been made mostly "above ground," little progress has been made "below ground"; and yet it is these root system traits that can improve productivity and resistance to drought stress. Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a hierarchical nonlinear mixed modeling approach that utilizes the complete field data for wheat genotypes to fit, under the Bayesian paradigm, an "idealized" relative intensity function for the root distribution over depth. Our approach was used to determine heritability : how much of the variation between field samples was purely random vs. being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our approach led to denoised profiles which exhibited rigorously discernible phenotypic traits. Profile-specific traits could be representative of a genotype, and thus, used as a quantitative tool to associate phenotypic traits with specific genotypes. This would allow breeders to select for whole root system distributions appropriate for sustainable intensification, and inform policy for mitigating crop yield risk and food insecurity.

Effects of dynamic agricultural decision making in an ecohydrological model

NASA Astrophysics Data System (ADS)

Reichenau, T. G.; Krimly, T.; Schneider, K.

2012-04-01

Due to various interdependencies between the cycles of water, carbon, nitrogen, and energy the impacts of climate change on ecohydrological systems can only be investigated in an integrative way. Furthermore, the human intervention in the environmental processes makes the system even more complex. On the one hand human impact affects natural systems. On the other hand the changing natural systems have a feedback on human decision making. One of the most important examples for this kind of interaction can be found in the agricultural sector. Management dates (planting, fertilization, harvesting) are chosen based on meteorological conditions and yield expectations. A faster development of crops under a warmer climate causes shorter cropping seasons. The choice of crops depends on their profitability, which is mainly determined by market prizes, the agro-political framework, and the (climate dependent) crop yield. This study investigates these relations for the district Günzburg located in the Upper Danube catchment in southern Germany. The modeling system DANUBIA was used to perform dynamically coupled simulations of plant growth, surface and soil hydrological processes, soil nitrogen transformations, and agricultural decision making. The agro-economic model simulates decisions on management dates (based on meteorological conditions and the crops' development state), on fertilization intensities (based on yield expectations), and on choice of crops (based on profitability). The environmental models included in DANUBIA are to a great extent process based to enable its use in a climate change scenario context. Scenario model runs until 2058 were performed using an IPCC A1B forcing. In consecutive runs, dynamic crop management, dynamic crop selection, and a changing agro-political framework were activated. Effects of these model features on hydrological and ecological variables were analyzed separately by comparing the results to a model run with constant crop distribution and constant management. Results show that the influence of the modeled dynamic management adaptation on variables like transpiration, carbon uptake, or nitrate leaching from the vadose zone is stronger than the influence of a dynamic choice of crops. Climate change was found to have a stronger impact on this modeled choice of crops than the agro-political framework. These results suggest that scenario studies in areas with a large share of arable land should take into account management adaptations to changing climate.

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.

Sensitivity of simulated maize crop yields to regional climate in the Southwestern United States

NASA Astrophysics Data System (ADS)

Kim, S.; Myoung, B.; Stack, D.; Kim, J.; Hatzopoulos, N.; Kafatos, M.

2013-12-01

The sensitivity of maize yield to the regional climate in the Southwestern United States (SW US) has been investigated by using a crop-yield simulation model (APSIM) in conjunction with meteorological forcings (daily minimum and maximum temperature, precipitation, and radiation) from the North American Regional Reanalysis (NARR) dataset. The primary focus of this study is to look at the effects of interannual variations of atmospheric components on the crop productivity in the SW US over the 21-year period (1991 to 2011). First of all, characteristics and performance of APSIM was examined by comparing simulated maize yields with observed yields from United States Department of Agriculture (USDA) and the leaf-area index (LAI) from MODIS satellite data. Comparisons of the simulated maize yield with the available observations show that the crop model can reasonably reproduce observed maize yields. Sensitivity tests were performed to assess the relative contribution of each climate driver to regional crop yield. Sensitivity experiments show that potential crop production responds nonlinearly to climate drivers and the yield sensitivity varied among geographical locations depending on their mean climates. Lastly, a detailed analysis of both the spatial and temporal variations of each climate driver in the regions where maize is actually grown in three states (CA, AZ, and NV) in the SW US was performed.

Agricultural Management Practices Explain Variation in Global Yield Gaps of Major Crops

NASA Astrophysics Data System (ADS)

Mueller, N. D.; Gerber, J. S.; Ray, D. K.; Ramankutty, N.; Foley, J. A.

2010-12-01

The continued expansion and intensification of agriculture are key drivers of global environmental change. Meeting a doubling of food demand in the next half-century will further induce environmental change, requiring either large cropland expansion into carbon- and biodiversity-rich tropical forests or increasing yields on existing croplands. Closing the “yield gaps” between the most and least productive farmers on current agricultural lands is a necessary and major step towards preserving natural ecosystems and meeting future food demand. Here we use global climate, soils, and cropland datasets to quantify yield gaps for major crops using equal-area climate analogs. Consistent with previous studies, we find large yield gaps for many crops in Eastern Europe, tropical Africa, and parts of Mexico. To analyze the drivers of yield gaps, we collected sub-national agricultural management data and built a global dataset of fertilizer application rates for over 160 crops. We constructed empirical crop yield models for each climate analog using the global management information for 17 major crops. We find that our climate-specific models explain a substantial amount of the global variation in yields. These models could be widely applied to identify management changes needed to close yield gaps, analyze the environmental impacts of agricultural intensification, and identify climate change adaptation techniques.

Wildlife-friendly farming increases crop yield: evidence for ecological intensification

PubMed Central

Pywell, Richard F.; Heard, Matthew S.; Woodcock, Ben A.; Hinsley, Shelley; Ridding, Lucy; Nowakowski, Marek; Bullock, James M.

2015-01-01

Ecological intensification has been promoted as a means to achieve environmentally sustainable increases in crop yields by enhancing ecosystem functions that regulate and support production. There is, however, little direct evidence of yield benefits from ecological intensification on commercial farms growing globally important foodstuffs (grains, oilseeds and pulses). We replicated two treatments removing 3 or 8% of land at the field edge from production to create wildlife habitat in 50–60 ha patches over a 900 ha commercial arable farm in central England, and compared these to a business as usual control (no land removed). In the control fields, crop yields were reduced by as much as 38% at the field edge. Habitat creation in these lower yielding areas led to increased yield in the cropped areas of the fields, and this positive effect became more pronounced over 6 years. As a consequence, yields at the field scale were maintained—and, indeed, enhanced for some crops—despite the loss of cropland for habitat creation. These results suggested that over a 5-year crop rotation, there would be no adverse impact on overall yield in terms of monetary value or nutritional energy. This study provides a clear demonstration that wildlife-friendly management which supports ecosystem services is compatible with, and can even increase, crop yields. PMID:26423846

7 CFR 400.651 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... unadjusted transitional yields and dividing the sum by the number of yields contained in the database, which will always contain at least four yields. The database may contain up to 10 consecutive crop years of... catastrophic risk protection. Crop of economic significance. A crop that has either contributed in the previous...

7 CFR 400.651 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... unadjusted transitional yields and dividing the sum by the number of yields contained in the database, which will always contain at least four yields. The database may contain up to 10 consecutive crop years of... catastrophic risk protection. Crop of economic significance. A crop that has either contributed in the previous...

7 CFR 400.651 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... unadjusted transitional yields and dividing the sum by the number of yields contained in the database, which will always contain at least four yields. The database may contain up to 10 consecutive crop years of... catastrophic risk protection. Crop of economic significance. A crop that has either contributed in the previous...

7 CFR 400.651 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... unadjusted transitional yields and dividing the sum by the number of yields contained in the database, which will always contain at least four yields. The database may contain up to 10 consecutive crop years of... catastrophic risk protection. Crop of economic significance. A crop that has either contributed in the previous...

7 CFR 400.651 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... unadjusted transitional yields and dividing the sum by the number of yields contained in the database, which will always contain at least four yields. The database may contain up to 10 consecutive crop years of... catastrophic risk protection. Crop of economic significance. A crop that has either contributed in the previous...

What's holding us back? Raising the alfalfa yield bar

USDA-ARS?s Scientific Manuscript database

Measuring yield of commodity crops is easy – weight and moisture content are determined on delivery. Consequently, reports of production or yield for grain crops can be made reliably to the agencies that track crop production, such as the USDA-National Agricultural Statistics Service (NASS). The s...

Temporally dependent pollinator competition and facilitation with mass flowering crops affects yield in co-blooming crops

PubMed Central

Grab, Heather; Blitzer, Eleanor J.; Danforth, Bryan; Loeb, Greg; Poveda, Katja

2017-01-01

One of the greatest challenges in sustainable agricultural production is managing ecosystem services, such as pollination, in ways that maximize crop yields. Most efforts to increase services by wild pollinators focus on management of natural habitats surrounding farms or non-crop habitats within farms. However, mass flowering crops create resource pulses that may be important determinants of pollinator dynamics. Mass bloom attracts pollinators and it is unclear how this affects the pollination and yields of other co-blooming crops. We investigated the effects of mass flowering apple on the pollinator community and yield of co-blooming strawberry on farms spanning a gradient in cover of apple orchards in the landscape. The effect of mass flowering apple on strawberry was dependent on the stage of apple bloom. During early and peak apple bloom, pollinator abundance and yield were reduced in landscapes with high cover of apple orchards. Following peak apple bloom, pollinator abundance was greater on farms with high apple cover and corresponded with increased yields on these farms. Spatial and temporal overlap between mass flowering and co-blooming crops alters the strength and direction of these dynamics and suggests that yields can be optimized by designing agricultural systems that avoid competition while maximizing facilitation. PMID:28345653

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.

Land Use, Yield and Quality Changes of Minor Field Crops: Is There Superseded Potential to Be Reinvented in Northern Europe?

PubMed

Peltonen-Sainio, Pirjo; Jauhiainen, Lauri; Lehtonen, Heikki

2016-01-01

Diversification of agriculture was one of the strengthened aims of the greening payment of European Agricultural Policy (CAP) as diversification provides numerous ecosystems services compared to cereal-intensive crop rotations. This study focuses on current minor crops in Finland that have potential for expanded production and considers changes in their cropping areas, yield trends, breeding gains, roles in crop rotations and potential for improving resilience. Long-term datasets of Natural Resources Institute Finland and farmers' land use data from the Agency of Rural Affairs were used to analyze the above-mentioned trends and changes. The role of minor crops in rotations declined when early and late CAP periods were compared and that of cereal monocultures strengthened. Genetic yield potentials of minor crops have increased as also genetic improvements in quality traits, although some typical trade-offs with improved yields have also appeared. However, the gap between potential and attained yields has expanded, depending on the minor crop, as national yield trends have either stagnated or declined. When comparing genetic improvements of minor crops to those of the emerging major crop, spring wheat, breeding achievements in minor crops were lower. It was evident that the current agricultural policies in the prevailing market and the price environment have not encouraged cultivation of minor crops but further strengthened the role of cereal monocultures. We suggest optimization of agricultural land use, which is a core element of sustainable intensification, as a future means to couple long-term environmental sustainability with better success in economic profitability and social acceptability. This calls for development of effective policy instruments to support farmer's diversification actions.

Land Use, Yield and Quality Changes of Minor Field Crops: Is There Superseded Potential to Be Reinvented in Northern Europe?

PubMed Central

Peltonen-Sainio, Pirjo; Jauhiainen, Lauri; Lehtonen, Heikki

2016-01-01

Diversification of agriculture was one of the strengthened aims of the greening payment of European Agricultural Policy (CAP) as diversification provides numerous ecosystems services compared to cereal-intensive crop rotations. This study focuses on current minor crops in Finland that have potential for expanded production and considers changes in their cropping areas, yield trends, breeding gains, roles in crop rotations and potential for improving resilience. Long-term datasets of Natural Resources Institute Finland and farmers’ land use data from the Agency of Rural Affairs were used to analyze the above-mentioned trends and changes. The role of minor crops in rotations declined when early and late CAP periods were compared and that of cereal monocultures strengthened. Genetic yield potentials of minor crops have increased as also genetic improvements in quality traits, although some typical trade-offs with improved yields have also appeared. However, the gap between potential and attained yields has expanded, depending on the minor crop, as national yield trends have either stagnated or declined. When comparing genetic improvements of minor crops to those of the emerging major crop, spring wheat, breeding achievements in minor crops were lower. It was evident that the current agricultural policies in the prevailing market and the price environment have not encouraged cultivation of minor crops but further strengthened the role of cereal monocultures. We suggest optimization of agricultural land use, which is a core element of sustainable intensification, as a future means to couple long-term environmental sustainability with better success in economic profitability and social acceptability. This calls for development of effective policy instruments to support farmer’s diversification actions. PMID:27870865

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

NASA Astrophysics Data System (ADS)

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

2018-03-01

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

Salinity's influence on boron toxicity in broccoli: I. Impacts on yield, biomass distribution, and water use

USDA-ARS?s Scientific Manuscript database

Research addressing the interactive effects of the dual plant stress factors, excess boron and salinity, on crop productivity has expanded considerably over the past few years. The purpose of this research was to determine and quantify the interactive effects of salinity, saltcomposition and boron ...

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

Influence of poultry litter and double cropping on soybean yield

USDA-ARS?s Scientific Manuscript database

Continuous cultivation of mono-cropping systems coupled with inorganic fertilizer consumption has led to a decline in soil fertility, negatively influencing crop yields. Poultry litter application and double cropping are two management practices that could be used with conservation tillage to increa...

Soil total carbon and crop yield affected by crop rotation and cultural practice

USDA-ARS?s Scientific Manuscript database

Stacked crop rotation and improved cultural practice have been used to control pests, but their impact on soil organic C (SOC) and crop yield are lacking. We evaluated the effects of stacked vs. alternate-year rotations and cultural practices on SOC at the 0- to 125-cm depth and annualized crop yiel...

Cura Annonae-Chemically Boosting Crop Yields Through Metabolic Feeding of a Plant Signaling Precursor.

PubMed

Vocadlo, David J

2017-05-22

The cream of the crop: With the world facing a projected shortfall of crops by 2050, new approaches are needed to boost crop yields. Metabolic feeding of plants with photocaged trehalose-6-phosphate (Tre6P) can increase levels of the signaling metabolite Tre6P in the plant. Reprogramming of cellular metabolism by Tre6P stimulates a program of plant growth and enhanced crop yields, while boosting starch content. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Management of Lesion Nematodes and Potato Early Dying with Rotation Crops

PubMed Central

LaMondia, J.A.

2006-01-01

Soil-incorporated rotation/green manure crops were evaluated for management of potato early dying caused by Verticillium dahliae and Pratylenchus penetrans. After two years of rotation/green manure and a subsequent potato crop, P. penetrans numbers were less after ‘Saia’ oat/‘Polynema’ marigold, ‘Triple S’ sorghum-sudangrass, or ‘Garry’ oat than ‘Superior’ potato or ‘Humus’ rapeseed. The area under the disease progress curve (AUDPC) for early dying was lowest after Saia oat/marigold, and tuber yields were greater than continuous potato after all crops except sorghum-sudangrass. Saia oat/marigold crops resulted in the greatest tuber yields. After one year of rotation/green manure, a marigold crop increased tuber yields and reduced AUDPC and P. penetrans. In the second potato crop after a single year of rotation, plots previously planted to marigolds had reduced P. penetrans densities and AUDPC and increased tuber yield. Rapeseed supported more P. penetrans than potato, but had greater yields. After two years of rotation/green manure crops and a subsequent potato crop, continuous potato had the highest AUDPC and lowest tuber weight. Rotation with Saia oats (2 yr) and Rudbeckia hirta (1 yr) reduced P. penetrans and increased tuber yields. AUDPC was lowest after R. hirta. Two years of sorghum-sudangrass did not affect P. penetrans, tuber yield or AUDPC. These results demonstrate that P. penetrans may be reduced by one or two years of rotation to non-host or antagonistic plants such as Saia oat, Polynema marigold, or R. hirta and that nematode control may reduce the severity of potato early dying. PMID:19259461

Adaptation Measures Evaliation on Agriculture Under Future Climate and Land Use Scenarios in Central Chile

NASA Astrophysics Data System (ADS)

Henriquez Dole, L. E.; Vicuna, S.; Gironas, J. A.; Meza, F. J.

2016-12-01

Future climate change scenarios threaten current practices in agriculture and therefore adaptation measures have been proposed to overcome this possible situation. Regional to local ideas apply for all kind of adaptation measures and can be found among literature for Central Chile, but their quantitative efficiency is rarely evaluated. Furthermore, land uses changes are commonly neglected in such evaluations. This research use the Water Evaluation and Planning (WEAP) model and the Plant Growth Model (PGM) to simulate weekly water distribution and consumption in Chile's rural areas up to 2050. Using information directly provided by the Water User Organizations (WUO), the developed model assesses possible future impacts on 2 crops (corn and plum) under 15 climate scenarios and land use trends. Results show that WEAP-PGM tool can represent satisfactorily crop sensitiveness to historic and future circumstances. Nine scenarios satisfy average crop water demands, but all of them present a diminished yield (1%-14%) and production (8%-20%). Just six scenarios cannot meet crop water demands (40-70% of reliability) if adaptation measures are not applied. Given this need, two adaptation measures were evaluated: a) using all water rights and b) irrigation improvements. The second option showed to be the most effective measure leading to the satisfaction of crop water demands under all the scenarios, but still a diminished yield and production remained.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

PubMed

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C

2014-09-01

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se. This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Closing the gap: global potential for increasing biofuel production through agricultural intensification

NASA Astrophysics Data System (ADS)

Johnston, Matt; Licker, R.; Foley, J.; Holloway, T.; Mueller, N. D.; Barford, C.; Kucharik, C.

2011-07-01

Since the end of World War II, global agriculture has undergone a period of rapid intensification achieved through a combination of increased applications of chemical fertilizers, pesticides, and herbicides, the implementation of best management practice techniques, mechanization, irrigation, and more recently, through the use of optimized seed varieties and genetic engineering. However, not all crops and not all regions of the world have realized the same improvements in agricultural intensity. In this study we examine both the magnitude and spatial variation of new agricultural production potential from closing of 'yield gaps' for 20 ethanol and biodiesel feedstock crops. With biofuels coming under increasing pressure to slow or eliminate indirect land-use conversion, the use of targeted intensification via established agricultural practices might offer an alternative for continued growth. We find that by closing the 50th percentile production gap—essentially improving global yields to median levels—the 20 crops in this study could provide approximately 112.5 billion liters of new ethanol and 8.5 billion liters of new biodiesel production. This study is intended to be an important new resource for scientists and policymakers alike—helping to more accurately understand spatial variation of yield and agricultural intensification potential, as well as employing these data to better utilize existing infrastructure and optimize the distribution of development and aid capital.

Statistical modeling of yield and variance instability in conventional and organic cropping systems

USDA-ARS?s Scientific Manuscript database

Cropping systems research was undertaken to address declining crop diversity and verify competitiveness of alternatives to the predominant conventional cropping system in the northern Corn Belt. To understand and capitalize on temporal yield variability within corn and soybean fields, we quantified ...

Simulating canopy temperature for modelling heat stress in cereals

USDA-ARS?s Scientific Manuscript database

Crop models must be improved to account for the large effects of heat stress effects on crop yields. To date, most approaches in crop models use air temperature despite evidence that crop canopy temperature better explains yield reductions associated with high temperature events. This study presents...

The California Biomass Crop Adoption Model estimates biofuel feedstock crop production across diverse agro-ecological zones within the state, under different future climates

NASA Astrophysics Data System (ADS)

Kaffka, S.; Jenner, M.; Bucaram, S.; George, N.

2012-12-01

Both regulators and businesses need realistic estimates for the potential production of biomass feedstocks for biofuels and bioproducts. This includes the need to understand how climate change will affect mid-tem and longer-term crop performance and relative advantage. The California Biomass Crop Adoption Model is a partial mathematical programming optimization model that estimates the profit level needed for new crop adoption, and the crop(s) displaced when a biomass feedstock crop is added to the state's diverse set of cropping systems, in diverse regions of the state. Both yield and crop price, as elements of profit, can be varied. Crop adoption is tested against current farmer preferences derived from analysis of 10 years crop production data for all crops produced in California, collected by the California Department of Pesticide Regulation. Analysis of this extensive data set resulted in 45 distinctive, representative farming systems distributed across the state's diverse agro-ecological regions. Estimated yields and water use are derived from field trials combined with crop simulation, reported elsewhere. Crop simulation is carried out under different weather and climate assumptions. Besides crop adoption and displacement, crop resource use is also accounted, derived from partial budgets used for each crop's cost of production. Systematically increasing biofuel crop price identified areas of the state where different types of crops were most likely to be adopted. Oilseed crops like canola that can be used for biodiesel production had the greatest potential to be grown in the Sacramento Valley and other northern regions, while sugar beets (for ethanol) had the greatest potential in the northern San Joaquin Valley region, and sweet sorghum in the southern San Joaquin Valley. Up to approximately 10% of existing annual cropland in California was available for new crop adoption. New crops are adopted if the entire cropping system becomes more profitable. In particular, canola production resulted in less overall water use but increased farm profits. Most crop substitutions were resource neutral. If future climate is drier, more winter annual crops like canola are likely to be adopted. Crop displacement is also important for determining market-mediated effects of biomass crop production. Correctly estimating crop displacement at the local scale greatly improves upon estimates for indirect land use change derived from the macro-scale PE and CGE models currently used by US EPA and the California Air Resources Board.

Long-term variation of Surface Ozone, NO2, temperature and relative humidity on crop yield over Andhra Pradesh (AP), India

NASA Astrophysics Data System (ADS)

Arunachalam, M. S.; Obili, Manjula; Srimurali, M.

2016-07-01

Long-term variation of Surface Ozone, NO2, Temperature, Relative humidity and crop yield datasets over thirteen districts of Andhra Pradesh(AP) has been studied with the help of OMI, MODIS, AIRS, ERA-Interim re-analysis and Directorate of Economics and Statistics (DES) of AP. Inter comparison of crop yield loss estimates according to exposure metrics such as AOT40 (accumulated ozone exposure over a threshold of 40) and non-linear variation of surface temperature for twenty and eighteen varieties of two major crop growing seasons namely, kharif (April-September) and rabi (October-March), respectively has been made. Study is carried to establish a new crop-yield-exposure relationship for different crop cultivars of AP. Both ozone and temperature are showing a correlation coefficient of 0.66 and 0.87 with relative humidity; and 0.72 and 0.80 with NO2. Alleviation of high surface ozone results in high food security and improves the economy thereby reduces the induced warming of the troposphere caused by ozone. Keywords: Surface Ozone, NO2, Temperature, Relative humidity, Crop yield, AOT 40.

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.

Tropical rotation crops influence nematode densities and vegetable yields.

PubMed

McSorley, R; Dickson, D W; de Brito, J A; Hochmuth, R C

1994-09-01

The effects of eight summer rotation crops on nematode densities and yields of subsequent spring vegetable crops were determined in field studies conducted in north Florida from 1991 to 1993. The crop sequence was as follows: (i) rotation crops during summer 1991; (ii) cover crop of rye (Secale cereale) during winter 1991-92; (iii) 'Lemondrop L' squash (Cucurbita pepo) during spring 1992; (iv) rotation crops during summer 1992; (v) rye during winter 1992-93; (vi) 'Classic' eggplant (Solanum melongena) during spring 1993. The eight summer crop rotation treatments were as follows: 'Hale' castor (Ricinus communis), velvetbean (Mucuna deeringiana), sesame (Sesamum indicum), American jointvetch (Aeschynomene americana), weed fallow, 'SX- 17' sorghum-sudangrass (Sorghum bicolor x S. sudanense), 'Kirby' soybean (Glycine max), and 'Clemson Spineless' okra (Hibiscus esculentus) as a control. Rotations with castor, velvetbean, American jointvetch, and sorghum-sudangrass were most effective in maintaining the lowest population densities of Meloidogyne spp. (a mixture of M. incognita race 1 and M. arenaria race 1), but Paratrichodorus minor built up in the sorghum-sudangrass rotation. Yield of squash was lower (P

Risk of water scarcity and water policy implications for crop production in the Ebro Basin in Spain

NASA Astrophysics Data System (ADS)

Quiroga, S.; Fernández-Haddad, Z.; Iglesias, A.

2010-08-01

The increasing pressure on water systems in the Mediterranean enhances existing water conflicts and threatens water supply for agriculture. In this context, one of the main priorities for agricultural research and public policy is the adaptation of crop yields to water pressures. This paper focuses on the evaluation of hydrological risk and water policy implications for food production. Our methodological approach includes four steps. For the first step, we estimate the impacts of rainfall and irrigation water on crop yields. However, this study is not limited to general crop production functions since it also considers the linkages between those economic and biophysical aspects which may have an important effect on crop productivity. We use statistical models of yield response to address how hydrological variables affect the yield of the main Mediterranean crops in the Ebro River Basin. In the second step, this study takes into consideration the effects of those interactions and analyzes gross value added sensitivity to crop production changes. We then use Montecarlo simulations to characterize crop yield risk to water variability. Finally we evaluate some policy scenarios with irrigated area adjustments that could cope in a context of increased water scarcity. A substantial decrease in irrigated land, of up to 30% of total, results in only moderate losses of crop productivity. The response is crop and region specific and may serve to prioritise adaptation strategies.

Achieving food security for one million sub-Saharan African poor through push-pull innovation by 2020.

PubMed

Khan, Zeyaur R; Midega, Charles A O; Pittchar, Jimmy O; Murage, Alice W; Birkett, Michael A; Bruce, Toby J A; Pickett, John A

2014-04-05

Food insecurity is a chronic problem in Africa and is likely to worsen with climate change and population growth. It is largely due to poor yields of the cereal crops caused by factors including stemborer pests, striga weeds and degraded soils. A platform technology, 'push-pull', based on locally available companion plants, effectively addresses these constraints resulting in substantial grain yield increases. It involves intercropping cereal crops with a forage legume, desmodium, and planting Napier grass as a border crop. Desmodium repels stemborer moths (push), and attracts their natural enemies, while Napier grass attracts them (pull). Desmodium is very effective in suppressing striga weed while improving soil fertility through nitrogen fixation and improved organic matter content. Both companion plants provide high-value animal fodder, facilitating milk production and diversifying farmers' income sources. To extend these benefits to drier areas and ensure long-term sustainability of the technology in view of climate change, drought-tolerant trap and intercrop plants are being identified. Studies show that the locally commercial brachiaria cv mulato (trap crop) and greenleaf desmodium (intercrop) can tolerate long droughts. New on-farm field trials show that using these two companion crops in adapted push-pull technology provides effective control of stemborers and striga weeds, resulting in significant grain yield increases. Effective multi-level partnerships have been established with national agricultural research and extension systems, non-governmental organizations and other stakeholders to enhance dissemination of the technology with a goal of reaching one million farm households in the region by 2020. These will be supported by an efficient desmodium seed production and distribution system in eastern Africa, relevant policies and stakeholder training and capacity development.

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

NASA Astrophysics Data System (ADS)

Souty, F.; Brunelle, T.; Dumas, P.; Dorin, B.; Ciais, P.; Crassous, R.; Müller, C.; Bondeau, A.

2012-10-01

Interactions between food demand, biomass energy and forest preservation are driving both food prices and land-use changes, regionally and globally. This study presents a new model called Nexus Land-Use version 1.0 which describes these interactions through a generic representation of agricultural intensification mechanisms within agricultural lands. The Nexus Land-Use model equations combine biophysics and economics into a single coherent framework to calculate crop yields, food prices, and resulting pasture and cropland areas within 12 regions inter-connected with each other by international trade. The representation of cropland and livestock production systems in each region relies on three components: (i) a biomass production function derived from the crop yield response function to inputs such as industrial fertilisers; (ii) a detailed representation of the livestock production system subdivided into an intensive and an extensive component, and (iii) a spatially explicit distribution of potential (maximal) crop yields prescribed from the Lund-Postdam-Jena global vegetation model for managed Land (LPJmL). The economic principles governing decisions about land-use and intensification are adapted from the Ricardian rent theory, assuming cost minimisation for farmers. In contrast to the other land-use models linking economy and biophysics, crops are aggregated as a representative product in calories and intensification for the representative crop is a non-linear function of chemical inputs. The model equations and parameter values are first described in details. Then, idealised scenarios exploring the impact of forest preservation policies or rising energy price on agricultural intensification are described, and their impacts on pasture and cropland areas are investigated.

Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Zhang, Qingyuan

2016-04-01

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05° (∼5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

High-resolution, regional-scale crop yield simulations for the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D. H.; Kafatos, M.; Medvigy, D.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Prasad, A. K.; Tremback, C.; Walko, R. L.; Asrar, G. R.

2012-12-01

Over the past few decades, there have been many process-based crop models developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields. These models simulate the growth and development of crops in response to environmental drivers. Traditionally, process-based crop models have been run at the individual farm level for yield optimization and management scenario testing. Few previous studies have used these models over broader geographic regions, largely due to the lack of gridded high-resolution meteorological and soil datasets required as inputs for these data intensive process-based models. In particular, assessment of regional-scale yield variability due to climate change requires high-resolution, regional-scale, climate projections, and such projections have been unavailable until recently. The goal of this study was to create a framework for extending the Agricultural Production Systems sIMulator (APSIM) crop model for use at regional scales and analyze spatial and temporal yield changes in the Southwestern United States (CA, AZ, and NV). Using the scripting language Python, an automated pipeline was developed to link Regional Climate Model (RCM) output with the APSIM crop model, thus creating a one-way nested modeling framework. This framework was used to combine climate, soil, land use, and agricultural management datasets in order to better understand the relationship between climate variability and crop yield at the regional-scale. Three different RCMs were used to drive APSIM: OLAM, RAMS, and WRF. Preliminary results suggest that, depending on the model inputs, there is some variability between simulated RCM driven maize yields and historical yields obtained from the United States Department of Agriculture (USDA). Furthermore, these simulations showed strong non-linear correlations between yield and meteorological drivers, with critical threshold values for some of the inputs (e.g. minimum and maximum temperature), beyond which the yields were negatively affected. These results are now being used for further regional-scale yield analysis as the aforementioned framework is adaptable to multiple geographic regions and crop types.

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.

Effects of fragmentation, supplementation and the addition of phase II compost to 2nd break compost on mushroom (Agaricus bisporus) yield.

PubMed

Royse, Daniel J

2010-01-01

Double-cropping offers growers an opportunity to increase production efficiency while reducing costs. We evaluated degree of fragmentation, supplementation, and addition of phase II compost (PIIC) to 2nd break compost (2BkC) on mushroom yield and biological efficiency (BE%). One crop was extended as a triple crop in which we evaluated effect of compost type, and addition of phase II compost and supplement. All crops involved removing the casing layer after 2nd break and then using 2BkC for the various treatments. Simple fragmentation of the compost increased mushroom yield by 30% compared to non-fragmented compost. Addition of a commercial supplement to fragmented compost increased mushroom yield by 53-56% over non-supplemented, fragmented 2BkC. Fragmented, supplemented 2BkC resulted in a 99% and 108% yield increase over the non-fragmented control depending on degree of fragmentation (3x, 1x, respectively). A 3rd crop of mushrooms was produced from 2BkC, but yields were about one-half that of the 1st and 2nd crops. Double-cropping (and even triple-cropping) offers growers an opportunity to increase bio-efficiency, reduce production costs, and increase profitability. The cost of producing Agaricus bisporus continues to rise due to increasing expenses including materials, energy, and labor. Optimizing production practices, through double- or triple-cropping, could help growers become more efficient and competitive, and ensure the availability of mushrooms for consumers.

Wheat yield and yield stability of eight dryland crop rotations

USDA-ARS?s Scientific Manuscript database

The winter wheat (Triticum aestivum L.)-fallow (WF) dryland production system employed in the Central Great Plains has evolved in the past 40 years to include a diversity of other crops, with a reduction in fallow frequency. Wheat remains the base crop for essentially all cropping systems. Decisions...

Effects of Management Practices on Meloidogyne incognita and Snap Bean Yield.

PubMed

Smittle, D A; Johnson, A W

1982-01-01

Phenamiphos applied at 6.7 kg ai/ha through a solid set or a center pivot irrigation system with 28 mm of water effectively controlled root-knot nematodes, Meloidogyne incognita, and resulted in greater snap bean growth and yields irrespective of growing season, tillage method, or cover crop system. The percentage yield increases attributed to this method of M. incognita control over nontreated controls were 45% in the spring crop, and 90% and 409% in the fall crops following winter rye and fallow, respectively. Root galling was not affected by tillage systems or cover crop, but disk tillage resulted in over 50% reduction in bean yield compared with yields from the subsoil-bed tillage system.

Meeting the demand for crop production: the challenge of yield decline in crops grown in short rotations.

PubMed

Bennett, Amanda J; Bending, Gary D; Chandler, David; Hilton, Sally; Mills, Peter

2012-02-01

There is a trend world-wide to grow crops in short rotation or in monoculture, particularly in conventional agriculture. This practice is becoming more prevalent due to a range of factors including economic market trends, technological advances, government incentives, and retailer and consumer demands. Land-use intensity will have to increase further in future in order to meet the demands of growing crops for both bioenergy and food production, and long rotations may not be considered viable or practical. However, evidence indicates that crops grown in short rotations or monoculture often suffer from yield decline compared to those grown in longer rotations or for the first time. Numerous factors have been hypothesised as contributing to yield decline, including biotic factors such as plant pathogens, deleterious rhizosphere microorganisms, mycorrhizas acting as pathogens, and allelopathy or autotoxicity of the crop, as well as abiotic factors such as land management practices and nutrient availability. In many cases, soil microorganisms have been implicated either directly or indirectly in yield decline. Although individual factors may be responsible for yield decline in some cases, it is more likely that combinations of factors interact to cause the problem. However, evidence confirming the precise role of these various factors is often lacking in field studies due to the complex nature of cropping systems and the numerous interactions that take place within them. Despite long-term knowledge of the yield-decline phenomenon, there are few tools to counteract it apart from reverting to longer crop rotations or break crops. Alternative cropping and management practices such as double-cropping or inter-cropping, tillage and organic amendments may prove valuable for combating some of the negative effects seen when crops are grown in short rotation. Plant breeding continues to be important, although this does require a specific breeding target to be identified. This review identifies gaps in our understanding of yield decline, particularly with respect to the complex interactions occurring between the different components of agro-ecosystems, which may well influence food security in the 21(st) Century. © 2011 The Authors. Biological Reviews © 2011 Cambridge Philosophical Society.

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;

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).

Understanding the weather signal in national crop-yield variability

NASA Astrophysics Data System (ADS)

Frieler, Katja; Schauberger, Bernhard; Arneth, Almut; Balkovič, Juraj; Chryssanthacopoulos, James; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Khabarov, Nikolay; Müller, Christoph; Olin, Stefan; Pugh, Thomas A. M.; Schaphoff, Sibyll; Schewe, Jacob; Schmid, Erwin; Warszawski, Lila; Levermann, Anders

2017-06-01

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

Patterns of Cereal Yield Growth across China from 1980 to 2010 and Their Implications for Food Production and Food Security

PubMed Central

Li, Xiaoyun; Liu, Nianjie; You, Liangzhi; Ke, Xinli; Liu, Haijun; Huang, Malan; Waddington, Stephen R.

2016-01-01

After a remarkable 86% increase in cereal production from 1980 to 2005, recent crop yield growth in China has been slow. County level crop production data between 1980 and 2010 from eastern and middle China were used to analyze spatial and temporal patterns of rice, wheat and maize yield in five major farming systems that include around 90% of China's cereal production. Site-specific yield trends were assessed in areas where those crops have experienced increasing yield or where yields have stagnated or declined. We find that rice yields have continued to increase on over 12.3 million hectares (m. ha) or 41.8% of the rice area in China between 1980 and 2010. However, yields stagnated on 50% of the rice area (around 14.7 m. ha) over this time period. Wheat yields increased on 13.8 m. ha (58.2% of the total harvest area), but stagnated on around 3.8 m. ha (15.8% of the harvest area). Yields increased on a smaller proportion of the maize area (17.7% of harvest area, 5.3 m. ha), while yields have stagnated on over 54% (16.3 m. ha). Many parts of the lowland rice and upland intensive sub-tropical farming systems were more prone to stagnation with rice, the upland intensive sub-tropical system with wheat, and maize in the temperate mixed system. Large areas where wheat yield continues to rise were found in the lowland rice and temperate mixed systems. Land and water constraints, climate variability, and other environmental limitations undermine increased crop yield and agricultural productivity in these systems and threaten future food security. Technology and policy innovations must be implemented to promote crop yields and the sustainable use of agricultural resources to maintain food security in China. In many production regions it is possible to better match the crop with input resources to raise crop yields and benefits. Investments may be especially useful to intensify production in areas where yields continue to improve. For example, increased support to maize production in southern China, where yields are still rising, seems justified. PMID:27404110

Correlation Between Precipitation and Crop Yield for Corn and Cotton Produced in Alabama

NASA Technical Reports Server (NTRS)

Hayes, Carol E.; Perkey, Donald J.

1998-01-01

In this study, variations in precipitation during the time of corn silking are compared to Alabama corn yields. Also, this study compares precipitation variations during bloom to Alabama cotton yield. The goal is to obtain mathematical correlations between rainfall during the crop's critical period and the crop amount harvested per acre.

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

NASA Astrophysics Data System (ADS)

Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph

2016-12-01

Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

The impact of soil moisture extremes and their spatiotemporal variability on Zambian maize yields

NASA Astrophysics Data System (ADS)

Zhao, Y.; Estes, L. D.; Vergopolan, N.

2017-12-01

Food security in sub-Saharan Africa is highly sensitive to climate variability. While it is well understood that extreme heat has substantial negative impacts on crop yield, the impacts of precipitation extremes, particularly over large spatial extents, are harder to quantify. There are three primary reasons for this difficulty, which are (1) lack of high quality, high resolution precipitation data, (2) rainfall data provide incomplete information on plant water availability, the variable that most directly affects crop performance, and (3) the type of rainfall extreme that most affects crop yields varies throughout the crop development stage. With respect to the first reason, the spatial and temporal variation of precipitation is much greater than that of temperature, yet the spatial resolution of rainfall data is typically even coarser than it is for temperature, particularly within Africa. Even if there were high-resolution rainfall data, the amount of water available to crops also depends on other physical factors that affect evapotranspiration, which are strongly influenced by heterogeneity in the land surface related to topography, soil properties, and land cover. In this context, soil moisture provides a better measure of crop water availability than rainfall. Furthermore, soil moisture has significantly different influences on crop yield depending on the crop's growth stage. The goal of this study is to understand how the spatiotemporal scales of soil moisture extremes interact with crops, more specifically, the timing and the spatial scales of extreme events like droughts and flooding. In this study, we simulate daily-1km soil moisture using HydroBlocks - a physically based land surface model - and compare it with precipitation and remote sensing derived maize yields between 2000 and 2016 in Zambia. We use a novel combination of the SCYM (scalable satellite-based yield mapper) method with DSSAT crop model, which is a mechanistic model responsive to water stress. Understanding the relationships between soil moisture spatiotemporal variability and yields can help to improve agricultural drought risk assessment and seasonal crop yield forecasting as well as early season warning of potential famines.

Droughts in India from 1981 to 2013 and Implications to Wheat Production

NASA Astrophysics Data System (ADS)

Zhang, Xiang; Obringer, Renee; Wei, Chehan; Chen, Nengcheng; Niyogi, Dev

2017-03-01

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.

Climate change and water scarcity effects on the rural income distribution in the Mediterranean

NASA Astrophysics Data System (ADS)

Quiroga, Sonia; Suárez, Cristina

2015-04-01

This paper examines the effects of climate change and water scarcity on the agricultural outputs in the Mediterranean region. By now the effects of water scarcity as a response to climate change or policy restrictions has been analyzed with response functions considering the direct effects on crop productivity. Here we consider a complementary indirect effect on social distribution of incomes which is essential in the long term. We estimate crop production functions for a range of Mediterranean crops in Spain and we use a decomposition of the Gini coefficient to estimate the impact of climate change and water scarcity on yield disparities. This social aspect is important for climate change policies since it can be determinant for the public acceptation of certain adaptation measures in a context of water scarcity. We provide the empirical estimations for the marginal effects on the two considered direct and indirect impacts. In our estimates we consider both bio-physical and socio-economic aspects to conclude that there are long term implications on both competitiveness and social disparities. We find disparities in the adaptation strategies depending on the crop and the region analyzed.

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.

Crop response to deep tillage - a meta-analysis

NASA Astrophysics Data System (ADS)

Schneider, Florian; Don, Axel; Hennings, Inga; Schmittmann, Oliver; Seidel, Sabine J.

2017-04-01

Subsoil, i.e. the soil layer below the topsoil, stores tremendous stocks of nutrients and can keep water even under drought conditions. Deep tillage may be a method to enhance the plant-availability of subsoil resources. However, in field trials, deep tillage effects on crop yields were inconsistent. Therefore, we conducted a meta-analysis of crop yield response to subsoiling, deep ploughing and deep mixing of soil profiles. Our search resulted in 1530 yield comparisons following deep and conventional control tillage on 67 experimental cropping sites. The vast majority of the data derived from temperate latitudes, from trials conducted in the USA (679 observations) and Germany (630 observations). On average, crop yield response to deep tillage was slightly positive (6% increase). However, individual deep tillage effects were highly scattered including about 40% yield depressions after deep tillage. Deep tillage on soils with root restrictive layers increased crop yields about 20%, while soils containing >70% silt increased the risk of yield depressions following deep tillage. Generally, deep tillage effects increased with drought intensity indicating deep tillage as climate adaptation measure at certain sites. Our results suggest that deep tillage can facilitate the plant-availability of subsoil nutrients, which increases crop yields if (i) nutrients in the topsoil are growth limiting, and (ii) deep tillage does not come at the cost of impairing topsoil fertility. On sites with root restrictive soil layers, deep tillage can be an effective measure to mitigate drought stress and improve the resilience of crops. However, deep tillage should only be performed on soils with a stable structure, i.e. <70% silt content. We will discuss the contribution of deep tillage options to enhance the sustainability of agricultural production by facilitating the uptake of nutrients and water from the subsoil.

Possible changes to arable crop yields by 2050

PubMed Central

Jaggard, Keith W.; Qi, Aiming; Ober, Eric S.

2010-01-01

By 2050, the world population is likely to be 9.1 billion, the CO2 concentration 550 ppm, the ozone concentration 60 ppb and the climate warmer by ca 2°C. In these conditions, what contribution can increased crop yield make to feeding the world? CO2 enrichment is likely to increase yields of most crops by approximately 13 per cent but leave yields of C4 crops unchanged. It will tend to reduce water consumption by all crops, but this effect will be approximately cancelled out by the effect of the increased temperature on evaporation rates. In many places increased temperature will provide opportunities to manipulate agronomy to improve crop performance. Ozone concentration increases will decrease yields by 5 per cent or more. Plant breeders will probably be able to increase yields considerably in the CO2-enriched environment of the future, and most weeds and airborne pests and diseases should remain controllable, so long as policy changes do not remove too many types of crop-protection chemicals. However, soil-borne pathogens are likely to be an increasing problem when warmer weather will increase their multiplication rates; control is likely to need a transgenic approach to breeding for resistance. There is a large gap between achievable yields and those delivered by farmers, even in the most efficient agricultural systems. A gap is inevitable, but there are large differences between farmers, even between those who have used the same resources. If this gap is closed and accompanied by improvements in potential yields then there is a good prospect that crop production will increase by approximately 50 per cent or more by 2050 without extra land. However, the demands for land to produce bio-energy have not been factored into these calculations. PMID:20713388

Possible changes to arable crop yields by 2050.

PubMed

Jaggard, Keith W; Qi, Aiming; Ober, Eric S

2010-09-27

By 2050, the world population is likely to be 9.1 billion, the CO(2) concentration 550 ppm, the ozone concentration 60 ppb and the climate warmer by ca 2 degrees C. In these conditions, what contribution can increased crop yield make to feeding the world? CO(2) enrichment is likely to increase yields of most crops by approximately 13 per cent but leave yields of C4 crops unchanged. It will tend to reduce water consumption by all crops, but this effect will be approximately cancelled out by the effect of the increased temperature on evaporation rates. In many places increased temperature will provide opportunities to manipulate agronomy to improve crop performance. Ozone concentration increases will decrease yields by 5 per cent or more. Plant breeders will probably be able to increase yields considerably in the CO(2)-enriched environment of the future, and most weeds and airborne pests and diseases should remain controllable, so long as policy changes do not remove too many types of crop-protection chemicals. However, soil-borne pathogens are likely to be an increasing problem when warmer weather will increase their multiplication rates; control is likely to need a transgenic approach to breeding for resistance. There is a large gap between achievable yields and those delivered by farmers, even in the most efficient agricultural systems. A gap is inevitable, but there are large differences between farmers, even between those who have used the same resources. If this gap is closed and accompanied by improvements in potential yields then there is a good prospect that crop production will increase by approximately 50 per cent or more by 2050 without extra land. However, the demands for land to produce bio-energy have not been factored into these calculations.

Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes

NASA Astrophysics Data System (ADS)

Zipper, Samuel C.; Qiu, Jiangxiao; Kucharik, Christopher J.

2016-09-01

Maximizing agricultural production on existing cropland is one pillar of meeting future global food security needs. To close crop yield gaps, it is critical to understand how climate extremes such as drought impact yield. Here, we use gridded, daily meteorological data and county-level annual yield data to quantify meteorological drought sensitivity of US maize and soybean production from 1958 to 2007. Meteorological drought negatively affects crop yield over most US crop-producing areas, and yield is most sensitive to short-term (1-3 month) droughts during critical development periods from July to August. While meteorological drought is associated with 13% of overall yield variability, substantial spatial variability in drought effects and sensitivity exists, with central and southeastern US becoming increasingly sensitive to drought over time. Our study illustrates fine-scale spatiotemporal patterns of drought effects, highlighting where variability in crop production is most strongly associated with drought, and suggests that management strategies that buffer against short-term water stress may be most effective at sustaining long-term crop productivity.

Spatial Mapping of Agricultural Water Productivity Using the SWAT Model

NASA Astrophysics Data System (ADS)

Thokal, Rajesh Tulshiram; Gorantiwar, S. D.; Kothari, Mahesh; Bhakar, S. R.; Nandwana, B. P.

2015-03-01

The Sina river basin is facing both episodic and chronic water shortages due to intensive irrigation development. The main objective of this study was to characterize the hydrologic processes of the Sina river basin and assess crop water productivity using the distributed hydrologic model, SWAT. In the simulation year (1998-1999), the inflow to reservoir from upstream side was the major contributor to the reservoir accounting for 92 % of the total required water release for irrigation purpose (119.5 Mm3), while precipitation accounted for 4.1 Mm3. Annual release of water for irrigation was 119.5 Mm3 out of which 54 % water was diverted for irrigation purpose, 26 % was wasted as conveyance loss, average discharge at the command outlet was estimated as 4 % and annual average ground-water recharge coefficient was in the range of 13-17 %. Various scenarios involving water allocation rule were tested with the goal of increasing economic water productivity values in the Sina Irrigation Scheme. Out of those, only most benefited allocation rule is analyzed in this paper. Crop yield varied from 1.98 to 25.9 t/ha, with the majority of the area between 2.14 and 2.78 t/ha. Yield and WP declined significantly in loamy soils of the irrigation command. Crop productivity in the basin was found in the lower range when compared with potential and global values. The findings suggested that there was a potential to improve further. Spatial variations in yield and WP were found to be very high for the crops grown during rabi season, while those were low for the crops grown during kharif season. The crop yields and WP during kharif season were more in the lower reach of the irrigation commands, where loamy soil is more concentrated. Sorghum in both seasons was most profitable. Sorghum fetched net income fivefold that of sunflower, two and half fold of pearl millet and one and half fold of mung beans as far as crop during kharif season were concerned and it fetched fourfold that of groundnut, threefold of wheat, twofold of onion during rabi season and was sevenfold of sugarcane. Analysis suggests that maximization of the area by provision of supplemental irrigation to rainfed areas as well as better on-farm water management practices can provide opportunities for improving water productivity.

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.

Predicting the Impacts of Climate Change on Agricultural Yields and Water Resources in the Maumee River Watershed

NASA Astrophysics Data System (ADS)

Nagelkirk, R. L.; Kendall, A. D.; Basso, B.; Hyndman, D. W.

2012-12-01

Climate change will likely have considerable effects on agriculture in the Midwestern United States. Under current climate projections, end-of-century temperatures rise by approximately 4 C, while precipitation stays relatively unchanged despite a potential increase in heavy rainfall events. These trends have already been observed over the last century: rising temperatures have extended the growing season two days per decade and heavy rainfall events have become twice as common. In an effort to understand the likely effects of climate change on agriculture, maize and soybean yields in the Maumee River Watershed were simulated using the Systems Approach to Land Use Sustainability (SALUS) crop model. SALUS calculates daily crop growth in response to changing climate, soil, and management conditions. We test the hypotheses that 1) despite any positive effects of CO2 fertilization and allowing for higher yielding varieties, longer and warmer growing seasons will lead to excessive water- and heat-stress, lowering yields under current management practices, and 2) that double-cropping maize and soybeans successively in the same season to offset these losses may become feasible if sufficient late-season soil moisture is made available. Outputs of daily Leaf Area Index (LAI) and root mass from a range of SALUS models are then distributed spatially to drive regional hydrologic simulations using the Integrated Landscape Hydrology Model (ILHM). These coupled simulations demonstrate the response of streamflow and groundwater levels to different management strategies.

What aspects of future rainfall changes matter for crop yields in West Africa?

NASA Astrophysics Data System (ADS)

Guan, Kaiyu; Sultan, Benjamin; Biasutti, Michela; Baron, Christian; Lobell, David B.

2015-10-01

How rainfall arrives, in terms of its frequency, intensity, the timing and duration of rainy season, may have a large influence on rainfed agriculture. However, a thorough assessment of these effects is largely missing. This study combines a new synthetic rainfall model and two independently validated crop models (APSIM and SARRA-H) to assess sorghum yield response to possible shifts in seasonal rainfall characteristics in West Africa. We find that shifts in total rainfall amount primarily drive the rainfall-related crop yield change, with less relevance to intraseasonal rainfall features. However, dry regions (total annual rainfall below 500 mm/yr) have a high sensitivity to rainfall frequency and intensity, and more intense rainfall events have greater benefits for crop yield than more frequent rainfall. Delayed monsoon onset may negatively impact yields. Our study implies that future changes in seasonal rainfall characteristics should be considered in designing specific crop adaptations in West Africa.

Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.

Analysis of strain distribution, migratory potential, and invasion history of fall armyworm populations in northern Sub-Saharan Africa

USDA-ARS?s Scientific Manuscript database

Fall armyworm (Spodoptera frugiperda, J.E. Smith) is a noctuid moth pest endemic throughout the Western Hemisphere that has recently become widespread in Sub-Saharan Africa. There is a strong expectation of significant damage to African maize crop yield and a high likelihood of further dispersal, pu...

Aerobic Decomposition and Organic Amendments Effects on Grain Yield of Triple-Cropped Rice in the Mekong Delta, Vietnam

USDA-ARS?s Scientific Manuscript database

Soil aeration during decomposition of incorporated crop residues and application of organic amendments might help improve soil quality and rice yield for sustainable intensive rice production. A field experiment was conducted on triple-cropped rice during three consecutive crops with five treatments...

Effects of Tropical Rotation Crops on Meloidogyne arenaria Population Densities and Vegetable Yields in Microplots.

PubMed

McSorley, R; Dickson, D W; de Brito, J A; Hewlett, T E; Frederick, J J

1994-06-01

The effects of 12 summer crop rotation treatments on population densities of Meloidogyne arenaria race 1 and on yields of subsequent spring vegetable crops were determined in microplots. The crop sequence was: (i) rotation crops during summer 1991 ; (ii) cover crop of rye (Secale cereale) during winter 1991-92; (iii) squash (Cucurbita pepo) during spring 1992; (iv) rotation crops during summer 1992; (v) rye during winter 1992-93; (vi) eggplant (Solanum melongena) during spring 1993. The 12 rotation treatments were castor (Ricinus communis), cotton (Gossypium hirsutum), velvetbean (Mucuna deeringiana), crotalaria (Crotalaria spectabilis), fallow, hairy indigo (Indigofera hirsuta), American jointvetch (Aeschynomene americana), sorghum-sudangrass (Sorghum bicolor x S. sudanense), soybean (Glycine max), horsebean (Canavalia ensiformis), sesame (Sesamum indicum), and peanut (Arachis hypogaea). Compared to peanut, the first eight rotation treatments resulted in lower (P

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.

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.

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.

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

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;

Development of transgenic crops based on photo-biotechnology.

PubMed

Ganesan, Markkandan; Lee, Hyo-Yeon; Kim, Jeong-Il; Song, Pill-Soon

2017-11-01

The phenotypes associated with plant photomorphogenesis such as the suppressed shade avoidance response and de-etiolation offer the potential for significant enhancement of crop yields. Of many light signal transducers and transcription factors involved in the photomorphogenic responses of plants, this review focuses on the transgenic overexpression of the photoreceptor genes at the uppermost stream of the signalling events, particularly phytochromes, crytochromes and phototropins as the transgenes for the genetic engineering of crops with improved harvest yields. In promoting the harvest yields of crops, the photoreceptors mediate the light regulation of photosynthetically important genes, and the improved yields often come with the tolerance to abiotic stresses such as drought, salinity and heavy metal ions. As a genetic engineering approach, the term photo-biotechnology has been coined to convey the idea that the greater the photosynthetic efficiency that crop plants can be engineered to possess, the stronger the resistance to biotic and abiotic stresses. Development of GM crops based on photoreceptor transgenes (mainly phytochromes, crytochromes and phototropins) is reviewed with the proposal of photo-biotechnology that the photoreceptors mediate the light regulation of photosynthetically important genes, and the improved yields often come with the added benefits of crops' tolerance to environmental stresses. © 2016 John Wiley & Sons Ltd.

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.

An assessment of irrigation needs and crop yield for the United States under potential climate changes

USGS Publications Warehouse

Brumbelow, Kelly; Georgakakos, Aris P.

2000-01-01

Past assessments of climate change on U.S. agriculture have mostly focused on changes in crop yield. Few studies have included the entire conterminous U.S., and few studies have assessed changing irrigation requirements. None have included the effects of changing soil moisture characteristics as determined by changing climatic forcing. This study assesses changes in irrigation requirements and crop yields for five crops in the areas of the U.S. where they have traditionally been grown. Physiologically-based crop models are used to incorporate inputs of climate, soils, agricultural management, and drought stress tolerance. Soil moisture values from a macroscale hydrologic model run under a future climate scenario are used to initialize soil moisture content at the beginning of each growing season. Historical crop yield data is used to calibrate model parameters and determine locally acceptable drought stress as a management parameter. Changes in irrigation demand and crop yield are assessed for both means and extremes by comparing results for atmospheric forcing close to the present climate with those for a future climate scenario. Assessments using the Canadian Center for Climate Modeling and Analysis General Circulation Model (CGCM1) indicate greater irrigation demands in the southern U.S. and decreased irrigation demands in the northern and western U.S. Crop yields typically increase except for winter wheat in the southern U.S. and corn. Variability in both irrigation demands and crop yields increases in most cases. Assessment results for the CGCM1 climate scenario are compared to those for the Hadley Centre for Climate Prediction and Research GCM (HadCM2) scenario for southwestern Georgia. The comparison shows significant differences in irrigation and yield trends, both in magnitude and direction. The differences reflect the high forecast uncertainty of current GCMs. Nonetheless, both GCMs indicate higher variability in future climatic forcing and, consequently, in the response of agricultural systems.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Educational Software for Illustration of Drainage, Evapotranspiration, and Crop Yield.

ERIC Educational Resources Information Center

Khan, A. H.; And Others

1996-01-01

Describes a study that developed a software package for illustrating drainage, evapotranspiration, and crop yield as influenced by water conditions. The software is a tool for depicting water's influence on crop production in western Kansas. (DDR)

Effects of meteorological droughts on agricultural water resources in southern China

NASA Astrophysics Data System (ADS)

Lu, Houquan; Wu, Yihua; Li, Yijun; Liu, Yongqiang

2017-05-01

With the global warming, frequencies of drought are rising in the humid area of southern China. In this study, the effects of meteorological drought on the agricultural water resource based on the agricultural water resource carrying capacity (AWRCC) in southern China were investigated. The entire study area was divided into three regions based on the distributions of climate and agriculture. The concept of the maximum available water resources for crops was used to calculate AWRCC. Meanwhile, an agricultural drought intensity index (ADI), which was suitable for rice planting areas, was proposed based on the difference between crop water requirements and precipitation. The actual drought area and crop yield in drought years from 1961 to 2010 were analyzed. The results showed that ADI and AWRCC were significantly correlated with the actual drought occurrence area and food yield in the study area, which indicated ADI and AWRCC could be used in drought-related studies. The effects of seasonal droughts on AWRCC strongly depended on both the crop growth season and planting structure. The influence of meteorological drought on agricultural water resources was pronounced in regions with abundant water resources, especially in Southwest China, which was the most vulnerable to droughts. In Southwest China, which has dry and wet seasons, reducing the planting area of dry season crops and rice could improve AWRCC during drought years. Likewise, reducing the planting area of double-season rice could improve AWRCC during drought years in regions with a double-season rice cropping system. Our findings highlight the importance of adjusting the proportions of crop planting to improve the utilization efficiency of agricultural water resources and alleviate drought hazards in some humid areas.

Supplemental irrigation as an initiative to support water and food security: A global evaluation of the potential to support and increase precipitation-fed wheat production

NASA Astrophysics Data System (ADS)

Smilovic, M.; Gleeson, T. P.; Adamowski, J. F.; Langhorn, C.; Kienzle, S. W.

2016-12-01

Supplemental irrigation is the practice of supporting precipitation-fed agriculture with limited irrigation. Precipitation-fed agriculture dominates the agricultural landscape, but is vulnerable to intraseasonal and interannual variability in precipitation and climate. The interplay between food security, water resources, ecosystem health, energy, and livelihoods necessitates evaluating and integrating initiatives that increase agricultural production while reducing demands on water resources. Supplemental irrigation is the practice of minimally irrigating in an effort to stabilize and increase agricultural production, as well as increase water productivity - the amount of crop produced per unit of water. The potential of supplemental irrigation to support both water and food security has yet to be evaluated at regional and global scales. We evaluate whether supplemental irrigation could stabilize and increase agricultural production of wheat by determining locally-calibrated water use-crop yield relationships, known as crop-water production functions. Crop-water production functions are functions of seasonal water use and crop yield, and previous efforts have largely ignored the effects of the temporal distribution of water use throughout the growing season. We significantly improve upon these efforts and provide an opportunity to evaluate supplemental irrigation that appropriately acknowledges the effects of irrigation scheduling. Integrating agroclimatic and crop data with the crop-water model Aquacrop, we determine the increases in wheat production achieved by maximizing water productivity, sharing limited water between different years, and other irrigation scenarios. The methodology presented and evaluation of supplemental irrigation provides water mangers, policy makers, governments, and non-governmental organizations the tools to appropriately understand and determine the potential of this initiative to support precipitation-fed agriculture.

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.

Drought mitigation in perennial crops by fertilization and adjustments of regional yield models for future climate variability

NASA Astrophysics Data System (ADS)

Kantola, I. B.; Blanc-Betes, E.; Gomez-Casanovas, N.; Masters, M. D.; Bernacchi, C.; DeLucia, E. H.

2017-12-01

Increased variability and intensity of precipitation in the Midwest agricultural belt due to climate change is a major concern. The success of perennial bioenergy crops in replacing maize for bioethanol production is dependent on sustained yields that exceed maize, and the marketing of perennial crops often emphasizes the resilience of perennial agriculture to climate stressors. Land conversion from maize for bioethanol to Miscanthus x giganteus (miscanthus) increases yields and annual evapotranspiration rates (ET). However, establishment of miscanthus also increases biome water use efficiency (the ratio between net ecosystem productivity after harvest and ET), due to greater belowground biomass in miscanthus than in maize or soybean. In 2012, a widespread drought reduced the yield of 5-year-old miscanthus plots in central Illinois by 36% compared to the previous two years. Eddy covariance data indicated continued soil water deficit during the hydrologically-normal growing season in 2013 and miscanthus yield failed to rebound as expected, lagging behind pre-drought yields by an average of 53% over the next three years. In early 2014, nitrogen fertilizer was applied to half of mature (7-year-old) miscanthus plots in an effort to improve yields. In plots with annual post-emergence application of 60 kg ha-1 of urea, peak biomass was 29% greater than unfertilized miscanthus in 2014, and 113% greater in 2015, achieving statistically similar yields to the pre-drought average. Regional-scale models of perennial crop productivity use 30-year climate averages that are inadequate for predicting long-term effects of short-term extremes on perennial crops. Modeled predictions of perennial crop productivity incorporating repeated extreme weather events, observed crop response, and the use of management practices to mitigate water deficit demonstrate divergent effects on predicted yields.

Variability in soybean yield in Brazil stemming from the interaction of heterogeneous management and climate variability

NASA Astrophysics Data System (ADS)

Cohn, A.; Bragança, A.; Jeffries, G. R.

2017-12-01

An increasing share of global agricultural production can be found in the humid tropics. Therefore, an improved understanding of the mechanisms governing variability in the output of tropical agricultural systems is of increasing importance for food security including through climate change adaptation. Yet, the long window over which many tropical crops can be sown, the diversity of crop varieties and management practices combine to challenge inference into climate risk to cropping output in analyses of tropical crop-climate sensitivity employing administrative data. In this paper, we leverage a newly developed spatially explicit dataset of soybean yields in Brazil to combat this problem. The dataset was built by training a model of remotely-sensed vegetation index data and land cover classification data using a rich in situ dataset of soybean yield and management variables collected over the period 2006 to 2016. The dataset contains soybean yields by plant date, cropping frequency, and maturity group for each 5km grid cell in Brazil. We model variation in these yields using an approach enabling the estimation of the influence of management factors on the sensitivity of soybean yields to variability in: cumulative solar radiation, extreme degree days, growing degree days, flooding rain in the harvest period, and dry spells in the rainy season. We find strong variation in climate sensitivity by management class. Planting date and maturity group each explained a great deal more variation in yield sensitivity than did cropping frequency. Brazil collects comparatively fine spatial resolution yield data. But, our attempt to replicate our results using administrative soy yield data revealed substantially lesser crop-climate sensitivity; suggesting that previous analyses employing administrative data may have underestimated climate risk to tropical soy production.

Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

NASA Astrophysics Data System (ADS)

B, Potgieter A.; D, Rodriguez; B, Power; J, Mclean; P, Davis

2014-02-01

Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (~1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible and 90m for thermal) satellite platforms. Results showed that spatial variations in crop yield were related to a satellite derived canopy stress index (CSIsat) and a moisture stress index (MSIsat). A weather station level canopy stress index (CSIws) calculated at midday was correlated to the CSIsat at late morning. In addition, a strong linear relationship was observed between EVI and LST at point scale throughout the crop growth period. Differences were smallest at anthesis when the canopy closure was highest. This suggests that LST imagery data around flowering could be used to calculate crop stress over large areas of the crop. The harvested yield was related (R2 = 0.67) to CSIsat using a fix date across all fields. This relationship improved (R2 = 0.92) using both indices from all five dates across all fields during the crop growth period. Here we successfully showed that satellite derived crop attributes (CSIsat and MSIsat) can account for most of the variability in final crop yield and that they can be used to predict crop yield at field scales. Applications of these results could enhance the ability of producers to hedge their financial on -farm crop production losses due to in-season water stress by taking crop insurance. This is likely to further improve their adaptive capacity and thus strengthening the long-term viability of the industry domestically and elsewhere.

Biochar derived from corn straw affected availability and distribution of soil nutrients and cotton yield.

PubMed

Tian, Xiaofei; Li, Chengliang; Zhang, Min; Wan, Yongshan; Xie, Zhihua; Chen, Baocheng; Li, Wenqing

2018-01-01

Biochar application as a soil amendment has been proposed as a strategy to improve soil fertility and increase crop yields. However, the effects of successive biochar applications on cotton yields and nutrient distribution in soil are not well documented. A three-year field study was conducted to investigate the effects of successive biochar applications at different rates on cotton yield and on the soil nutrient distribution in the 0-100 cm soil profile. Biochar was applied at 0, 5, 10, and 20 t ha-1 (expressed as Control, BC5, BC10, and BC20, respectively) for each cotton season, with identical doses of chemical fertilizers. Biochar enhanced the cotton lint yield by 8.0-15.8%, 9.3-13.9%, and 9.2-21.9% in 2013, 2014, and 2015, respectively, and high levels of biochar application achieved high cotton yields each year. Leaching of soil nitrate was reduced, while the pH values, soil organic carbon, total nitrogen (N), and available K content of the 0-20 cm soil layer were increased in 2014 and 2015. However, the changes in the soil available P content were less substantial. This study suggests that successive biochar amendments have the potential to enhance cotton productivity and soil fertility while reducing nitrate leaching.

Performance of some Ethiopian fenugreek (Trigonella foenum-graecum L.) germplasm collections as compared with the commercial variety Challa.

PubMed

Fikreselassie, Million

2012-05-01

Systematic breeding efforts on fenugreek have so far been neglected in Ethiopia. For this, 143 random samples of fenugreek accessions along with a commercial variety were used in this study to evaluate the potential of the land races. The field experiment was conducted at Haramaya University research station during 2011 main cropping season. Treatments were arranged in a 12x12 simple lattice design. The highest biomass and seed yielding accessions were generally concentrated more in the categories of yellow and green seed colors. When compared with the commercial variety, above 27% of the tested accessions performed significantly better in terms of seed yield indicating that significant yield gains could be secured by simple selection. However, further evaluation over wider environments is necessary to arrive at conclusive points for such quantitative traits. Green and yellow seeded accessions are widely distributed over all the country and over half of the accessions (63%) had green seed color. High seed yield bearing accessions were those collected from northwest and central part of Ethiopia, while accessions collected from eastern and northwestern Ethiopia were strikingly bold seed size. This variability would provide a basis for improving the crop in breeding program.

The gut bacterial communities associated with lab-raised and field-collected ants of Camponotus fragilis (Formicidae: Formicinae).

PubMed

He, Hong; Wei, Cong; Wheeler, Diana E

2014-09-01

Camponotus is the second largest ant genus and known to harbor the primary endosymbiotic bacteria of the genus Blochmannia. However, little is known about the effect of diet and environment changes on the gut bacterial communities of these ants. We investigated the intestinal bacterial communities in the lab-raised and field-collected ants of Camponotus fragilis which is found in the southwestern United States and northern reaches of Mexico. We determined the difference of gut bacterial composition and distribution among the crop, midgut, and hindgut of the two types of colonies. Number of bacterial species varied with the methods of detection and the source of the ants. Lab-raised ants yielded 12 and 11 species using classical microbial culture methods and small-subunit rRNA genes (16S rRNAs) polymerase chain reaction-restriction fragment-length polymorphism analysis, respectively. Field-collected ants yielded just 4 and 1-3 species using the same methods. Most gut bacterial species from the lab-raised ants were unevenly distributed among the crop, midgut, and hindgut, and each section had its own dominant bacterial species. Acetobacter was the prominent bacteria group in crop, accounting for about 55 % of the crop clone library. Blochmannia was the dominant species in midgut, nearly reaching 90 % of the midgut clone library. Pseudomonas aeruginosa dominated the hindgut, accounting for over 98 % of the hindgut clone library. P. aeruginosa was the only species common to all three sections. A comparison between lab-raised and field-collected ants, and comparison with other species, shows that gut bacterial communities vary with local environment and diet. The bacterial species identified here were most likely commensals with little effect on their hosts or mild pathogens deleterious to colony health.

Design and analysis of mixed cropping experiments for indigenous Pacific Islands

Treesearch

Mareko P. Tofinga

1993-01-01

Mixed cropping (including agroforestry) often gives yield advan-tages as opposed to monocropping. Many criteria have been used to assess yield advantage in crop mixtures. Some of these are presented. In addition, the relative merits of replacement, additive and bivariate factorial designs are discussed. The concepts of analysis of mixed cropping are applied to an...

Impacts of climate change on peanut yield in China simulated by CMIP5 multi-model ensemble projections

NASA Astrophysics Data System (ADS)

Xu, Hanqing; Tian, Zhan; Zhong, Honglin; Fan, Dongli; Shi, Runhe; Niu, Yilong; He, Xiaogang; Chen, Maosi

2017-09-01

Peanut is one of the major edible vegetable oil crops in China, whose growth and yield are very sensitive to climate change. In addition, agriculture climate resources are expected to be redistributed under climate change, which will further influence the growth, development, cropping patterns, distribution and production of peanut. In this study, we used the DSSAT-Peanut model to examine the climate change impacts on peanut production, oil industry and oil food security in China. This model is first calibrated using site observations including 31 years' (1981-2011) climate, soil and agronomy data. This calibrated model is then employed to simulate the future peanut yield based on 20 climate scenarios from 5 Global Circulation Models (GCMs) developed by the InterSectoral Impact Model Intercomparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs). Results indicate that the irrigated peanut yield will decrease 2.6% under the RCP 2.6 scenario, 9.9% under the RCP 4.5 scenario and 29% under the RCP 8.5 scenario, respectively. Similarly, the rain-fed peanut yield will also decrease, with a 2.5% reduction under the RCP 2.6 scenario, 11.5% reduction under the RCP 4.5 scenario and 30% reduction under the RCP 8.5 scenario, respectively.

How changes of climate extremes affect summer and winter crop yields and water productivity in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, D.; Cammarano, D.

2017-12-01

Modeling changes of crop production at regional scale is important to make adaptation measures for sustainably food supply under global change. In this study, we explore how changing climate extremes in the 20th and 21st century affect maize (summer crop) and wheat (winter crop) yields in an agriculturally important region: the southeast United States. We analyze historical (1950-1999) and projected (2006-2055) precipitation and temperature extremes by calculating the changes of 18 climate extreme indices using the statistically downscaled CMIP5 data from 10 general circulation models (GCMs). To evaluate how these climate extremes affect maize and wheat yields, historical baseline and projected maize and wheat yields under RCP4.5 and RCP8.5 scenarios are simulated using the DSSAT-CERES maize and wheat models driven by the same downscaled GCMs data. All of the changes are examined at 110 locations over the study region. The results show that most of the precipitation extreme indices do not have notable change; mean precipitation, precipitation intensity, and maximum 1-day precipitation are generally increased; the number of rainy days is decreased. The temperature extreme indices mostly showed increased values on mean temperature, number of high temperature days, diurnal temperature range, consecutive high temperature days, maximum daily maximum temperature, and minimum daily minimum temperature; the number of low temperature days and number of consecutive low temperature days are decreased. The conditional probabilistic relationships between changes in crop yields and changes in extreme indices suggested different responses of crop yields to climate extremes during sowing to anthesis and anthesis to maturity periods. Wheat yields and crop water productivity for wheat are increased due to an increased CO2 concentration and minimum temperature; evapotranspiration, maize yields, and crop water productivity for wheat are decreased owing to the increased temperature extremes. We found the effects of precipitation changes on both yields are relatively uncertain.

LACIE performance predictor final operational capability program description, volume 3

NASA Technical Reports Server (NTRS)

1976-01-01

The requirements and processing logic for the LACIE Error Model program (LEM) are described. This program is an integral part of the Large Area Crop Inventory Experiment (LACIE) system. LEM is that portion of the LPP (LACIE Performance Predictor) which simulates the sample segment classification, strata yield estimation, and production aggregation. LEM controls repetitive Monte Carlo trials based on input error distributions to obtain statistical estimates of the wheat area, yield, and production at different levels of aggregation. LEM interfaces with the rest of the LPP through a set of data files.

Midwest Agriculture: A comparison of AVHRR NDVI3g data and crop yields in Corn Belt region of the United States from 1982 to 2014

NASA Astrophysics Data System (ADS)

Glennie, E.; Anyamba, A.; Eastman, R.

2016-12-01

A time series of Advanced Very High Resolution Radiometer (AVHRR) derived normalized difference vegetation index (NDVI) images was compared to National Agricultural Statistics Service (NASS) corn yield data in the Corn Belt of the United States from 1982 to 2014. The relationship between NDVI and crop yields under El Nino, neutral, and La Nina conditions was used to assess 1) the reliability of using NDVI as an indicator of crop productivity, and 2) the response of the Corn Belt to El Nino/ Southern Oscillation (ENSO) teleconnection effects. First, bi-monthly NDVI data were combined into monthly data using the maximum value compositing technique to reduce cloud contamination and other effects. The most representative seasonal curve of NDVI values over the course of the study period was extracted to define the growing season in the region - May to October. Standardized NDVI anomalies were calculated and averaged to produce a growing season NDVI metrics to represent each Agricultural Statistics Division (ASD) for each year in the study period. The corn yields were detrended in order to remove effects of technological advancements on crop productivity (use of genetically modified seeds, fertilizer, herbicides). Correlation (R) values between the NDVI anomalies and detrended corn yields varied across the Corn Belt, with a maximum of 0.81 and mean of 0.49. While corn is the dominant crop in the region, some inconsistencies between corn yield and NDVI may be accounted for by an increase in soy yield for a given year due to crop rotation practices. The 10 El Nino events and 9 La Nina events that occurred between 1982 and 2014 are not reflected in a consistent manner in NDVI or corn yield data. However, composites of NDVI and crop yields for all El Nino events indicate there is a tendency for higher than normal NDVI and increased corn yields. Conversely, the composite crop yield image for La Nina events shows a slight decrease in productivity.

Nation-wide assessment of climate change impacts on crops in the Philippines and Peru as part of multi-disciplinary modelling framework

NASA Astrophysics Data System (ADS)

Fujisawa, Mariko; Kanamaru, Hideki

2016-04-01

Agriculture is vulnerable to environmental changes, and climate change has been recognized as one of the most devastating factors. In many developing countries, however, few studies have focused on nation-wide assessment of crop yield and crop suitability in the future, and hence there is a large pressure on science to provide policy makers with solid predictions for major crops in the countries in support of climate risk management policies and programmes. FAO has developed the tool MOSAICC (Modelling System for Agricultural Impacts of Climate Change) where statistical climate downscaling is combined with crop yield projections under climate change scenarios. Three steps are required to get the results: 1. The historical meteorological data such as temperature and precipitation for about 30 years were collected, and future climates were statistically downscaled to the local scale, 2. The historical crop yield data were collected and regression functions were made to estimate the yield by using observed climatic data and water balance during the growing period for each crop, and 3. The yield changes in the future were estimated by using the future climate data, produced by the first step, as an input to the yield regression functions. The yield was first simulated at sub-national scale and aggregated to national scale, which is intended to provide national policies with adaptation options. The methodology considers future changes in characteristics of extreme weather events as the climate projections are on daily scale while crop simulations are on 10-daily scale. Yields were simulated with two greenhouse gas concentration pathways (RCPs) for three GCMs per crop to account for uncertainties in projections. The crop assessment constitutes a larger multi-disciplinary assessment of climate change impacts on agriculture and vulnerability of livelihoods in terms of food security (e.g. water resources, agriculture market, household-level food security from socio-economic perspective). In our presentation we will show the cases of Peru and the Philippines, and discuss the implications for agriculture policies and risk management.

Using observed warming to identify hazards to Mozambique maize production

USGS Publications Warehouse

Funk, Christopher C.; Harrison, Laura; Eilerts, Gary

2011-01-01

New Perspectives on Crop Yield Constraints because of Climate Change. Climate change impact assessments usually focus on changes to precipitation because most global food production is from rainfed cropping systems; however, other aspects of climate change may affect crop growth and potential yields.A recent (2011) study by the University of California, Santa Barbara (UCSB) Climate Hazards Group, determined that climate change may be affecting Mozambique's primary food crop in a usually overlooked, but potentially significant way (Harrison and others, 2011). The study focused on the direct relation between maize crop development and growing season temperature. It determined that warming during the past three decades in Mozambique may be causing more frequent crop stress and yield reductions in that country's maize crop, independent of any changes occurring in rainfall. This report summarizes the findings and conclusions of that study.

Trading carbon for food: global comparison of carbon stocks vs. crop yields on agricultural land.

PubMed

West, Paul C; Gibbs, Holly K; Monfreda, Chad; Wagner, John; Barford, Carol C; Carpenter, Stephen R; Foley, Jonathan A

2010-11-16

Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha(-1) vs. ∼63 tons·ha(-1)) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha(-1)·y(-1) vs. 3.84 tons·ha(-1)·y(-1)). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.

Network Candidate Genes in Breeding for Drought Tolerant Crops

PubMed Central

Krannich, Christoph Tim; Maletzki, Lisa; Kurowsky, Christina; Horn, Renate

2015-01-01

Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance. PMID:26193269

Network Candidate Genes in Breeding for Drought Tolerant Crops.

PubMed

Krannich, Christoph Tim; Maletzki, Lisa; Kurowsky, Christina; Horn, Renate

2015-07-17

Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance.

Modelling crop yield in Iberia under drought conditions

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

2017-04-01

The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining vegetation and hydro-meteorological drought indices for the assessment of cereal yield. Moreover, the present study will provide some guidance on user's decision making process in agricultural practices in the IP, assisting farmers in deciding whether to purchase crop insurance. Acknowledgements: This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMDROFLOOD (WaterJPI/0004/2014). Ana Russo thanks FCT for granted support (SFRH/BPD/99757/2014). Andreia Ribeiro also thanks FCT for grant PD/BD/114481/2016.

Roguing with replacement in perennial crops: conditions for successful disease management.

PubMed

Sisterson, Mark S; Stenger, Drake C

2013-02-01

Replacement of diseased plants with healthy plants is commonly used to manage spread of plant pathogens in perennial cropping systems. This strategy has two potential benefits. First, removing infected plants may slow pathogen spread by eliminating inoculum sources. Second, replacing infected plants with uninfected plants may offset yield losses due to disease. The extent to which these benefits are realized depends on multiple factors. In this study, sensitivity analyses of two spatially explicit simulation models were used to evaluate how assumptions concerning implementation of a plant replacement program and pathogen spread interact to affect disease suppression. In conjunction, effects of assumptions concerning yield loss associated with disease and rates of plant maturity on yields were simultaneously evaluated. The first model was used to evaluate effects of plant replacement on pathogen spread and yield on a single farm, consisting of a perennial crop monoculture. The second model evaluated effects of plant replacement on pathogen spread and yield in a 100 farm crop growing region, with all farms maintaining a monoculture of the same perennial crop. Results indicated that efficient replacement of infected plants combined with a high degree of compliance among farms effectively slowed pathogen spread, resulting in replacement of few plants and high yields. In contrast, inefficient replacement of infected plants or limited compliance among farms failed to slow pathogen spread, resulting in replacement of large numbers of plants (on farms practicing replacement) with little yield benefit. Replacement of infected plants always increased yields relative to simulations without plant replacement provided that infected plants produced no useable yield. However, if infected plants produced useable yields, inefficient removal of infected plants resulted in lower yields relative to simulations without plant replacement for perennial crops with long maturation periods in some cases.

Strengths and Limitations of Operational Use of 1 Km EO Biophysical Products for Regional Prediction of Grain Yelds in Europe (wheat, barley and maize)

NASA Astrophysics Data System (ADS)

Meroni, M.; LEO, O.; Lopez-Lozano, R.; Baruth, B.; Duveiller, G.; Garcia-Condado, S.; Hooker, J.; Seguini, L.

2014-12-01

The site-specific relationship between EO indicators and actual crop yields has been explored in many different studies, describing semi-empirical regression models between spatially aggregated biophysical parameters or vegetation indices and observed yields (from field measurements or official statistics). However, when considering larger extensions -from countries to continents- agro-climatic conditions and crop management may differ substantially among regions, and these differences may greatly influence the relationship between biophysical indicators and the observed yields, which may be also driven by limiting factors other than green biomass formation. The present study aims to better assess the contribution of EO indicators within an operational crop yield forecasting system in Europe and neighbouring countries, by evaluating how these above mentioned geographic differences influence the relationship between biophysical indicators and crop yield. We therefore explore, as a first step, the correspondence between fAPAR time-series (1999-2013) and the inter-annual yield variability of wheat, barley and grain maize, at sub-national level across Europe (270-450 Administrative Units, depending on crop). In a second step, we map the agro-climatic contexts in which EO indicators better explain the observed yield inter-annual variability, identify the influence of some meteorological events on the fAPAR -yield relationship and provide some recommendations for further investigation. The results indicate that in water-limited environments (e.g. Mediterranean and Black Sea areas), fAPAR is highly correlated with yields whereas in northern Europe, crop yield appears much less limited by leaf area expansion along the season, and the relationship between yield and EO products becomes more difficult to interpret.

[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.

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 .

Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C

NASA Astrophysics Data System (ADS)

Chen, Yi; Zhang, Zhao; Tao, Fulu

2018-05-01

A new temperature goal of holding the increase in global average temperature well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0 °C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106-2115 in warming scenarios of 1.5 and 2.0 °C using a method of ensemble simulation with well-validated Model to capture the Crop-Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and the probability of crop yield decrease. Results showed that climate change would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the major cultivation areas, due to a decrease in crop growth duration and an increase in extreme events. By contrast, with moderate increases in temperature, solar radiation, precipitation and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resources could be ameliorated, which would enhance canopy photosynthesis and consequently biomass accumulations and yields. The moderate climate change would slightly worsen the maize growth environment but would result in a much more appropriate growth environment for wheat and rice. As a result, wheat, rice and maize yields would change by +3.9 (+8.6), +4.1 (+9.4) and +0.2 % (-1.7 %), respectively, in a warming scenario of 1.5 °C (2.0 °C). In general, the warming scenarios would bring more opportunities than risks for crop development and food security in China. Moreover, although the variability of crop yield would increase from 1.5 °C warming to 2.0 °C warming, the probability of a crop yield decrease would decrease. Our findings highlight that the 2.0 °C warming scenario would be more suitable for crop production in China, but more attention should be paid to the expected increase in extreme event impacts.

Using a basin-scale hydrological model to estimate crop transpiration and soil evaporation

NASA Astrophysics Data System (ADS)

Kite, G.

2000-03-01

Increasing populations and expectations, declining crop yields and the resulting increased competition for water necesitate improvements in irrigation management and productivity. A key factor in defining agricultural productivity is to be able to simulate soil evaporation and crop transpiration. In agribusiness terms, crop transpiration is a useful process while soil and open-water evaporations are wasteful processes. In this study a distributed hydrological model was used to compute daily evaporation and transpiration for a variety of crops and other land covers within the 17,200 km 2 Gediz Basin in western Turkey. The model, SLURP, describes the complete hydrological cycle for each land cover within a series of sub-basins including all dams, reservoirs, regulators and irrigation schemes in the basin. The sub-basins and land covers are defined by analysing a digital elevation model and NOAA AVHRR satellite data. In this study, the model uses the FAO implementation of the Penman-Monteith equation to simulate soil evaporation and crop transpiration. The results of the model runs provide time series of data on streamflow at many points along the river system, abstractions and return flows from crops within the irrigation schemes and areally distributed soil evaporation and crop transpiration across the entire basin on each day of an 11 year period. The results show that evaporation and transpiration vary widely across the basin on any one day and over the irrigation season and can be used to evaluate the effectiveness of the various irrigation strategies used in the basin. The advantages of using such a model as compared to deriving evapotranspiration from satellite data are that the model obtains results for each day of an indefinitely long period, as opposed to occasional snapshots, and can also be used to simulate alternate scenarios.

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1976-01-01

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.

Climate change impacts on crop yield: evidence from China.

PubMed

Wei, Taoyuan; Cherry, Todd L; Glomrød, Solveig; Zhang, Tianyi

2014-11-15

When estimating climate change impact on crop yield, a typical assumption is constant elasticity of yield with respect to a climate variable even though the elasticity may be inconstant. After estimating both constant and inconstant elasticities with respect to temperature and precipitation based on provincial panel data in China 1980-2008, our results show that during that period, the temperature change contributes positively to total yield growth by 1.3% and 0.4% for wheat and rice, respectively, but negatively by 12% for maize. The impacts of precipitation change are marginal. We also compare our estimates with other studies and highlight the implications of the inconstant elasticities for crop yield, harvest and food security. We conclude that climate change impact on crop yield would not be an issue in China if positive impacts of other socio-economic factors continue in the future. Copyright © 2014 Elsevier B.V. All rights reserved.

Recent patterns of crop yield growth and stagnation.

PubMed

Ray, Deepak K; Ramankutty, Navin; Mueller, Nathaniel D; West, Paul C; Foley, Jonathan A

2012-01-01

In the coming decades, continued population growth, rising meat and dairy consumption and expanding biofuel use will dramatically increase the pressure on global agriculture. Even as we face these future burdens, there have been scattered reports of yield stagnation in the world's major cereal crops, including maize, rice and wheat. Here we study data from ∼2.5 million census observations across the globe extending over the period 1961-2008. We examined the trends in crop yields for four key global crops: maize, rice, wheat and soybeans. Although yields continue to increase in many areas, we find that across 24-39% of maize-, rice-, wheat- and soybean-growing areas, yields either never improve, stagnate or collapse. This result underscores the challenge of meeting increasing global agricultural demands. New investments in underperforming regions, as well as strategies to continue increasing yields in the high-performing areas, are required.

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.

Monitoring Crop Productivity over the U.S. Corn Belt using an Improved Light Use Efficiency Model

NASA Astrophysics Data System (ADS)

Wu, X.; Xiao, X.; Zhang, Y.; Qin, Y.; Doughty, R.

2017-12-01

Large-scale monitoring of crop yield is of great significance for forecasting food production and prices and ensuring food security. Satellite data that provide temporally and spatially continuous information that by themselves or in combination with other data or models, raises possibilities to monitor and understand agricultural productivity regionally. In this study, we first used an improved light use efficiency model-Vegetation Photosynthesis Model (VPM) to simulate the gross primary production (GPP). Model evaluation showed that the simulated GPP (GPPVPM) could well captured the spatio-temporal variation of GPP derived from FLUXNET sites. Then we applied the GPPVPM to further monitor crop productivity for corn and soybean over the U.S. Corn Belt and benchmarked with county-level crop yield statistics. We found VPM-based approach provides pretty good estimates (R2 = 0.88, slope = 1.03). We further showed the impacts of climate extremes on the crop productivity and carbon use efficiency. The study indicates the great potential of VPM in estimating crop yield and in understanding of crop yield responses to climate variability and change.

Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

NASA Astrophysics Data System (ADS)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

NASA Technical Reports Server (NTRS)

Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon;

Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence.

PubMed

Guan, Kaiyu; Berry, Joseph A; Zhang, Yongguang; Joiner, Joanna; Guanter, Luis; Badgley, Grayson; Lobell, David B

2016-02-01

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change. © 2015 John Wiley & Sons Ltd.

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

NASA Technical Reports Server (NTRS)

Guan, Kaiyu; Berry, Joseph A.; Zhang, Yongguang; Joiner, Joanna; Guanter, Luis; Badgley, Grayson; Lobell, David B.

2015-01-01

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Effect of Tillage Practices on Soil Properties and Crop Productivity in Wheat-Mungbean-Rice Cropping System under Subtropical Climatic Conditions

PubMed Central

Islam, Md. Monirul; Hasanuzzaman, Mirza

2014-01-01

This study was conducted to know cropping cycles required to improve OM status in soil and to investigate the effects of medium-term tillage practices on soil properties and crop yields in Grey Terrace soil of Bangladesh under wheat-mungbean-T. aman cropping system. Four different tillage practices, namely, zero tillage (ZT), minimum tillage (MT), conventional tillage (CT), and deep tillage (DT), were studied in a randomized complete block (RCB) design with four replications. Tillage practices showed positive effects on soil properties and crop yields. After four cropping cycles, the highest OM accumulation, the maximum root mass density (0–15 cm soil depth), and the improved physical and chemical properties were recorded in the conservational tillage practices. Bulk and particle densities were decreased due to tillage practices, having the highest reduction of these properties and the highest increase of porosity and field capacity in zero tillage. The highest total N, P, K, and S in their available forms were recorded in zero tillage. All tillage practices showed similar yield after four years of cropping cycles. Therefore, we conclude that zero tillage with 20% residue retention was found to be suitable for soil health and achieving optimum yield under the cropping system in Grey Terrace soil (Aeric Albaquept). PMID:25197702

Climate analogues suggest limited potential for intensification of production on current croplands under climate change

PubMed Central

Pugh, T.A.M.; Müller, C.; Elliott, J.; Deryng, D.; Folberth, C.; Olin, S.; Schmid, E.; Arneth, A.

2016-01-01

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand. PMID:27646707

Climate Analogues Suggest Limited Potential for Intensification of Production on Current Croplands Under Climate Change

NASA Technical Reports Server (NTRS)

Pugh, T. A. M.; Mueller, C.; Elliott, J.; Deryng, D.; Folberth, C.; Olin, S.; Schmid, E.; Arneth, A.

2016-01-01

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand.

Climate analogues suggest limited potential for intensification of production on current croplands under climate change

NASA Astrophysics Data System (ADS)

Pugh, T. A. M.; Müller, C.; Elliott, J.; Deryng, D.; Folberth, C.; Olin, S.; Schmid, E.; Arneth, A.

2016-09-01

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand.

Differentiating Wheat Genotypes by Bayesian Hierarchical Nonlinear Mixed Modeling of Wheat Root Density

PubMed Central

Wasson, Anton P.; Chiu, Grace S.; Zwart, Alexander B.; Binns, Timothy R.

2017-01-01

Ensuring future food security for a growing population while climate change and urban sprawl put pressure on agricultural land will require sustainable intensification of current farming practices. For the crop breeder this means producing higher crop yields with less resources due to greater environmental stresses. While easy gains in crop yield have been made mostly “above ground,” little progress has been made “below ground”; and yet it is these root system traits that can improve productivity and resistance to drought stress. Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a hierarchical nonlinear mixed modeling approach that utilizes the complete field data for wheat genotypes to fit, under the Bayesian paradigm, an “idealized” relative intensity function for the root distribution over depth. Our approach was used to determine heritability: how much of the variation between field samples was purely random vs. being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our approach led to denoised profiles which exhibited rigorously discernible phenotypic traits. Profile-specific traits could be representative of a genotype, and thus, used as a quantitative tool to associate phenotypic traits with specific genotypes. This would allow breeders to select for whole root system distributions appropriate for sustainable intensification, and inform policy for mitigating crop yield risk and food insecurity. PMID:28303148

Crop responses to climatic variation

PubMed Central

Porter, John R; Semenov, Mikhail A

2005-01-01

The yield and quality of food crops is central to the well being of humans and is directly affected by climate and weather. Initial studies of climate change on crops focussed on effects of increased carbon dioxide (CO2) level and/or global mean temperature and/or rainfall and nutrition on crop production. However, crops can respond nonlinearly to changes in their growing conditions, exhibit threshold responses and are subject to combinations of stress factors that affect their growth, development and yield. Thus, climate variability and changes in the frequency of extreme events are important for yield, its stability and quality. In this context, threshold temperatures for crop processes are found not to differ greatly for different crops and are important to define for the major food crops, to assist climate modellers predict the occurrence of crop critical temperatures and their temporal resolution. This paper demonstrates the impacts of climate variability for crop production in a number of crops. Increasing temperature and precipitation variability increases the risks to yield, as shown via computer simulation and experimental studies. The issue of food quality has not been given sufficient importance when assessing the impact of climate change for food and this is addressed. Using simulation models of wheat, the concentration of grain protein is shown to respond to changes in the mean and variability of temperature and precipitation events. The paper concludes with discussion of adaptation possibilities for crops in response to drought and argues that characters that enable better exploration of the soil and slower leaf canopy expansion could lead to crop higher transpiration efficiency. PMID:16433091

Satellite-based mapping of field-scale stress indicators for crop yield forecasting: an application over Mead, NE

NASA Astrophysics Data System (ADS)

Yang, Y.; Anderson, M. C.; Gao, F.; Wardlow, B.; Hain, C.; Otkin, J.; Sun, L.; Dulaney, W.

2017-12-01

In agricultural regions, water is one of the most widely limiting factors of crop performance and production. Evapotranspiration (ET) describes crop water use through transpiration and water lost through direct soil evaporation, which makes it a good indicator of soil moisture availability and vegetation health and thus has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) describes temporal anomalies in a normalized evapotranspiration metric (fRET) as derived from satellite remote sensing and has demonstrated capacity to explain regional yield variability in water limited crop growing regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded. In this study we generated maps of ET, fRET, and ESI at high spatiotemporal resolution (30-m pixels, daily timesteps) using a multi-sensor data fusion method, integrating information from satellite platforms with good temporal coverage and other platforms that provide field-scale spatial detail. The study was conducted over the period 2010-2014, covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of corn yield are investigated at both the field and county level to assess the value of ESI as a yield forecasting tool. To examine the role of phenology in ESI-yield correlations, annual input fRET timeseries were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). Results demonstrate that mapping of fRET and ESI at 30-m has the advantage of being able to resolve different crop types with varying phenology. The study also suggests that incorporating phenological information significantly improves yield-correlations by accounting for effects of phenology such as variable planting date and emergence date. The yield-ESI relationship in this study well captures the inter-annual variability of yields and thus has potential to be used for yield prediction, or for ingestion into a crop simulation model as a crop-specific moisture stress function.

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.

Application of Thermal Infrared Remote Sensing for Quantitative Evaluation of Crop Characteristics

NASA Technical Reports Server (NTRS)

Shaw, J.; Luvall, J.; Rickman, D.; Mask, P.; Wersinger, J.; Sullivan, D.; Arnold, James E. (Technical Monitor)

2002-01-01

Evidence suggests that thermal infrared emittance (TIR) at the field-scale is largely a function of the integrated crop/soil moisture continuum. Because soil moisture dynamics largely determine crop yields in non-irrigated farming (85 % of Alabama farms are non-irrigated), TIR may be an effective method of mapping within field crop yield variability, and possibly, absolute yields. The ability to map yield variability at juvenile growth stages can lead to improved soil fertility and pest management, as well as facilitating the development of economic forecasting. Researchers at GHCC/MSFC/NASA and Auburn University are currently investigating the role of TIR in site-specific agriculture. Site-specific agriculture (SSA), or precision farming, is a method of crop production in which zones and soils within a field are delineated and managed according to their unique properties. The goal of SSA is to improve farm profits and reduce environmental impacts through targeted agrochemical applications. The foundation of SSA depends upon the spatial and temporal characterization of soil and crop properties through the creation of management zones. Management zones can be delineated using: 1) remote sensing (RS) data, 2) conventional soil testing and soil mapping, and 3) yield mapping. Portions of this research have concentrated on using remote sensing data to map yield variability in corn (Zea mays L.) and soybean (Glycine max L.) crops. Remote sensing data have been collected for several fields in the Tennessee Valley region at various crop growth stages during the last four growing seasons. Preliminary results of this study will be presented.

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.

Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

2017-11-01

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize ( Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

Opposing effects of different soil organic matter fractions on crop yields.

PubMed

Wood, Stephen A; Sokol, Noah; Bell, Colin W; Bradford, Mark A; Naeem, Shahid; Wallenstein, Matthew D; Palm, Cheryl A

2016-10-01

Soil organic matter is critical to sustainable agriculture because it provides nutrients to crops as it decomposes and increases nutrient- and water-holding capacity when built up. Fast- and slow-cycling fractions of soil organic matter can have different impacts on crop production because fast-cycling fractions rapidly release nutrients for short-term plant growth and slow-cycling fractions bind nutrients that mineralize slowly and build up water-holding capacity. We explored the controls on these fractions in a tropical agroecosystem and their relationship to crop yields. We performed physical fractionation of soil organic matter from 48 farms and plots in western Kenya. We found that fast-cycling, particulate organic matter was positively related to crop yields, but did not have a strong effect, while slower-cycling, mineral-associated organic matter was negatively related to yields. Our finding that slower-cycling organic matter was negatively related to yield points to a need to revise the view that stabilization of organic matter positively impacts food security. Our results support a new paradigm that different soil organic matter fractions are controlled by different mechanisms, potentially leading to different relationships with management outcomes, like crop yield. Effectively managing soils for sustainable agriculture requires quantifying the effects of specific organic matter fractions on these outcomes. © 2016 by the Ecological Society of America.

The importance of key floral bioactive compounds to honey bees for the detection and attraction of hybrid vegetable crops and increased seed yield.

PubMed

Mas, Flore; Harper, Aimee; Horner, Rachael; Welsh, Taylor; Jaksons, Peter; Suckling, David M

2018-02-15

Crop breeding programmes generally select for traits for improved yield and human consumption preferences. Yet, they often overlook one fundamental trait essential for insect-pollinated crops: pollinator attraction. This is even more critical for hybrid plants that rely on cross-pollination between the male-fertile line and the male-sterile line to set seeds. This study investigated the role of floral odours for honey bee pollination that could explain the poor seed yield in hybrid crops. The key floral bioactive compounds that honey bees detect were identified for three vegetable hybrid crops. It was found that 30% of the variation in bioactive compound quantities was explained by variety. Differences in quantities of the bioactive compounds triggered different degrees of olfactory response and were also associated with varied appetitive response. Correlating the abundance of each bioactive compound with seed yield, it was found that aldehydes such as nonanal and decanal can have a strong negative influence on seed yield with increasing quantity. Using these methodologies to identify relevant bioactive compounds associated with honey bee pollination, plant breeding programmes should also consider selecting for floral traits attractive to honey bees to improve crop pollination for enhanced seed yield. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Contrasting effects of landscape composition on crop yield mediated by specialist herbivores.

PubMed

Perez-Alvarez, Ricardo; Nault, Brian A; Poveda, Katja

2018-04-01

Landscape composition not only affects a variety of arthropod-mediated ecosystem services, but also disservices, such as herbivory by insect pests that may have negative effects on crop yield. Yet, little is known about how different habitats influence the dynamics of multiple herbivore species, and ultimately their collective impact on crop production. Using cabbage as a model system, we examined how landscape composition influenced the incidence of three specialist cruciferous pests (aphids, flea beetles, and leaf-feeding Lepidoptera), lepidopteran parasitoids, and crop yield across a gradient of landscape composition in New York, USA. We expected that landscapes with a higher proportion of cropland and lower habitat diversity would lead to an increase in pest pressure of the specialist herbivores and a reduction in crop yield. However, results indicated that neither greater cropland area nor lower landscape diversity influenced pest pressure or yield. Rather, pest pressure and yield were best explained by the presence of non-crop habitats (i.e., meadows) in the landscape. Specifically, cabbage was infested with fewer Lepidoptera in landscapes with a higher proportion of meadows likely resulting from increased parasitism. Conversely, cabbage was infested with more flea beetles and aphids as the proportion of meadows in the landscape increased, suggesting that these pests benefit from non-crop habitats. Furthermore, path analysis confirmed that these landscape-mediated effects on pest populations can have either positive or negative cascading effects on crop yield. Our findings illustrate how different pest species within the same cropping system show contrasting responses to landscape composition with respect to both the direction and spatial scale of the relationship. Such tradeoffs resulting from the complex interaction between multiple-pests, natural enemies, and landscape composition must be considered, if we are to manage landscapes for pest suppression benefits. © 2018 by the Ecological Society of America.

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.

Variation in canopy duration in the perennial biofuel crop Miscanthus reveals complex associations with yield.

PubMed

Robson, Paul R H; Farrar, Kerrie; Gay, Alan P; Jensen, Elaine F; Clifton-Brown, John C; Donnison, Iain S

2013-05-01

Energy crops can provide a sustainable source of power and fuels, and mitigate the negative effects of CO2 emissions associated with fossil fuel use. Miscanthus is a perennial C4 energy crop capable of producing large biomass yields whilst requiring low levels of input. Miscanthus is largely unimproved and therefore there could be significant opportunities to increase yield. Further increases in yield will improve the economics, energy balance, and carbon mitigation of the crop, as well as reducing land-take. One strategy to increase yield in Miscanthus is to maximize the light captured through an extension of canopy duration. In this study, canopy duration was compared among a diverse collection of 244 Miscanthus genotypes. Canopy duration was determined by calculating the number of days between canopy establishment and senescence. Yield was positively correlated with canopy duration. Earlier establishment and later senescence were also both separately correlated with higher yield. However, although genotypes with short canopy durations were low yielding, not all genotypes with long canopy durations were high yielding. Differences of yield between genotypes with long canopy durations were associated with variation in stem and leaf traits. Different methodologies to assess canopy duration traits were investigated, including visual assessment, image analysis, light interception, and different trait thresholds. The highest correlation coefficients were associated with later assessments of traits and the use of quantum sensors for canopy establishment. A model for trait optimization to enable yield improvement in Miscanthus and other bioenergy crops is discussed.

Variation in canopy duration in the perennial biofuel crop Miscanthus reveals complex associations with yield

PubMed Central

Robson, Paul R.H.; Farrar, Kerrie; Gay, Alan P.; Jensen, Elaine F.; Clifton-Brown, John C.; Donnison, Iain S.

2013-01-01

Energy crops can provide a sustainable source of power and fuels, and mitigate the negative effects of CO2 emissions associated with fossil fuel use. Miscanthus is a perennial C4 energy crop capable of producing large biomass yields whilst requiring low levels of input. Miscanthus is largely unimproved and therefore there could be significant opportunities to increase yield. Further increases in yield will improve the economics, energy balance, and carbon mitigation of the crop, as well as reducing land-take. One strategy to increase yield in Miscanthus is to maximize the light captured through an extension of canopy duration. In this study, canopy duration was compared among a diverse collection of 244 Miscanthus genotypes. Canopy duration was determined by calculating the number of days between canopy establishment and senescence. Yield was positively correlated with canopy duration. Earlier establishment and later senescence were also both separately correlated with higher yield. However, although genotypes with short canopy durations were low yielding, not all genotypes with long canopy durations were high yielding. Differences of yield between genotypes with long canopy durations were associated with variation in stem and leaf traits. Different methodologies to assess canopy duration traits were investigated, including visual assessment, image analysis, light interception, and different trait thresholds. The highest correlation coefficients were associated with later assessments of traits and the use of quantum sensors for canopy establishment. A model for trait optimization to enable yield improvement in Miscanthus and other bioenergy crops is discussed. PMID:23599277

Assessment of energy crops alternative to maize for biogas production in the Greater Region.

PubMed

Mayer, Frédéric; Gerin, Patrick A; Noo, Anaïs; Lemaigre, Sébastien; Stilmant, Didier; Schmit, Thomas; Leclech, Nathael; Ruelle, Luc; Gennen, Jerome; von Francken-Welz, Herbert; Foucart, Guy; Flammang, Jos; Weyland, Marc; Delfosse, Philippe

2014-08-01

The biomethane yield of various energy crops, selected among potential alternatives to maize in the Greater Region, was assessed. The biomass yield, the volatile solids (VS) content and the biochemical methane potential (BMP) were measured to calculate the biomethane yield per hectare of all plant species. For all species, the dry matter biomass yield and the VS content were the main factors that influence, respectively, the biomethane yield and the BMP. Both values were predicted with good accuracy by linear regressions using the biomass yield and the VS as independent variable. The perennial crop miscanthus appeared to be the most promising alternative to maize when harvested as green matter in autumn and ensiled. Miscanthus reached a biomethane yield of 5.5 ± 1 × 10(3)m(3)ha(-1) during the second year after the establishment, as compared to 5.3 ± 1 × 10(3)m(3)ha(-1) for maize under similar crop conditions. Copyright © 2014. Published by Elsevier Ltd.

Rice Crop Monitoring and Yield Assessment with MODIS 250m Gridded Vegetation Products: A Case Study of Sa Kaeo Province, Thailand

NASA Astrophysics Data System (ADS)

Wijesingha, J. S. J.; Deshapriya, N. L.; Samarakoon, L.

2015-04-01

Billions of people in the world depend on rice as a staple food and as an income-generating crop. Asia is the leader in rice cultivation and it is necessary to maintain an up-to-date rice-related database to ensure food security as well as economic development. This study investigates general applicability of high temporal resolution Moderate Resolution Imaging Spectroradiometer (MODIS) 250m gridded vegetation product for monitoring rice crop growth, mapping rice crop acreage and analyzing crop yield, at the province-level. The MODIS 250m Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) time series data, field data and crop calendar information were utilized in this research in Sa Kaeo Province, Thailand. The following methodology was used: (1) data pre-processing and rice plant growth analysis using Vegetation Indices (VI) (2) extraction of rice acreage and start-of-season dates from VI time series data (3) accuracy assessment, and (4) yield analysis with MODIS VI. The results show a direct relationship between rice plant height and MODIS VI. The crop calendar information and the smoothed NDVI time series with Whittaker Smoother gave high rice acreage estimation (with 86% area accuracy and 75% classification accuracy). Point level yield analysis showed that the MODIS EVI is highly correlated with rice yield and yield prediction using maximum EVI in the rice cycle predicted yield with an average prediction error 4.2%. This study shows the immense potential of MODIS gridded vegetation product for keeping an up-to-date Geographic Information System of rice cultivation.

Soil properties, greenhouse gas emissions and crop yield under compost, biochar and co-composted biochar in two tropical agronomic systems.

PubMed

Bass, Adrian M; Bird, Michael I; Kay, Gavin; Muirhead, Brian

2016-04-15

The addition of organic amendments to agricultural soils has the potential to increase crop yields, reduce dependence on inorganic fertilizers and improve soil condition and resilience. We evaluated the effect of biochar (B), compost (C) and co-composted biochar (COMBI) on the soil properties, crop yield and greenhouse gas emissions from a banana and a papaya plantation in tropical Australia in the first harvest cycle. Biochar, compost and COMBI organic amendments improved soil properties, including significant increases in soil water content, CEC, K, Ca, NO3, NH4 and soil carbon content. However, increases in soil nutrient content and improvements in physical properties did not translate to improved fruit yield. Counter to our expectations, banana crop yield (weight per bunch) was reduced by 18%, 12% and 24% by B, C and COMBI additions respectively, and no significant effect was observed on the papaya crop yield. Soil efflux of CO2 was elevated by addition of C and COMBI amendments, likely due to an increase in labile carbon for microbial processing. Our data indicate a reduction in N2O flux in treatments containing biochar. The application of B, C and COMBI amendments had a generally positive effect on soil properties, but this did not translate into a crop productivity increase in this study. The benefits to soil nutrient content, soil carbon storage and N2O emission reduction need to be carefully weighed against potentially deleterious effects on crop yield, at least in the short-term. Copyright © 2016 Elsevier B.V. All rights reserved.

Changes in yields and their variability at different levels of global warming

NASA Astrophysics Data System (ADS)

Childers, Katelin

2015-04-01

An assessment of climate change impacts at different levels of global warming is crucial to inform the political discussion about mitigation targets as well as for the inclusion of climate change impacts in Integrated Assessment Models (IAMs) that generally only provide global mean temperature change as an indicator of climate change. While there is a well-established framework for the scalability of regional temperature and precipitation changes with global mean temperature change we provide an assessment of the extent to which impacts such as crop yield changes can also be described in terms of global mean temperature changes without accounting for the specific underlying emissions scenario. Based on multi-crop-model simulations of the four major cereal crops (maize, rice, soy, and wheat) on a 0.5 x 0.5 degree global grid generated within ISI-MIP, we show the average spatial patterns of projected crop yield changes at one half degree warming steps. We find that emissions scenario dependence is a minor component of the overall variance of projected yield changes at different levels of global warming. Furthermore, scenario dependence can be reduced by accounting for the direct effects of CO2 fertilization in each global climate model (GCM)/impact model combination through an inclusion of the global atmospheric CO2 concentration as a second predictor. The choice of GCM output used to force the crop model simulations accounts for a slightly larger portion of the total yield variance, but the greatest contributor to variance in both global and regional crop yields and at all levels of warming, is the inter-crop-model spread. The unique multi impact model ensemble available with ISI-MIP data also indicates that the overall variability of crop yields is projected to increase in conjunction with increasing global mean temperature. This result is consistent throughout the ensemble of impact models and across many world regions. Such a hike in yield volatility could have significant policy implications by affecting food prices and supplies.

7 CFR 760.602 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... territory or possession of the United States. Subsequent crop means any crop planted after an initial crop... itself to the greatest level of accuracy, as determined by the FSA State committee. USDA means United... history yield means the average of the actual production history yields for each insurable or noninsurable...

7 CFR 760.602 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... territory or possession of the United States. Subsequent crop means any crop planted after an initial crop... itself to the greatest level of accuracy, as determined by the FSA State committee. USDA means United... history yield means the average of the actual production history yields for each insurable or noninsurable...

How are arbuscular mycorrhizal associations related to maize growth performance during short-term cover crop rotation?

PubMed

Higo, Masao; Takahashi, Yuichi; Gunji, Kento; Isobe, Katsunori

2018-03-01

Better cover crop management options aiming to maximize the benefits of arbuscular mycorrhizal fungi (AMF) to subsequent crops are largely unknown. We investigated the impact of cover crop management methods on maize growth performance and assemblages of AMF colonizing maize roots in a field trial. The cover crop treatments comprised Italian ryegrass, wheat, brown mustard and fallow in rotation with maize. The diversity of AMF communities among cover crops used for maize management was significantly influenced by the cover crop and time course. Cover crops did not affect grain yield and aboveground biomass of subsequent maize but affected early growth. A structural equation model indicated that the root colonization, AMF diversity and maize phosphorus uptake had direct strong positive effects on yield performance. AMF variables and maize performance were related directly or indirectly to maize grain yield, whereas root colonization had a positive effect on maize performance. AMF may be an essential factor that determines the success of cover crop rotational systems. Encouraging AMF associations can potentially benefit cover cropping systems. Therefore, it is imperative to consider AMF associations and crop phenology when making management decisions. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Impacts of climate variability and change on crop yield in sub-Sahara Africa

NASA Astrophysics Data System (ADS)

Pan, S.; Zhang, J.; Yang, J.; Chen, G.; Xu, R.; Zhang, B.; Lou, Y.

2017-12-01

Much concern has been raised about the impacts of climate change and climate extremes on Africa's food security. The impact of climate change on Africa's agriculture is likely to be severe compared to other continents due to high rain-fed agricultural dependence, and limited ability to mitigate and adapt to climate change. In recent decades, warming in Africa is more pronounced and faster than the global average and this trend is likely to continue in the future. However, quantitative assessment on impacts of climate extremes and climate change on crop yield has not been well investigated yet. By using an improved agricultural module of the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed impacts of historical climate variability and future climate change on food crop yield across the sub-Sahara Africa during1980-2016 and the rest of the 21st century (2017-2099). Our simulated results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Droughts have largely reduced crop yield in the most vulnerable regions of Sub-Sahara Africa. Future projections with DLEM-AG2 show that food crop production in Sub-Sahara Africa would be favored with limiting end-of-century warming to below 1.50 C.

Simulated Near-term Climate Change Impacts on Major Crops across Latin America and the Caribbean

NASA Astrophysics Data System (ADS)

Gourdji, S.; Mesa-Diez, J.; Obando-Bonilla, D.; Navarro-Racines, C.; Moreno, P.; Fisher, M.; Prager, S.; Ramirez-Villegas, J.

2016-12-01

Robust estimates of climate change impacts on agricultural production can help to direct investments in adaptation in the coming decades. In this study commissioned by the Inter-American Development Bank, near-term climate change impacts (2020-2049) are simulated relative to a historical baseline period (1971-2000) for five major crops (maize, rice, wheat, soybean and dry bean) across Latin America and the Caribbean (LAC) using the DSSAT crop model. No adaptation or technological change is assumed, thereby providing an analysis of existing climatic stresses on yields in the region and a worst-case scenario in the coming decades. DSSAT is run across irrigated and rain-fed growing areas in the region at a 0.5° spatial resolution for each crop. Crop model inputs for soils, planting dates, crop varieties and fertilizer applications are taken from previously-published datasets, and also optimized for this study. Results show that maize and dry bean are the crops most affected by climate change, followed by wheat, with only minimal changes for rice and soybean. Generally, rain-fed production sees more severe yield declines than irrigated production, although large increases in irrigation water are needed to maintain yields, reducing the yield-irrigation productivity in most areas and potentially exacerbating existing supply limitations in watersheds. This is especially true for rice and soybean, the two crops showing the most neutral yield changes. Rain-fed yields for maize and bean are projected to decline most severely in the sub-tropical Caribbean, Central America and northern South America, where climate models show a consistent drying trend. Crop failures are also projected to increase in these areas, necessitating switches to other crops or investment in adaptation measures. Generally, investment in agricultural adaptation to climate change (such as improved seed and irrigation infrastructure) will be needed throughout the LAC region in the 21st century.

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.

From experiments to simulations: tracing Na+ distribution around roots under different transpiration rates and salinity levels

NASA Astrophysics Data System (ADS)

Perelman, Adi; Jorda, Helena; Vanderborght, Jan; Pohlmeier, Andreas; Lazarovitch, Naftali

2017-04-01

When salinity increases beyond a certain threshold it will result in reduced crop yield at a fixed rate, according to Maas and Hoffman model (1976). Thus, there is a great importance of predicting salinization and its impact on crops. Current models do not consider the impact of environmental conditions on plants salt tolerance, even though these conditions are affecting plant water uptake and therefore salt accumulation around the roots. Different factors, such as transpiration rates, can influence the plant sensitivity to salinity by influencing salt concentrations around the roots. Better parametrization of a model can help improving predicting the real effects of salinity on crop growth and yield. The aim of this research is to study Na+ distribution around roots at different scales using different non-invasive methods, and study how this distribution is being affected by transpiration rate and plant water uptake. Results from tomato plants growing on Rhizoslides (capillary paper growth system), show that Na+ concentration is higher at the root- substrate interface, compared with the bulk. Also, Na+ accumulation around the roots decreased under low transpiration rate, which is supporting our hypothesis. Additionally, Rhizoslides enable to study roots' growth rate and architecture under different salinity levels. Root system architecture was retrieved from photos taken during the experiment and enabled us to incorporate real root systems into a simulation. To observe the correlation of root system architectures and Na+ distribution in three dimensions, we used magnetic resonance imaging (MRI). MRI provides fine resolution of Na+ accumulation around a single root without disturbing the root system. With time, Na+ was accumulating only where roots were found in the soil and later on around specific roots. These data are being used for model calibration, which is expected to predict root water uptake in saline soils for different climatic conditions and different soil water availabilities.

Maize diversity and ethnolinguistic diversity in Chiapas, Mexico

PubMed Central

Perales, Hugo R.; Benz, Bruce F.; Brush, Stephen B.

2005-01-01

The objective of this study is to investigate whether ethnolinguistic diversity influences crop diversity. Factors suggest a correlation between biological diversity of crops and cultural diversity. Although this correlation has been noted, little systematic research has focused on the role of culture in shaping crop diversity. This paper reports on research in the Maya highlands (altitude > 1,800 m) of central Chiapas in southern Mexico that examined the distribution of maize (Zea mays) types among communities of two groups, the Tzeltal and Tzotzil. The findings suggest that maize populations are distinct according to ethnolinguistic group. However, a study of isozymes indicates no clear separation of the region's maize into two distinct populations based on ethnolin-guistic origin. A reciprocal garden experiment shows that there is adaptation of maize to its environment but that Tzeltal maize sometimes out-yields Tzotzil maize in Tzotzil environments. Because of the proximity of the two groups and selection for yield, we would expect that the superior maize would dominate both groups' maize populations, but we find that such domination is not the case. The role of ethnolinguistic identity in shaping social networks and information exchange is discussed in relation to landrace differentiation. PMID:15640353

Maize diversity and ethnolinguistic diversity in Chiapas, Mexico.

PubMed

Perales, Hugo R; Benz, Bruce F; Brush, Stephen B

2005-01-18

The objective of this study is to investigate whether ethnolinguistic diversity influences crop diversity. Factors suggest a correlation between biological diversity of crops and cultural diversity. Although this correlation has been noted, little systematic research has focused on the role of culture in shaping crop diversity. This paper reports on research in the Maya highlands (altitude >1,800 m) of central Chiapas in southern Mexico that examined the distribution of maize (Zea mays) types among communities of two groups, the Tzeltal and Tzotzil. The findings suggest that maize populations are distinct according to ethnolinguistic group. However, a study of isozymes indicates no clear separation of the region's maize into two distinct populations based on ethnolinguistic origin. A reciprocal garden experiment shows that there is adaptation of maize to its environment but that Tzeltal maize sometimes out-yields Tzotzil maize in Tzotzil environments. Because of the proximity of the two groups and selection for yield, we would expect that the superior maize would dominate both groups' maize populations, but we find that such domination is not the case. The role of ethnolinguistic identity in shaping social networks and information exchange is discussed in relation to landrace differentiation.

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

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

Nges, Ivo Achu, E-mail: Nges.Ivo_Achu@biotek.lu.se; Escobar, Federico; Fu Xinmei

2012-01-15

Highlights: Black-Right-Pointing-Pointer This study demonstrates the feasibility of co-digestion food industrial waste with energy crops. Black-Right-Pointing-Pointer Laboratory batch co-digestion led to improved methane yield and carbon to nitrogen ratio as compared to mono-digestion of industrial waste. Black-Right-Pointing-Pointer Co-digestion was also seen as a means of degrading energy crops with nutrients addition as crops are poor in nutrients. Black-Right-Pointing-Pointer Batch co-digestion methane yields were used to predict co-digestion methane yield in full scale operation. Black-Right-Pointing-Pointer It was concluded that co-digestion led an over all economically viable process and ensured a constant supply of feedstock. - Abstract: Currently, there is increasing competitionmore » 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.« less

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

Independent Peer Evaluation of the Large Area Crop Inventory Experiment (LACIE): The LACIE Symposium

NASA Technical Reports Server (NTRS)

1978-01-01

Yield models and crop estimate accuracy are discussed within the Large Area Crop Inventory Experiment. The wheat yield estimates in the United States, Canada, and U.S.S.R. are emphasized. Experimental results design, system implementation, data processing systems, and applications were considered.

Row and forage crop rotation effects on maize mineral nutrition and yield

USDA-ARS?s Scientific Manuscript database

Extended crop rotations provide many attributes in support of sustainable agriculture. Objectives were to investigate rotations that included row crops and forages in terms of their effects on soil characteristics as well as on maize (Zea mays L.) stover biomass, grain yield, and mineral components...

7 CFR 760.602 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... history yield means the average of the actual production history yields for each insurable or noninsurable..., excluding value loss crops, the product obtained by multiplying: (i) 100 percent of the per unit price for... established price for the crop, times (ii) The relevant per unit quantity of the crop produced on the farm...

Droughts in India from 1981 to 2013 and Implications to Wheat Production

PubMed Central

Zhang, Xiang; Obringer, Renee; Wei, Chehan; Chen, Nengcheng; Niyogi, Dev

2017-01-01

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation. PMID:28294189

Climate driven crop planting date in the ACME Land Model (ALM): Impacts on productivity and yield

NASA Astrophysics Data System (ADS)

Drewniak, B.

2017-12-01

Climate is one of the key drivers of crop suitability and productivity in a region. The influence of climate and weather on the growing season determine the amount of time crops spend in each growth phase, which in turn impacts productivity and, more importantly, yields. Planting date can have a strong influence on yields with earlier planting generally resulting in higher yields, a sensitivity that is also present in some crop models. Furthermore, planting date is already changing and may continue, especially if longer growing seasons caused by future climate change drive early (or late) planting decisions. Crop models need an accurate method to predict plant date to allow these models to: 1) capture changes in crop management to adapt to climate change, 2) accurately model the timing of crop phenology, and 3) improve crop simulated influences on carbon, nutrient, energy, and water cycles. Previous studies have used climate as a predictor for planting date. Climate as a plant date predictor has more advantages than fixed plant dates. For example, crop expansion and other changes in land use (e.g., due to changing temperature conditions), can be accommodated without additional model inputs. As such, a new methodology to implement a predictive planting date based on climate inputs is added to the Accelerated Climate Model for Energy (ACME) Land Model (ALM). The model considers two main sources of climate data important for planting: precipitation and temperature. This method expands the current temperature threshold planting trigger and improves the estimated plant date in ALM. Furthermore, the precipitation metric for planting, which synchronizes the crop growing season with the wettest months, allows tropical crops to be introduced to the model. This presentation will demonstrate how the improved model enhances the ability of ALM to capture planting date compared with observations. More importantly, the impact of changing the planting date and introducing tropical crops will be explored. Those impacts include discussions on productivity, yield, and influences on carbon and energy fluxes.

Separating out the influence of climatic trend, fluctuations, and extreme events on crop yield: a case study in Hunan Province, China

NASA Astrophysics Data System (ADS)

Wang, Zhu; Shi, Peijun; Zhang, Zhao; Meng, Yongchang; Luan, Yibo; Wang, Jiwei

2017-09-01

Separating out the influence of climatic trend, fluctuations and extreme events on crop yield is of paramount importance to climate change adaptation, resilience, and mitigation. Previous studies lack systematic and explicit assessment of these three fundamental aspects of climate change on crop yield. This research attempts to separate out the impacts on rice yields of climatic trend (linear trend change related to mean value), fluctuations (variability surpassing the "fluctuation threshold" which defined as one standard deviation (1 SD) of the residual between the original data series and the linear trend value for each climatic variable), and extreme events (identified by absolute criterion for each kind of extreme events related to crop yield). The main idea of the research method was to construct climate scenarios combined with crop system simulation model. Comparable climate scenarios were designed to express the impact of each climate change component and, were input to the crop system model (CERES-Rice), which calculated the related simulated yield gap to quantify the percentage impacts of climatic trend, fluctuations, and extreme events. Six Agro-Meteorological Stations (AMS) in Hunan province were selected to study the quantitatively impact of climatic trend, fluctuations and extreme events involving climatic variables (air temperature, precipitation, and sunshine duration) on early rice yield during 1981-2012. The results showed that extreme events were found to have the greatest impact on early rice yield (-2.59 to -15.89%). Followed by climatic fluctuations with a range of -2.60 to -4.46%, and then the climatic trend (4.91-2.12%). Furthermore, the influence of climatic trend on early rice yield presented "trade-offs" among various climate variables and AMS. Climatic trend and extreme events associated with air temperature showed larger effects on early rice yield than other climatic variables, particularly for high-temperature events (-2.11 to -12.99%). Finally, the methodology use to separate out the influences of the climatic trend, fluctuations, and extreme events on crop yield was proved to be feasible and robust. Designing different climate scenarios and feeding them into a crop system model is a potential way to evaluate the quantitative impact of each climate variable.

Agricultural biotechnology and smallholder farmers in developing countries.

PubMed

Anthony, Vivienne M; Ferroni, Marco

2012-04-01

Agricultural biotechnology holds much potential to contribute towards crop productivity gains and crop improvement for smallholder farmers in developing countries. Over 14 million smallholder farmers are already benefiting from biotech crops such as cotton and maize in China, India and other Asian, African and Central/South American countries. Molecular breeding can accelerate crop improvement timescales and enable greater use of diversity of gene sources. Little impact has been realized to date with fruits and vegetables because of development timescales for molecular breeding and development and regulatory costs and political considerations facing biotech crops in many countries. Constraints to the development and adoption of technology-based solutions to reduce yield gaps need to be overcome. Full integration with broader commercial considerations such as farmer access to seed distribution systems that facilitate dissemination of improved varieties and functioning markets for produce are critical for the benefits of agricultural biotechnology to be fully realized by smallholders. Public-private partnerships offer opportunities to catalyze new approaches and investment while accelerating integrated research and development and commercial supply chain-based solutions. Copyright © 2011. Published by Elsevier Ltd.

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

Reduce pests, enhance production: benefits of intercropping at high densities for okra farmers in Cameroon.

PubMed

Singh, Akanksha; Weisser, Wolfgang W; Hanna, Rachid; Houmgny, Raissa; Zytynska, Sharon E

2017-10-01

Intercropping can help reduce insect pest populations. However, the results of intercropping can be pest- and crop-species specific, with varying effects on crop yield, and pest suppression success. In Cameroon, okra vegetable is often grown in intercropped fields and sown with large distances between planting rows (∼ 2 m). Dominant okra pests include cotton aphids, leaf beetles and whiteflies. In a field experiment, we intercropped okra with maize and bean in different combinations (okra monoculture, okra-bean, okra-maize and okra-bean-maize) and altered plant densities (high and low) to test for the effects of diversity, crop identity and planting distances on okra pests, their predators and yield. We found crop identity and plant density, but not crop diversity to influence okra pests, their predators and okra yield. Only leaf beetles decreased okra yield and their abundance reduced at high plant density. Overall, okra grown with bean at high density was the most economically profitable combination. We suggest that when okra is grown at higher densities, legumes (e.g. beans) should be included as an additional crop. Intercropping with a leguminous crop can enhance nitrogen in the soil, benefiting other crops, while also being harvested and sold at market for additional profit. Manipulating planting distances and selecting plants based on their beneficial traits may thus help to eliminate yield gaps in sustainable agriculture. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Weed Diversity Affects Soybean and Maize Yield in a Long Term Experiment in Michigan, USA.

PubMed

Ferrero, Rosana; Lima, Mauricio; Davis, Adam S; Gonzalez-Andujar, Jose L

2017-01-01

Managing production environments in ways that promote weed community diversity may enhance both crop production and the development of a more sustainable agriculture. This study analyzed data of productivity of maize (corn) and soybean in plots in the Main Cropping System Experiment (MCSE) at the W. K. Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) in Michigan, USA, from 1996 to 2011. We used models derived from population ecology to explore how weed diversity, temperature, and precipitation interact with crop yields. Using three types of models that considered internal and external (climate and weeds) factors, with additive or non-linear variants, we found that changes in weed diversity were associated with changes in rates of crop yield increase over time for both maize and soybeans. The intrinsic capacity for soybean yield increase in response to the environment was greater under more diverse weed communities. Soybean production risks were greatest in the least weed diverse systems, in which each weed species lost was associated with progressively greater crop yield losses. Managing for weed community diversity, while suppressing dominant, highly competitive weeds, may be a helpful strategy for supporting long term increases in soybean productivity. In maize, there was a negative and non-additive response of yields to the interaction between weed diversity and minimum air temperatures. When cold temperatures constrained potential maize productivity through limited resources, negative interactions with weed diversity became more pronounced. We suggest that: (1) maize was less competitive in cold years allowing higher weed diversity and the dominance of some weed species; or (2) that cold years resulted in increased weed richness and prevalence of competitive weeds, thus reducing crop yields. Therefore, we propose to control dominant weed species especially in the years of low yield and extreme minimum temperatures to improve maize yields. Results of our study indicate that through the proactive management of weed diversity, it may be possible to promote both high productivity of crops and environmental sustainability.

Weed Diversity Affects Soybean and Maize Yield in a Long Term Experiment in Michigan, USA

PubMed Central

Ferrero, Rosana; Lima, Mauricio; Davis, Adam S.; Gonzalez-Andujar, Jose L.

2017-01-01

Managing production environments in ways that promote weed community diversity may enhance both crop production and the development of a more sustainable agriculture. This study analyzed data of productivity of maize (corn) and soybean in plots in the Main Cropping System Experiment (MCSE) at the W. K. Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) in Michigan, USA, from 1996 to 2011. We used models derived from population ecology to explore how weed diversity, temperature, and precipitation interact with crop yields. Using three types of models that considered internal and external (climate and weeds) factors, with additive or non-linear variants, we found that changes in weed diversity were associated with changes in rates of crop yield increase over time for both maize and soybeans. The intrinsic capacity for soybean yield increase in response to the environment was greater under more diverse weed communities. Soybean production risks were greatest in the least weed diverse systems, in which each weed species lost was associated with progressively greater crop yield losses. Managing for weed community diversity, while suppressing dominant, highly competitive weeds, may be a helpful strategy for supporting long term increases in soybean productivity. In maize, there was a negative and non-additive response of yields to the interaction between weed diversity and minimum air temperatures. When cold temperatures constrained potential maize productivity through limited resources, negative interactions with weed diversity became more pronounced. We suggest that: (1) maize was less competitive in cold years allowing higher weed diversity and the dominance of some weed species; or (2) that cold years resulted in increased weed richness and prevalence of competitive weeds, thus reducing crop yields. Therefore, we propose to control dominant weed species especially in the years of low yield and extreme minimum temperatures to improve maize yields. Results of our study indicate that through the proactive management of weed diversity, it may be possible to promote both high productivity of crops and environmental sustainability. PMID:28286509

Spectral considerations for modeling yield of canola

USDA-ARS?s Scientific Manuscript database

Conspicuous yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A ...

Increasing temperature cuts back crop yields in Hungary over the last 90 years.

PubMed

Pinke, Zsolt; Lövei, Gábor L

2017-12-01

The transformation of climatic regime has an undeniable impact on plant production, but we rarely have long enough date series to examine the unfolding of such effects. The clarification of the relationship between crop plants and climate has a near-immediate importance due to the impending human-made global change. This study investigated the relationship between temperature, precipitation, drought intensity and the yields of four major cereals in Hungary between 1921 and 2010. The analysis of 30-year segments indicated a monotonously increasing negative impact of temperature on crop yields. A 1°C temperature increase reduced the yield of the four main cereals by 9.6%-14.8% in 1981-2010, which revealed the vulnerability of Eastern European crop farming to recent climate change. Climate accounted for 17%-39% of yield variability over the past 90 years, but this figure reached 33%-67% between 1981 and 2010. Our analysis supports the claim that the mid-20th century green revolution improved yields "at the mercy of the weather": during this period, the impact of increasing fertilization and mechanisation coincided with climatic conditions that were more favourable than today. Crop yields in Eastern Europe have been stagnating or decreasing since the mid-1980s. Although usually attributed to the large socio-economic changes sweeping the region, our analysis indicates that a warming climate is at least partially responsible for this trend. Such a robust impact of increasing temperatures on crop yields also constitutes an obvious warning for this core grain-growing region of the world. © 2017 John Wiley & Sons Ltd.

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.

Annual Crop-Yield Variation, Child Survival, and Nutrition Among Subsistence Farmers in Burkina Faso.

PubMed

Belesova, Kristine; Gasparrini, Antonio; Sié, Ali; Sauerborn, Rainer; Wilkinson, Paul

2018-02-01

Whether year-to-year variation in crop yields affects the nutrition, health, and survival of subsistence-farming populations is relevant to the understanding of the potential impacts of climate change. However, the empirical evidence is limited. We examined the associations of child survival with interannual variation in food crop yield and middle-upper arm circumference (MUAC) in a subsistence-farming population of rural Burkina Faso. The study was of 44,616 children aged <5 years included in the Nouna Health and Demographic Surveillance System, 1992-2012, whose survival was analyzed in relation to the food crop yield in the year of birth (which ranged from 65% to 120% of the period average) and, for a subset of 16,698 children, to MUAC, using shared-frailty Cox proportional hazards models. Survival was appreciably worse in children born in years with low yield (full-adjustment hazard ratio = 1.11 (95% confidence interval: 1.02, 1.20) for a 90th- to 10th-centile decrease in annual crop yield) and in children with small MUAC (hazard ratio = 2.72 (95% confidence interval: 2.15, 3.44) for a 90th- to 10th-centile decrease in MUAC). These results suggest an adverse impact of variations in crop yields, which could increase under climate change. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield and production is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to provide temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (10-30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat yield assessment at regional scale. For the former, we adapt a previously developed approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument at 250 m resolution that allows automatic mapping of winter crops taking into account a priori knowledge on crop calendar. For the latter, we use a generalized winter wheat yield forecasting model that is based on estimation of the peak Normalized Difference Vegetation Index (NDVI) from MODIS image time-series, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A improves both winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times compared to using a single satellite.

Effects of ditch-buried straw return on water percolation, nitrogen leaching and crop yields in a rice-wheat rotation system.

PubMed

Yang, Haishui; Xu, Mingmin; Koide, Roger T; Liu, Qian; Dai, Yajun; Liu, Ling; Bian, Xinmin

2016-03-15

Crop residue management and nitrogen loss are two important environmental problems in the rice-wheat rotation system in China. This study investigated the effects of burial of straw on water percolation, nitrogen loss by leaching, crop growth and yield. Greenhouse mesocosm experiments were conducted over the course of three simulated cropping seasons in a rice1-wheat-rice2 rotation. Greater amounts of straw resulted in more water percolation, irrespective of crop season. Burial at 20 and 35 cm significantly reduced, but burial at 50 cm increased nitrogen leaching. Straw at 500 kg ha(-1) reduced, but at 1000 kg ha(-1) and at 1500 kg ha(-1) straw increased nitrogen leaching in three consecutive crop rotations. In addition, straw at 500 kg ha(-1) buried at 35 cm significantly increased yield and its components for both crops. This study suggests that N losses via leaching from the rice-wheat rotation may be reduced by the burial of the appropriate amount of straw at the appropriate depth. Greater amounts of buried straw, however, may promote nitrogen leaching and negatively affect crop growth and yields. Complementary field experiments must be performed to make specific agronomic recommendations. © 2015 Society of Chemical Industry.

Analyzing and modelling the effect of long-term fertilizer management on crop yield and soil organic carbon in China.

PubMed

Zhang, Jie; Balkovič, Juraj; Azevedo, Ligia B; Skalský, Rastislav; Bouwman, Alexander F; Xu, Guang; Wang, Jinzhou; Xu, Minggang; Yu, Chaoqing

2018-06-15

This study analyzes the influence of various fertilizer management practices on crop yield and soil organic carbon (SOC) based on the long-term field observations and modelling. Data covering 11 years from 8 long-term field trials were included, representing a range of typical soil, climate, and agro-ecosystems in China. The process-based model EPIC (Environmental Policy Integrated Climate model) was used to simulate the response of crop yield and SOC to various fertilization regimes. The results showed that the yield and SOC under additional manure application treatment were the highest while the yield under control treatment was the lowest (30%-50% of NPK yield) at all sites. The SOC in northern sites appeared more dynamic than that in southern sites. The variance partitioning analysis (VPA) showed more variance of crop yield could be explained by the fertilization factor (42%), including synthetic nitrogen (N), phosphorus (P), potassium (K) fertilizers, and fertilizer NPK combined with manure. The interactive influence of soil (total N, P, K, and available N, P, K) and climate factors (mean annual temperature and precipitation) determine the largest part of the SOC variance (32%). EPIC performs well in simulating both the dynamics of crop yield (NRMSE = 32% and 31% for yield calibration and validation) and SOC (NRMSE = 13% and 19% for SOC calibration and validation) under diverse fertilization practices in China. EPIC can assist in predicting the impacts of different fertilization regimes on crop growth and soil carbon dynamics, and contribute to the optimization of fertilizer management for different areas in China. Copyright © 2018. Published by Elsevier B.V.

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.

Increasing Crop Yields in Water Stressed Countries by Combining Operations of Freshwater Reservoir and Wastewater Reclamation Plant

NASA Astrophysics Data System (ADS)

Bhushan, R.; Ng, T. L.

2015-12-01

Freshwater resources around the world are increasing in scarcity due to population growth, industrialization and climate change. This is a serious concern for water stressed countries, including those in Asia and North Africa where future food production is expected to be negatively affected by this. To address this problem, we investigate the potential of combining freshwater reservoir and wastewater reclamation operations. Reservoir water is the cheaper source of irrigation, but is often limited and climate sensitive. Treated wastewater is a more reliable alternative for irrigation, but often requires extensive further treatment which can be expensive. We propose combining the operations of a reservoir and a wastewater reclamation plant (WWRP) to augment the supply from the reservoir with reclaimed water for increasing crop yields in water stressed regions. The joint system of reservoir and WWRP is modeled as a multi-objective optimization problem with the double objective of maximizing the crop yield and minimizing total cost, subject to constraints on reservoir storage, spill and release, and capacity of the WWRP. We use the crop growth model Aquacrop, supported by The Food and Agriculture Organization of the United Nations (FAO), to model crop growth in response to water use. Aquacrop considers the effects of water deficit on crop growth stages, and from there estimates crop yield. We generate results comparing total crop yield under irrigation with water from just the reservoir (which is limited and often interrupted), and yield with water from the joint system (which has the potential of higher supply and greater reliability). We will present results for locations in India and Africa to evaluate the potential of the joint operations for improving food security in those areas for different budgets.

The Response of Durum Wheat to the Preceding Crop in a Mediterranean Environment

PubMed Central

Ercoli, Laura; Masoni, Alessandro; Pampana, Silvia; Mariotti, Marco; Arduini, Iduna

2014-01-01

Crop sequence is an important management practice that may affect durum wheat (Triticum durum Desf.) production. Field research was conducted in 2007-2008 and 2008-2009 seasons in a rain-fed cold Mediterranean environment to examine the impact of the preceding crops alfalfa (Medicago sativa L.), maize (Zea mays L.), sunflower (Helianthus annuus L.), and bread wheat (Triticum aestivum L.) on yield and N uptake of four durum wheat varieties. The response of grain yield of durum wheat to the preceding crop was high in 2007-2008 and was absent in the 2008-2009 season, because of the heavy rainfall that negatively impacted establishment, vegetative growth, and grain yield of durum wheat due to waterlogging. In the first season, durum wheat grain yield was highest following alfalfa, and was 33% lower following wheat. The yield increase of durum wheat following alfalfa was mainly due to an increased number of spikes per unit area and number of kernels per spike, while the yield decrease following wheat was mainly due to a reduction of spike number per unit area. Variety growth habit and performance did not affect the response to preceding crop and varieties ranked in the order Levante > Saragolla = Svevo > Normanno. PMID:25401153

Sugarcane for bioenergy production: an assessment of yield and regulation of sucrose content.

PubMed

Waclawovsky, Alessandro J; Sato, Paloma M; Lembke, Carolina G; Moore, Paul H; Souza, Glaucia M

2010-04-01

An increasing number of plant scientists, including breeders, agronomists, physiologists and molecular biologists, are working towards the development of new and improved energy crops. Research is increasingly focused on how to design crops specifically for bioenergy production and increased biomass generation for biofuel purposes. The most important biofuel to date is bioethanol produced from sugars (sucrose and starch). Second generation bioethanol is also being targeted for studies to allow the use of the cell wall (lignocellulose) as a source of carbon. If a crop is to be used for bioenergy production, the crop should be high yielding, fast growing, low lignin content and requiring relatively small energy inputs for its growth and harvest. Obtaining high yields in nonprime agricultural land is a key for energy crop development to allow sustainability and avoid competition with food production. Sugarcane is the most efficient bioenergy crop of tropical and subtropical regions, and biotechnological tools for the improvement of this crop are advancing rapidly. We focus this review on the studies of sugarcane genes associated with sucrose content, biomass and cell wall metabolism and the preliminary physiological characterization of cultivars that contrast for sugar and biomass yield.

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.

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.

Population dynamics of plant nematodes in cultivated soil: length of rotation in newly cleared and old agricultural land.

PubMed

Good, J M; Murphy, W S; Brodie, B B

1973-04-01

During a 6-year study of 1-, 2-, and 3-year crop rotations, population densities of Pratylenchus brachyurus, Trichodorus christiei, and Meloidogyne incognita were significantly affected by the choice of crops but not by length of crop rotation. The density of P. brachyurus and T. christiei increased rapidly on milo (Sorghum vulgate). In addition, populations of P. brachyurus increased significantly in cropping systems that involved crotalaria (C. rnucronata), millet (Setaria italica), and sudangrass (Sorghum sudanense). Lowest numbers of P. brachyurus occurred where okra (Hibiscus esculentus) was grown or where land was fallow. The largest increase in populations of T. christiei occurred in cropping systems that involved millet, sudangrass, and okra whereas the smallest increase occurred in cropping systems that involved crotalaria or fallow. A winter cover of rye (Secale cereale) had no distinguishable effect on population densities of P. brachyurus or T. christiei. Meloidogyne incognita was detected during the fourth year in both newly cleared and old agricultural land when okra was included in the cropping system. Detectable populations of M. incognita did not develop in any of the other cropping systems. Yields of tomato transplants were higher on the newly cleared land than on the old land. Highest yields were obtained when crotalaria was included in the cropping system. Lowest yields were obtained when milo, or fallow were included in the cropping system. Length of rotation had no distinguishable effect on yields of tomato transplants.

Analysis of a large dataset of mycorrhiza inoculation field trials on potato shows highly significant increases in yield.

PubMed

Hijri, Mohamed

2016-04-01

An increasing human population requires more food production in nutrient-efficient systems in order to simultaneously meet global food needs while reducing the environmental footprint of agriculture. Arbuscular mycorrhizal fungi (AMF) have the potential to enhance crop yield, but their efficiency has yet to be demonstrated in large-scale crop production systems. This study reports an analysis of a dataset consisting of 231 field trials in which the same AMF inoculant (Rhizophagus irregularis DAOM 197198) was applied to potato over a 4-year period in North America and Europe under authentic field conditions. The inoculation was performed using a liquid suspension of AMF spores that was sprayed onto potato seed pieces, yielding a calculated 71 spores per seed piece. Statistical analysis showed a highly significant increase in marketable potato yield (ANOVA, P < 0.0001) for inoculated fields (42.2 tons/ha) compared with non-inoculated controls (38.3 tons/ha), irrespective of trial year. The average yield increase was 3.9 tons/ha, representing 9.5 % of total crop yield. Inoculation was profitable with a 0.67-tons/ha increase in yield, a threshold reached in almost 79 % of all trials. This finding clearly demonstrates the benefits of mycorrhizal-based inoculation on crop yield, using potato as a case study. Further improvements of these beneficial inoculants will help compensate for crop production deficits, both now and in the future.

Strategies for soil-based precision agriculture in cotton

NASA Astrophysics Data System (ADS)

Neely, Haly L.; Morgan, Cristine L. S.; Stanislav, Scott; Rouze, Gregory; Shi, Yeyin; Thomasson, J. Alex; Valasek, John; Olsenholler, Jeff

2016-05-01

The goal of precision agriculture is to increase crop yield while maximizing the use efficiency of farm resources. In this application, UAV-based systems are presenting agricultural researchers with an opportunity to study crop response to environmental and management factors in real-time without disturbing the crop. The spatial variability soil properties, which drive crop yield and quality, cannot be changed and thus keen agronomic choices with soil variability in mind have the potential to increase profits. Additionally, measuring crop stress over time and in response to management and environmental conditions may enable agronomists and plant breeders to make more informed decisions about variety selection than the traditional end-of-season yield and quality measurements. In a previous study, seed-cotton yield was measured over 4 years and compared with soil variability as mapped by a proximal soil sensor. It was found that soil properties had a significant effect on seed-cotton yield and the effect was not consistent across years due to different precipitation conditions. However, when seed-cotton yield was compared to the normalized difference vegetation index (NDVI), as measured using a multispectral camera from a UAV, predictions improved. Further improvement was seen when soil-only pixels were removed from the analysis. On-going studies are using UAV-based data to uncover the thresholds for stress and yield potential. Long-term goals of this research include detecting stress before yield is reduced and selecting better adapted varieties.

Engineering crop nutrient efficiency for sustainable agriculture.

PubMed

Chen, Liyu; Liao, Hong

2017-10-01

Increasing crop yields can provide food, animal feed, bioenergy feedstocks and biomaterials to meet increasing global demand; however, the methods used to increase yield can negatively affect sustainability. For example, application of excess fertilizer can generate and maintain high yields but also increases input costs and contributes to environmental damage through eutrophication, soil acidification and air pollution. Improving crop nutrient efficiency can improve agricultural sustainability by increasing yield while decreasing input costs and harmful environmental effects. Here, we review the mechanisms of nutrient efficiency (primarily for nitrogen, phosphorus, potassium and iron) and breeding strategies for improving this trait, along with the role of regulation of gene expression in enhancing crop nutrient efficiency to increase yields. We focus on the importance of root system architecture to improve nutrient acquisition efficiency, as well as the contributions of mineral translocation, remobilization and metabolic efficiency to nutrient utilization efficiency. © 2017 Institute of Botany, Chinese Academy of Sciences.

Mutually beneficial pollinator diversity and crop yield outcomes in small and large farms.

PubMed

Garibaldi, Lucas A; Carvalheiro, Luísa G; Vaissière, Bernard E; Gemmill-Herren, Barbara; Hipólito, Juliana; Freitas, Breno M; Ngo, Hien T; Azzu, Nadine; Sáez, Agustín; Åström, Jens; An, Jiandong; Blochtein, Betina; Buchori, Damayanti; Chamorro García, Fermín J; Oliveira da Silva, Fabiana; Devkota, Kedar; Ribeiro, Márcia de Fátima; Freitas, Leandro; Gaglianone, Maria C; Goss, Maria; Irshad, Mohammad; Kasina, Muo; Pacheco Filho, Alípio J S; Kiill, Lucia H Piedade; Kwapong, Peter; Parra, Guiomar Nates; Pires, Carmen; Pires, Viviane; Rawal, Ranbeer S; Rizali, Akhmad; Saraiva, Antonio M; Veldtman, Ruan; Viana, Blandina F; Witter, Sidia; Zhang, Hong

2016-01-22

Ecological intensification, or the improvement of crop yield through enhancement of biodiversity, may be a sustainable pathway toward greater food supplies. Such sustainable increases may be especially important for the 2 billion people reliant on small farms, many of which are undernourished, yet we know little about the efficacy of this approach. Using a coordinated protocol across regions and crops, we quantify to what degree enhancing pollinator density and richness can improve yields on 344 fields from 33 pollinator-dependent crop systems in small and large farms from Africa, Asia, and Latin America. For fields less than 2 hectares, we found that yield gaps could be closed by a median of 24% through higher flower-visitor density. For larger fields, such benefits only occurred at high flower-visitor richness. Worldwide, our study demonstrates that ecological intensification can create synchronous biodiversity and yield outcomes. Copyright © 2016, American Association for the Advancement of Science.

Estimation of Rice Crop Yields Using Random Forests in Taiwan

NASA Astrophysics Data System (ADS)

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

2017-12-01

Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2.9%, respectively. Although there are several uncertainties attributed to the data quality of input layers, our study demonstrates the promising application of random forests for estimating rice crop yields at the national level in Taiwan. This approach could be transferable to other regions of the world for improving large-scale estimation of rice crop yields.

Biochar derived from corn straw affected availability and distribution of soil nutrients and cotton yield

PubMed Central

Tian, Xiaofei; Zhang, Min; Wan, Yongshan; Xie, Zhihua; Chen, Baocheng; Li, Wenqing

2018-01-01

Biochar application as a soil amendment has been proposed as a strategy to improve soil fertility and increase crop yields. However, the effects of successive biochar applications on cotton yields and nutrient distribution in soil are not well documented. A three-year field study was conducted to investigate the effects of successive biochar applications at different rates on cotton yield and on the soil nutrient distribution in the 0–100 cm soil profile. Biochar was applied at 0, 5, 10, and 20 t ha-1 (expressed as Control, BC5, BC10, and BC20, respectively) for each cotton season, with identical doses of chemical fertilizers. Biochar enhanced the cotton lint yield by 8.0–15.8%, 9.3–13.9%, and 9.2–21.9% in 2013, 2014, and 2015, respectively, and high levels of biochar application achieved high cotton yields each year. Leaching of soil nitrate was reduced, while the pH values, soil organic carbon, total nitrogen (N), and available K content of the 0–20 cm soil layer were increased in 2014 and 2015. However, the changes in the soil available P content were less substantial. This study suggests that successive biochar amendments have the potential to enhance cotton productivity and soil fertility while reducing nitrate leaching. PMID:29324750

Enhancing crop yield with the use of N-based fertilizers co-applied with plant hormones or growth regulators.

PubMed

Zaman, Mohammad; Kurepin, Leonid V; Catto, Warwick; Pharis, Richard P

2015-07-01

Crop yield, vegetative or reproductive, depends on access to an adequate supply of essential mineral nutrients. At the same time, a crop plant's growth and development, and thus yield, also depend on in situ production of plant hormones. Thus optimizing mineral nutrition and providing supplemental hormones are two mechanisms for gaining appreciable yield increases. Optimizing the mineral nutrient supply is a common and accepted agricultural practice, but the co-application of nitrogen-based fertilizers with plant hormones or plant growth regulators is relatively uncommon. Our review discusses possible uses of plant hormones (gibberellins, auxins, cytokinins, abscisic acid and ethylene) and specific growth regulators (glycine betaine and polyamines) to enhance and optimize crop yield when co-applied with nitrogen-based fertilizers. We conclude that use of growth-active gibberellins, together with a nitrogen-based fertilizer, can result in appreciable and significant additive increases in shoot dry biomass of crops, including forage crops growing under low-temperature conditions. There may also be a potential for use of an auxin or cytokinin, together with a nitrogen-based fertilizer, for obtaining additive increases in dry shoot biomass and/or reproductive yield. Further research, though, is needed to determine the potential of co-application of nitrogen-based fertilizers with abscisic acid, ethylene and other growth regulators. © 2014 Society of Chemical Industry.

Assessment of crop yield losses in Punjab and Haryana using two years of continuous in-situ ozone measurements

NASA Astrophysics Data System (ADS)

Sinha, B.; Singh Sangwan, K.; Maurya, Y.; Kumar, V.; Sarkar, C.; Chandra, B. P.; Sinha, V.

2015-01-01

In this study we use a high quality dataset of in-situ ozone measurements at a suburban site called Mohali in the state of Punjab to estimate ozone related crop yield losses for wheat, rice, cotton and maize for Punjab and the neighbouring state Haryana for the years 2011-2013. We inter-compare crop yield loss estimates according to different exposure metrics such as AOT40 and M7 for the two major crop growing seasons of Kharif (June-October) and Rabi (November-April) and establish a new crop yield exposure relationship for South Asian wheat and rice cultivars. These are a factor of two more sensitive to ozone induced crop yield losses compared to their European and American counterparts. Relative yield losses based on the AOT40 metrics ranged from 27-41% for wheat, 21-26% for rice, 9-11% for maize and 47-58% for cotton. Crop production losses for wheat amounted to 20.8 million t in fiscal year 2012-2013 and 10.3 million t in fiscal year 2013-2014 for Punjab and Haryana jointly. Crop production losses for rice totalled 5.4 million t in fiscal year 2012-2013 and 3.2 million t year 2013-2014 for Punjab and Haryana jointly. The Indian National Food Security Ordinance entitles ~ 820 million of India's poor to purchase about 60 kg of rice/wheat per person annually at subsidized rates. The scheme requires 27.6 Mt of wheat and 33.6 Mt of rice per year. Mitigation of ozone related crop production losses in Punjab and Haryana alone could provide >50% of the wheat and ~10% of the rice required for the scheme. The total economic cost losses in Punjab and Haryana amounted to USD 6.5 billion in the fiscal year 2012-2013 and USD 3.7 billion in the fiscal year 2013-2014. This economic loss estimate represents a very conservative lower limit based on the minimum support price of the crop, which is lower than the actual production costs. The upper limit for ozone related crop yield losses in entire India currently amounts to 3.5-20% of India's GDP. Mitigation of high surface ozone would require relatively little investment in comparison to economic losses incurred presently. Therefore, ozone mitigation can yield massive benefits in terms of ensuring food security and boosting the economy. Co-benefits of ozone mitigation also include a decrease in the ozone related mortality, morbidity and a reduction of the ozone induced warming in the lower troposphere.

Climate change impacts on crop yield in the Euro-Mediterranean region

NASA Astrophysics Data System (ADS)

Toreti, Andrea; Ceglar, Andrej; Dentener, Frank; Niemeyer, Stefan; Dosio, Alessandro; Fumagalli, Davide

2017-04-01

Agriculture is strongly influenced by climate variability, climate extremes and climate changes. Recent studies on past decades have identified and analysed the effects of climate variability and extremes on crop yields in the Euro-Mediterranean region. As these effects could be amplified in a changing climate context, it is essential to analyse available climate projections and investigate the possible impacts on European agriculture in terms of crop yield. In this study, five model runs from the Euro-CORDEX initiative under two scenarios (RCP4.5 and RCP8.5) have been used. Climate model data have been bias corrected and then used to feed a mechanistic crop growth model. The crop model has been run under different settings to better sample the intrinsic uncertainties. Among the main results, it is worth to report a weak but significant and spatially homogeneous increase in potential wheat yield at mid-century (under a CO2 fertilisation effect scenario). While more complex changes seem to characterise potential maize yield, with large areas in the region showing a weak-to-moderate decrease.

Can novel management practice improve soil and environmental quality and sustain crop yield simultaneously?

USDA-ARS?s Scientific Manuscript database

Little is known about management practices that can simultaneously improve soil and environmental quality and sustain crop yields. The effect of a combination of tillage, crop rotation, and N fertilization on soil C and N, global warming potential (GWP), greenhouse gas intensity (GHGI), and malt bar...

Long-term conventional and no-tillage effects on field hydrology and yields of a dryland crop rotation

USDA-ARS?s Scientific Manuscript database

Semiarid dryland crop yields with no-till, NT, residue management are often greater than stubble-mulch, SM, tillage as a result of improved soil conditions and water conservation, but information on long-term tillage effects on field hydrology and sustained crop production are needed. Our objective ...

3% Yield Increase (HH3), All Energy Crops scenario of the 2016 Billion Ton Report

DOE Data Explorer

Davis, Maggie R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000181319328); Hellwinkel, Chad [University of Tennessee] (ORCID:0000000173085058); Eaton, Laurence [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000312709626); Langholtz, Matthew H. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000281537154); Turhollow, Anthony [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000228159350); Brandt, Craig [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000214707379); Myers, Aaron (ORCID:0000000320373827)

2016-07-13

Scientific reason for data generation: to serve as an alternate high-yield scenario for the BT16 volume 1 agricultural scenarios to compare these projections of potential biomass supplies against a reference case (agricultural baseline 10.11578/1337885). The simulation runs from 2015 through 2040; a starting year of 2014 is used but not reported. Date the data set was last modified: 02/02/2016 How each parameter was produced (methods), format, and relationship to other data in the data set: This exogenous price simulations (also referred to as “specified-price” simulations) introduces a farmgate price, and POLYSYS solves for biomass supplies that may be brought to market in response to these prices. In specified-price scenarios, a specified farmgate price is offered constantly in all counties over all years of the simulation. This simulation begins in 2015 with an offered farmgate price for primary crop residues only between 2015 and 2018 and long-term contracts for dedicated crops beginning in 2019. Expected mature energy crop yield grows at a compounding rate of 3% beginning in 2016. The yield growth assumptions are fixed after crops are planted such that yield gains do not apply to crops already planted, but new plantings do take advantage of the gains in expected yield growth. Instruments used: Policy Analysis System –POLYSYS (version POLYS2015_V10_alt_JAN22B), an agricultural policy modeling system of U.S. agriculture (crops and livestock), supplied by the University of Tennessee Institute of Agriculture, Agricultural Policy Analysis Center.

2% Yield Increase (HH2), All Energy Crops scenario of the 2016 Billion Ton Report

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

Davis, Maggie R.; Hellwinkel, Chad; Eaton, Laurence

Scientific reason for data generation: to serve as an alternate high-yield scenario for the BT16 volume 1 agricultural scenarios to compare these projections of potential biomass supplies against a reference case (agricultural baseline 10.11578/1337885). The simulation runs from 2015 through 2040; a starting year of 2014 is used but not reported. Date the data set was last modified: 02/02/2016 How each parameter was produced (methods), format, and relationship to other data in the data set: This exogenous price simulations (also referred to as “specified-price” simulations) introduces a farmgate price, and POLYSYS solves for biomass supplies that may be brought tomore » market in response to these prices. In specified-price scenarios, a specified farmgate price is offered constantly in all counties over all years of the simulation. This simulation begins in 2015 with an offered farmgate price for primary crop residues only between 2015 and 2018 and long-term contracts for dedicated crops beginning in 2019. Expected mature energy crop yield grows at a compounding rate of 2% beginning in 2016. The yield growth assumptions are fixed after crops are planted such that yield gains do not apply to crops already planted, but new plantings do take advantage of the gains in expected yield growth. Instruments used: Policy Analysis System –POLYSYS (version POLYS2015_V10_alt_JAN22B), an agricultural policy modeling system of U.S. agriculture (crops and livestock), supplied by the University of Tennessee Institute of Agriculture, Agricultural Policy Analysis Center.« less

Spectrally-Based Assessment of Crop Seasonal Performance and Yield

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Borisova, Denitsa; Georgiev, Georgy

The rapid advances of space technologies concern almost all scientific areas from aeronautics to medicine, and a wide range of application fields from communications to crop yield predictions. Agricultural monitoring is among the priorities of remote sensing observations for getting timely information on crop development. Monitoring agricultural fields during the growing season plays an important role in crop health assessment and stress detection provided that reliable data is obtained. Successfully spreading is the implementation of hyperspectral data to precision farming associated with plant growth and phenology monitoring, physiological state assessment, and yield prediction. In this paper, we investigated various spectral-biophysical relationships derived from in-situ reflectance measurements. The performance of spectral data for the assessment of agricultural crops condition and yield prediction was examined. The approach comprisesd development of regression models between plant spectral and state-indicative variables such as biomass, vegetation cover fraction, leaf area index, etc., and development of yield forecasting models from single-date (growth stage) and multitemporal (seasonal) reflectance data. Verification of spectral predictions was performed through comparison with estimations from biophysical relationships between crop growth variables. The study was carried out for spring barley and winter wheat. Visible and near-infrared reflectance data was acquired through the whole growing season accompanied by detailed datasets on plant phenology and canopy structural and biochemical attributes. Empirical relationships were derived relating crop agronomic variables and yield to various spectral predictors. The study findings were tested using airborne remote sensing inputs. A good correspondence was found between predicted and actual (ground-truth) estimates

Meta-analysis of climate impacts and uncertainty on crop yields in Europe

NASA Astrophysics Data System (ADS)

Knox, Jerry; Daccache, Andre; Hess, Tim; Haro, David

2016-11-01

Future changes in temperature, rainfall and soil moisture could threaten agricultural land use and crop productivity in Europe, with major consequences for food security. We assessed the projected impacts of climate change on the yield of seven major crop types (viz wheat, barley, maize, potato, sugar beet, rice and rye) grown in Europe using a systematic review (SR) and meta-analysis of data reported in 41 original publications from an initial screening of 1748 studies. Our approach adopted an established SR procedure developed by the Centre for Evidence Based Conservation constrained by inclusion criteria and defined methods for literature searches, data extraction, meta-analysis and synthesis. Whilst similar studies exist to assess climate impacts on crop yield in Africa and South Asia, surprisingly, no comparable synthesis has been undertaken for Europe. Based on the reported results (n = 729) we show that the projected change in average yield in Europe for the seven crops by the 2050s is +8%. For wheat and sugar beet, average yield changes of +14% and +15% are projected, respectively. There were strong regional differences with crop impacts in northern Europe being higher (+14%) and more variable compared to central (+6%) and southern (+5) Europe. Maize is projected to suffer the largest negative mean change in southern Europe (-11%). Evidence of climate impacts on yield was extensive for wheat, maize, sugar beet and potato, but very limited for barley, rice and rye. The implications for supporting climate adaptation policy and informing climate impacts crop science research in Europe are discussed.

An economic assessment of the health effects and crop yield losses caused by air pollution in mainland China.

PubMed

Miao, Weijie; Huang, Xin; Song, Yu

2017-06-01

Air pollution is severe in China, and pollutants such as PM 2.5 and surface O 3 may cause major damage to human health and crops, respectively. Few studies have considered the health effects of PM 2.5 or the loss of crop yields due to surface O 3 using model-simulated air pollution data in China. We used gridded outputs from the WRF-Chem model, high resolution population data, and crop yield data to evaluate the effects on human health and crop yield in mainland China. Our results showed that outdoor PM 2.5 pollution was responsible for 1.70-1.99 million cases of all-cause mortality in 2006. The economic costs of these health effects were estimated to be 151.1-176.9 billion USD, of which 90% were attributed to mortality. The estimated crop yield losses for wheat, rice, maize, and soybean were approximately 9, 4.6, 0.44, and 0.34 million tons, respectively, resulting in economic losses of 3.4 billion USD. The total economic losses due to ambient air pollution were estimated to be 154.5-180.3 billion USD, accounting for approximately 5.7%-6.6% of the total GDP of China in 2006. Our results show that both population health and staple crop yields in China have been significantly affected by exposure to air pollution. Measures should be taken to reduce emissions, improve air quality, and mitigate the economic loss. Copyright © 2016. Published by Elsevier B.V.

7 CFR 1437.3 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... (including ornamental fish), and floriculture, an environment in which everything that can practicably be..., as determined by CCC. Good farming practices means the cultural practices generally used for the crop... good farming practices. T-Yield means the yield which is based on the county expected yield of the crop...

Relationship between cotton yield and soil electrical conductivity, topography, and landsat imagery

USDA-ARS?s Scientific Manuscript database

Understanding spatial and temporal variability in crop yield is a prerequisite to implementing site-specific management of crop inputs. Apparent soil electrical conductivity (ECa), soil brightness, and topography are easily obtained data that can explain yield variability. The objectives of this stu...

7 CFR 1437.102 - Yield determinations.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Determining Yield Coverage Using Actual Production History § 1437.102 Yield determinations. (a) An actual... used in the actual production history base period when less than four consecutive crop years of actual... calculated, in the actual production history base period when the producer reports acreage for the crop but...

7 CFR 1437.102 - Yield determinations.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Determining Yield Coverage Using Actual Production History § 1437.102 Yield determinations. (a) An actual... used in the actual production history base period when less than four consecutive crop years of actual... calculated, in the actual production history base period when the producer reports acreage for the crop but...

7 CFR 1437.102 - Yield determinations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Determining Yield Coverage Using Actual Production History § 1437.102 Yield determinations. (a) An actual... used in the actual production history base period when less than four consecutive crop years of actual... calculated, in the actual production history base period when the producer reports acreage for the crop but...

Linear unmixing of multidate hyperspectral imagery for crop yield estimation

USDA-ARS?s Scientific Manuscript database

In this paper, we have evaluated an unsupervised unmixing approach, vertex component analysis (VCA), for the application of crop yield estimation. The results show that abundance maps of the vegetation extracted by the approach are strongly correlated to the yield data (the correlation coefficients ...

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

A meteorologically-driven yield reduction model for spring and winter wheat

NASA Technical Reports Server (NTRS)

Ravet, F. W.; Cremins, W. J.; Taylor, T. W.; Ashburn, P.; Smika, D.; Aaronson, A. (Principal Investigator)

1983-01-01

A yield reduction model for spring and winter wheat was developed for large-area crop condition assessment. Reductions are expressed in percentage from a base yield and are calculated on a daily basis. The algorithm contains two integral components: a two-layer soil water budget model and a crop calendar routine. Yield reductions associated with hot, dry winds (Sukhovey) and soil moisture stress are determined. Input variables include evapotranspiration, maximum temperature and precipitation; subsequently crop-stage, available water holding percentage and stress duration are evaluated. No specific base yield is required and may be selected by the user; however, it may be generally characterized as the maximum likely to be produced commercially at a location.

Olive response to water availability: yield response functions, soil water content indicators and evaluation of adaptability to climate change

NASA Astrophysics Data System (ADS)

Riccardi, Maria; Alfieri, Silvia Maria; Basile, Angelo; Bonfante, Antonello; Menenti, Massimo; Monaco, Eugenia; De Lorenzi, Francesca

2013-04-01

Climate evolution, with the foreseen increase of temperature and frequency of drought events during the summer, could cause significant changes in the availability of water resources specially in the Mediterranean region. European countries need to encourage sustainable agriculture practices, reducing inputs, especially of water, and minimizing any negative impact on crop quantity and quality. Olive is an important crop in the Mediterranean region that has traditionally been cultivated with no irrigation and is known to attain acceptable production under dry farming. Therefore this crop will not compete for foreseen reduced water resources. However, a good quantitative knowledge must be available about effects of reduced precipitation and water availability on yield. Yield response functions, coupled with indicators of soil water availability, provide a quantitative description of the cultivar- specific behavior in relation to hydrological conditions. Yield response functions of 11 olive cultivars, typical of Mediterranean environment, were determined using experimental data (unpublished or reported in scientific literature). The yield was expressed as relative yield (Yr); the soil water availability was described by means of different indicators: relative soil water deficit (RSWD), relative evapotranspiration (RED) and transpiration deficit (RTD). Crops can respond nonlinearly to changes in their growing conditions and exhibit threshold responses, so for the yield functions of each olive cultivar both linear regression and threshold-slope models were considered to evaluate the best fit. The level of relative yield attained in rain-fed conditions was identified and defined as the acceptable yield level (Yrrainfed). The value of the indicator (RSWD, RED and RTD) corresponding to Yrrainfed was determined for each cultivar and indicated as the critical value of water availability. The error in the determination of the critical value was estimated. By means of a simulation model of the water flow in the soil-plant-atmosphere system, the indicators of soil water availability were calculated for different soil units in an area of Southern Italy, traditionally cultivated with olive. Simulations were performed for two climate scenarios: reference (1961-90) and future climate (2021-50). The potentiality of the indicators RSWD, RED and RTD to describe soil water availability was evaluated using simulated and experimental data. The analysis showed that RED values were correlated to RTD. The analysis demonstrated that RTD was more effective than RED in representing crop water availability RSWD is very well correlated to RTD and the degree of correlation depends of the period of deficit considered. The probability of adaptation of each cultivar was calculated for both climatic periods by comparing the critical values (and their error distribution) with soil availability indicators. Keywords: Olea europaea, soil water deficit, water availability critical value. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

Effect of chemical and mechanical weed control on cassava yield, soil quality and erosion under cassava cropping system

NASA Astrophysics Data System (ADS)

Islami, Titiek; Wisnubroto, Erwin; Utomo, Wani

2016-04-01

Three years field experiments were conducted to study the effect of chemical and mechanical weed control on soil quality and erosion under cassava cropping system. The experiment were conducted at University Brawijaya field experimental station, Jatikerto, Malang, Indonesia. The experiments were carried out from 2011 - 2014. The treatments consist of three cropping system (cassava mono culture; cassava + maize intercropping and cassava + peanut intercropping), and two weed control method (chemical and mechanical methods). The experimental result showed that the yield of cassava first year and second year did not influenced by weed control method and cropping system. However, the third year yield of cassava was influence by weed control method and cropping system. The cassava yield planted in cassava + maize intercropping system with chemical weed control methods was only 24 t/ha, which lower compared to other treatments, even with that of the same cropping system used mechanical weed control. The highest cassava yield in third year was obtained by cassava + peanuts cropping system with mechanical weed control method. After three years experiment, the soil of cassava monoculture system with chemical weed control method possessed the lowest soil organic matter, and soil aggregate stability. During three years of cropping soil erosion in chemical weed control method, especially on cassava monoculture, was higher compared to mechanical weed control method. The soil loss from chemical control method were 40 t/ha, 44 t/ha and 54 t/ha for the first, second and third year crop. The soil loss from mechanical weed control method for the same years was: 36 t/ha, 36 t/ha and 38 t/ha. Key words: herbicide, intercropping, soil organic matter, aggregate stability.

Assessing the impacts of current and future concentrations of surface ozone on crop yield with meta-analysis

NASA Astrophysics Data System (ADS)

Feng, Zhaozhong; Kobayashi, Kazuhiko

Meta-analysis was conducted to quantitatively assess the effects of rising ozone concentrations ([O 3]) on yield and yield components of major food crops: potato, barley, wheat, rice, bean and soybean in 406 experimental observations. Yield loss of the crops under current and future [O 3] was expressed relative to the yield under base [O 3] (≤26 ppb). With potato, current [O 3] (31-50 ppb) reduced the yield by 5.3%, and it reduced the yield of barley, wheat and rice by 8.9%, 9.7% and 17.5%, respectively. In bean and soybean, the yield losses were 19.0% and 7.7%, respectively. Compared with yield loss at current [O 3], future [O 3] (51-75 ppb) drove a further 10% loss in yield of soybean, wheat and rice, and 20% loss in bean. Mass of individual grain, seed, or tuber was often the major cause of the yield loss at current and future [O 3], whereas other yield components also contributed to the yield loss in some cases. No significant difference was found between the responses in crops grown in pots and those in the ground for any yield parameters. The ameliorating effect of elevated [CO 2] was significant in the yields of wheat and potato, and the individual grain weight in wheat exposed to future [O 3]. These findings confirm the rising [O 3] as a threat to food security for the growing global population in this century.

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

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.

Modeling global yield growth of major crops under multiple socioeconomic pathways

NASA Astrophysics Data System (ADS)

Iizumi, T.; Kim, W.; Zhihong, S.; Nishimori, M.

2016-12-01

Global gridded crop models (GGCMs) are a key tool in deriving global food security scenarios under climate change. However, it is difficult for GGCMs to reproduce the reported yield growth patterns—rapid growth, yield stagnation and yield collapse. Here, we propose a set of parameterizations for GGCMs to capture the contributions to yield from technological improvements at the national and multi-decadal scales. These include country annual per capita gross domestic product (GDP)-based parameterizations for the nitrogen application rate and crop tolerance to stresses associated with high temperature, low temperature, water deficit and water excess. Using a GGCM combined with the parameterizations, we present global 140-year (1961-2100) yield growth simulations for maize, soybean, rice and wheat under multiple shared socioeconomic pathways (SSPs) and no climate change. The model reproduces the major characteristics of reported global and country yield growth patterns over the 1961-2013 period. Under the most rapid developmental pathway SSP5, the simulated global yields for 2091-2100, relative to 2001-2010, are the highest (1.21-1.82 times as high, with variations across the crops), followed by SSP1 (1.14-1.56 times as high), SSP2 (1.12-1.49 times as high), SSP4 (1.08-1.38 times as high) and SSP3 (1.08-1.36 times as high). Future country yield growth varies substantially by income level as well as by crop and by SSP. These yield pathways offer a new baseline for addressing the interdisciplinary questions related to global agricultural development, food security and climate change.

Assessment of potential greenhouse gas mitigation from changes to crop root mass and architecture

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

Paustian, Keith; Campbell, Nell; Dorich, Chris

Reducing (and eventually reversing) the increase in greenhouse gases (GHGs) in the atmosphere due to human activities, and thus reducing the extent and severity of anthropogenic climate change, is one of the great challenges facing humanity. While most of the man-caused increase in GHGs has been due to fossil fuel use, land use (including agriculture) currently accounts for about 25% of total GHG emissions and thus there is a need to include emission reductions from the land use sector as part of an effective climate change mitigation strategy. In addition, analyses included in the recent IPCC 5th Climate Change Assessmentmore » report suggests that it may not be possible to achieve large enough emissions reductions in the energy, transport and industrial sectors alone to stabilize GHG concentrations at a level commensurate with a less than 2°C global average temperature increase, without the help of a substantial CO 2 sink (i.e., atmospheric CO 2 removal) from the land use sector. One of the potential carbon sinks that could contribute to this goal is increasing C storage in soil organic matter on managed lands. This report details a preliminary scoping analysis, to assess the potential agricultural area in the US – where appropriate soil, climate and land use conditions exist – to determine the land area on which ‘improved root phenotype’ crops could be deployed and to evaluate the potential long-term soil C storage, given a set of ‘bounding scenarios’ of increased crop root input and/or rooting depth for major crop species (e.g., row crops (corn, sorghum, soybeans), small grains (wheat, barley, oats), and hay and pasture perennial forages). The enhanced root phenotype scenarios assumed 25, 50 and 100% increase in total root C inputs, in combination with five levels of modifying crop root distributions (i.e., no change and four scenarios with increasing downward shift in root distributions). We also analyzed impacts of greater root production on the soil-crop nitrogen balance, from the standpoint of increased need for additional N inputs and consequences for increased N 2O flux, as well as potential impacts if more and deeper roots contributed to reduced N leaching. In the enhanced root phenotype scenarios, the implicit assumption was that increases in overall plant production could be achieved (e.g., through increased CO 2 assimilation, greater growth efficiency) without reducing the harvested yield – that is, we did not include potential leakage and land substitution effects from potential decreased crop yield in the analysis.« less

Assessment of different gridded weather data for soybean yield simulations in Brazil

NASA Astrophysics Data System (ADS)

Battisti, R.; Bender, F. D.; Sentelhas, P. C.

2018-01-01

A high-density, well-distributed, and consistent historical weather data series is of major importance for agricultural planning and climatic risk evaluation. A possible option for regions where weather station network is irregular is the use of gridded weather data (GWD), which can be downloaded online from different sources. Based on that, the aim of this study was to assess the suitability of two GWD, AgMERRA and XAVIER, by comparing them with measured weather data (MWD) for estimating soybean yield in Brazil. The GWD and MWD were obtained for 24 locations across Brazil, considering the period between 1980 and 2010. These data were used to estimate soybean yield with DSSAT-CROPGRO-Soybean model. The comparison of MWD with GWD resulted in a good agreement between climate variables, except for solar radiation. The crop simulations with GWD and MWD resulted in a good agreement for vegetative and reproductive phases. Soybean potential yield (Yp) simulated with AgMERRA and XAVIER had a high correlation (r > 0.88) when compared to the estimates with MWD, with the RMSE of about 400 kg ha-1. For attainable yield (Ya), estimates with XAVIER resulted in a RMSE of 700 kg ha-1 against 864 kg ha-1 from AgMERRA, both compared to the simulations using MWD. Even with these differences in Ya simulations, both GWD can be considered suitable for simulating soybean growth, development, and yield in Brazil; however, with XAVIER GWD presenting a better performance for weather and crop variables assessed.

Precision agriculture in dry land: spatial variability of crop yield and roles of soil surveys, aerial photos, and digital elevation models

NASA Astrophysics Data System (ADS)

Nachabe, Mahmood; Ahuja, Laj; Shaffer, Mary Lou; Ascough, J.; Flynn, Brian; Cipra, J.

1998-12-01

In dryland, yield of crop varies substantially in space, often changing by an order of magnitude within few meters. Precision agriculture aims at exploiting this variability by changing agriculture management practices in space according to site specific conditions. Thus instead of managing a field (typical area 50 to 100 hectares) as a single unit using average conditions, the field is partitioned into small pieces of land known as management units. The size of management units can be in the order of 100 to 1,000 m2 to capture the patterns of variation of yield in the field. Agricultural practices like seeding rate, type of crop, and tillage and fertilizers are applied at the scale of the management unit to suit local agronomic conditions in unit. If successfully practiced, precision agriculture has the potential of increasing income and minimizing environmental impacts by reducing over application of crop production inputs. In the 90s, the implementation of precision agriculture was facilitated tremendously due to the wide availability and use of three technologies: (1) the Global Positioning System (GPS), (2) the Geographic Information System (GIS), and (3) remote sensing. The introduction of the GPS allowed the farmer to determine his coordinate location as equipments are moved in the field. Thus, any piece of equipment can be easily programmed to vary agricultural practices according to coordinate location over the field. The GIS allowed the storage and manipulation of large sets of data and the production of yield maps. Yield maps can be correlated with soil attributes from soil survey, and/or topographical attributes from a Digital Elevation Model (DEM). This helps predicting variation of potential yield over the landscape based on the spatial distribution of soil and topographical attributes. Soil attributes may include soil PH, Organic Matter, porosity, and hydraulic conductivity, whereas topographical attributes involve the estimations of elevation, slope, aspect, curvature, and specific catchment area. Finally remote sensing provided a means of assessing soil and crop conditions over large scales from the air, without excessive sampling on the ground. There are two objectives for this work. The first objective is to analyze the spatial variability of yield across a spectrum of scales to identify the spatial characteristics of yield variation; in essence, we are trying to answer the following questions, at what scale of management unit we should resolve the field level variability and what is the relationship between this resolution and the observed variability form a yield map? The second objective is to identify the soil and topographical attributes that control yield variation over the landscape topography. We already know that, because erosion and deposition are major processes in the formation of a catena, soil variations occur in response to surface and subsurface flow over the landscape. Also landscape positions corresponding to low elevation tend to have high catchment area which usually results in high soil water content in the root zone and thick A horizon. Can topographical attributes explain yield variation observed in the landscape? Will topographical attributes extracted from a DEM compensate for the relatively poor spatial resolution from a soil survey?

Development of a Distributed Parallel Computing Framework to Facilitate Regional/Global Gridded Crop Modeling with Various Scenarios

NASA Astrophysics Data System (ADS)

Jang, W.; Engda, T. A.; Neff, J. C.; Herrick, J.

2017-12-01

Many crop models are increasingly used to evaluate crop yields at regional and global scales. However, implementation of these models across large areas using fine-scale grids is limited by computational time requirements. In order to facilitate global gridded crop modeling with various scenarios (i.e., different crop, management schedule, fertilizer, and irrigation) using the Environmental Policy Integrated Climate (EPIC) model, we developed a distributed parallel computing framework in Python. Our local desktop with 14 cores (28 threads) was used to test the distributed parallel computing framework in Iringa, Tanzania which has 406,839 grid cells. High-resolution soil data, SoilGrids (250 x 250 m), and climate data, AgMERRA (0.25 x 0.25 deg) were also used as input data for the gridded EPIC model. The framework includes a master file for parallel computing, input database, input data formatters, EPIC model execution, and output analyzers. Through the master file for parallel computing, the user-defined number of threads of CPU divides the EPIC simulation into jobs. Then, Using EPIC input data formatters, the raw database is formatted for EPIC input data and the formatted data moves into EPIC simulation jobs. Then, 28 EPIC jobs run simultaneously and only interesting results files are parsed and moved into output analyzers. We applied various scenarios with seven different slopes and twenty-four fertilizer ranges. Parallelized input generators create different scenarios as a list for distributed parallel computing. After all simulations are completed, parallelized output analyzers are used to analyze all outputs according to the different scenarios. This saves significant computing time and resources, making it possible to conduct gridded modeling at regional to global scales with high-resolution data. For example, serial processing for the Iringa test case would require 113 hours, while using the framework developed in this study requires only approximately 6 hours, a nearly 95% reduction in computing time.

Estimating variability in grain legume yields across Europe and the Americas

NASA Astrophysics Data System (ADS)

Cernay, Charles; Ben-Ari, Tamara; Pelzer, Elise; Meynard, Jean-Marc; Makowski, David

2015-06-01

Grain legume production in Europe has recently come under scrutiny. Although legume crops are often promoted to provide environmental services, European farmers tend to turn to non-legume crops. It is assumed that high variability in legume yields explains this aversion, but so far this hypothesis has not been tested. Here, we estimate the variability of major grain legume and non-legume yields in Europe and the Americas from yield time series over 1961-2013. Results show that grain legume yields are significantly more variable than non-legume yields in Europe. These differences are smaller in the Americas. Our results are robust at the level of the statistical methods. In all regions, crops with high yield variability are allocated to less than 1% of cultivated areas. Although the expansion of grain legumes in Europe may be hindered by high yield variability, some species display risk levels compatible with the development of specialized supply chains.

Integrating a Detailed Agricultural Model in a Global Economic Framework: New methods for assessment of climate mitigation and adaptation opportunities

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Calvin, K.; Zhang, X.; Wise, M.; West, T. O.

2010-12-01

Climate change and food security are global issues increasingly linked through human decision making that takes place across all scales from on-farm management actions to international climate negotiations. Understanding how agricultural systems can respond to climate change, through mitigation or adaptation, while still supplying sufficient food to feed a growing global population, thus requires a multi-sector tool in a global economic framework. Integrated assessment models are one such tool, however they are typically driven by historical aggregate statistics of production in combination with exogenous assumptions of future trends in agricultural productivity; they are not yet capable of exploring agricultural management practices as climate adaptation or mitigation strategies. Yet there are agricultural models capable of detailed biophysical modeling of farm management and climate impacts on crop yield, soil erosion and C and greenhouse gas emissions, although these are typically applied at point scales that are incompatible with coarse resolution integrated assessment modeling. To combine the relative strengths of these modeling systems, we are using the agricultural model EPIC (Environmental Policy Integrated Climate), applied in a geographic data framework for regional analyses, to provide input to the global economic model GCAM (Global Change Assessment Model). The initial phase of our approach focuses on a pilot region of the Midwest United States, a highly productive agricultural area. We apply EPIC, a point based biophysical process model, at 60 m spatial resolution within this domain and aggregate the results to GCAM agriculture and land use subregions for the United States. GCAM is then initialized with multiple management options for key food and bioenergy crops. Using EPIC to distinguish these management options based on grain yield, residue yield, soil C change and cost differences, GCAM then simulates the optimum distribution of the available management options to meet demands for food and energy over the next century. The coupled models provide a new platform for evaluating future changes in agricultural management based on food demand, bioenergy demand, and changes in crop yield and soil C under a changing climate. This framework can be applied to evaluate the economically and biophysically optimal distribution of management under future climates.

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.

Targeting carbon for crop yield and drought resilience

PubMed Central

Griffiths, Cara A

2017-01-01

Abstract Current methods of crop improvement are not keeping pace with projected increases in population growth. Breeding, focused around key traits of stem height and disease resistance, delivered the step‐change yield improvements of the green revolution of the 1960s. However, subsequently, yield increases through conventional breeding have been below the projected requirement of 2.4% per year required by 2050. Genetic modification (GM) mainly for herbicide tolerance and insect resistance has been transformational, akin to a second green revolution, although GM has yet to make major inroads into intrinsic yield processes themselves. Drought imposes the major restriction on crop yields globally but, as yet, has not benefited substantially from genetic improvement and still presents a major challenge to agriculture. Much still has to be learnt about the complex process of how drought limits yield and what should be targeted. Mechanisms of drought adaptation from the natural environment cannot be taken into crops without significant modification for the agricultural environment because mechanisms of drought tolerance are often in contrast with mechanisms of high productivity required in agriculture. However, through convergence of fundamental and translational science, it would appear that a mechanism of sucrose allocation in crops can be modified for both productivity and resilience to drought and other stresses. Recent publications show how this mechanism can be targeted by GM, natural variation and a new chemical approach. Here, with an emphasis on drought, we highlight how understanding fundamental science about how crops grow, develop and what limits their growth and yield can be combined with targeted genetic selection and pioneering chemical intervention technology for transformational yield improvements. © 2017 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. PMID:28653336

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

Xiong, Wei; Balkovic, Juraj; van der Velde, M.

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER)more » and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.« less

Meteorological fluctuations define long-term crop yield patterns in conventional and organic production systems

USDA-ARS?s Scientific Manuscript database

Periodic variability in meteorological patterns presents significant challenges to crop production consistency and yield stability. Meteorological influences on corn and soybean grain yields were analyzed over an 18-year period at a long-term experiment in Beltsville, Maryland, U.S.A., comparing c...

Surprising yields with no-till cropping systems

USDA-ARS?s Scientific Manuscript database

Producers using no-till systems have found that crop yields often exceed their expectation based on nutrient and water supply. For example, corn yields 7% higher in a no-till system in central South Dakota than in a tilled system in eastern South Dakota. This is surprising because rainfall is 5 in...

Surprising yields with no-till cropping systems

USDA-ARS?s Scientific Manuscript database

Producers using no-till practices have observed that crop yields can greatly exceed expectations based on nutrient and water supply. For example, Ralph Holzwarth, who farms near Gettysburg, SD, has averaged 150 bu/ac of corn on his farm for the past 6 years. We were surprised with this yield, as c...

An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

NASA Astrophysics Data System (ADS)

Huang, Qing; Zhou, Qing-bo; Zhang, Li

2009-07-01

China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

Airborne monitoring of crop canopy temperatures for irrigation scheduling and yield prediction

NASA Technical Reports Server (NTRS)

Millard, J. P.; Jackson, R. D.; Goettelman, R. C.; Reginato, R. J.; Idso, S. B.; Lapado, R. L.

1977-01-01

Airborne and ground measurements were made on April 1 and 29, 1976, over a USDA test site consisting mostly of wheat in various stages of water stress, but also including alfalfa and bare soil. These measurements were made to evaluate the feasibility of measuring crop temperatures from aircraft so that a parameter termed stress degree day, SDD, could be computed. Ground studies have shown that SDD is a valuable indicator of a crop's water needs, and that it can be related to irrigation scheduling and yield. The aircraft measurement program required predawn and afternoon flights coincident with minimum and maximum crop temperatures. Airborne measurements were made with an infrared line scanner and with color IR photography. The scanner data were registered, subtracted, and color-coded to yield pseudo-colored temperature-difference images. Pseudo-colored images reading directly in daily SDD increments were also produced. These maps enable a user to assess plant water status and thus determine irrigation needs and crop yield potentials.

Application of a CROPWAT Model to Analyze Crop Yields in Nicaragua

NASA Astrophysics Data System (ADS)

Doria, R.; Byrne, J. M.

2013-12-01

ABSTRACT Changes in climate are likely to influence crop yields due to varying evapotranspiration and precipitation over agricultural regions. In Nicaragua, agriculture is extensive, with new areas of land brought into production as the population increases. Nicaraguan staple food items (maize and beans) are produced mostly by small scale farmers with less than 10 hectares, but they are critical for income generation and food security for rural communities. Given that the majority of these farmers are dependent on rain for crop irrigation, and that maize and beans are sensitive to variations in temperature and rainfall patterns, the present study was undertaken to assess the impact of climate change on these crop yields. Climate data were generated per municipio representing the three major climatic zones of the country: the wet Pacific lowland, the cooler Central highland, and the Caribbean lowland. Historical normal climate data from 1970-2000 (baseline period) were used as input to CROPWAT model to analyze the potential and actual evapotranspiration (ETo and ETa, respectively) that affects crop yields. Further, generated local climatic data of future years (2030-2099) under various scenarios were inputted to the CROPWAT to determine changes in ETo and ETa from the baseline period. Spatial variability maps of both ETo and ETa as well as crop yields were created. Results indicated significant variation in seasonal rainfall depth during the baseline period and predicted decreasing trend in the future years that eventually affects yields. These maps enable us to generate appropriate adaptation measures and best management practices for small scale farmers under future climate change scenarios. KEY WORDS: Climate change, evapotranspiration, CROPWAT, yield, Nicaragua

Evaluation of Historical and Projected Agricultural Climate Risk Over the Continental US

NASA Astrophysics Data System (ADS)

Zhu, X.; Troy, T. J.; Devineni, N.

2016-12-01

Food demands are rising due to an increasing population with changing food preferences, which places pressure on agricultural systems. In addition, in the past decade climate extremes have highlighted the vulnerability of our agricultural production to climate variability. Quantitative analyses in the climate-agriculture research field have been performed in many studies. However, climate risk still remains difficult to evaluate at large scales yet shows great potential of help us better understand historical climate change impacts and evaluate the future risk given climate projections. In this study, we developed a framework to evaluate climate risk quantitatively by applying statistical methods such as Bayesian regression, distribution fitting, and Monte Carlo simulation. We applied the framework over different climate regions in the continental US both historically and for modeled climate projections. The relative importance of any major growing season climate index, such as maximum dry period or heavy precipitation, was evaluated to determine what climate indices play a role in affecting crop yields. The statistical modeling framework was applied using county yields, with irrigated and rainfed yields separated to evaluate the different risk. This framework provides estimates of the climate risk facing agricultural production in the near-term that account for the full uncertainty of climate occurrences, range of crop response, and spatial correlation in climate. In particular, the method provides robust estimates of importance of irrigation in mitigating agricultural climate risk. The results of this study can contribute to decision making about crop choice and water use in an uncertain climate.

Dryland pea production and water use in responses to tillage, crop rotation, and weed management practice

USDA-ARS?s Scientific Manuscript database

Pea has been used to replace fallow and sustain dryland crop yields in arid and semiarid regions, but information to optimize its management is required. We evaluated pea growth, yield, and water use in response to tillage, crop rotation, and weed management practice from 2005 to 2010 in the norther...

Gypsum effects on crop yield and chemistry of soil, crop tissue, and vadose zone water: A meta-analysis.

USDA-ARS?s Scientific Manuscript database

Gypsum has various potential benefits as a soil amendment, but data are lacking on gypsum effects on crop yields and on environmental impacts across diverse field sites. Gypsum studies were conducted in six states using a common design with three rates each of mined and flue gas desulfurization (FGD...

7 CFR 457.131 - Macadamia nut crop insurance provisions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... the orchard for the purpose of picking all or a portion of the crop. Graft. The uniting of a macadamia.... Picking of mature macadamia nuts from the ground. Interplanted. Acreage on which two or more crops are... information that we request in order to establish your approved yield. We will reduce the yield used to...

Are GM Crops for Yield and Resilience Possible?

PubMed

Paul, Matthew J; Nuccio, Michael L; Basu, Shib Sankar

2018-01-01

Crop yield improvements need to accelerate to avoid future food insecurity. Outside Europe, genetically modified (GM) crops for herbicide- and insect-resistance have been transformative in agriculture; other traits have also come to market. However, GM of yield potential and stress resilience has yet to impact on food security. Genes have been identified for yield such as grain number, size, leaf growth, resource allocation, and signaling for drought tolerance, but there is only one commercialized drought-tolerant GM variety. For GM and genome editing to impact on yield and resilience there is a need to understand yield-determining processes in a cell and developmental context combined with evaluation in the grower environment. We highlight a sugar signaling mechanism as a paradigm for this approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effect of plant density and mixing ratio on crop yield in sweet corn/mungbean intercropping.

PubMed

Sarlak, S; Aghaalikhani, M; Zand, B

2008-09-01

In order to evaluate the ear and forage yield of sweet corn (Zea mays L. var. Saccarata) in pure stand and intercropped with mung bean (Vigna radiata L.), a field experiment was conducted at Varamin region on summer 2006. Experiment was carried out in a split plot design based on randomized complete blocks with 4 replications. Plant density with 3 levels [Low (D1), Mean (D2) and High (D3) respecting 6, 8 and 10 m(-2) for sweet corn, cultivar S.C.403 and 10, 20 and 30 m(-2) for mung bean cultivar, Partow] was arranged in main plots and 5 mixing ratios [(P1) = 0/100, (P2) = 25/75, (P3) = 50/50, (P4) = 75/25, (P5) = 100/0% for sweet corn/mung bean, respectively] were arranged in subplots. Quantitative attributes such as plant height, sucker numbers, LER, dry matter distribution in different plant organs were measured in sweet corn economical maturity. Furthermore the yield of cannable ear corn and yield components of sweet corn and mung bean were investigated. Results showed that plant density has not any significant effect on evaluated traits, while the effect of mixing ratio was significant (p < 0.01). Therefore, the mixing ratio of 75/25 (sweet corn/mung bean) could be introduced as the superior mixing ratio; because of it's maximum rate of total sweet corn's biomass, forage yield, yield and yield components of ear corn in intercropping. Regarding to profitability indices of intercropping, the mixing ratio 75/25 (sweet corn/mung bean) in low density (D1P2) which showed the LER = 1.03 and 1.09 for total crop yield before ear harvesting and total forage yield after ear harvest respectively, was better than corn or mung bean monoculture.

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

NASA Astrophysics Data System (ADS)

Chen, Huili; Liang, Zhongyao; Liu, Yong; Liang, Qiuhua; Xie, Shuguang

2017-10-01

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses.

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.

Linkages among climate change, crop yields and Mexico–US cross-border migration

PubMed Central

Feng, Shuaizhang; Krueger, Alan B.; Oppenheimer, Michael

2010-01-01

Climate change is expected to cause mass human migration, including immigration across international borders. This study quantitatively examines the linkages among variations in climate, agricultural yields, and people's migration responses by using an instrumental variables approach. Our method allows us to identify the relationship between crop yields and migration without explicitly controlling for all other confounding factors. Using state-level data from Mexico, we find a significant effect of climate-driven changes in crop yields on the rate of emigration to the United States. The estimated semielasticity of emigration with respect to crop yields is approximately −0.2, i.e., a 10% reduction in crop yields would lead an additional 2% of the population to emigrate. We then use the estimated semielasticity to explore the potential magnitude of future emigration. Depending on the warming scenarios used and adaptation levels assumed, with other factors held constant, by approximately the year 2080, climate change is estimated to induce 1.4 to 6.7 million adult Mexicans (or 2% to 10% of the current population aged 15–65 y) to emigrate as a result of declines in agricultural productivity alone. Although the results cannot be mechanically extrapolated to other areas and time periods, our findings are significant from a global perspective given that many regions, especially developing countries, are expected to experience significant declines in agricultural yields as a result of projected warming. PMID:20660749

Linkages among climate change, crop yields and Mexico-US cross-border migration.

PubMed

Feng, Shuaizhang; Krueger, Alan B; Oppenheimer, Michael

2010-08-10

Climate change is expected to cause mass human migration, including immigration across international borders. This study quantitatively examines the linkages among variations in climate, agricultural yields, and people's migration responses by using an instrumental variables approach. Our method allows us to identify the relationship between crop yields and migration without explicitly controlling for all other confounding factors. Using state-level data from Mexico, we find a significant effect of climate-driven changes in crop yields on the rate of emigration to the United States. The estimated semielasticity of emigration with respect to crop yields is approximately -0.2, i.e., a 10% reduction in crop yields would lead an additional 2% of the population to emigrate. We then use the estimated semielasticity to explore the potential magnitude of future emigration. Depending on the warming scenarios used and adaptation levels assumed, with other factors held constant, by approximately the year 2080, climate change is estimated to induce 1.4 to 6.7 million adult Mexicans (or 2% to 10% of the current population aged 15-65 y) to emigrate as a result of declines in agricultural productivity alone. Although the results cannot be mechanically extrapolated to other areas and time periods, our findings are significant from a global perspective given that many regions, especially developing countries, are expected to experience significant declines in agricultural yields as a result of projected warming.

Effect of a 5-Year Multi-Crop Rotation on Mineral N and Hard Red Spring Wheat Yield, Protein, Test Weight and Economics in Western North Dakota, USA

NASA Astrophysics Data System (ADS)

Landblom, Douglas; Senturklu, Songul; Cihacek, Larry; Brevik, Eric

2016-04-01

The objectives of this non-irrigated cropping study was to employ the principles of soil health and determine the effect of rotation on seasonal mineral N, HRSW production, protein, test weight, and economics. Prior to the initiation of this research, the cropping study area had been previously seeded to hard red spring wheat (HRSW). The cropping systems consisted of a continuous HRSW control (C) compared to HRSW grown in a multi-crop 5-year rotation (R). The 5-yr rotation consisted of HRSW, cover crop (dual crop winter triticale-hairy vetch harvested for hay in June and immediately reseeded to a 7-species cover crop mix grazed by cows after weaning from mid-November to mid-December), forage corn, field pea-forage barley, and sunflower. The cereal grains, cover crops, and pea-barley intercrop were seeded using a JD 1590 no-till drill, 19 cm row spacing, and seed depth of 2.54 cm Cereal grain plant population was 3,088,750 plants/ha. The row crops were planted using a JD 7000 no-till planter, 76.2 cm row spacing, and seed depth of 5.08 cm. Plant population for the row crops was 46,947 plants/ha. Weeds were controlled using a pre-plant burn down and post-emergence control except for cover crops and pea-barley where a pre-plant burn down was the only chemical applied. Fertilizer application was based on soil test results and recommendations from the North Dakota State University Soil Testing Laboratory. During the 1st three years of the study 31.8 kg of N was applied to the C HRSW and then none the last two years of the 5-year period. The R HRSW was fertilized with 13.6 kg of N the 1st two years of the study and none the remaining three years of the 5-year period. However, chloride was low; therefore, 40.7-56.1 kg/ha were applied each year to both the C and R treatments. Based on 2014 and 2015 seasonal mineral N values, the data suggests that N levels were adequate to meet the 2690 kg/ha yield goal. In 2015, however, the R yield goal was exceeded by 673 kg/ha whereas the C yield goal of 2690 kg/ha was not achieved indicating that the multi-crop rotation enhanced soil quality and increased N cycling within the rotation management system. The 5-year average HRSW yield (C: 2690 vs. R: 2757 kg/ha; P=0.76), protein (C: 13.9 vs. R: 13.3%; P=0.06), and test weight (C: 28.1 vs. R: 28.0 kg/bu; P=0.81) did not differ between management treatments. Improved production is the result of enhanced nutrient cycling of available nutrients. Yields for crop years 1-5 were the same year 1, but in year 2, C wheat yield was 24.4% higher than R wheat (3,766 vs. 3,026 kg/ha). Change that started when the rotation was initiated became more evident in year three, when the yield margin between the two management practices began to narrow, but remained 20.5% higher for the C (3,161 vs. 2,623 kg/ha). Yield reversal became fully realized by year 4, when the R wheat yield was 9.1% higher (2,959 vs. 3,228 kg/ha), and by the 5th crop year R wheat yield was 38.9% higher than the C wheat yield (2,421 vs. 3,363 kg/ha). The 5-yr average input cost (C: 477 vs. R: 440/ha) and gross return (C: 650 vs. R: 638/ha) resulted in a net return that was 25 higher for R HRSW compared to the C HRSW (CTRL 173 vs. ROT 198/ha). The 5-yr net return from the C, R, and combination of all of the R crops was 173, 198, and 213/ha suggesting that growing continuous HRSW is less intensive, but also 14.5% less profitable.

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.

Base-Case 1% Yield Increase (BC1), All Energy Crops scenario of the 2016 Billion Ton Report

DOE Data Explorer

Davis, Maggie R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000181319328); Hellwinkel, Chad [University of Tennessee] (ORCID:0000000173085058); Eaton, Laurence [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000312709626); Langholtz, Matthew H. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000281537154); Turhollow, Anthony [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000228159350); Brandt, Craig [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000214707379); Myers, Aaron (ORCID:0000000320373827)

2016-07-13

Scientific reason for data generation: to serve as the base-case scenario for the BT16 volume 1 agricultural scenarios to compare these projections of potential biomass supplies against a reference case (agricultural baseline 10.11578/1337885). The simulation runs from 2015 through 2040; a starting year of 2014 is used but not reported. How each parameter was produced (methods), format, and relationship to other data in the data set: This exogenous price simulations (also referred to as “specified-price” simulations) introduces a farmgate price, and POLYSYS solves for biomass supplies that may be brought to market in response to these prices. In specified-price scenarios, a specified farmgate price is offered constantly in all counties over all years of the simulation. This simulation begins in 2015 with an offered farmgate price for primary crop residues only between 2015 and 2018 and long-term contracts for dedicated crops beginning in 2019. Expected mature energy crop yield grows at a compounding rate of 1% beginning in 2016. The yield growth assumptions are fixed after crops are planted such that yield gains do not apply to crops already planted, but new plantings do take advantage of the gains in expected yield growth. Instruments used: Policy Analysis System –POLYSYS (version POLYS2015_V10_alt_JAN22B), an agricultural policy modeling system of U.S. agriculture (crops and livestock), supplied by the University of Tennessee Institute of Agriculture, Agricultural Policy Analysis Center.

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

Research in the application of spectral data to crop identification and assessment, volume 2

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T. (Principal Investigator); Hixson, M. M.; Bauer, M. E.

1980-01-01

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates.

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale

PubMed Central

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2018-01-01

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3–5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage. PMID:29888751

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.

PubMed

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.

Effect of crop residue harvest on long-term crop yield, soil erosion, and carbon balance: tradeoffs for a sustainable bioenergy feedstock

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

Gregg, Jay S.; Izaurralde, Roberto C.

2010-08-26

Agricultural residues are a potential feedstock for bioenergy production, if residue harvest can be done sustainably. The relationship between crop residue harvest, soil erosion, crop yield and carbon balance was modeled with the Erosion Productivity Impact Calculator/ Environment Policy Integrated Climate (EPIC) using a factorial design. Four crop rotations (winter wheat [Triticum aestivum (L.)] – sunflower [Helianthus annuus]; spring wheat [Triticum aestivum (L.)] – canola [Brassica napus]; corn [Zea mays L.] – soybean [Glycine max (L.) Merr.]; and cotton [Gossypium hirsutum] – peanut [Arachis hypogaea]) were simulated at four US locations each, under different topographies (0-10% slope), and management practicesmore » [crop residue removal rates (0-75%), conservation practices (no till, contour cropping, strip cropping, terracing)].« less

Plant species evaluated for new crop potential

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

Carr, M.E.

1985-01-01

Ninety-two plant species from various regions of the USA were screened for their energy-producing potential. Samples were analysed for oil, polyphenol, hydrocarbon and protein. Oil fractions of some species were analysed for classes of lipid constituents and yields of unsaponifiable matter and fatty acids were determined. Hydrocarbon fractions of some species were analysed for rubber, gutta and waxes. Average MW and MW distribution of rubber and gutta were determined. Complete analytical data for 16 species is presented. Large quantities of oil were obtained from Philadelphus coronarius, Cacalia muhlenbergii, Lindera benzoin and Koelreuteria paniculata. High yields of polyphenols came from Acermore » ginnala, Cornus obliqua and Salix caprea and maximum yields of hydrocarbon and protein were from Elymus virginicus and Lindera benzoin, respectively.« less

Australian wheat production expected to decrease by the late 21st century.

PubMed

Wang, Bin; Liu, De L; O'Leary, Garry J; Asseng, Senthold; Macadam, Ian; Lines-Kelly, Rebecca; Yang, Xihua; Clark, Anthony; Crean, Jason; Sides, Timothy; Xing, Hongtao; Mi, Chunrong; Yu, Qiang

2018-06-01

Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat-growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041-2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO 2 emission scenario by 2081-2100 due to increasing losses in suitable wheat-growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australia's wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production. © 2017 John Wiley & Sons Ltd.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

USGS Publications Warehouse

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-01-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

NASA Astrophysics Data System (ADS)

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-08-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia's cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

Humans as Sensors: Assessing the Information Value of Qualitative Farmer's Crop Condition Surveys for Crop Yield Monitoring and Forecasting

NASA Astrophysics Data System (ADS)

Beguería, S.

2017-12-01

While large efforts are devoted to developing crop status monitoring and yield forecasting systems trough the use of Earth observation data (mostly remotely sensed satellite imagery) and observational and modeled weather data, here we focus on the information value of qualitative data on crop status from direct observations made by humans. This kind of data has a high value as it reflects the expert opinion of individuals directly involved in the development of the crop. However, they have issues that prevent their direct use in crop monitoring and yield forecasting systems, such as their non-spatially explicit nature, or most importantly their qualitative nature. Indeed, while the human brain is good at categorizing the status of physical systems in terms of qualitative scales (`very good', `good', `fair', etcetera), it has difficulties in quantifying it in physical units. This has prevented the incorporation of this kind of data into systems that make extensive use of numerical information. Here we show an example of using qualitative crop condition data to estimate yields of the most important crops in the US early in the season. We use USDA weekly crop condition reports, which are based on a sample of thousands of reporters including mostly farmers and people in direct contact with them. These reporters provide subjective evaluations of crop conditions, in a scale including five levels ranging from `very poor' to `excellent'. The USDA report indicates, for each state, the proportion of reporters fort each condition level. We show how is it possible to model the underlying non-observed quantitative variable that reflects the crop status on each state, and how this model is consistent across states and years. Furthermore, we show how this information can be used to monitor the status of the crops and to produce yield forecasts early in the season. Finally, we discuss approaches for blending this information source with other, more classical earth data sources such as remote sensing or weather data, in the context of hierarchical regression models.

The effect of nitrogen rate on vine kill, tuber skinning injury, tuber yield and size distribution, and tuber nutrients and phytonutrients in two potato cultivars grown for early potato production

USDA-ARS?s Scientific Manuscript database

Early potatoes are typically produced using less nitrogen than a full season potato crop as high rates of nitrogen may delay tuber set and lead to excessive vine growth that is difficult to terminate prior to harvest. Bintje and Ciklamen potato cultivars were grown with preplant soil nitrogen levels...

Future crop production threatened by extreme heat

NASA Astrophysics Data System (ADS)

Siebert, Stefan; Ewert, Frank

2014-04-01

Heat is considered to be a major stress limiting crop growth and yields. While important findings on the impact of heat on crop yield have been made based on experiments in controlled environments, little is known about the effects under field conditions at larger scales. The study of Deryng et al (2014 Global crop yield response to extreme heat stress under multiple climate change futures Environ. Res. Lett. 9 034011), analysing the impact of heat stress on maize, spring wheat and soya bean under climate change, represents an important contribution to this emerging research field. Uncertainties in the occurrence of heat stress under field conditions, plant responses to heat and appropriate adaptation measures still need further investigation.

Biochemical Disincentives to Fertilizing Cellulosic Ethanol Crops

NASA Astrophysics Data System (ADS)

Gallagher, M. E.; Hockaday, W. C.; Snapp, S.; McSwiney, C.; Baldock, J.

2010-12-01

Corn grain biofuel crops produce the highest yields when the cropping ecosystem is not nitrogen (N)-limited, achieved by application of fertilizer. There are environmental consequences for excessive fertilizer application to crops, including greenhouse gas emissions, hypoxic “dead zones,” and health problems from N runoff into groundwater. The increase in corn acreage in response to demand for alternative fuels (i.e. ethanol) could exacerbate these problems, and divert food supplies to fuel production. A potential substitute for grain ethanol that could reduce some of these impacts is cellulosic ethanol. Cellulosic ethanol feedstocks include grasses (switchgrass), hardwoods, and crop residues (e.g. corn stover, wheat straw). It has been assumed that these feedstocks will require similar N fertilization rates to grain biofuel crops to maximize yields, but carbohydrate yield versus N application has not previously been monitored. We report the biochemical stocks (carbohydrate, protein, and lignin in Mg ha-1) of a corn ecosystem grown under varying N levels. We measured biochemical yield in Mg ha-1 within the grain, leaf and stem, and reproductive parts of corn plants grown at seven N fertilization rates (0-202 kg N ha-1), to evaluate the quantity and quality of these feedstocks across a N fertilization gradient. The N fertilization rate study was performed at the Kellogg Biological Station-Long Term Ecological Research Site (KBS-LTER) in Michigan. Biochemical stocks were measured using 13C nuclear magnetic resonance spectroscopy (NMR), combined with a molecular mixing model (Baldock et al. 2004). Carbohydrate and lignin are the main biochemicals of interest in ethanol production since carbohydrate is the ethanol feedstock, and lignin hinders the carbohydrate to ethanol conversion process. We show that corn residue carbohydrate yields respond only weakly to N fertilization compared to grain. Grain carbohydrate yields plateau in response to fertilization at moderate levels (67 kg N ha-1). Increasing fertilizer application beyond the point of diminishing returns for grain (67 kg N ha-1) to double the regionally-recommended amount (202 kg N ha-1) resulted in only marginal increases (25%) in crop residue carbohydrate yield, while increasing lignin yields 41%. In the case of at least this ecosystem, high fertilization rates did not result in large carbohydrate yield increases in the crop residue, and instead produced a lower quality feedstock for cellulosic ethanol production.

Robust features of future climate change impacts on sorghum yields in West Africa

NASA Astrophysics Data System (ADS)

Sultan, B.; Guan, K.; Kouressy, M.; Biasutti, M.; Piani, C.; Hammer, G. L.; McLean, G.; Lobell, D. B.

2014-10-01

West Africa is highly vulnerable to climate hazards and better quantification and understanding of the impact of climate change on crop yields are urgently needed. Here we provide an assessment of near-term climate change impacts on sorghum yields in West Africa and account for uncertainties both in future climate scenarios and in crop models. Towards this goal, we use simulations of nine bias-corrected CMIP5 climate models and two crop models (SARRA-H and APSIM) to evaluate the robustness of projected crop yield impacts in this area. In broad agreement with the full CMIP5 ensemble, our subset of bias-corrected climate models projects a mean warming of +2.8 °C in the decades of 2031-2060 compared to a baseline of 1961-1990 and a robust change in rainfall in West Africa with less rain in the Western part of the Sahel (Senegal, South-West Mali) and more rain in Central Sahel (Burkina Faso, South-West Niger). Projected rainfall deficits are concentrated in early monsoon season in the Western part of the Sahel while positive rainfall changes are found in late monsoon season all over the Sahel, suggesting a shift in the seasonality of the monsoon. In response to such climate change, but without accounting for direct crop responses to CO2, mean crop yield decreases by about 16-20% and year-to-year variability increases in the Western part of the Sahel, while the eastern domain sees much milder impacts. Such differences in climate and impacts projections between the Western and Eastern parts of the Sahel are highly consistent across the climate and crop models used in this study. We investigate the robustness of impacts for different choices of cultivars, nutrient treatments, and crop responses to CO2. Adverse impacts on mean yield and yield variability are lowest for modern cultivars, as their short and nearly fixed growth cycle appears to be more resilient to the seasonality shift of the monsoon, thus suggesting shorter season varieties could be considered a potential adaptation to ongoing climate changes. Easing nitrogen stress via increasing fertilizer inputs would increase absolute yields, but also make the crops more responsive to climate stresses, thus enhancing the negative impacts of climate change in a relative sense. Finally, CO2 fertilization would significantly offset the negative climate impacts on sorghum yields by about 10%, with drier regions experiencing the largest benefits, though the net impacts of climate change remain negative even after accounting for CO2.

Bayesian Inference of Baseline Fertility and Treatment Effects via a Crop Yield-Fertility Model

PubMed Central

Chen, Hungyen; Yamagishi, Junko; Kishino, Hirohisa

2014-01-01

To effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this paper, we focus on the application of these concepts to agriculture. We define the baseline fertility of soil as the level of fertility that a crop can acquire for growth from the soil. With this strict definition, we propose a new crop yield-fertility model that enables quantification of the process of improving baseline fertility and the effects of treatments solely from the time series of crop yields. The model was modified from Michaelis-Menten kinetics and measured the additional effects of the treatments given the baseline fertility. Using more than 30 years of experimental data, we used the Bayesian framework to estimate the improvements in baseline fertility and the effects of fertilizer and farmyard manure (FYM) on maize (Zea mays), barley (Hordeum vulgare), and soybean (Glycine max) yields. Fertilizer contributed the most to the barley yield and FYM contributed the most to the soybean yield among the three crops. The baseline fertility of the subsurface soil was very low for maize and barley prior to fertilization. In contrast, the baseline fertility in this soil approximated half-saturated fertility for the soybean crop. The long-term soil fertility was increased by adding FYM, but the effect of FYM addition was reduced by the addition of fertilizer. Our results provide evidence that long-term soil fertility under continuous farming was maintained, or increased, by the application of natural nutrients compared with the application of synthetic fertilizer. PMID:25405353

Using FACE systems to screen wheat cultivars for yield increases at elevated CO2

USDA-ARS?s Scientific Manuscript database

Because of continuing increases in atmospheric CO2, identifying cultivars of crops with larger yield increases at elevated CO2 may provide an avenue to increase crop yield potential in future climates. Free-air CO2 enrichment (FACE) systems have most often been used with multiple replications of ea...

Increased yield stability of field-grown winter barley (Hordeum vulgare L.) varietal mixtures through ecological processes

PubMed Central

Creissen, Henry E.; Jorgensen, Tove H.; Brown, James K.M.

2016-01-01

Crop variety mixtures have the potential to increase yield stability in highly variable and unpredictable environments, yet knowledge of the specific mechanisms underlying enhanced yield stability has been limited. Ecological processes in genetically diverse crops were investigated by conducting field trials with winter barley varieties (Hordeum vulgare), grown as monocultures or as three-way mixtures in fungicide treated and untreated plots at three sites. Mixtures achieved yields comparable to the best performing monocultures whilst enhancing yield stability despite being subject to multiple predicted and unpredicted abiotic and biotic stresses including brown rust (Puccinia hordei) and lodging. There was compensation through competitive release because the most competitive variety overyielded in mixtures thereby compensating for less competitive varieties. Facilitation was also identified as an important ecological process within mixtures by reducing lodging. This study indicates that crop varietal mixtures have the capacity to stabilise productivity even when environmental conditions and stresses are not predicted in advance. Varietal mixtures provide a means of increasing crop genetic diversity without the need for extensive breeding efforts. They may confer enhanced resilience to environmental stresses and thus be a desirable component of future cropping systems for sustainable arable farming. PMID:27375312

An experimental case study to estimate Pre-harvest Wheat Acreage/Production in Hilly and Plain region of Uttarakhand state: Challenges and solutions of problems by using satellite data

NASA Astrophysics Data System (ADS)

Uniyal, D.; Kimothi, M. M.; Bhagya, N.; Ram, R. D.; Patel, N. K.; Dhaundiya, V. K.

2014-11-01

Wheat is an economically important Rabi crop for the state, which is grown on around 26 % of total available agriculture area in the state. There is a variation in productivity of wheat crop in hilly and tarai region. The agricultural productivity is less in hilly region in comparison of tarai region due to terrace cultivation, traditional system of agriculture, small land holdings, variation in physiography, top soil erosion, lack of proper irrigation system etc. Pre-harvest acreage/yield/production estimation of major crops is being done with the help of conventional crop cutting method, which is biased, inaccurate and time consuming. Remote Sensing data with multi-temporal and multi-spectral capabilities has shown new dimension in crop discrimination analysis and acreage/yield/production estimation in recent years. In view of this, Uttarakhand Space Applications Centre (USAC), Dehradun with the collaboration of Space Applications Centre (SAC), ISRO, Ahmedabad and Uttarakhand State Agriculture Department, have developed different techniques for the discrimination of crops and estimation of pre-harvest wheat acreage/yield/production. In the 1st phase, five districts (Dehradun, Almora, Udham Singh Nagar, Pauri Garhwal and Haridwar) with distinct physiography i.e. hilly and plain regions, have been selected for testing and verification of techniques using IRS (Indian Remote Sensing Satellites), LISS-III, LISS-IV satellite data of Rabi season for the year 2008-09 and whole 13 districts of the Uttarakhand state from 2009-14 along with ground data were used for detailed analysis. Five methods have been developed i.e. NDVI (Normalized Differential Vegetation Index), Supervised classification, Spatial modeling, Masking out method and Programming on visual basics methods using multitemporal satellite data of Rabi season along with the collateral and ground data. These methods were used for wheat discriminations and preharvest acreage estimations and subsequently results were compared with Bureau of Estimation Statistics (BES). Out of these five different methods, wheat area that was estimated by spatial modeling and programming on visual basics has been found quite near to Bureau of Estimation Statistics (BES). But for hilly region, maximum fields were going in shadow region, so it was difficult to estimate accurate result, so frequency distribution curve method has been used and frequency range has been decided to discriminate wheat pixels from other pixels in hilly region, digitized those regions and result shows good result. For yield estimation, an algorithm has been developed by using soil characteristics i.e. texture, depth, drainage, temperature, rainfall and historical yield data. To get the production estimation, estimated yield multiplied by acreage of crop per hectare. Result shows deviation for acreage estimation from BES is around 3.28 %, 2.46 %, 3.45 %, 1.56 %, 1.2 % and 1.6 % (estimation not declared till now by state Agriculture dept. For the year 2013-14) estimation and deviation for production estimation is around 4.98 %, 3.66 % 3.21 % , 3.1 % NA and 2.9 % for the consecutive above mentioned years i.e. 2008-09, 2009-10, 2010-11, 2011-12, 2012-13 and 2013-14. The estimated data has been provided to State Agriculture department for their use. To forecast production before harvest facilitate the formulation of workable marketing strategies leading to better export/import of crop in the state, which will help to lead better economic condition of the state. Yield estimation would help agriculture department in assessment of productivity of land for specific crop. Pre-harvest wheat acreage/production estimation, is useful to facilitate the reliable and timely estimates and enable the administrators and planners to take strategic decisions on import-export policy matters and trade negotiations.

Maximum soil organic carbon storage in Midwest U.S. cropping systems when crops are optimally nitrogen-fertilized

USDA-ARS?s Scientific Manuscript database

Nitrogen fertilizer is critical to optimize short-term crop yield, but its long-term effect on soil organic C (SOC) is actively debated. Using 60 site-years of maize (Zea mays L.) yield response to a wide range of N fertilizer rates in continuous maize and annually rotated maize-soybean [Glycine max...

Organic weed conrol and cover crop residue integration impacts on weed control, quality, and yield and economics in conservation tillage tomato - A case study

USDA-ARS?s Scientific Manuscript database

The increased use of conservation tillage in vegetable production requires more information be developed on the role of cover crops in weed control, tomato quality and yield. Three conservation-tillage systems utilizing crimson clover, brassica and cereal rye as winter cover crops were compared to ...

A diversified no-till crop rotation reduces nitrous oxide emissions, increases soybean yields, and promotes soil C accrual

USDA-ARS?s Scientific Manuscript database

We evaluated the impact of crop rotational diversity on greenhouse gas (GHG) emissions, global warming potential (GWP), and crop yields. Under no-till, rain-fed conditions, a two-yr (corn (Zea mays L.)-soybean (Glycine max (L.) Merr.)) rotation and a four-yr (corn-field peas (Pisum sativum L.)-winte...

The potential of cover crops for improving soil function

NASA Astrophysics Data System (ADS)

Stoate, Chris; Crotty, Felicity

2017-04-01

Cover crops can be grown over the autumn and winter ensuring green cover throughout the year. They have been described as improving soil structure, reducing soil erosion and potentially even a form of grass weed control. These crops retain nutrients within the plant, potentially making them available for future crops, as well as increasing soil organic matter. Over the last three years, we have investigated how different cover crop regimes affect soil quality. Three separate experiments over each autumn/winter period have investigated how different cover crops affect soil biology, physics and chemistry, with each experiment building on the previous one. There have been significant effects of cover crops on soil structure, as well as significantly lower weed biomass and increased yields in the following crop - in comparison to bare stubble. For example, the effect of drilling the cover crops on soil structure in comparison to a bare stubble control that had not been driven on by machinery was quantified, and over the winter period the soil structure of the cover crop treatments changed, with compaction reduced in the cover crop treatments, whilst the bare stubble control remained unchanged. Weeds were found in significantly lower biomass in the cover crop mixes in comparison to the bare stubble control, and significantly lower weed biomass continued to be found in the following spring oat crop where the cover crops had been, indicating a weed suppressive effect that has a continued legacy in the following crop. The following spring oats have shown similar results in the last two years, with higher yields in the previous cover crop areas compared to the bare stubble controls. Overall, these results are indicating that cover crops have the potential to provide improvements to soil quality, reduce weeds and improve yields. We discuss the economic implications.

Importance of rhizobia in Agriculture: potential of the commercial inoculants and native strains for improving legume yields in different land-use systems

NASA Astrophysics Data System (ADS)

Lesueur, D.; Atieno, M.; Mathu, S.; Herrmann, L.

2012-04-01

Legumes play an important role in the traditional diets of many regions throughout the world because they provide a multitude of benefits to both the soil and other crops grown in combination with them or following them in several cropping systems. The ability of legumes to fix atmospheric nitrogen in association with rhizobia gives them the capacity to grow in very degraded soils. But do we have to systematically inoculate legumes? For example our results suggested that the systematic inoculation of both cowpea and green gram in Kenya with commercial inoculants to improve yields is not really justified, native strains performing better than inoculated strains. But when native rhizobia nodulating legumes are not naturally present, application of rhizobial inoculants is very commonly used. Our results showed that the utilization of effective good-quality rhizobial inoculants by farmers have a real potential to improve legume yields in unfertile soils requesting high applications of mineral fertilizers. For example an effective soybean commercial inoculants was tested in different locations in Kenya (in about 150 farms in 3 mandate areas presenting different soil characteristics and environmental conditions). Application of the rhizobial inoculant significantly increased the soybean yields in all mandate areas (about 75% of the farms). Nodule occupancy analysis showed that a high number of nodules occupied by the inoculated strain did not obviously lead to an increase of soybean production. Soil factors (pH, P, C, N…) seemed to affect the inoculant efficiency whether the strain is occupying the nodules or not. Our statistic analysis showed that soil pH significantly affected nodulation and yield, though the effect was variable depending on the region. We concluded that the competitiveness of rhizobial strains might not be the main factor explaining the effect (or lack of) of legumes inoculation in the field. Another study was aiming to assess if several factors such as cropping systems, N fertilization and application of crop residues affect the genetic diversity of native strains of rhizobia nodulating soybean in Kenya without any inoculation. Results showed that nodulation was not significantly affected by the different factors except N fertilization, regardless the season. Nodule occupancy revealed only 3 main profiles representing 93.6% and 92.5% of all the RFLP profiles obtained from 2008 and 2009 nodules respectively. This suggested a low diversity of native rhizobial strains capable to nodulate the promiscuous variety. The cropping system, Nitrogen and Residue applications didn't increase the diversity of the rhizobia but results indicated an effect on the distribution of the 3 profiles within the nodules of the plants. Within same treatments, significant differences were found between the two seasons in term of strains occupying the nodules. It could be explained by the shorter rainfall received in 2008 compared to 2009. Results suggest that cropping systems and both N and crop residues applications affect more specifically plant growth and grain yields than the diversity of the native rhizobia nodulating promiscuous soybean variety. Our work shows how diverse are the factors influencing the success of the field rhizobial inoculation of legumes.

PERSPECTIVE: Climate change, biofuels, and global food security

NASA Astrophysics Data System (ADS)

Cassman, Kenneth G.

2007-03-01

There is a new urgency to improve the accuracy of predicting climate change impact on crop yields because the balance between food supply and demand is shifting abruptly from surplus to deficit. This reversal is being driven by a rapid rise in petroleum prices and, in response, a massive global expansion of biofuel production from maize, oilseed, and sugar crops. Soon the price of these commodities will be determined by their value as feedstock for biofuel rather than their importance as human food or livestock feed [1]. The expectation that petroleum prices will remain high and supportive government policies in several major crop producing countries are providing strong momentum for continued expansion of biofuel production capacity and the associated pressures on global food supply. Farmers in countries that account for a majority of the world's biofuel crop production will enjoy the promise of markedly higher commodity prices and incomesNote1. In contrast, urban and rural poor in food-importing countries will pay much higher prices for basic food staples and there will be less grain available for humanitarian aid. For example, the developing countries of Africa import about 10 MMt of maize each year; another 3 5 MMt of cereal grains are provided as humanitarian aid (figure 1). In a world where more than 800 million are already undernourished and the demand for crop commodities may soon exceed supply, alleviating hunger will no longer be solely a matter of poverty alleviation and more equitable food distribution, which has been the situation for the past thirty years. Instead, food security will also depend on accelerating the rate of gain in crop yields and food production capacity at both local and global scales. Maize imports and cereal donations as humanitarian aid to the developing countries of Africa Figure 1. Maize imports (yellow bar) and cereal donations as humanitarian aid to the developing countries of Africa, 2001 2003. MMT = million metric tons. Data source: faostat.fao.org/site/395/default.aspx. Given this situation, the question of whether global climate change will have a net positive, negative, or negligible impact on crop yields takes on a larger significance because additional hundreds of millions of people could be at risk of hunger and the window of opportunity for mounting an effective response is closing. To answer this question, Lobell and Field use an innovative empirical/geostatistical approach to estimate the impact of increased temperature since 1980 on crop yields—a period when global mean temperature increased ~0.4 °C [2]. For three major crops—maize, wheat, and barley—there was a significant negative response to increased temperature. For all six crops evaluated (also including rice, soybean, and sorghum), the net impact of climate trends on yield since 1980 was negative. While the approach used by Lobell and Field can be questioned on several pointsNote2, the body of their work represents an ambitious global assessment of recent climate impact on crop yields. Most noteworthy is their conclusion that: the combined effects of increased atmospheric CO2 concentration and climate trends have largely cancelled each other over the past two decades. They contrast their finding with the conclusion of the International Panel on Climate Change (IPCC) that CO2 benefits will exceed temperature-related yield reductions up to a 2 °C increase in mean temperature [3]. It should be noted, however, that the IPCC is coming out with a new assessment to be released in April 2007 (www.ipcc.ch/), and it remains to be seen if this conclusion still holds. The purpose here is not to support or challenge the conclusions of either Lobell and Field or the IPCC, but rather to highlight the fact that there are substantive differences between results obtained from geostatistical assessments based on recent climate trends and actual crop yields versus assessments based on results from controlled experiments in growth chambers, greenhouses, and field enclosures and crop modeling. And while there appears to be good agreement on the predicted impact of atmospheric CO2 enrichment on crop yields across a wide range of studies conducted using different approaches [4], there is less convincing evidence on the impact of warming temperatures. There are three reasons for greater uncertainty about temperature effects. First, it is logistically more difficult to control temperature at elevated levels in studies that allow crops to grow in an 'open-air' environment comparable to field-grown plants. The 'free-air carbon dioxide enrichment' (FACE) systems were specifically designed to avoid such problems for study of CO2 effects and appear to have been largely successful [4]. In contrast, growth chamber, greenhouse, and small-enclosure studies used for temperature-effect experiments have confounding effects associated with differences in humidity, air turbulence, and reduced light intensity that result from the need to more fully enclose experimental units with a transparent barrier to achieve adequate temperature control. Second, unlike CO2 effects, yield response to temperature is often discontinuous. In many crops, pollination fails if temperatures rise above a critical threshold, which can result in dramatic yield reductions due to very small changes in temperature. Also, because climate change is predicted to increase both average temperature and temperature variability, changes in both factors must be evaluated in experiments with realistic growth conditions to fully understand climate change impact on crop yields. Such experiments would require expensive infrastructure with creative new designs—studies that have yet to be conducted, in part due to lack of adequate funding. A third factor is the interactive effect of temperature and plant nitrogen (protein) content on respiration, which is poorly understood. In the absence of such studies, it is sobering to note that one long-term field study in which the effect of temperature on rice yield could be isolated from other factors documented a 15% decrease in yield for every 1 °C increase in mean temperature [5]. The magnitude of this decrease is considerably larger than predictions of yield decreases from higher temperature obtained from crop simulation models. Like the results of Lobell and Field [2], we see a discrepancy between estimates of the effects of warmer temperatures on crop yields based on the relationship between crop yields and temperature under field conditions versus those derived from modeling and experiments conducted under controlled conditions. As we make the historic transition from an extended period of surplus food production to one in which demand for staple crop commodities exceeds supply, there is a vital need to better understand the impact of warming temperatures on current and future crop yields. References [1] Council for Agricultural Science and Technology 2006 Convergence of agriculture and energy: Implications for Research and Policy CAST Commentary QTA 2006-3 (Ames, Iowa: CAST) (www.cast-science.org) [2] Lobell D B and Field C B 2007 Global scale climate-crop yield relationships and the impacts of recent warming Environ. Res. Lett. 2 014002 [3] Intergovernmental Panel on Climate Change, Working Group 2 Climate Change 2001 Impacts, Adaptation and Vulnerability IPCC Working Group 2, Third Assessment (New York: Cambridge University Press) [4] Tubiello F N et al 2006 Crop response to elevated CO2 and world food supply: A comment on 'Food for Thought...' by Long et al, Science 312:1918-1921, 2006 Eur. J. Agron. 26 215 23 [5] Peng S, Huang J, Sheehy J E, Laza R, Visperas R M, Zhong X, Centeno G S, Khush G and Cassman K G 2004 Rice yields decline with higher night temperature from global warming Proc. Natl Acad. Sci. 101 9971 5 Notes Note1 USA (40% of global maize, 56% of global maize exports), Brazil (33% of global sugar, 36% of global sugar exports), Indonesia and Malaysia (81% of global palm oil, 88% of global palm oil exports)—2005 data from FAOSTAT: faostat.fao.org/site/395/default.aspx. Note2 For example, the use of a 'global season' for calculating temperatures is problematic. In the case of soybean, a substantial portion of global soybean production occurs in the southern hemisphere, mostly in Brazil and Argentina, yet the global season for temperature was July August—a time when soybean is not grown in these countries. Likewise the global season for rice was January October, a period in which two consecutive rice crops are grown in tropical and subtropical irrigated systems of Asia—systems that account for a large portion of global rice production. Photo of Kenneth G Cassman Dr Cassman is Director of the Nebraska Center for Energy Science Research at the University of Nebraska and the Heuermann Professor of Agronomy. His work focuses on ensuring local and global food security while improving environmental quality in many of the world's most productive cropping systems. Previous positions include: research agronomist in Brazil, Egypt and the Philippines; faculty member at the University of California-Davis; division/department head at the International Rice Research Institute and the University of Nebraska. He received a PhD from the University of Hawaii's College of Tropical Agriculture (1979) and a BS in Biology from the University of California, San Diego (1975).

Energy balance in rainfed herbaceous crops in a semiarid environment for a 15-year experiment. 1. Impact of farming systems

NASA Astrophysics Data System (ADS)

Moreno, M. M.; Moreno, C.; Lacasta, C.; Tarquis, A. M.; Meco, R.

2012-04-01

During the last years, agricultural practices have led to increase yields by means of the massive consumption on non-renewable fossil energy. However, the viability of a production system does not depend solely on crop yield, but also on its efficiency in the use of available resources. This work is part of a larger study assessing the effects of three farming systems (conventional, conservation with zero tillage, and organic) and four barley-based crop rotations (barley monoculture and in rotation with vetch, sunflower and fallow) on the energy balance of crop production under the semi-arid conditions over a 15 year period. However, the present work is focused on the farming system effect, so crop rotations and years are averaged. Experiments were conducted at "La Higueruela" Experimental Farm (4°26' W, 40°04' N, altitude 450 m) (Spanish National Research Council, Santa Olalla, Toledo, central Spain). The climate is semi-arid Mediterranean, with an average seasonal rainfall of 480 mm irregularly distributed and a 4-month summer drought period. Conventional farming included the use of moldboard plow for tillage, chemical fertilizers and herbicides. Conservation farming was developed with zero tillage, direct sowing and chemical fertilizers and herbicides. Organic farming included the use of cultivator and no chemical fertilizers or herbicides. The energy balance method used required the identification and quantification of all the inputs and outputs implied, and the conversion to energy values by corresponding coefficients. The parameters considered were (i) energy inputs (EI) (diesel, machines, fertilizers, herbicides, seeds) (ii) energy outputs (EO) (energy in the harvested biomass), (iii) net energy produced (NE) (EI - EO), (iv) the energy output/input ratio (O/I), and (v) energy productivity (EP) (Crop yield/EI). EI was 3.0 and 3.5 times higher in conservation (10.4 GJ ha-1 year-1) and conventional (11.7 GJ ha-1 year-1) than in organic farming (3.41 GJ ha-1 year-1). The difference between conservation and conventional systems was as result of the greater use of machinery and, consequently, of fuel in conventional, though the use of herbicides was slightly lower. In both systems, fertilizer was the most important energy input. EO was lower for organic (17.9 GJ ha-1 year-1) than for either conventional or conservation systems (25.7 and 23.4 GJ ha-1 year-1, respectively), a result of the lower barley grain and vetch hay yields. The highest NE was obtained in organic (14.5 GJ ha-1 year-1), and the lowest in conservation (13.0 GJ ha-1 year-1). In relation to O/I, organic farming were about 2.3 times more energetically efficient (5.36) than either the conventional or conservation systems (about 2.35). EP ranged from 400 kg GJ-1 in organic to 177 kg GJ-1 in conventional. No differences in all the energy variables considered were recorded between the conventional and conservation managements. As conclusions and in terms of energy efficiency, farming systems requiring agrochemicals in semi-arid Mediterranean conditions, whether conventional or conservation, appeared to be little efficient. Chemical fertilizer was the most important energy input in these two systems, but their use did not lead to an equivalent increase in yield because of the irregular distribution in many years. Organic farming would improve the energy efficiency in these environmental conditions, offering a sustainable production with minimal inputs.

Projected Climate Impacts to South African Maize and Wheat Production in 2055: A Comparison of Empirical and Mechanistic Modeling Approaches

NASA Technical Reports Server (NTRS)

Estes, Lyndon D.; Beukes, Hein; Bradley, Bethany A.; Debats, Stephanie R.; Oppenheimer, Michael; Ruane, Alex C.; Schulze, Roland; Tadross, Mark

2013-01-01

Crop model-specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs' median-projected maize and wheat yield changes were 3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water-use efficiency gains simulated by the MMs under elevated CO2 accounted for much of the EMMM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EMMM comparisons to provide a fuller picture of crop-climate response uncertainties.

Yield Model Development (YMD) implementation plan for fiscal years 1981 and 1982

NASA Technical Reports Server (NTRS)

Ambroziak, R. A. (Principal Investigator)

1981-01-01

A plan is described for supporting USDA crop production forecasting and estimation by (1) testing, evaluating, and selecting crop yield models for application testing; (2) identifying areas of feasible research for improvement of models; and (3) conducting research to modify existing models and to develop new crop yield assessment methods. Tasks to be performed for each of these efforts are described as well as for project management and support. The responsibilities of USDA, USDC, USDI, and NASA are delineated as well as problem areas to be addressed.

Comparative analysis of maize (Zea mays) crop performance: natural variation, incremental improvements and economic impacts.

PubMed

Leibman, Mark; Shryock, Jereme J; Clements, Michael J; Hall, Michael A; Loida, Paul J; McClerren, Amanda L; McKiness, Zoe P; Phillips, Jonathan R; Rice, Elena A; Stark, Steven B

2014-09-01

Grain yield from maize hybrids continues to improve through advances in breeding and biotechnology. Despite genetic improvements to hybrid maize, grain yield from distinct maize hybrids is expected to vary across growing locations due to numerous environmental factors. In this study, we examine across-location variation in grain yield among maize hybrids in three case studies. The three case studies examine hybrid improvement through breeding, introduction of an insect protection trait or introduction of a transcription factor trait associated with increased yield. In all cases, grain yield from each hybrid population had a Gaussian distribution. Across-location distributions of grain yield from each hybrid partially overlapped. The hybrid with a higher mean grain yield typically outperformed its comparator at most, but not all, of the growing locations (a 'win rate'). These results suggest that a broad set of environmental factors similarly impacts grain yields from both conventional- and biotechnology-derived maize hybrids and that grain yields among two or more hybrids should be compared with consideration given to both mean yield performance and the frequency of locations at which each hybrid 'wins' against its comparators. From an economic standpoint, growers recognize the value of genetically improved maize hybrids that outperform comparators in the majority of locations. Grower adoption of improved maize hybrids drives increases in average U.S. maize grain yields and contributes significant value to the economy. © 2014 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Spatiotemporal analysis of projected impacts of climate change on the major C3 and C4 crop yield under representative concentration pathway 4.5: Insight from the coasts of Tamil Nadu, South India

PubMed Central

A, Ramachandran; Praveen, Dhanya; R, Jaganathan; D, RajaLakshmi; K, Palanivelu

2017-01-01

India's dependence on a climate sensitive sector like agriculture makes it highly vulnerable to its impacts. However, agriculture is highly heterogeneous across the country owing to regional disparities in exposure, sensitivity, and adaptive capacity. It is essential to know and quantify the possible impacts of changes in climate on crop yield for successful agricultural management and planning at a local scale. The Hadley Centre Global Environment Model version 2-Earth System (HadGEM-ES) was employed to generate regional climate projections for the study area using the Regional Climate Model (RCM) RegCM4.4. The dynamics in potential impacts at the sub-district level were evaluated using the Representative Concentration Pathway 4.5 (RCPs). The aim of this study was to simulate the crop yield under a plausible change in climate for the coastal areas of South India through the end of this century. The crop simulation model, the Decision Support System for Agrotechnology Transfer (DSSAT) 4.5, was used to understand the plausible impacts on the major crop yields of rice, groundnuts, and sugarcane under the RCP 4.5 trajectory. The findings reveal that under the RCP 4.5 scenario there will be decreases in the major C3 and C4 crop yields in the study area. This would affect not only the local food security, but the livelihood security as well. This necessitates timely planning to achieve sustainable crop productivity and livelihood security. On the other hand, this situation warrants appropriate adaptations and policy intervention at the sub-district level for achieving sustainable crop productivity in the future. PMID:28753605

Spatiotemporal analysis of projected impacts of climate change on the major C3 and C4 crop yield under representative concentration pathway 4.5: Insight from the coasts of Tamil Nadu, South India.

PubMed

A, Ramachandran; Praveen, Dhanya; R, Jaganathan; D, RajaLakshmi; K, Palanivelu

2017-01-01

India's dependence on a climate sensitive sector like agriculture makes it highly vulnerable to its impacts. However, agriculture is highly heterogeneous across the country owing to regional disparities in exposure, sensitivity, and adaptive capacity. It is essential to know and quantify the possible impacts of changes in climate on crop yield for successful agricultural management and planning at a local scale. The Hadley Centre Global Environment Model version 2-Earth System (HadGEM-ES) was employed to generate regional climate projections for the study area using the Regional Climate Model (RCM) RegCM4.4. The dynamics in potential impacts at the sub-district level were evaluated using the Representative Concentration Pathway 4.5 (RCPs). The aim of this study was to simulate the crop yield under a plausible change in climate for the coastal areas of South India through the end of this century. The crop simulation model, the Decision Support System for Agrotechnology Transfer (DSSAT) 4.5, was used to understand the plausible impacts on the major crop yields of rice, groundnuts, and sugarcane under the RCP 4.5 trajectory. The findings reveal that under the RCP 4.5 scenario there will be decreases in the major C3 and C4 crop yields in the study area. This would affect not only the local food security, but the livelihood security as well. This necessitates timely planning to achieve sustainable crop productivity and livelihood security. On the other hand, this situation warrants appropriate adaptations and policy intervention at the sub-district level for achieving sustainable crop productivity in the future.

Assessing and modelling ecohydrologic processes at the agricultural field scale

NASA Astrophysics Data System (ADS)

Basso, Bruno

2015-04-01

One of the primary goals of agricultural management is to increase the amount of crop produced per unit of fertilizer and water used. World record corn yields demonstrated that water use efficiency can increase fourfold with improved agronomic management and cultivars able to tolerate high densities. Planting crops with higher plant density can lead to significant yield increases, and increase plant transpiration vs. soil water evaporation. Precision agriculture technologies have been adopted for the last twenty years but seldom have the data collected been converted to information that led farmers to different agronomic management. These methods are intuitively appealing, but yield maps and other spatial layers of data need to be properly analyzed and interpreted to truly become valuable. Current agro-mechanic and geospatial technologies allow us to implement a spatially variable plan for agronomic inputs including seeding rate, cultivars, pesticides, herbicides, fertilizers, and water. Crop models are valuable tools to evaluate the impact of management strategies (e.g., cover crops, tile drains, and genetically-improved cultivars) on yield, soil carbon sequestration, leaching and greenhouse gas emissions. They can help farmers identify adaptation strategies to current and future climate conditions. In this paper I illustrate the key role that precision agriculture technologies (yield mapping technologies, within season soil and crop sensing), crop modeling and weather can play in dealing with the impact of climate variability on soil ecohydrologic processes. Case studies are presented to illustrate this concept.

Improving the Yield and Nutritional Quality of Forage Crops

PubMed Central

Capstaff, Nicola M.; Miller, Anthony J.

2018-01-01

Despite being some of the most important crops globally, there has been limited research on forages when compared with cereals, fruits, and vegetables. This review summarizes the literature highlighting the significance of forage crops, the current improvements and some of future directions for improving yield and nutritional quality. We make the point that the knowledge obtained from model plant and grain crops can be applied to forage crops. The timely development of genomics and bioinformatics together with genome editing techniques offer great scope to improve forage crops. Given the social, environmental and economic importance of forage across the globe and especially in poorer countries, this opportunity has enormous potential to improve food security and political stability. PMID:29740468

Worldwide Dissemination of Radopholus similis and Its Importance in Crop Production

PubMed Central

O'Bannon, J. H.

1977-01-01

The burrowing nematode, Radopholus similis, attacks agronomic and horticultural crops and many weeds, and is reported to reproduce on more than 250 plant species. Two races of R. similis are recognized. Although one race attacks citrus and the other race does not, they are morphologically similar. At present, the citrus race is found attacking citrus only in Florida, U.S.A., but it is known to infect more than 250 species and varieties of noncitrus plants. Although it has many hosts, R. similis is probably most widely distributed on banana and is found worldwide. Although best known as a pest of Piper nigrum, Musa spp., and Citrus spp., it also attacks many crops that are important in world commerce and in subsistence-type agriculture, a factor which makes it a significant agricultural pest. Worldwide dissemination occurs primarily when parasitized plants are moved into areas where the pest could adapt. Yield losses of 12.5 tons/ha in bananas have been reported from R. similis infection. Infections suppress orange and grapefruit yields as much as 70-80%. Because of the severity of R. similis damage (particularly to banana and citrus), extensive control programs have been developed. Prevention, cultural practices, resistant varieties, and chemical pesticides interact to reduce losses. PMID:19305565

SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change.

PubMed

Fereidoon, Majid; Koch, Manfred

2018-07-15

Agriculture is one of the environmental/economic sectors that may adversely be affected by climate change, especially, in already nowadays water-scarce regions, like the Middle East. One way to cope with future changes in absolute as well as seasonal (irrigation) water amounts can be the adaptation of the agricultural crop pattern in a region, i.e. by planting crops which still provide high yields and so economic benefits to farmers under such varying climate conditions. To do this properly, the whole cascade starting from climate change, effects on hydrology and surface water availability, subsequent effects on crop yield, agricultural areas available, and, finally, economic value of a multi-crop cultivation pattern must be known. To that avail, a complex coupled simulation-optimization tool SWAT-LINGO-MODSIM-PSO (SLMP) has been developed here and used to find the future optimum cultivation area of crops for the maximization of the economic benefits in five irrigation-fed agricultural plains in the south of the Karkheh River Basin (KRB) southwest Iran. Starting with the SWAT distributed hydrological model, the KR-streamflow as well as the inflow into the Karkheh-reservoir, as the major storage of irrigation water, is calibrated and validated, based on 1985-2004 observed discharge data. In the subsequent step, the SWAT-predicted streamflow is fed into the MODSIM river basin Decision Support System to simulate and optimize the water allocation between different water users (agricultural, environmental, municipal and industrial) under standard operating policy (SOP) rules. The final step is the maximization of the economic benefit in the five agricultural plains through constrained PSO (particle swarm optimization) by adjusting the cultivation areas (decision variables) of different crops (wheat, barley, maize and "others"), taking into account their specific prizes and optimal crop yields under water deficiency, with the latter computed in the LINGO-sub-optimization module embedded in the SLMP-tool. For the optimization of the agricultural benefits in the KRB in the near future (2038-2060), quantile-mapping (QM) bias-corrected downscaled predictors for daily precipitation and temperatures of the HadGEM2-ES GCM-model under RCP4.5- and RCP8.5-emission scenarios are used as climate drivers in the streamflow- and crop yield simulations of the SWAT-model, leading to corresponding changes in the final outcome (economic benefit) of the SLMP-tool. In fact, whereas for the historical period (1985-2004) a total annual benefit of 94.2 million US$ for all multi-crop areas in KRB is computed, there is a decrease to 88.3 million US$ and 72.1 million US$ for RCP4.5 and RCP8.5, respectively, in the near future (2038-2060) prediction period. In fact, this future income decrease is due to a substantial shift from cultivation areas devoted nowadays to high-price wheat and barley in the winter season to low-price maize-covered areas in the future summers, owing to a future seasonal change of SWAT-predicted irrigation water available, i.e. less in the winter and more in the summer. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of Water and Nutrient Cycles in the North China Plain and U.S. High Plains related to Climate Forcing

NASA Astrophysics Data System (ADS)

Scanlon, B. R.; Pei, H.; Shen, Y.

2014-12-01

The North China Plain (NCP) and U.S. High Plains play critical roles in food production, which relies heavily on groundwater resources for irrigation and nutrients. Here we evaluate food production in terms of resource availability (water and nutrients) and impacts on resources (groundwater quantity and quality) within the context of climate forcing. Double cropping of corn and wheat in the NCP under intensive irrigation (80 - 90% of cropland) and massive N fertilization (384 kg/ha) resulted in total corn plus wheat yields of 13.4 kg/ha (2002 - 2011). In contrast, single cropping of corn on the USHP under less intensive irrigation (40% of cropland) and N fertilization (90 kg/ha) resulted in only 15% lower yield in the USHP (11.7 kg/ha) than in the NCP. However, irrigation essentially decouples crop production from climate extremes. Average corn and wheat yield in the NCP over the past three decades is not correlated with precipitation. Irrigated corn yield in the north and central USHP was actually higher during the recent 2012 drought by up to ~ 30% relative to the 30 year long-term mean yield whereas rainfed corn yield decreased by ~50% during the drought. The main impact of climate extremes on the aquifers is indirect through increased irrigation pumpage for crop production rather than direct through changes in recharge. Effects of crop production on groundwater quality should be much greater in the NCP because of ~4 times higher fertilizer application relative to that in the USHP. Field research experiments in the NCP indicate that much of this fertilizer application (> 200 kg N/ha) does not impact yield and could potentially leach into underlying aquifers. Projected groundwater depletion in these aquifers should result in a shift from intensive irrigation to more rainfed crop production, increasing vulnerability of crop production to climate extremes.

Integrating multiple satellite data for crop monitoring

USDA-ARS?s Scientific Manuscript database

Remote sensing provides a valuable data source for detecting crop types, monitoring crop condition and predicting crop yields from space. Routine and continuous remote sensing data are critical for agricultural research and operational applications. Since crop field dimensions tend to be relatively ...

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 Use of Cover Crops as Climate-Smart Management in Midwest Cropping Systems

NASA Astrophysics Data System (ADS)

Basche, A.; Miguez, F.; Archontoulis, S.; Kaspar, T.

2014-12-01

The observed trends in the Midwestern United States of increasing rainfall variability will likely continue into the future. Events such as individual days of heavy rain as well as seasons of floods and droughts have large impacts on agricultural productivity and the natural resource base that underpins it. Such events lead to increased soil erosion, decreased water quality and reduced corn and soybean yields. Winter cover crops offer the potential to buffer many of these impacts because they essentially double the time for a living plant to protect and improve the soil. However, at present, cover crops are infrequently utilized in the Midwest (representing 1-2% of row cropped land cover) in particular due to producer concerns over higher costs and management, limited time and winter growing conditions as well as the potential harm to corn yields. In order to expand their use, there is a need to quantify how cover crops impact Midwest cropping systems in the long term and namely to understand how to optimize the benefits of cover crops while minimizing their impacts on cash crops. We are working with APSIM, a cropping systems platform, to specifically quantify the long term future impacts of cover crop incorporation in corn-based cropping systems. In general, our regional analysis showed only minor changes to corn and soybean yields (<1% differences) when a cover crop was or was not included in the simulation. Further, a "bad spring" scenario (where every third year had an abnormally wet/cold spring and cover crop termination and planting cash crop were within one day) did not result in any major changes to cash crop yields. Through simulations we estimate an average increase of 4-9% organic matter improvement in the topsoil and an average decrease in soil erosion of 14-32% depending on cover crop planting date and growth. Our work is part of the Climate and Corn-based Cropping Systems Coordinated Agriculture Project (CSCAP), a collaboration of eleven Midwestern institutions established to evaluate how conservation practices, including cover crops, improve the resilience of Midwest agriculture to future change. Such collaborations can help better quantify long term impacts of conservation practices on the landscape that ultimately lead to more climate-smart management of such agricultural systems.

Breeding and Domesticating Crops Adapted to Drought and Salinity: A New Paradigm for Increasing Food Production

PubMed Central

Fita, Ana; Rodríguez-Burruezo, Adrián; Boscaiu, Monica; Prohens, Jaime; Vicente, Oscar

2015-01-01

World population is expected to reach 9.2 × 109 people by 2050. Feeding them will require a boost in crop productivity using innovative approaches. Current agricultural production is very dependent on large amounts of inputs and water availability is a major limiting factor. In addition, the loss of genetic diversity and the threat of climate change make a change of paradigm in plant breeding and agricultural practices necessary. Average yields in all major crops are only a small fraction of record yields, and drought and soil salinity are the main factors responsible for yield reduction. Therefore there is the need to enhance crop productivity by improving crop adaptation. Here we review the present situation and propose the development of crops tolerant to drought and salt stress for addressing the challenge of dramatically increasing food production in the near future. The success in the development of crops adapted to drought and salt depends on the efficient and combined use of genetic engineering and traditional breeding tools. Moreover, we propose the domestication of new halophilic crops to create a ‘saline agriculture’ which will not compete in terms of resources with conventional agriculture. PMID:26617620

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.

Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

NASA Astrophysics Data System (ADS)

Jaafar, H. H.; Ahmad, F. A.

2015-04-01

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.

Effects of Land Use Change for Crops on Water and Carbon Budgets in the Midwest USA

DOE PAGES

Sun, Jian; Twine, Tracy; Hill, Jason; ...

2017-02-07

By increasing the demand for food and bioenergy, the global landscape has altered dramatically in recent years. Land use and land cover change affects the environmental system in many ways through biophysical and biogeochemical mechanisms. Here, we evaluate the impacts of land use and land cover change driven by recent crop expansion and conversion on the water budget, carbon exchange, and carbon storage in the Midwest USA. A dynamic global vegetation model was used to simulate and examine the impacts of landscape change in a historical case based on crop distribution data from the United States Department of Agriculture Nationalmore » Agricultural Statistics Services. Furthermore, the simulation results indicate that recent crop expansion not only decreased soil carbon sequestration (60 Tg less of soil organic carbon) and net carbon flux into ecosystems (3.7 Tg • year -1 less of net biome productivity), but also lessened water consumption through evapotranspiration (1.04 x 10 10 m 3 • year -1 less) over 12 states in the Midwest. More water yield at the land surface does not necessarily make more water available for vegetation. Crop residue removal might also exacerbate the soil carbon loss.« less

Effects of Land Use Change for Crops on Water and Carbon Budgets in the Midwest USA

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

Sun, Jian; Twine, Tracy; Hill, Jason

By increasing the demand for food and bioenergy, the global landscape has altered dramatically in recent years. Land use and land cover change affects the environmental system in many ways through biophysical and biogeochemical mechanisms. Here, we evaluate the impacts of land use and land cover change driven by recent crop expansion and conversion on the water budget, carbon exchange, and carbon storage in the Midwest USA. A dynamic global vegetation model was used to simulate and examine the impacts of landscape change in a historical case based on crop distribution data from the United States Department of Agriculture Nationalmore » Agricultural Statistics Services. Furthermore, the simulation results indicate that recent crop expansion not only decreased soil carbon sequestration (60 Tg less of soil organic carbon) and net carbon flux into ecosystems (3.7 Tg • year -1 less of net biome productivity), but also lessened water consumption through evapotranspiration (1.04 x 10 10 m 3 • year -1 less) over 12 states in the Midwest. More water yield at the land surface does not necessarily make more water available for vegetation. Crop residue removal might also exacerbate the soil carbon loss.« less

Dams and Intergovernmental Transfers

NASA Astrophysics Data System (ADS)

Bao, X.

2012-12-01

Gainers and Losers are always associated with large scale hydrological infrastructure construction, such as dams, canals and water treatment facilities. Since most of these projects are public services and public goods, Some of these uneven impacts cannot fully be solved by markets. This paper tried to explore whether the governments are paying any effort to balance the uneven distributional impacts caused by dam construction or not. It showed that dam construction brought an average 2% decrease in per capita tax revenue in the upstream counties, a 30% increase in the dam-location counties and an insignificant increase in downstream counties. Similar distributional impacts were observed for other outcome variables. like rural income and agricultural crop yields, though the impacts differ across different crops. The paper also found some balancing efforts from inter-governmental transfers to reduce the unevenly distributed impacts caused by dam construction. However, overall the inter-governmental fiscal transfer efforts were not large enough to fully correct those uneven distributions, reflected from a 2% decrease of per capita GDP in upstream counties and increase of per capita GDP in local and downstream counties. This paper may shed some lights on the governmental considerations in the decision making process for large hydrological infrastructures.

Cotton yield estimation using very high-resolution digital images acquired on a low-cost small unmanned aerial vehicle

USDA-ARS?s Scientific Manuscript database

Yield estimation is a critical task in crop management. A number of traditional methods are available for crop yield estimation but they are costly, time-consuming and difficult to expand to a relatively large field. Remote sensing provides techniques to develop quick coverage over a field at any sc...

7 CFR 400.55 - Qualification for actual production history coverage program.

Code of Federal Regulations, 2013 CFR

2013-01-01

... APH yield is calculated from a database containing a minimum of four yields and will be updated each subsequent crop year. The database may contain a maximum of the 10 most recent crop years and may include... only occur in the database when there are less than four years of actual and/or assigned yields. (b...

7 CFR 400.55 - Qualification for actual production history coverage program.

Code of Federal Regulations, 2011 CFR

2011-01-01

... APH yield is calculated from a database containing a minimum of four yields and will be updated each subsequent crop year. The database may contain a maximum of the 10 most recent crop years and may include... only occur in the database when there are less than four years of actual and/or assigned yields. (b...

7 CFR 400.55 - Qualification for actual production history coverage program.

Code of Federal Regulations, 2010 CFR

2010-01-01

... APH yield is calculated from a database containing a minimum of four yields and will be updated each subsequent crop year. The database may contain a maximum of the 10 most recent crop years and may include... only occur in the database when there are less than four years of actual and/or assigned yields. (b...

7 CFR 400.55 - Qualification for actual production history coverage program.

Code of Federal Regulations, 2012 CFR

2012-01-01

... APH yield is calculated from a database containing a minimum of four yields and will be updated each subsequent crop year. The database may contain a maximum of the 10 most recent crop years and may include... only occur in the database when there are less than four years of actual and/or assigned yields. (b...

Genotypic diversity in the responses of yield and yield components to elevated ozone of diverse inbred and hybrid maize

USDA-ARS?s Scientific Manuscript database

Current tropospheric ozone concentrations ([O3]), an important air pollutant, are phytotoxic and detrimental to crop yield causing significant losses of ~14-26 billion in 4 of the world’s major crops. Until recent years, it was believed that agricultural and economically important C4 plants, such as...

Global warming threatens agricultural productivity in Africa and South Asia

NASA Astrophysics Data System (ADS)

Sultan, Benjamin

2012-12-01

The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC; Christensen et al 2007) has, with greater confidence than previous reports, warned the international community that the increase in anthropogenic greenhouse gases emissions will result in global climate change. One of the most direct and threatening impacts it may have on human societies is the potential consequences on global crop production. Indeed agriculture is considered as the most weather-dependent of all human activities (Hansen 2002) since climate is a primary determinant for agricultural productivity. The potential impact of climate change on crop productivity is an additional strain on the global food system which is already facing the difficult challenge of increasing food production to feed a projected 9 billion people by 2050 with changing consumption patterns and growing scarcity of water and land (Beddington 2010). In some regions such as Sub-Saharan Africa or South Asia that are already food insecure and where most of the population increase and economic development will take place, climate change could be the additional stress that pushes systems over the edge. A striking example, if needed, is the work from Collomb (1999) which estimates that by 2050 food needs will more than quintuple in Africa and more than double in Asia. Better knowledge of climate change impacts on crop productivity in those vulnerable regions is crucial to inform policies and to support adaptation strategies that may counteract the adverse effects. Although there is a growing literature on the impact of climate change on crop productivity in tropical regions, it is difficult to provide a consistent assessment of future yield changes because of large uncertainties in regional climate change projections, in the response of crops to environmental change (rainfall, temperature, CO2 concentration), in the coupling between climate models and crop productivity functions, and in the adaptation of agricultural systems to progressive climate change (Roudier et al 2011, Challinor et al 2007). These uncertainties result in a large spread of crop yield projections indicating a low confidence in future yield projections. A recent study by Knox et al (2012) is among the first to provide robust evidence of how climate change will impact productivity of major crops in Africa and South Asia. Using a meta-analysis, which is widely used in epidemiology and medicine and consists in comparing and combining results from different independent published studies, Knox et al (2012) show a consistent yield loss by the 2050s of major crops (wheat, maize, sorghum and millet) in both regions. This systematic review and meta-analysis of data in 52 original publications from an initial screen of 1144 studies nicely extend previous works by Müller et al (2011) and Roudier et al (2011), confirming the threat of negative climate change impacts in Africa but also in South Asia. Knox et al (2012) estimate that mean yield change for all crops is -8% by the 2050s with strong variations among crops and regions. For instance evidence of yield reduction up to -40% are detected for some regions of Africa while no mean yield change is detected for rice in India. Variations in crop yield projections decrease when considering a large number of climate models confirming the relevance of the expanded use of multi-model ensembles of projections of future climate change adopted in the IPCC Fourth Assessment Report. Conversely, variations in crop yield projections increase with the crop model complexity especially when using process-based crop models over statistical models. Such differences in crop yield variations may be attributed either to the structural differences between crop model approaches or to the spatial scale differences; biophysical crop models operating at finer spatial scales and thus reproducing the higher variability of impacts at these scales. Such robust evidence of future yield change in Africa and South Asia can be surprising in regards to the diverging projections in a warmer climate of summer monsoon rainfall, the primary driver for rainfed crop productivity in the region, especially in West Africa where some studies make projections of wetter conditions and some predict more frequent droughts (Druyan 2011). This is because of the adverse role of higher temperatures in shortening the crop cycle duration and increasing evapotranspiration demand and thus reducing crop yields, irrespective of rainfall changes (Berg et al 2012, Roudier et al 2011, Schlenker and Lobell 2010). Potential wetter conditions or elevated CO2 concentrations hardly counteract the adverse effect of higher temperatures. Although such systematic reviews and meta-analyses conducted by Knox et al (2012), Müller et al (2011) or Roudier et al (2011) can provide important insights about sign, magnitude and uncertainty of climate change impacts, direct comparison among studies suffers from inevitable limitations. In particular the diversity of the studies selected for the meta-analysis, encompassing a range of different countries, scales, crops and methods (climate models and scenarios, crop models, downscaling technique), makes it difficult to aggregate crop yield projections to provide a consistent and precise impact assessment. A rigorous multi-ensembles approach, with varying climate models, emissions scenarios, crop models, and downscaling techniques, as recommended by Challinor et al (2007), would enable a move towards a more complete sampling of uncertainty in crop yield projections. In that sense, coordinated modeling experiments such as the ones conducted throughout the Agricultural Model Intercomparison and Improvement Project (AgMIP; www.agmip.org/) are likely to improve substantially the characterization of the threat of crop yield losses and food insecurity due to climate change. In spite of the threat of crop yield losses in a warmer climate, it is important to keep in mind, as discussed by Berg et al (2012), that developing countries in the tropics have the potential to more than offset such adverse impacts by implementing more intensive agricultural practices and adapting agriculture to climate and environmental change. Indeed Africa and in a lesser extend South Asia are among the only regions of the world where there is an untapped potential for raising agricultural productivity since poor soil fertility and low input levels, combined with extensive agricultural practices, contribute to a large gap between actual and potential yields (Licker et al 2010). References Beddington J 2010 Food security: contributions from science to a new and greener revolution Phil. Trans. R. Soc. B 365 61-71 Berg A, de Noblet-Ducoudré N, Sultan B, Lengaigne N and Guimberteau M 2012 Projections of climate change impacts on potential crop productivity over tropical regions Agric. For. Meteorol. at press (doi:10.1016/j.agrformet.2011.12.003) Challinor A, Wheeler T, Garforth C, Craufurd P and Kassam A 2007 Assessing the vulnerability of food crop systems in Africa to climate change Clim. Change 83 381-99 Christensen J H et al 2007 Regional climate projections Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ed S Solomon, D Qin, M Manning, Z Chen, M Marquis, K B Averyt, M Tignor and H L Miller (Cambridge: Cambridge University Press) Collomb P 1999 A narrow road to food security from now to 2050 FAO Economica (Paris: FAO) Druyan L M 2011 Studies of 21st-century precipitation trends over West Africa Int. J. Climatol. 31 1415-572 Hansen J W 2002 Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges Agric. Syst. 74 309-30 Knox J, Hess T, Daccache A and Wheeler T 2012 Climate change impacts on crop productivity in Africa and South Asia Environ. Res. Lett. 7 034032 Licker R, Johnston M, Foley J A, Barford C, Kucharik C J, Monfreda C and Ramankutty N 2010 Mind the gap: how do climate and agricultural management explain the 'yield gap' of croplands around the world? Glob. Ecol. Biogeogr. 19 769-82 Müller C, Cramer W, Hare W L and Lotze-Campen H 2011 Climate change risks for African agriculture Proc. Natl Acad. Sci. USA 108 4313-5 Roudier P, Sultan S, Quirion P and Berg A 2011 The impact of future climate change on West African crop yields: what does the recent literature say? Glob. Environ. Change 21 1073-83 Schlenker W and Lobell D 2010 Robust negative impacts of climate change on African agriculture Environ. Res. Lett. 5 014010

Plausible rice yield losses under future climate warming.

PubMed

Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Huang, Yao; Ciais, Philippe; Elliott, Joshua; Huang, Mengtian; Janssens, Ivan A; Li, Tao; Lian, Xu; Liu, Yongwen; Müller, Christoph; Peng, Shushi; Wang, Tao; Zeng, Zhenzhong; Peñuelas, Josep

2016-12-19

Rice is the staple food for more than 50% of the world's population 1-3 . Reliable prediction of changes in rice yield is thus central for maintaining global food security. This is an extraordinary challenge. Here, we compare the sensitivity of rice yield to temperature increase derived from field warming experiments and three modelling approaches: statistical models, local crop models and global gridded crop models. Field warming experiments produce a substantial rice yield loss under warming, with an average temperature sensitivity of -5.2 ± 1.4% K -1 . Local crop models give a similar sensitivity (-6.3 ± 0.4% K -1 ), but statistical and global gridded crop models both suggest less negative impacts of warming on yields (-0.8 ± 0.3% and -2.4 ± 3.7% K -1 , respectively). Using data from field warming experiments, we further propose a conditional probability approach to constrain the large range of global gridded crop model results for the future yield changes in response to warming by the end of the century (from -1.3% to -9.3% K -1 ). The constraint implies a more negative response to warming (-8.3 ± 1.4% K -1 ) and reduces the spread of the model ensemble by 33%. This yield reduction exceeds that estimated by the International Food Policy Research Institute assessment (-4.2 to -6.4% K -1 ) (ref. 4). Our study suggests that without CO 2 fertilization, effective adaptation and genetic improvement, severe rice yield losses are plausible under intensive climate warming scenarios.

Balancing Immunity and Yield in Crop Plants.

PubMed

Ning, Yuese; Liu, Wende; Wang, Guo-Liang

2017-12-01

Crop diseases cause enormous yield losses and threaten global food[ED1] security. The use of highly resistant cultivars can effectively control plant diseases, but in crops, genetic immunity to disease often comes with an unintended reduction in growth and yield. Here, we review recent advances in understanding how nucleotide-binding domain, leucine-rich repeat (NLR) receptors and cell wall-associated kinase (WAK) proteins function in balancing immunity and yield. We also discuss the role of plant hormones and transcription factors in regulating the trade-offs between plant growth and immunity. Finally, we describe how a novel mechanism of translational control of defense proteins can enhance immunity without the reduction in fitness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Soil Moisture Anomaly as Predictor of Crop Yield Deviation in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Thober, Stephan; Schwarze, Reimund; Meyer, Volker; Samaniego, Luis

2016-04-01

Natural hazards, such as droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany (COPA-COGECA 2003). Predicting crop yields allows to economize the mitigation of risks of weather extremes. Economic approaches for quantifying agricultural impacts of natural hazards mainly rely on temperature and related concepts. For instance extreme heat over the growing season is considered as best predictor of corn yield (Auffhammer and Schlenker 2014). However, those measures are only able to provide a proxy for the available water content in the root zone that ultimately determines plant growth and eventually crop yield. The aim of this paper is to analyse whether soil moisture has a causal effect on crop yield that can be exploited in improving adaptation measures. For this purpose, reduced form fixed effect panel models are developed with yield as dependent variable for both winter wheat and silo maize crops. The explanatory variables used are soil moisture anomalies, precipitation and temperature. The latter two are included to estimate the current state of the water balance. On the contrary, soil moisture provides an integrated signal over several months. It is also the primary source of water supply for plant growth. For each crop a single model is estimated for every month within the growing period to study the variation of the effects over time. Yield data is available for Germany as a whole on the level of administrative districts from 1990 to 2010. Station data by the German Weather Service are obtained for precipitation and temperature and are aggregated to the same spatial units. Simulated soil moisture computed by the mesoscale Hydrologic Model (mHM, www.ufz.de/mhm) is transformed into Soil Moisture Index (SMI), which represents the monthly soil water quantile and hence accounts directly for the water content available to plants. The results indicate that wet and dry soil moisture anomalies have a causal effect on crop yields. However, the effects vary in magnitude and direction for each crop depending on the month. For instance dry soil moisture anomalies in July, August and September reduce silo maize yield more than ten percent with respect to average conditions. Extreme wetness, however, increases silo maize yield in the same time period. A negative effect is observed for winter wheat during this period for both wet and dry anomalies. The reduction due to dry anomalies is smaller for winter wheat than for silo maize. This study shows that the impact of soil moisture anomalies varies dependent on months and crops. These evolving patterns provide new insights to improve adaptation measures for extreme soil moisture conditions. References Auffhammer, M., and W. Schlenker. 2014. "Empirical studies on agricultural impacts and adaptation." Energy Economics 46:555-561. COPA-COGECA. 2003. "Assessment of the impact of the heat wave and drought of the summer 2003 on agriculture and forestry." In Committee of Agricultural Organisations in the European Union General Committee for Agricultural Cooperation in the European Union, Brussels. p. 15.

Soil Moisture as an Estimator for Crop Yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan

2015-04-01

Annual crop yield depends on various factors such as soil properties, management decisions, and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields allows to mitigate negative effects of weather extremes which are assumed to occur more often in the future due to climate change. A standard approach in economics is to predict the impact of climate change on agriculture as a function of temperature and precipitation. This approach has been developed further using concepts like growing degree days. Other econometric models use nonlinear functions of heat or vapor pressure deficit. However, none of these approaches uses soil moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to explain stress on plant growth than estimations based on precipitation and temperature. This is the case because the latter variables do not explicitly account for the available water content in the root zone, which is the primary source of water supply for plant growth. In this study, a reduced form panel approach is applied to estimate a multivariate econometric production function for the years 1999 to 2010. Annual crop yield data of various crops on the administrative district level serve as depending variables. The explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4 km2 grid resolution covering entire Germany. A reduced model approach is suitable because the SMI is the result of a stochastic weather process and therefore can be considered exogenous. For the ease of interpretation a linear functionality is preferred. Meteorological, phenological, geological, agronomic, and socio-economic variables are also considered to extend the model in order to reveal the proper causal relation. First results show that dry as well as wet extremes of SMI have a negative impact on crop yield for winter wheat. This indicates that soil moisture has at least a limiting affect on crop production.

Using a time-series statistical framework to quantify trends and abrupt change in US corn, soybean, and wheat yields from 1970-2016

NASA Astrophysics Data System (ADS)

Zhang, J.; Ives, A. R.; Turner, M. G.; Kucharik, C. J.

2017-12-01

Previous studies have identified global agricultural regions where "stagnation" of long-term crop yield increases has occurred. These studies have used a variety of simple statistical methods that often ignore important aspects of time series regression modeling. These methods can lead to differing and contradictory results, which creates uncertainty regarding food security given rapid global population growth. Here, we present a new statistical framework incorporating time series-based algorithms into standard regression models to quantify spatiotemporal yield trends of US maize, soybean, and winter wheat from 1970-2016. Our primary goal was to quantify spatial differences in yield trends for these three crops using USDA county level data. This information was used to identify regions experiencing the largest changes in the rate of yield increases over time, and to determine whether abrupt shifts in the rate of yield increases have occurred. Although crop yields continue to increase in most maize-, soybean-, and winter wheat-growing areas, yield increases have stagnated in some key agricultural regions during the most recent 15 to 16 years: some maize-growing areas, except for the northern Great Plains, have shown a significant trend towards smaller annual yield increases for maize; soybean has maintained an consistent long-term yield gains in the Northern Great Plains, the Midwest, and southeast US, but has experienced a shift to smaller annual increases in other regions; winter wheat maintained a moderate annual increase in eastern South Dakota and eastern US locations, but showed a decline in the magnitude of annual increases across the central Great Plains and western US regions. Our results suggest that there were abrupt shifts in the rate of annual yield increases in a variety of US regions among the three crops. The framework presented here can be broadly applied to additional yield trend analyses for different crops and regions of the Earth.

Nutritional composition and in vitro digestibility of grass and legume winter (cover) crops.

PubMed

Brown, A N; Ferreira, G; Teets, C L; Thomason, W E; Teutsch, C D

2018-03-01

In dairy farming systems, growing winter crops for forage is frequently limited to annual grasses grown in monoculture. The objectives of this study were to determine how cropping grasses alone or in mixtures with legumes affects the yield, nutritional composition, and in vitro digestibility of fresh and ensiled winter crops and the yield, nutritional composition, and in vitro digestibility of the subsequent summer crops. Experimental plots were planted with 15 different winter crops at 3 locations in Virginia. At each site, 4 plots of each treatment were planted in a randomized complete block design. The 15 treatments included 5 winter annual grasses [barley (BA), ryegrass (RG), rye (RY), triticale (TR), and wheat (WT)] in monoculture [i.e., no legumes (NO)] or with 1 of 2 winter annual legumes [crimson clover (CC) and hairy vetch (HV)]. After harvesting the winter crops, corn and forage sorghum were planted within the same plots perpendicular to the winter crop plantings. The nutritional composition and the in vitro digestibility of winter and summer crops were determined for fresh and ensiled samples. Growing grasses in mixtures with CC increased forage dry matter (DM) yield (2.84 Mg/ha), but the yield of mixtures with HV (2.47 Mg/ha) was similar to that of grasses grown in monoculture (2.40 Mg/ha). Growing grasses in mixtures with legumes increased the crude protein concentration of the fresh forage from 13.0% to 15.5% for CC and to 17.3% for HV. For neutral detergent fiber (NDF) concentrations, the interaction between grasses and legumes was significant for both fresh and ensiled forages. Growing BA, RY, and TR in mixtures with legumes decreased NDF concentrations, whereas growing RG and WT with legumes did not affect the NDF concentrations of either the fresh or the ensiled forages. Growing grasses in mixtures with legumes decreased the concentration of sugars of fresh forages relative to grasses grown in monoculture. Primarily, this decrease can be attributed to low concentrations of sugars of mixtures with HV (10.5%). Growing grasses in mixtures with legumes reduced the fiber digestibility of both winter crops (75.7% to 72.8% NDF). Growing grasses in mixtures with legumes did not affect estimated DM yield, nutritional composition, or digestibility of the succeeding summer crops. In conclusion, growing grasses in mixtures with legumes as winter forage crops can increase forage estimated DM yields and its nutritional quality in dairy farming sytems. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Regional simulation of soil nitrogen dynamics and balance in Swiss cropping systems

NASA Astrophysics Data System (ADS)

Lee, Juhwan; Necpalova, Magdalena; Six, Johan

2017-04-01

We evaluated the regional-scale potential of various crop and soil management practices to reduce the dependency of crop N demand on external N inputs and N losses to the environment. The estimates of soil N balance were simulated and compared under alternative and conventional crop production across all Swiss cropland. Alternative practices were all combinations of organic fertilization, reduced tillage and winter cover cropping. Using the DayCent model, we simulated changes in crop N yields as well as the contribution of inputs and outputs to soil N balance by alternative practices, which was complemented with corresponding measurements from available long-term field experiments and site-level simulations. In addition, the effects of reducing (between 0% and 80% of recommended application rates) or increasing chemical fertilizer input rates (between 120% and 300% of recommended application rates) on system-level N dynamics were also simulated. Modeled yields at recommended N rates were only 37-87% of the maximum yield potential across common Swiss crops, and crop productivity were sensitive to the level of external N inputs, except for grass-clover mixture, soybean and peas. Overall, differences in soil N input and output decreased or increased proportionally with changing the amount of N input only from the recommended rate. As a result, there was no additional difference in soil N balance in response to N application rates. Nitrate leaching accounted for 40-81% of total N output differences, while up to 47% of total N output occurred through harvest and straw removal. Regardless of crops, yield potential became insensitive to high N rates. Differences in N2O and N2 emissions slightly increased with increasing N inputs, in which each gas was only responsible for about 1% of changes in total N output. Overall, there was a positive soil N balance under alternative practices. Particularly, considerable improvement in soil N balance was expected when slowly decomposed organic fertilizer was used in combination with cover cropping and/or reduced tillage. However, the increase in soil N balance was due to the decreases in harvested yield and nitrate leaching under these organic cropping based practices. Instead, the use of fast decomposed organic matter with cover cropping could be considered to avoid any yield penalty while decreasing nitrate leaching, hence reducing total N output. In order to effectively reduce N losses from soils, approaches to utilize multiple alternative options should be taken into account at the regional scale.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

DOE PAGES

Calvin, Kate; Fisher-Vanden, Karen

2017-10-27

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparisonmore » between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.« less

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

NASA Astrophysics Data System (ADS)

Calvin, Kate; Fisher-Vanden, Karen

2017-11-01

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between -12% and +15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

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

Calvin, Kate; Fisher-Vanden, Karen

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparisonmore » between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.« less

An image based method for crop yield prediction using remotely sensed and crop canopy data: the case of Paphos district, western Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.

2014-08-01

Remote sensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remote sensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future.

Linking crop yield anomalies to large-scale atmospheric circulation in Europe.

PubMed

Ceglar, Andrej; Turco, Marco; Toreti, Andrea; Doblas-Reyes, Francisco J

2017-06-15

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

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.

Weed interference with peppermint (Mentha x piperita L.) and spearmint (Mentha spicata L.) crops under different herbicide treatments: effects on biomass and essential oil yield.

PubMed

Karkanis, Anestis; Lykas, Christos; Liava, Vasiliki; Bezou, Anna; Petropoulos, Spyridon; Tsiropoulos, Nikolaos

2018-01-01

'Minor crops' such as spearmint and peppermint are high added value crops, despite the fact that their production area is comparably small worldwide. The main limiting factor in mint commercial cultivation is weed competition. Thus, field experiments were carried out to evaluate the effects of weed interference on growth, biomass and essential oil yield in peppermint and spearmint under different herbicide treatments. The application of pendimethalin and oxyfluorfen provided better control of annual weeds resulting in higher crop yield. Additionally, when treated with herbicides both crops were more competitive against annual weeds in the second year than in the first year. All pre-emergence herbicides increased biomass yield, since pendimethalin, linuron and oxyfluorfen reduced the density of annual weeds by 71-92%, 63-74% and 86-95%, respectively. Weed interference and herbicide application had no effect on essential oil content; however, a relatively strong impact on essential oil production per cultivated area unit was observed, mainly due to the adverse effect of weed interference on plant growth. Considering that pendimethalin and oxyfluorfen were effective against annual weeds in both spearmint and peppermint crops, these herbicides should be included in integrated weed management systems for better weed management in mint crops. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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.

The Technology Roadmap for Plant/Crop-Based Renewable Resources 2020

DTIC Science & Technology

2005-01-01

field. Poultry Swine Cattle Feed for Livestock Export (grain) Export (food) Food and Industrial Ethanol High Fructose Corn Syrup In a similar manner...terrestrial nutrients. The United States has significant resources in good soils, extensive natural water distribution, and a technology base that allows...yield to provide a 2-fold (vs 98) increase in carbon output per unit input. Develop systems approaches to minimize impact on land, air, and water

Using LANDSAT to provide potato production estimates to Columbia Basin farmers and processors

NASA Technical Reports Server (NTRS)

1991-01-01

The estimation of potato yields in the Columbia basin is described. The fundamental objective is to provide CROPIX with working models of potato production. A two-pronged approach was used to yield estimation: (1) using simulation models, and (2) using purely empirical models. The simulation modeling approach used satellite observations to determine certain key dates in the development of the crop for each field identified as potatoes. In particular, these include planting dates, emergence dates, and harvest dates. These critical dates are fed into simulation models of crop growth and development to derive yield forecasts. Purely empirical models were developed to relate yield to some spectrally derived measure of crop development. Two empirical approaches are presented: one relates tuber yield to estimates of cumulative intercepted solar radiation, the other relates tuber yield to the integral under GVI (Global Vegetation Index) curve.

Winter cover crop effect on corn seedling pathogens

USDA-ARS?s Scientific Manuscript database

Cover crops are an excellent management tool to improve the sustainability of agriculture. Winter rye cover crops have been used successfully in Iowa corn-soybean rotations. Unfortunately, winter rye cover crops occasionally reduce yields of the following corn crop. We hypothesize that one potential...

Evaluation of weather-based rice yield models in India.

PubMed

Sudharsan, D; Adinarayana, J; Reddy, D Raji; Sreenivas, G; Ninomiya, S; Hirafuji, M; Kiura, T; Tanaka, K; Desai, U B; Merchant, S N

2013-01-01

The objective of this study was to compare two different rice simulation models--standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])--with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.