Generation of multi annual land use and crop rotation data for regional agro-ecosystem modeling

NASA Astrophysics Data System (ADS)

Waldhoff, G.; Lussem, U.; Sulis, M.; Bareth, G.

2017-12-01

For agro-ecosystem modeling on a regional scale with systems like the Community Land Model (CLM), detailed crop type and crop rotation information on the parcel-level is of key importance. Only with this, accurate assessments of the fluxes associated with the succession of crops and their management are possible. However, sophisticated agro-ecosystem modeling for large regions is only feasible at grid resolutions, which are much coarser than the spatial resolution of modern land use maps (usually ca. 30 m). As a result, much of the original information content of the maps has to be dismissed during resampling. Here we present our mapping approach for the Rur catchment (located in the west of Germany), which was developed to address these demands and issues. We integrated remote sensing and geographic information system (GIS) methods to classify multi temporal images of (e.g.) Landsat, RapidEye and Sentinel-2 to generate annual crop maps for the years 2008-2017 at 15 m spatial resolution (accuracy always ca. 90 %). A key aspect of our method is the consideration of crop phenology for the data selection and the analysis. In a GIS, the annul crop maps were integrated to a crop sequence dataset from which the major crop rotations were derived (based on the 10-years). To retain the multi annual crop succession and crop area information at coarser grid resolutions, cell-based land use fractions, including other land use classes were calculated for each year and for various target cell sizes (1-32 arc seconds). The resulting datasets contain the contribution (in percent) of every land use class to each cell. Our results show that parcels with the major crop types can be differentiated with a high accuracy and on an annual basis. The analysis of the crop sequence data revealed a very large number of different crop rotations, but only relatively few crop rotations cover larger areas. This strong diversity emphasizes the importance of information on crop rotations to reduce uncertainties in agro-ecosystem modeling. Through the combination of the multi annual land use fractions, the resulting datasets additionally inform about land use changes and trends within the coarser grid cells. We see this as a major advantage, because we are able to maintain much more precise land use information when a coarser cell size is used.

Integrating Satellite and Surface Sensor Networks for Irrigation Management Applications in California

NASA Astrophysics Data System (ADS)

Melton, F. S.; Johnson, L.; Post, K. M.; Guzman, A.; Zaragoza, I.; Spellenberg, R.; Rosevelt, C.; Michaelis, A.; Nemani, R. R.; Cahn, M.; Frame, K.; Temesgen, B.; Eching, S.

2016-12-01

Satellite mapping of evapotranspiration (ET) from irrigated agricultural lands can provide agricultural producers and water managers with information that can be used to optimize agricultural water use, especially in regions with limited water supplies. The timely delivery of information on agricultural crop water requirements has the potential to make irrigation scheduling more practical, convenient, and accurate. We present a system for irrigation scheduling and management support in California and describe lessons learned from the development and implementation of the system. The Satellite Irrigation Management Support (SIMS) framework integrates satellite data with information from agricultural weather networks to map crop canopy development, basal crop coefficients (Kcb), and basal crop evapotranspiration (ETcb) at the scale of individual fields. Information is distributed to agricultural producers and water managers via a web-based irrigation management decision support system and web data services. SIMS also provides an application programming interface (API) that facilitates integration with other irrigation decision support tools, estimation of total crop evapotranspiration (ETc) and calculation of on-farm water use efficiency metrics. Accuracy assessments conducted in commercial fields for more than a dozen crop types to date have shown that SIMS seasonal ETcb estimates are within 10% mean absolute error (MAE) for well-watered crops and within 15% across all crop types studied, and closely track daily ETc and running totals of ETc measured in each field. Use of a soil water balance model to correct for soil evaporation and crop water stress reduces this error to less than 8% MAE across all crop types studied to date relative to field measurements of ETc. Results from irrigation trials conducted by the project for four vegetable crops have also demonstrated the potential for use of ET-based irrigation management strategies to reduce total applied water by 20-40% relative to grower standard practices while maintaining crop yields and quality.

The Effect of Five Biomass Cropping Systems on Soil-Saturated Hydraulic Conductivity Across a Topographic Gradient

Treesearch

Usman Anwar; Lisa A. Schulte; Matthew Helmers; Randall K. Kolka

2017-01-01

Understanding the environmental impact of bioenergy crops is needed to inform bioenergy policy development. We determined the effects of five biomass cropping systems—continuous maize (Zea mays), soybean (Glycine max)-triticale (Triticosecale ×)/soybean-maize, maize-switchgrass (Panicum virgatum...

Center pivot mounted infrared sensors: Retrieval of ET and interface with satellite systems

USDA-ARS?s Scientific Manuscript database

Infrared sensors mounted aboard cener pivot irrigation systems can remotely sense the surface temperatures of the crops and soils, which provides important information on crop water status. This can be used for irrigation management and irrigation automation, which can increase crop water productivi...

Soil phosphatase and urease activities impacted by cropping systems and water management

USDA-ARS?s Scientific Manuscript database

Soil enzymes can play an important role in nutrient availability to plants. Consequently, soil enzyme measurements can provide useful information on soil fertility for crop production. We examined the impact of cropping system and water management on phosphatase, urease, and microbial biomass C in s...

Agroforestry Systems in Zimbabwe: Promoting Trees in Agriculture.

ERIC Educational Resources Information Center

Vukasin, Helen L., Ed.

Agroforestry has been defined as a sustainable crop management system which combines the production of forest crops with field crops. In June, 1987, an agroforestry workshop took place in Nyanga, Manicaland, Zimbabwe. This document was prepared to share the information presented at this workshop with other non-government organizations around the…

Timely precipitation drives cover crop outcomes

USDA-ARS?s Scientific Manuscript database

Cover crops can expand ecosystem services, though sound management recommendations for their use within semi-arid cropping systems is currently constrained by a lack of information. This study was conducted to determine agroecosystem responses to late-summer seeded cover crops under no-till managem...

WEBGIS based CropWatch online agriculture monitoring system

NASA Astrophysics Data System (ADS)

Zhang, X.; Wu, B.; Zeng, H.; Zhang, M.; Yan, N.

2015-12-01

CropWatch, which was developed by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), has achieved breakthrough results in the integration of methods, independence of the assessments and support to emergency response by periodically releasing global agricultural information. Taking advantages of the multi-source remote sensing data and the openness of the data sharing policies, CropWatch group reported their monitoring results by publishing four bulletins one year. In order to better analysis and generate the bulletin and provide an alternative way to access agricultural monitoring indicators and results in CropWatch, The CropWatch online system based on the WEBGIS techniques has been developed. Figure 1 shows the CropWatch online system structure and the system UI in Clustering mode. Data visualization is sorted into three different modes: Vector mode, Raster mode and Clustering mode. Vector mode provides the statistic value for all the indicators over each monitoring units which allows users to compare current situation with historical values (average, maximum, etc.). Users can compare the profiles of each indicator over the current growing season with the historical data in a chart by selecting the region of interest (ROI). Raster mode provides pixel based anomaly of CropWatch indicators globally. In this mode, users are able to zoom in to the regions where the notable anomaly was identified from statistic values in vector mode. Data from remote sensing image series at high temporal and low spatial resolution provide key information in agriculture monitoring. Clustering mode provides integrated information on different classes in maps, the corresponding profiles for each class and the percentage of area of each class to the total area of all classes. The time series data is categorized into limited types by the ISODATA algorithm. For each clustering type, pixels on the map, profiles, and percentage legend are all linked together. All the three visualization methods are applied to four scales including 65 monitoring and reporting units (MRUs), 7 major production zones (MPZs), 173 countries and sub-countries for 9 large countries. Agro-Climatic information, Agronomic information and indicators related with crop area, crop yield and crop production are provided.

Crop diversification and livelihoods of smallholder farmers in Zimbabwe: adaptive management for environmental change.

PubMed

Makate, Clifton; Wang, Rongchang; Makate, Marshall; Mango, Nelson

2016-01-01

This paper demonstrates how crop diversification impacts on two outcomes of climate smart agriculture; increased productivity (legume and cereal crop productivity) and enhanced resilience (household income, food security, and nutrition) in rural Zimbabwe. Using data from over 500 smallholder farmers, we jointly estimate crop diversification and each of the outcome variables within a conditional (recursive) mixed process framework that corrects for selectivity bias arising due to the voluntary nature of crop diversification. We find that crop diversification depends on the land size, farming experience, asset wealth, location, access to agricultural extension services, information on output prices, low transportation costs and general information access. Our results also indicate that an increase in the rate of adoption improves crop productivity, income, food security and nutrition at household level. Overall, our results are indicative of the importance of crop diversification as a viable climate smart agriculture practice that significantly enhances crop productivity and consequently resilience in rural smallholder farming systems. We, therefore, recommend wider adoption of diversified cropping systems notably those currently less diversified for greater adaptation to the ever-changing climate.

Economic Benefits of Improved Information on Worldwide Crop Production: An Optimal Decision Model of Production and Distribution with Application to Wheat, Corn, and Soybeans

NASA Technical Reports Server (NTRS)

Andrews, J.

1977-01-01

An optimal decision model of crop production, trade, and storage was developed for use in estimating the economic consequences of improved forecasts and estimates of worldwide crop production. The model extends earlier distribution benefits models to include production effects as well. Application to improved information systems meeting the goals set in the large area crop inventory experiment (LACIE) indicates annual benefits to the United States of $200 to $250 million for wheat, $50 to $100 million for corn, and $6 to $11 million for soybeans, using conservative assumptions on expected LANDSAT system performance.

Operationalizing crop monitoring system for informed decision making related to food security in Nepal

NASA Astrophysics Data System (ADS)

Qamer, F. M.; Shah, S. N. Pd.; Murthy, M. S. R.; Baidar, T.; Dhonju, K.; Hari, B. G.

2014-11-01

In Nepal, two thirds of the total population depend on agriculture for their livelihoods and more than one third of Gross Domestic Product (GDP) comes from the agriculture sector. However, effective agriculture production across the country remains a serious challenge due to various factors, such as a high degree of spatial and temporal climate variability, irrigated and rain-fed agriculture systems, farmers' fragile social and economic fabric, and unique mountain practices. ICIMOD through SERVIR-Himalaya initiative with collaboration of Ministry of Agricultural Development (MoAD) is working on developing a comprehensive crop monitoring system which aims to provide timely information on crop growth and drought development conditions. This system analyzes historical climate and crop conditions patterns and compares this data with the current growing season to provide timely assessment of crop growth. Using remote sensing data for vegetation indices, temperature and rainfall, the system generated anomaly maps are inferred to predict the increase or shortfall in production. Comparisons can be made both spatially and in graphs and figures at district and Village Developmental Committee (VDC) levels. Timely information on possible anomaly in crop production is later used by the institutions like Ministry of Agricultural Development, Nepal and World Food Programme, Nepal to trigger appropriate management response. Future potential includes integrating data on agricultural inputs, socioeconomics, demographics, and transportation to holistically assess food security in the region served by SERVIR-Himalaya.

Growth of tropical legume cover crops as influenced by nitrogen fertilization and Rhizobia

USDA-ARS?s Scientific Manuscript database

Tropical legume cover crops are important components in cropping systems due to their role in improving soil quality. Information is limited on the influence of nitrogen (N) fertilization on growth of tropical legume cover crops grown on Oxisols. A greenhouse experiment was conducted to evaluate the...

Short-term soil responses to late-seeded cover crops in a semi-arid environment

USDA-ARS?s Scientific Manuscript database

Cover crops can expand ecosystem services, though sound management recommendations for their use within semi-arid cropping systems is currently constrained by a lack of information. This study was conducted to determine agroecosystem responses to late-summer seeded cover crops under no-till managem...

Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System

USGS Publications Warehouse

Ward, M.H.; Nuckols, J.R.; Weigel, S. J.; Cantor, K.P.; Miller, Roger S.

2000-01-01

Pesticides used in agriculture may cause adverse health effects among the population living near agricultural areas. However, identifying the populations most likely to be exposed is difficult. We conducted a feasibility study to determine whether satellite imagery could be used to reconstruct historical crop patterns. We used historical Farm Service Agency records as a source of ground reference data to classify a late summer 1984 satellite image into crop species in a three-county area in south central Nebraska. Residences from a population-based epidemiologic study of non-Hodgkin lymphoma were located on the crop maps using a geographic information system (GIS). Corn, soybeans, sorghum, and alfalfa were the major crops grown in the study area. Eighty-five percent of residences could be located, and of these 22% had one of the four major crops within 500 m of the residence, an intermediate distance for the range of drift effects from pesticides applied in agriculture. We determined the proximity of residences to specific crop species and calculated crop-specific probabilities of pesticide use based on available data. This feasibility study demonstrated that remote sensing data and historical records on crop location can be used to create historical crop maps. The crop pesticides that were likely to have been applied can be estimated when information about crop-specific pesticide use is available. Using a GIS, zones of potential exposure to agricultural pesticides and proximity measures can be determined for residences in a study.

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

NASA Astrophysics Data System (ADS)

di, L.; Yang, Z.

2009-12-01

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

Synthetic aperture radar for a crop information system: A multipolarization and multitemporal approach

NASA Astrophysics Data System (ADS)

Ban, Yifang

Acquisition of timely information is a critical requirement for successful management of an agricultural monitoring system. Crop identification and crop-area estimation can be done fairly successfully using satellite sensors operating in the visible and near-infrared (VIR) regions of the spectrum. However, data collection can be unreliable due to problems of cloud cover at critical stages of the growing season. The all-weather capability of synthetic aperture radar (SAR) imagery acquired from satellites provides data over large areas whenever crop information is required. At the same time, SAR is sensitive to surface roughness and should be able to provide surface information such as tillage-system characteristics. With the launch of ERS-1, the first long-duration SAR system became available. The analysis of airborne multipolarization SAR data, multitemporal ERS-1 SAR data, and their combinations with VIR data, is necessary for the development of image-analysis methodologies that can be applied to RADARSAT data for extracting agricultural crop information. The overall objective of this research is to evaluate multipolarization airborne SAR data, multitemporal ERS-1 SAR data, and combinations of ERS-1 SAR and satellite VIR data for crop classification using non-conventional algorithms. The study area is situated in Norwich Township, an agricultural area in Oxford County, southern Ontario, Canada. It has been selected as one of the few representative agricultural 'supersites' across Canada at which the relationships between radar data and agriculture are being studied. The major field crops are corn, soybeans, winter wheat, oats, barley, alfalfa, hay, and pasture. Using airborne C-HH and C-HV SAR data, it was found that approaches using contextual information, texture information and per-field classification for improving agricultural crop classification proved to be effective, especially the per-field classification method. Results show that three of the four best per-field classification accuracies (\\ K=0.91) are achieved using combinations of C-HH and C-VV SAR data. This confirms the strong potential of multipolarization data for crop classification. The synergistic effects of multitemporal ERS-1 SAR and Landsat TM data are evaluated for crop classification using an artificial neural network (ANN) approach. The results show that the per-field approach using a feed-forward ANN significantly improves the overall classification accuracy of both single-date and multitemporal SAR data. Using the combination of TM3,4,5 and Aug. 5 SAR data, the best per-field ANN classification of 96.8% was achieved. It represents an 8.5% improvement over a single TM3,4,5 classification alone. Using multitemporal ERS-1 SAR data acquired during the 1992 and 1993 growing seasons, the radar backscatter characteristics of crops and their underlying soils are analyzed. The SAR temporal backscatter profiles were generated for each crop type and the earliest times of the year for differentiation of individual crop types were determined. Orbital (incidence-angle) effects were also observed on all crops. The average difference between the two orbits was about 3 dB. Thus attention should be given to the local incidence-angle effects when using ERS-1 SAR data, especially when comparing fields from different scenes or different areas within the same scene. Finally, early- and mid-season multitemporal SAR data for crop classification using sequential-masking techniques are evaluated, based on the temporal backscatter profiles. It was found that all crops studied could be identified by July 21.

A plan for application system verification tests: The value of improved meteorological information, volume 1. [economic consequences of improved meteorological information

NASA Technical Reports Server (NTRS)

1976-01-01

The framework within which the Applications Systems Verification Tests (ASVTs) are performed and the economic consequences of improved meteorological information demonstrated is described. This framework considers the impact of improved information on decision processes, the data needs to demonstrate the economic impact of the improved information, the data availability, the methodology for determining and analyzing the collected data and demonstrating the economic impact of the improved information, and the possible methods of data collection. Three ASVTs are considered and program outlines and plans are developed for performing experiments to demonstrate the economic consequences of improved meteorological information. The ASVTs are concerned with the citrus crop in Florida, the cotton crop in Mississippi and a group of diverse crops in Oregon. The program outlines and plans include schedules, manpower estimates and funding requirements.

Farm-scale costs and returns for second generation bioenergy cropping systems in the US Corn Belt

NASA Astrophysics Data System (ADS)

Manatt, Robert K.; Hallam, Arne; Schulte, Lisa A.; Heaton, Emily A.; Gunther, Theo; Hall, Richard B.; Moore, Ken J.

2013-09-01

While grain crops are meeting much of the initial need for biofuels in the US, cellulosic or second generation (2G) materials are mandated to provide a growing portion of biofuel feedstocks. We sought to inform development of a 2G crop portfolio by assessing the profitability of novel cropping systems that potentially mitigate the negative effects of grain-based biofuel crops on food supply and environmental quality. We analyzed farm-gate costs and returns of five systems from an ongoing experiment in central Iowa, USA. The continuous corn cropping system was most profitable under current market conditions, followed by a corn-soybean rotation that incorporated triticale as a 2G cover crop every third year, and a corn-switchgrass system. A novel triticale-hybrid aspen intercropping system had the highest yields over the long term, but could only surpass the profitability of the continuous corn system when biomass prices exceeded foreseeable market values. A triticale/sorghum double cropping system was deemed unviable. We perceive three ways 2G crops could become more cost competitive with grain crops: by (1) boosting yields through substantially greater investment in research and development, (2) increasing demand through substantially greater and sustained investment in new markets, and (3) developing new schemes to compensate farmers for environmental benefits associated with 2G crops.

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.

Large scale maps of cropping intensity in Asia from MODIS

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Crop Management to Cope with Global Change: A Systems Perspective Aided by Information Technologies

USDA-ARS?s Scientific Manuscript database

Optimizing crop management must consider the dynamic interaction of abiotic and biotic factors within the context of economic, environmental, sociological, and policy constraints. A wide array of information technologies exists to assist producers, consultants, scientists, agribusiness, action agenc...

Priority regions for research on dryland cereals and legumes

PubMed Central

Hyman, Glenn; Barona, Elizabeth; Biradar, Chandrashekhar; Guevara, Edward; Dixon, John; Beebe, Steve; Castano, Silvia Elena; Alabi, Tunrayo; Gumma, Murali Krishna; Sivasankar, Shoba; Rivera, Ovidio; Espinosa, Herlin; Cardona, Jorge

2016-01-01

Dryland cereals and legumes  are important crops in farming systems across the world.  Yet they are frequently neglected among the priorities for international agricultural research and development, often due to lack of information on their magnitude and extent. Given what we know about the global distribution of dryland cereals and legumes, what regions should be high priority for research and development to improve livelihoods and food security? This research evaluated the geographic dimensions of these crops and the farming systems where they are found worldwide. The study employed geographic information science and data to assess the key farming systems and regions for these crops. Dryland cereal and legume crops should be given high priority in 18 farming systems worldwide, where their cultivated area comprises more than 160 million ha. These regions include the dryer areas of South Asia, West and East Africa, the Middle East and North Africa, Central America and other parts of Asia. These regions are prone to drought and heat stress, have limiting soil constraints, make up half of the global population and account for 60 percent of the global poor and malnourished. The dryland cereal and legume crops and farming systems merit more research and development attention to improve productivity and address development problems. This project developed an open access dataset and information resource that provides the basis for future analysis of the geographic dimensions of dryland cereals and legumes. PMID:27303632

GIS and crop simulation modelling applications in climate change research

USDA-ARS?s Scientific Manuscript database

The challenges that climate change presents humanity require an unprecedented ability to predict the responses of crops to environment and management. Geographic information systems (GIS) and crop simulation models are two powerful and highly complementary tools that are increasingly used for such p...

A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops.

PubMed

Bengochea-Guevara, José M; Andújar, Dionisio; Sanchez-Sardana, Francisco L; Cantuña, Karla; Ribeiro, Angela

2017-12-24

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, "on ground crop inspection" potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. "On ground monitoring" is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows.

A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops

PubMed Central

Andújar, Dionisio; Sanchez-Sardana, Francisco L.; Cantuña, Karla

2017-01-01

Crop monitoring is an essential practice within the field of precision agriculture since it is based on observing, measuring and properly responding to inter- and intra-field variability. In particular, “on ground crop inspection” potentially allows early detection of certain crop problems or precision treatment to be carried out simultaneously with pest detection. “On ground monitoring” is also of great interest for woody crops. This paper explores the development of a low-cost crop monitoring system that can automatically create accurate 3D models (clouds of coloured points) of woody crop rows. The system consists of a mobile platform that allows the easy acquisition of information in the field at an average speed of 3 km/h. The platform, among others, integrates an RGB-D sensor that provides RGB information as well as an array with the distances to the objects closest to the sensor. The RGB-D information plus the geographical positions of relevant points, such as the starting and the ending points of the row, allow the generation of a 3D reconstruction of a woody crop row in which all the points of the cloud have a geographical location as well as the RGB colour values. The proposed approach for the automatic 3D reconstruction is not limited by the size of the sampled space and includes a method for the removal of the drift that appears in the reconstruction of large crop rows. PMID:29295536

Mixed cropping regimes promote the soil fungal community under zero tillage.

PubMed

Silvestro, L B; Biganzoli, F; Stenglein, S A; Forjan, H; Manso, L; Moreno, M V

2018-07-01

Fungi of yield soils represent a significant portion of the microbial biomass and reflect sensitivity to changes in the ecosystem. Our hypothesis was that crops included in cropping regimes under the zero tillage system modify the structure of the soil fungi community. Conventional and molecular techniques provide complementary information for the analysis of diversity of fungal species and successful information to accept our hypothesis. The composition of the fungal community varied according to different crops included in the cropping regimes. However, we detected other factors as sources of variation among them, season and sampling depth. The mixed cropping regimes including perennial pastures and one crop per year promote fungal diversity and species with potential benefit to soil and crop. The winter season and 0-5 cm depth gave the largest evenness and fungal diversity. Trichoderma aureoviride and Rhizopus stolonifer could be used for monitoring changes in soil under zero tillage.

GEOGLAM Crop Monitor Assessment Tool: Developing Monthly Crop Condition Assessments

NASA Astrophysics Data System (ADS)

McGaughey, K.; Becker Reshef, I.; Barker, B.; Humber, M. L.; Nordling, J.; Justice, C. O.; Deshayes, M.

2014-12-01

The Group on Earth Observations (GEO) developed the Global Agricultural Monitoring initiative (GEOGLAM) to improve existing agricultural information through a network of international partnerships, data sharing, and operational research. This presentation will discuss the Crop Monitor component of GEOGLAM, which provides the Agricultural Market Information System (AMIS) with an international, multi-source, and transparent consensus assessment of crop growing conditions, status, and agro-climatic conditions likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soybean) within the main agricultural producing regions of the AMIS countries. These assessments have been produced operationally since September 2013 and are published in the AMIS Market Monitor Bulletin. The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type. This presentation will focus on the building of international networks, data collection, and data dissemination.

A Modernized System for Agricultural Monitoring for Food Security in Tanzania

NASA Astrophysics Data System (ADS)

Dempewolf, J.; Nakalembe, C. L.; Becker-Reshef, I.; Justice, C. J.; Tumbo, S.; Mbilinyi, B.; Maurice, S.; Mtalo, M.

2016-12-01

Accurate and timely information on agriculture, particularly in many countries dominated by complex smallholder, subsistence agricultural systems is often difficult to obtain or not available. This includes up-to-date information during the growing season on crop type, crop area and crop condition such as developmental stage, damage from pests and diseases, drought or flooding. These data are critical for government decision making on production forecasts, planning for commodity market transactions, food aid delivery, responding to disease outbreaks and for implementing agricultural extension and development efforts. In Tanzania we have been working closely with the National Food Security Division (NFSD) at the Ministry of Agriculture, Livestock and Fisheries (MALF) on designing and implementing an advanced agricultural monitoring system, utilizing satellite remote sensing, smart phone and internet technologies. Together with our local implementing partner, the Sokoine University of Agriculture we trained a large number of agricultural extension agents in different regions of Tanzania to deliver field data in near-realtime. Using our collaborative internet portal (Crop Monitor) the team of analysts compiles pertinent information on current crop and weather conditions from throughout the country in a standardized, consistent manner. Using the portal traditionally collected data are combined with electronically collected field data and MODIS satellite image time series from GLAM East-Africa (Global Agricultural Monitoring System, customized for stakeholders in East Africa). The main outcome of this work has been the compilation of the National Food Security Bulletin for Tanzania with plans for a public release and the intention for it to become the main avenue to dispense current updates and analysis on agriculture in the country. The same information is also a potential contribution to the international Early Warning Crop Monitor, which currently covers Tanzania mainly through assessments provided by international agencies.

Herbicide and cover crop residue integration in conservation tillage tomato

USDA-ARS?s Scientific Manuscript database

The increased adoption of conservation tillage in vegetable production requires more information on the role of various cover crops in weed control, tomato quality, and yield. Three conservation-tillage systems utilizing crimson clover, turnip, and cereal rye as winter cover crops were compared to a...

The GRIN-Taxonomy Crop Wild Relative Inventory. Pp 453-457 in Maxted, N., Mulloo, M.E., Ford-Lloyd, B.V. Enhancing crop genepool use: capturing wild relative and landrace diversity for crop improvement

USDA-ARS?s Scientific Manuscript database

In order to provide an informational tool for assessing and prioritizing germplasm needs for ex situ conservation in the U.S. National Plant Germplasm System (NPGS), the USDA Agricultural Research Service in 2008 initiated a project to identify crop wild relatives (CWR) of major and minor crops. Eac...

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Targeting the right input data to improve crop modeling at global level

NASA Astrophysics Data System (ADS)

Adam, M.; Robertson, R.; Gbegbelegbe, S.; Jones, J. W.; Boote, K. J.; Asseng, S.

2012-12-01

Designed for location-specific simulations, the use of crop models at a global level raises important questions. Crop models are originally premised on small unit areas where environmental conditions and management practices are considered homogeneous. Specific information describing soils, climate, management, and crop characteristics are used in the calibration process. However, when scaling up for global application, we rely on information derived from geographical information systems and weather generators. To run crop models at broad, we use a modeling platform that assumes a uniformly generated grid cell as a unit area. Specific weather, specific soil and specific management practices for each crop are represented for each of the cell grids. Studies on the impacts of the uncertainties of weather information and climate change on crop yield at a global level have been carried out (Osborne et al, 2007, Nelson et al., 2010, van Bussel et al, 2011). Detailed information on soils and management practices at global level are very scarce but recognized to be of critical importance (Reidsma et al., 2009). Few attempts to assess the impact of their uncertainties on cropping systems performances can be found. The objectives of this study are (i) to determine sensitivities of a crop model to soil and management practices, inputs most relevant to low input rainfed cropping systems, and (ii) to define hotspots of sensitivity according to the input data. We ran DSSAT v4.5 globally (CERES-CROPSIM) to simulate wheat yields at 45arc-minute resolution. Cultivar parameters were calibrated and validated for different mega-environments (results not shown). The model was run for nitrogen-limited production systems. This setting was chosen as the most representative to simulate actual yield (especially for low-input rainfed agricultural systems) and assumes crop growth to be free of any pest and diseases damages. We conducted a sensitivity analysis on contrasting management practices, initial soil conditions, and soil characteristics information. Management practices were represented by planting date and the amount of fertilizer, initial conditions estimates for initial nitrogen, soil water, and stable soil carbon, and soil information is based on a simplified version of the WISE database, characterized by soil organic matter, texture and soil depth. We considered these factors as the most important determinants of nutrient supply to crops during their growing season. Our first global results demonstrate that the model is most sensitive to the initial conditions in terms of soil carbon and nitrogen (CN): wheat yields decreased by 45% when soil CN is null and increase by 15% when twice the soil CN content of the reference run is used. The yields did not appear to be very sensitive to initial soil water conditions, varying from 0% yield increase when initial soil water is set to wilting point to 6% yield increase when it was set to field capacity. They are slightly sensitive to nitrogen application: 8% yield decrease when no N is applied to 9% yield increase when 150 kg.ha-1 is applied. However, with closer examination of results, the model is more sensitive to nitrogen application than to initial soil CN content in Vietnam, Thailand and Japan compared to the rest of the world. More analyses per region and results on the planting dates and soil properties will be presented.

Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation

NASA Astrophysics Data System (ADS)

Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.

2016-06-01

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

Life-cycle analysis of dryland greenhouse gases affected by cropping sequence and nitrogen fertilization

USDA-ARS?s Scientific Manuscript database

Little information is available about management practices effect on net global warming potential (GWP) and greenhouse gas intensity (GHGI) under dryland cropping systems. We evaluated the effects of cropping sequences (conventional till malt barley-fallow [CTB-F], no-till malt barley-pea [NTB-P], a...

Soil water infiltration affected by topsoil thickness in row crop and switchgrass production systems

USDA-ARS?s Scientific Manuscript database

Conversion of annual grain crop systems to biofuel production systems can restore soil hydrologic function; however, information on these effects is limited. Hence, the objective of this study was to evaluate the influence of topsoil thickness on water infiltration in claypan soils for grain and swi...

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2011-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

Expert system for controlling plant growth in a contained environment

NASA Technical Reports Server (NTRS)

May, George A. (Inventor); Lanoue, Mark Allen (Inventor); Bethel, Matthew (Inventor); Ryan, Robert E. (Inventor)

2009-01-01

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

A Biophysical Modeling Framework for Assessing the Environmental Impact of Biofuel Production

NASA Astrophysics Data System (ADS)

Zhang, X.; Izaurradle, C.; Manowitz, D.; West, T. O.; Post, W. M.; Thomson, A. M.; Nichols, J.; Bandaru, V.; Williams, J. R.

2009-12-01

Long-term sustainability of a biofuel economy necessitates environmentally friendly biofuel production systems. We describe a biophysical modeling framework developed to understand and quantify the environmental value and impact (e.g. water balance, nutrients balance, carbon balance, and soil quality) of different biomass cropping systems. This modeling framework consists of three major components: 1) a Geographic Information System (GIS) based data processing system, 2) a spatially-explicit biophysical modeling approach, and 3) a user friendly information distribution system. First, we developed a GIS to manage the large amount of geospatial data (e.g. climate, land use, soil, and hydrograhy) and extract input information for the biophysical model. Second, the Environmental Policy Integrated Climate (EPIC) biophysical model is used to predict the impact of various cropping systems and management intensities on productivity, water balance, and biogeochemical variables. Finally, a geo-database is developed to distribute the results of ecosystem service variables (e.g. net primary productivity, soil carbon balance, soil erosion, nitrogen and phosphorus losses, and N2O fluxes) simulated by EPIC for each spatial modeling unit online using PostgreSQL. We applied this framework in a Regional Intensive Management Area (RIMA) of 9 counties in Michigan. A total of 4,833 spatial units with relatively homogeneous biophysical properties were derived using SSURGO, Crop Data Layer, County, and 10-digit watershed boundaries. For each unit, EPIC was executed from 1980 to 2003 under 54 cropping scenarios (eg. corn, switchgrass, and hybrid poplar). The simulation results were compared with historical crop yields from USDA NASS. Spatial mapping of the results show high variability among different cropping scenarios in terms of the simulated ecosystem services variables. Overall, the framework developed in this study enables the incorporation of environmental factors into economic and life-cycle analysis in order to optimize biomass cropping production scenarios.

Trade-Offs between Economic and Environmental Impacts of Introducing Legumes into Cropping Systems

PubMed Central

Reckling, Moritz; Bergkvist, Göran; Watson, Christine A.; Stoddard, Frederick L.; Zander, Peter M.; Walker, Robin L.; Pristeri, Aurelio; Toncea, Ion; Bachinger, Johann

2016-01-01

Europe's agriculture is highly specialized, dependent on external inputs and responsible for negative environmental impacts. Legume crops are grown on less than 2% of the arable land and more than 70% of the demand for protein feed supplement is imported from overseas. The integration of legumes into cropping systems has the potential to contribute to the transition to a more resource-efficient agriculture and reduce the current protein deficit. Legume crops influence the production of other crops in the rotation making it difficult to evaluate the overall agronomic effects of legumes in cropping systems. A novel assessment framework was developed and applied in five case study regions across Europe with the objective of evaluating trade-offs between economic and environmental effects of integrating legumes into cropping systems. Legumes resulted in positive and negative impacts when integrated into various cropping systems across the case studies. On average, cropping systems with legumes reduced nitrous oxide emissions by 18 and 33% and N fertilizer use by 24 and 38% in arable and forage systems, respectively, compared to systems without legumes. Nitrate leaching was similar with and without legumes in arable systems and reduced by 22% in forage systems. However, grain legumes reduced gross margins in 3 of 5 regions. Forage legumes increased gross margins in 3 of 3 regions. Among the cropping systems with legumes, systems could be identified that had both relatively high economic returns and positive environmental impacts. Thus, increasing the cultivation of legumes could lead to economic competitive cropping systems and positive environmental impacts, but achieving this aim requires the development of novel management strategies informed by the involvement of advisors and farmers. PMID:27242870

Trade-Offs between Economic and Environmental Impacts of Introducing Legumes into Cropping Systems.

PubMed

Reckling, Moritz; Bergkvist, Göran; Watson, Christine A; Stoddard, Frederick L; Zander, Peter M; Walker, Robin L; Pristeri, Aurelio; Toncea, Ion; Bachinger, Johann

2016-01-01

Europe's agriculture is highly specialized, dependent on external inputs and responsible for negative environmental impacts. Legume crops are grown on less than 2% of the arable land and more than 70% of the demand for protein feed supplement is imported from overseas. The integration of legumes into cropping systems has the potential to contribute to the transition to a more resource-efficient agriculture and reduce the current protein deficit. Legume crops influence the production of other crops in the rotation making it difficult to evaluate the overall agronomic effects of legumes in cropping systems. A novel assessment framework was developed and applied in five case study regions across Europe with the objective of evaluating trade-offs between economic and environmental effects of integrating legumes into cropping systems. Legumes resulted in positive and negative impacts when integrated into various cropping systems across the case studies. On average, cropping systems with legumes reduced nitrous oxide emissions by 18 and 33% and N fertilizer use by 24 and 38% in arable and forage systems, respectively, compared to systems without legumes. Nitrate leaching was similar with and without legumes in arable systems and reduced by 22% in forage systems. However, grain legumes reduced gross margins in 3 of 5 regions. Forage legumes increased gross margins in 3 of 3 regions. Among the cropping systems with legumes, systems could be identified that had both relatively high economic returns and positive environmental impacts. Thus, increasing the cultivation of legumes could lead to economic competitive cropping systems and positive environmental impacts, but achieving this aim requires the development of novel management strategies informed by the involvement of advisors and farmers.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

S.I.I.A for monitoring crop evolution and anomaly detection in Andalusia by remote sensing

NASA Astrophysics Data System (ADS)

Rodriguez Perez, Antonio Jose; Louakfaoui, El Mostafa; Munoz Rastrero, Antonio; Rubio Perez, Luis Alberto; de Pablos Epalza, Carmen

2004-02-01

A new remote sensing application was developed and incorporated to the Agrarian Integrated Information System (S.I.I.A), project which is involved on integrating the regional farming databases from a geographical point of view, adding new values and uses to the original information. The project is supported by the Studies and Statistical Service, Regional Government Ministry of Agriculture and Fisheries (CAP). The process integrates NDVI values from daily NOAA-AVHRR and monthly IRS-WIFS images, and crop classes location maps. Agrarian local information and meteorological information is being included in the working process to produce a synergistic effect. An updated crop-growing evaluation state is obtained by 10-days periods, crop class, sensor type (including data fusion) and administrative geographical borders. Last ten years crop database (1992-2002) has been organized according to these variables. Crop class database can be accessed by an application which helps users on the crop statistical analysis. Multi-temporal and multi-geographical comparative analysis can be done by the user, not only for a year but also for a historical point of view. Moreover, real time crop anomalies can be detected and analyzed. Most of the output products will be available on Internet in the near future by a on-line application.

Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya.

PubMed

Richard, Kyalo; Abdel-Rahman, Elfatih M; Subramanian, Sevgan; Nyasani, Johnson O; Thiel, Michael; Jozani, Hosein; Borgemeister, Christian; Landmann, Tobias

2017-11-03

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Incorporating agricultural management into an earth system model for the Pacific Northwest region: Interactions between climate, hydrology, agriculture, and economics

NASA Astrophysics Data System (ADS)

Chinnayakanahalli, K.; Adam, J. C.; Stockle, C.; Nelson, R.; Brady, M.; Rajagopalan, K.; Barber, M. E.; Dinesh, S.; Malek, K.; Yorgey, G.; Kruger, C.; Marsh, T.; Yoder, J.

2011-12-01

For better management and decision making in the face of climate change, earth system models must explicitly account for natural resource and agricultural management activities. Including crop system, water management, and economic models into an earth system modeling framework can help in answering questions related to the impacts of climate change on irrigation water and crop productivity, how agricultural producers can adapt to anticipated climate change, and how agricultural practices can mitigate climate change. Herein we describe the coupling of the Variability Infiltration Capacity (VIC) land surface model, which solves the water and energy balances of the hydrologic cycle at regional scales, with a crop-growth model, CropSyst. This new model, VIC-CropSyst, is the land surface model that will be used in a new regional-scale model development project focused on the Pacific Northwest, termed BioEarth. Here we describe the VIC-CropSyst coupling process and its application over the Columbia River basin (CRB) using agricultural-specific land cover information. The Washington State Department of Agriculture (WSDA) and U. S. Department of Agriculture (USDA) cropland data layers were used to identify agricultural land use patterns, in which both irrigated and dry land crops were simulated. The VIC-CropSyst model was applied over the CRB for the historical period of 1976 - 2006 to establish a baseline for surface water availability, irrigation demand, and crop production. The model was then applied under future (2030s) climate change scenarios derived from statistically-downscaled Global Circulation Models output under two emission scenarios (A1B and B1). Differences between simulated future and historical irrigation demand, irrigation water availability, and crop production were used in an economics model to identify the most economically-viable future cropping pattern. The economics model was run under varying scenarios of regional growth, trade, water pricing, and water capacity providing a spectrum of possible future cropping patterns. The resulting cropping patterns were then used in VIC-CropSyst to quantify the impacts of climate change, economic, and water management scenarios on crop production, and water resources availability. This modeling framework provides opportunities to study the interactions between human activities and complex natural processes and is a valuable tool for inclusion in an earth system model with the goal of informing land use and water management.

Rice crop growth and outlook monitoring using SAR in Asia

NASA Astrophysics Data System (ADS)

Hamamoto, K.; Sobue, S.; Oyoshi, K.; Ikehata, Y.

2016-12-01

The Asia-RiCE initiative (http://www.asia-rice.org) has been organized to enhance rice production estimates through the use of Earth observation satellites data, and seeks to ensure that Asian rice crops are appropriately represented within GEO Global Agriculture Monitoring (GEO-GLAM) to support FAO Agriculture Market Information System (FAO-AMIS). Asia-RiCE is composed of national teams that are actively contributing to the Crop Monitor for AMIS and developing technical demonstrations of rice crop monitoring activities using both Synthetic Aperture Radar (SAR) data (Radarsat-2 from 2013; Sentinel-1 and ALOS-2 from 2015; TerraSAR-X, Cosmo-SkyMed, RISAT, and others) and optical imagery (such as from MODIS, SPOT-5, Landsat, and Sentinel-2) for 100x100km Technical Demonstration Sites (TDS) as a phase 1 (2013-2015) in Asia. with satellite -based cultivated area and growing stage map. The Asia-RiCE teams are also developing satellite-based agro-met information for rice crop outlook, crop calendars and damage assessment in cooperation with ASEAN food security information system (AFSIS) for selected countries (currently Indonesia, Thailand, Vietnam, Philippine, and Japan; http://www.afsisnc.org/blog), using JAXA's Satellite-based MonItoring Network system as a contribution to the FAO AMIS outlook (JASMIN) with University of Tokyo (http://suzaku.eorc.jaxa.jp/cgi-bin/gcomw/jasm/jasm_top.cgi). Because of continous El Nino in South East Asia, there are less precipitation and rain fall pattern change in South East Asia, crop pattern has been changed and production may be decreased, especially for dry season crop. JAXA provides drought index (KBDI) and accumulated precipitation of Tak province, Thailand where main reservior is located, to AFSIS and national experts to assess rice crop outlook and NDVI time seriese to Ang Tong province where is main rice production area in downstream area of that reservior.From 2016 as a phase 2, Asia-RiCE initiative deploy up-scaling activity from a province (100x100km) to major crop areas or entire country to implement operational use for rice crop production information in low Mekong, Vietnam and top 10 provinces in Indonesia using space based technology. This paper reports this year activity of 2016 accomplishment and way forward.

Soil quality differences in a mature alley cropping system in temperate North America

USDA-ARS?s Scientific Manuscript database

Alley cropping in agroforestry practices has been shown to improve soil quality, however information on long-term effects (>10 years) of alley cropping on soils in the temperate zone is very limited. The objective of this study was to examine effects of management, landscape, and soil depth on soil...

Weeds and their effect on the performance of maize and fingermillet in the mid-hills of Nepal

USDA-ARS?s Scientific Manuscript database

Relay cropping of maize with fingermillet (maize/fingermillet) is the predominant cropping system for sustaining food security in the hilly regions of Nepal. In this region weed pressure severely reduces crop yields, yet basic information on weed species composition, biomass production and their eff...

Strengthening Agricultural Decisions in Countries at Risk of Food Insecurity: The GEOGLAM Crop Monitor for Early Warning

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Barker, B.; McGaughey, K.; Humber, M. L.; Sanchez, A.; Justice, C. O.; Rembold, F.; Verdin, J. P.

2016-12-01

Timely, reliable information on crop conditions, and prospects at the subnational scale, is critical for making informed policy and agricultural decisions for ensuring food security, particularly for the most vulnerable countries. However, such information is often incomplete or lacking. As such, the Crop Monitor for Early Warning (CM for EW) was developed with the goal to reduce uncertainty and strengthen decision support by providing actionable information on a monthly basis to national, regional and global food security agencies through timely consensus assessments of crop conditions. This information is especially critical in recent years, given the extreme weather conditions impacting food supplies including the most recent El Nino event. This initiative brings together the main international food security monitoring agencies and organizations to develop monthly crop assessments based on satellite observations, meteorological information, field observations and ground reports, which reflect an international consensus. This activity grew out of the successful Crop Monitor for the G20 Agricultural Market Information System (AMIS), which provides operational monthly crop assessments of the main producing countries of the world. The CM for EW was launched in February 2016 and has already become a trusted source of information internationally and regionally. Its assessments have been featured in a large number of news articles, reports, and press releases, including a joint statement by the USAID's FEWS NET, UN World Food Program, European Commission Joint Research Center, and the UN Food and Agriculture Organziation, on the devastating impacts of the southern African drought due to El Nino. One of the main priorities for this activity going forward is to expand its partnership with regional and national monitoring agencies, and strengthen capacity for national crop condition assessments.

The Joint Experiment for Crop Assessment and Monitoring (JECAM) Initiative: Developing methods and best practices for global agricultural monitoring

NASA Astrophysics Data System (ADS)

Champagne, C.; Jarvis, I.; Defourny, P.; Davidson, A.

2014-12-01

Agricultural systems differ significantly throughout the world, making a 'one size fits all' approach to remote sensing and monitoring of agricultural landscapes problematic. The Joint Experiment for Crop Assessment and Monitoring (JECAM) was established in 2009 to bring together the global scientific community to work towards a set of best practices and recommendations for using earth observation data to map, monitor and report on agricultural productivity globally across an array of diverse agricultural systems. These methods form the research and development component of the Group on Earth Observation Global Agricultural Monitoring (GEOGLAM) initiative to harmonize global monitoring efforts and increase market transparency. The JECAM initiative brings together researchers from a large number of globally distributed, well monitored agricultural test sites that cover a range of crop types, cropping systems and climate regimes. Each test site works independently as well as together across multiple sites to test methods, sensors and field data collection techniques to derive key agricultural parameters, including crop type, crop condition, crop yield and soil moisture. The outcome of this project will be a set of best practices that cover the range of remote sensing monitoring and reporting needs, including satellite data acquisition, pre-processing techniques, information retrieval and ground data validation. These outcomes provide the research and development foundation for GEOGLAM and will help to inform the development of the GEOGLAM "system of systems" for global agricultural monitoring. The outcomes of the 2014 JECAM science meeting will be discussed as well as examples of methods being developed by JECAM scientists.

Assessing COSMO-SkyMed capability for crops identification and monitoring

NASA Astrophysics Data System (ADS)

Guarini, R.; Dini, L.

2015-12-01

In the last decade, it has been possible to better understand the impact of agricultural human practices on the global environmental change at different spatial (from local to global) and time (from seasonal to decadal) scales. This has been achieved thanks to: big dataset continuously acquired by Earth Observation (EO) satellites; the improved capabilities of remote sensing techniques in extracting valuable information from the EO datasets; the new EO data policy which allowed unrestricted data usage; the net technologies which allowed to quickly and easily share national, international and market-derived information; an increasingly performing computing technology which allows to massively process large amount of data easier and at decreasing costs. To better understand the environmental impacts of agriculture and to monitor the consequences of human agricultural activities on the biosphere, scientists require to better identify crops and monitor crop conditions over time and space. Traditionally, NDVI time series maps derived from optical sensors have been used to this aim. As well-known this important source of information is conditioned by cloud cover. Unlike passive systems, synthetic aperture radar (SAR) ones are almost insensitive to atmospheric influences; thus, they are especially suitable for crop identification and condition monitoring. Among the other SAR systems currently in orbit, the Italian Space Agency (ASI) COSMO Sky-Med® (CSK®) constellation (X-band, frequency 9.6 GHz, wavelength 3.1 cm), especially for its peculiar high revisit capability (up to four images in 16 days with same acquisition geometry) seems to be particular suitable for providing information in addition and/or in alternative to other optical EO systems. To assess the capability of the CSK® constellation in identifying crops and in monitoring crops condition in 2013 ASI started the "AGRICIDOT" project. Some of the main project achievements will be presented at the congress.

Hydroponics Database and Handbook for the Advanced Life Support Test Bed

NASA Technical Reports Server (NTRS)

Nash, Allen J.

1999-01-01

During the summer 1998, I did student assistance to Dr. Daniel J. Barta, chief plant growth expert at Johnson Space Center - NASA. We established the preliminary stages of a hydroponic crop growth database for the Advanced Life Support Systems Integration Test Bed, otherwise referred to as BIO-Plex (Biological Planetary Life Support Systems Test Complex). The database summarizes information from published technical papers by plant growth experts, and it includes bibliographical, environmental and harvest information based on plant growth under varying environmental conditions. I collected 84 lettuce entries, 14 soybean, 49 sweet potato, 16 wheat, 237 white potato, and 26 mix crop entries. The list will grow with the publication of new research. This database will be integrated with a search and systems analysis computer program that will cross-reference multiple parameters to determine optimum edible yield under varying parameters. Also, we have made preliminary effort to put together a crop handbook for BIO-Plex plant growth management. It will be a collection of information obtained from experts who provided recommendations on a particular crop's growing conditions. It includes bibliographic, environmental, nutrient solution, potential yield, harvest nutritional, and propagation procedure information. This handbook will stand as the baseline growth conditions for the first set of experiments in the BIO-Plex facility.

The Joint Experiment for Crop Assessment and Monitoring (JECAM): Update on Multisite Inter-comparison Experiments

NASA Astrophysics Data System (ADS)

Jarvis, I.; Gilliams, S. J. B.; Defourny, P.

2016-12-01

Globally there is significant convergence on agricultural monitoring research questions. The focus of interest usually revolves around crop type, crop area estimation and near real time crop condition and yield forecasting. Notwithstanding this convergence, agricultural systems differ significantly throughout the world, reflecting the diversity of ecosystems they are located in. Consequently, a global system of systems for operational monitoring must be based on multiple approaches. Research is required to compare and assess these approaches to identify which are most appropriate for any given location. To this end the Joint Experiments for Crop Assessment and Monitoring (JECAM) was established in 2009 to as a research platform to allow the global agricultural monitoring community to work towards a set of best practices and recommendations for using earth observation data to map, monitor and report on agricultural productivity globally. The JECAM initiative brings together researchers from a large number of globally distributed, well monitored agricultural test sites that cover a range of crop types, cropping systems and climate regimes. The results of JECAM optical inter-comparison research taking place in the Stimulating Innovation for Global Monitoring of Agriculture (SIGMA) project and the Sentinel-2 for Agriculture project will be discussed. The presentation will also highlight upcoming work on a Synthetic Aperture Radar (SAR) inter-comparison study. The outcome of these projects will result in a set of best practices that cover the range of remote sensing monitoring and reporting needs, including satellite data acquisition, pre-processing techniques, information retrieval and ground data validation. These outcomes provide the R&D foundation for GEOGLAM and will help to inform the development of the GEOGLAM system of systems for global agricultural monitoring.

Integrating NASA Earth Science Enterprise (ESE) Data Into Global Agricultural Decision Support Systems

NASA Astrophysics Data System (ADS)

Teng, W.; Kempler, S.; Chiu, L.; Doraiswamy, P.; Liu, Z.; Milich, L.; Tetrault, R.

2003-12-01

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of U.S. agricultural products and for global food security. Two major operational users of satellite remote sensing for global crop monitoring are the USDA Foreign Agricultural Service (FAS) and the U.N. World Food Programme (WFP). The primary goal of FAS is to improve foreign market access for U.S. agricultural products. The WFP uses food to meet emergency needs and to support economic and social development. Both use global agricultural decision support systems that can integrate and synthesize a variety of data sources to provide accurate and timely information on global crop conditions. The Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (GES DAAC) has begun a project to provide operational solutions to FAS and WFP, by fully leveraging results from previous work, as well as from existing capabilities of the users. The GES DAAC has effectively used its recently developed prototype TRMM Online Visualization and Analysis System (TOVAS) to provide ESE data and information to the WFP for its agricultural drought monitoring efforts. This prototype system will be evolved into an Agricultural Information System (AIS), which will operationally provide ESE and other data products (e.g., rainfall, land productivity) and services, to be integrated into and thus enhance the existing GIS-based, decision support systems of FAS and WFP. Agriculture-oriented, ESE data products (e.g., MODIS-based, crop condition assessment product; TRMM derived, drought index product) will be input to a crop growth model in collaboration with the USDA Agricultural Research Service, to generate crop condition and yield prediction maps. The AIS will have the capability for remotely accessing distributed data, by being compliant with community-based interoperability standards, enabling easy access to agriculture-related products from other data producers. The AIS? system approach will provide a generic mechanism for easily incorporating new products and making them accessible to users.

Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Thornton, Philip K.; Herrero, Mario

2015-09-01

Mixed crop-livestock systems are the backbone of African agriculture, providing food security and livelihood options for hundreds of millions of people. Much is known about the impacts of climate change on the crop enterprises in the mixed systems, and some, although less, on the livestock enterprises. The interactions between crops and livestock can be managed to contribute to environmentally sustainable intensification, diversification and risk management. There is relatively little information on how these interactions may be affected by changes in climate and climate variability. This is a serious gap, because these interactions may offer some buffering capacity to help smallholders adapt to climate change.

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang*

PubMed Central

Fang, Bin; Wang, Guang-huo; Van den berg, Marrit; Roetter, Reimund

2005-01-01

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China’s Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops. PMID:16187411

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang.

PubMed

Fang, Bin; Wang, Guang-Huo; Van, Den Berg Marrit; Roetter, Reimund

2005-10-01

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China's Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

NASA Astrophysics Data System (ADS)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2's continuous and manifold data stream.

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

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Zhang, B.; Deng, M.; Yang, Z.

2011-12-01

Crop progress is an important piece of information for food security and agricultural commodities. Timely monitoring and reporting are mandated for the operation of agricultural statistical agencies. Traditionally, the weekly reporting issued by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is based on reports from the knowledgeable state and county agricultural officials and farmers. The results are spatially coarse and subjective. In this project, a remote-sensing-supported crop progress monitoring system is being developed intensively using the data and derived products from NASA Earth Observing satellites. Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 product - MOD09 (Surface Reflectance) is used for deriving daily normalized vegetation index (NDVI), vegetation condition index (VCI), and mean vegetation condition index (MVCI). Ratio change to previous year and multiple year mean can be also produced on demand. The time-series vegetation condition indices are further combined with the NASS' remote-sensing-derived Cropland Data Layer (CDL) to estimate crop condition and progress crop by crop. To facilitate the operational requirement and increase the accessibility of data and products by different users, each component of the system has being developed and implemented following open specifications under the Web Service reference model of Open Geospatial Consortium Inc. Sensor observations and data are accessed through Web Coverage Service (WCS), Web Feature Service (WFS), or Sensor Observation Service (SOS) if available. Products are also served through such open-specification-compliant services. For rendering and presentation, Web Map Service (WMS) is used. A Web-service based system is set up and deployed at dss.csiss.gmu.edu/NDVIDownload. Further development will adopt crop growth models, feed the models with remotely sensed precipitation and soil moisture information, and incorporate the model results with vegetation-index time series for crop progress stage estimation.

Decision Support Systems To Manage Water Resources At Irrigation District Level In Southern Italy Using Remote Sensing Information. An Integrated Project (AQUATER)

NASA Astrophysics Data System (ADS)

Rinaldi, M.; Castrignanò, A.; Mastrorilli, M.; Rana, G.; Ventrella, D.; Acutis, M.; D'Urso, G.; Mattia, F.

2006-08-01

An efficient management of water resources is crucial point for Italy and in particular for southern areas characterized by Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. A three-year Project (2005-2008) has been funded by the Italian Ministry of Agriculture and Forestry Policies; it involves four Italian research institutions: the Agricultural Research Council (ISA, Bari), the National Research Council (ISSIA, Bari) and two Universities (Federico II-Naples and Milan). It is focused on the remote sensing, the plant and the climate and, for interdisciplinary relationships, the project working group consists of agronomists, engineers and physicists. The aims of the Project are: a) to produce a Decision Support System (DSS) combining remote sensing information, spatial data and simulation models to manage water resources in irrigation districts; b) to simulate irrigation scenarios to evaluate the effects of water stress on crop yield using agro-ecological indicators; c) to identify the most sensitive areas to drought risk in Southern Italy. The tools used in this Project will be: 1. Remote sensing images, topographic maps, soil and land use maps; 2. Geographic Information Systems; 3. Geostatistic methodologies; 4. Ground truth measurements (land use, canopy and soil temperatures, soil and plant water status, Normalized Difference Vegetation Index, Crop Water Stress Index, Leaf Area Index, actual evapotranspiration, crop coefficients, crop yield, agro-ecological indicators); 5. Crop simulation models. The Project is structured in four work packages with specific objectives, high degree of interaction and information exchange: 1) Remote Sensing and Image Analysis; 2) Cropping Systems; 3) Modelling and Softwares Development; 4) Stakeholders. The final product will be a DSS with the purpose of integrating remote sensing images, to estimate crop and soil variables related to drought, to assimilate these variables into a simulation model at district scale and, finally, to estimate evapotranspiration, plant water status and drought indicators. A project Web home page, a technical course about DSS for the employers of irrigation authorities and dissemination of results (meetings, publications, reports), are also planned.

Monitoring and Modeling Crop Health and Water Use via in-situ, Airborne and Space-based Platforms

NASA Astrophysics Data System (ADS)

McCabe, M. F.

2014-12-01

The accurate retrieval of plant water use, health and function together with soil state and condition, represent key objectives in the management and monitoring of large-scale agricultural production. In regions of water shortage or stress, understanding the sustainable use of available water supplies is critical. Unfortunately, this need is all too often limited by a lack of reliable observations. Techniques that balance the demand for reliable ground-based data with the rapid retrieval of spatially distributed crop characteristics represent a needed line of research. Data from in-situ monitoring coupled with advances in satellite retrievals of key land surface variables, provide the information necessary to characterize many crop health and water use features, including evaporation, leaf-chlorophyll and other common vegetation indices. With developments in UAV and quadcopter solutions, the opportunity to bridge the spatio-temporal gap between satellite and ground based sensing now exists, along with the capacity for customized retrievals of crop information. While there remain challenges in the routine application of autonomous airborne systems, the state of current technology and sensor developments provide the capacity to explore the operational potential. While this presentation will focus on the multi-scale estimation of crop-water use and crop-health characteristics from satellite-based sensors, the retrieval of high resolution spatially distributed information from near-surface airborne and ground-based systems will also be examined.

International Global Crop Condition Assessments in the framework of GEOGLAM

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.; Vermote, E.; Whitcraft, A. K.; Claverie, M.

2013-12-01

The Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative in response to the growing calls for improved agricultural information. The goal of GEOGLAM is to strengthen the international community's capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through the use of Earth observations. This initiative is designed to build on existing agricultural monitoring initiatives at national, regional and global levels and to enhance and strengthen them through international networking, operationally focused research, and data/method sharing. GEOGLAM was adopted by the G20 as part of the action plan on food price volatility and agriculture and is being implemented through building on the extensive GEO Agricultural Community of Practice (CoP) that was initiated in 2007 and includes key national and international agencies, organizations, and universities involved in agricultural monitoring. One of the early GEOGLAM activities is to provide harmonized global crop outlooks that offer timely qualitative consensus information on crop status and prospects. This activity is being developed in response to a request from the G-20 Agricultural Market Information System (AMIS) and is implemented within the global monitoring systems component of GEOGLAM. The goal is to develop a transparent, international, multi-source, consensus assessment of crop growing conditions, status, and agro-climatic conditions, likely to impact global production. These assessments are focused on the four primary crop types (corn, wheat, soy and rice) within the main agricultural producing regions of the world. The GEOGLAM approach is to bring together international experts from global, regional and national monitoring systems that can share and discuss information from a variety of independent complementary sources in order to reach a convergence of evidence based assessment. Information types include earth observations (EO) data and products, agro-meteorological data, crop models and field reports. To date, representatives from close to 20 different agencies have participated in these outlook assessments, which are submitted to AMIS on a monthly basis as well as shared with the international community. This talk will discuss the process, operational R&D, and progress towards developing these harmonized global crop assessments.

Study on an agricultural environment monitoring server system using Wireless Sensor Networks.

PubMed

Hwang, Jeonghwan; Shin, Changsun; Yoe, Hyun

2010-01-01

This paper proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural environment monitoring server system collects environmental and soil information on the outdoors through WSN-based environmental and soil sensors, collects image information through CCTVs, and collects location information using GPS modules. This collected information is converted into a database through the agricultural environment monitoring server consisting of a sensor manager, which manages information collected from the WSN sensors, an image information manager, which manages image information collected from CCTVs, and a GPS manager, which processes location information of the agricultural environment monitoring server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural environment monitoring server system could even monitor the environmental information on the outdoors remotely, and it could be expected that the use of such a system could contribute to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information.

VIC-CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions

NASA Astrophysics Data System (ADS)

Malek, Keyvan; Stöckle, Claudio; Chinnayakanahalli, Kiran; Nelson, Roger; Liu, Mingliang; Rajagopalan, Kirti; Barik, Muhammad; Adam, Jennifer C.

2017-08-01

Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively). A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly coupled framework using the macroscale variable infiltration capacity (VIC) hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module was also developed for inclusion in this framework. Because VIC-CropSyst combines two widely used and mechanistic models (for crop phenology, growth, management, and macroscale hydrology), it can provide realistic and hydrologically consistent simulations of water availability, crop water requirements for irrigation, and agricultural productivity for both irrigated and dryland systems. This allows VIC-CropSyst to provide managers and decision makers with reliable information on regional water stresses and their impacts on food production. Additionally, VIC-CropSyst is being used in conjunction with socioeconomic models, river system models, and atmospheric models to simulate feedback processes between regional water availability, agricultural water management decisions, and land-atmosphere interactions. The performance of VIC-CropSyst was evaluated on both regional (over the US Pacific Northwest) and point scales. Point-scale evaluation involved using two flux tower sites located in agricultural fields in the US (Nebraska and Illinois). The agreement between recorded and simulated evapotranspiration (ET), applied irrigation water, soil moisture, leaf area index (LAI), and yield indicated that, although the model is intended to work on regional scales, it also captures field-scale processes in agricultural areas.

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 ...

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Developing a Satellite Based Automatic System for Crop Monitoring: Kenya's Great Rift Valley, A Case Study

NASA Astrophysics Data System (ADS)

Lucciani, Roberto; Laneve, Giovanni; Jahjah, Munzer; Mito, Collins

2016-08-01

The crop growth stage represents essential information for agricultural areas management. In this study we investigate the feasibility of a tool based on remotely sensed satellite (Landsat 8) imagery, capable of automatically classify crop fields and how much resolution enhancement based on pan-sharpening techniques and phenological information extraction, useful to create decision rules that allow to identify semantic class to assign to an object, can effectively support the classification process. Moreover we investigate the opportunity to extract vegetation health status information from remotely sensed assessment of the equivalent water thickness (EWT). Our case study is the Kenya's Great Rift valley, in this area a ground truth campaign was conducted during August 2015 in order to collect crop fields GPS measurements, leaf area index (LAI) and chlorophyll samples.

The Area IV Soil Conservation Districts Cooperative Research Farm: Thirty years of collaborative research to improve cropping system sustainability in the Northern Plains

USDA-ARS?s Scientific Manuscript database

Findings and interpretations generated from long-term cropping system studies serve to inform the status and trajectory of ecosystem services, while concurrently providing opportunities for further inquiry related to basic/fundamental research. Recent calls for increased investment in long-term cro...

GRIN-Global: An International Project to Develop a Global Plant Genebank and Information Management System

USDA-ARS?s Scientific Manuscript database

The mission of the GRIN-Global Project is to create a new, scalable version of the Germplasm Resource Information System (GRIN) to provide the world’s crop genebanks with a powerful, flexible, easy-to-use plant genetic resource (PGR) information management system. The system will help safeguard PGR ...

GRIN-Global: An International Project to Develop a Global Plant Genebank and Information Management System

USDA-ARS?s Scientific Manuscript database

The mission of the GRIN-Global Project is to create a new, scalable version of the Germplasm Resource Information System (GRIN) to provide the world's crop genebanks with a powerful, flexible, easy-to-use plant genetic resource (PGR) information management system. The system will help safeguard PGR ...

GRIN-Global: An International Project to Develop a Global Plant Genebank and Information Management System

USDA-ARS?s Scientific Manuscript database

The mission of the GRIN-Global Project is to create a new, scalable version of the Germplasm Resource Information System (GRIN) to provide the world’s crop genebanks with a powerful, flexible, easy-to-use plant genetic resource (PGR) information management system. The system will help safeguard PGR...

GRIN-Global: An International Project to Develop a Global Plant Genebank Information Management System

USDA-ARS?s Scientific Manuscript database

The mission of the GRIN-Global Project is to create a new, scalable version of the Germplasm Resource Information System (GRIN) to provide the world’s crop genebanks with a powerful, flexible, easy-to-use plant genetic resource (PGR) information management system. The system will help safeguard PGR ...

The GRIN-Global Information Management System – A Preview and Opportunity for Public User Input

USDA-ARS?s Scientific Manuscript database

The GRIN-Global Information Management System, under development for the past two years, will provide the world's crop genebanks and plant genetic resource (PGR) users with a powerful, flexible, easy-to-use PGR information management system. Developed jointly by the USDA Agricultural Research Servi...

The GRIN-Global Information Management System – Public Interface Demonstration and Input Opportunity

USDA-ARS?s Scientific Manuscript database

The GRIN-Global (GG) Information Management System, under development for the past three years, provides the world's crop genebanks and plant genetic resource (PGR) users with a powerful, flexible, easy-to-use PGR information management system. Developed jointly by the USDA Agricultural Research Ser...

Use of Satellite-based Remote Sensing to inform Evapotranspiration parameters in Cropping System Models

NASA Astrophysics Data System (ADS)

Dhungel, S.; Barber, M. E.

2016-12-01

The objectives of this paper are to use an automated satellite-based remote sensing evapotranspiration (ET) model to assist in parameterization of a cropping system model (CropSyst) and to examine the variability of consumptive water use of various crops across the watershed. The remote sensing model is a modified version of the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC™) energy balance model. We present the application of an automated python-based implementation of METRIC to estimate ET as consumptive water use for agricultural areas in three watersheds in Eastern Washington - Walla Walla, Lower Yakima and Okanogan. We used these ET maps with USDA crop data to identify the variability of crop growth and water use for the major crops in these three watersheds. Some crops, such as grapes and alfalfa, showed high variability in water use in the watershed while others, such as corn, had comparatively less variability. The results helped us to estimate the range and variability of various crop parameters that are used in CropSyst. The paper also presents a systematic approach to estimate parameters of CropSyst for a crop in a watershed using METRIC results. Our initial application of this approach was used to estimate irrigation application rate for CropSyst for a selected farm in Walla Walla and was validated by comparing crop growth (as Leaf Area Index - LAI) and consumptive water use (ET) from METRIC and CropSyst. This coupling of METRIC with CropSyst will allow for more robust parameters in CropSyst and will enable accurate predictions of changes in irrigation practices and crop rotation, which are a challenge in many cropping system models.

Mobile open-source plant-canopy monitoring system

USDA-ARS?s Scientific Manuscript database

Many agricultural applications, including improved crop production, precision agriculture, and phenotyping, rely on detailed field and crop information to detect and react to spatial variabilities. Mobile farm vehicles, such as tractors and sprayers, have the potential to operate as mobile sensing ...

Classification and Mapping of Agricultural Land for National Water-Quality Assessment

USGS Publications Warehouse

Gilliom, Robert J.; Thelin, Gail P.

1997-01-01

Agricultural land use is one of the most important influences on water quality at national and regional scales. Although there is great diversity in the character of agricultural land, variations follow regional patterns that are influenced by environmental setting and economics. These regional patterns can be characterized by the distribution of crops. A new approach to classifying and mapping agricultural land use for national water-quality assessment was developed by combining information on general land-use distribution with information on crop patterns from agricultural census data. Separate classification systems were developed for row crops and for orchards, vineyards, and nurseries. These two general categories of agricultural land are distinguished from each other in the land-use classification system used in the U.S. Geological Survey national Land Use and Land Cover database. Classification of cropland was based on the areal extent of crops harvested. The acreage of each crop in each county was divided by total row-crop area or total orchard, vineyard, and nursery area, as appropriate, thus normalizing the crop data and making the classification independent of total cropland area. The classification system was developed using simple percentage criteria to define combinations of 1 to 3 crops that account for 50 percent or more or harvested acreage in a county. The classification system consists of 21 level I categories and 46 level II subcategories for row crops, and 26 level I categories and 19 level II subcategories for orchards, vineyards, and nurseries. All counties in the United States with reported harvested acreage are classified in these categories. The distribution of agricultural land within each county, however, must be evaluated on the basis of general land-use data. This can be done at the national scale using 'Major Land Uses of the United States,' at the regional scale using data from the national Land Use and Land Cover database, or at smaller scales using locally available data.

Towards Developing a Regional Drought Information System for Lower Mekong

NASA Astrophysics Data System (ADS)

Dutta, R.; Jayasinghe, S.; Basnayake, S. B.; Apirumanekul, C.; Pudashine, J.; Granger, S. L.; Andreadis, K.; Das, N. N.

2016-12-01

With the climate and weather patterns changing over the years, the Lower Mekong Basin have been experiencing frequent and prolonged droughts resulting in severe damage to the agricultural sector affecting food security and livelihoods of the farming community. However, the Regional Drought Information System (RDIS) for Lower Mekong countries would help prepare vulnerable communities from frequent and severe droughts through monitoring, assessing and forecasting of drought conditions and allowing decision makers to take effective decisions in terms of providing early warning, incentives to farmers, and adjustments to cropping calendars and so on. The RDIS is an integrated system that is being designed for drought monitoring, analysis and forecasting based on the need to meet the growing demand of an effective monitoring system for drought by the lower Mekong countries. The RDIS is being built on four major components that includes earth observation component, meteorological data component, database storage and Regional Hydrologic Extreme Assessment System (RHEAS) framework while the outputs from the system will be made open access to the public through a web-based user interface. The system will run on the RHEAS framework that allows both nowcasting and forecasting using hydrological and crop simulation models such as the Variable Infiltration Capacity (VIC) model and the Decision Support System for Agro-Technology Transfer (DSSAT) model respectively. The RHEAS allows for a tightly constrained observation based drought and crop yield information system that can provide customized outputs on drought that includes root zone soil moisture, Standard Precipitation Index (SPI), Standard Runoff Index (SRI), Palmer Drought Severity Index (PDSI) and Crop Yield and can integrate remote sensing products, along with evapotranspiration and soil moisture data. The anticipated outcomes from the RDIS is to improve the operational, technological and institutional capabilities of lower Mekong countries to prepare for and respond towards drought situations and providing policy makers with current and forecast drought indices for decision making on adjusting cropping calendars as well as planning short and long term mitigation measures.

Benefits of an improved wheat crop information system

NASA Technical Reports Server (NTRS)

Kinne, I. L.

1976-01-01

The ECON work and the results of the independent reviews are summarized. Attempts are made to put this information into layman's terms and to present the benefits that can realistically be expected from a LANDSAT-type remote sensing system. Further the mechanisms by which these benefits can be expected to accrue are presented. The benefits are given including the nature of expected information improvements, how and why they can lead to benefits to society, and the estimated magnitude of the expected benefits. A brief description is presented of the ECON models, how they work, their results, and a summary of the pertinent aspects of each review. The ECON analyses show that substantial benefits will accrue from implementation of an improved wheat crop information system based on remote sensing.

Efforts Toward an Early Warning Crop Monitor for Countries at Risk

NASA Astrophysics Data System (ADS)

Budde, M. E.; Verdin, J. P.; Barker, B.; Humber, M. L.; Becker-Reshef, I.; Justice, C. O.; Magadzire, T.; Galu, G.; Rodriguez, M.; Jayanthi, H.

2015-12-01

Assessing crop growing conditions is a crucial aspect of monitoring food security in the developing world. One of the core components of the Group on Earth Observations - Global Agricultural Monitoring (GEOGLAM) targets monitoring Countries at Risk (component 3). The Famine Early Warning Systems Network (FEWS NET) has a long history of utilizing remote sensing and crop modeling to address food security threats in the form of drought, floods, pest infestation, and climate change in some of the world's most at risk countries. FEWS NET scientists at the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center and the University of Maryland Department of Geography have undertaken efforts to address component 3, by promoting the development of a collaborative Early Warning Crop Monitor (EWCM) that would specifically address Countries at Risk. A number of organizations utilize combinations of satellite earth observations, field campaigns, network partner inputs, and crop modeling techniques to monitor crop conditions throughout the world. Agencies such as the Food and Agriculture Organization of the United Nations (FAO), United Nations World Food Programme (WFP), and the European Commission's Joint Research Centre (JRC) provide agricultural monitoring information and reporting across a broad number of areas at risk and in many cases, organizations routinely report on the same countries. The latter offers an opportunity for collaboration on crop growing conditions among agencies. The reduction of uncertainty and achievement of consensus will help strengthen confidence in decisions to commit resources for mitigation of acute food insecurity and support for resilience and development programs. In addition, the development of a collaborative global EWCM will provide each of the partner agencies with the ability to quickly gather crop condition information for areas where they may not typically work or have access to local networks. Using a framework developed by GEOGLAM for monitoring crop conditions in support of the Agricultural Market Information System, we developed an EWCM system for countries at risk. We present the current status of that implementation and highlight achievements to date along with future plans to support the needs of the global agricultural monitoring community.

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.

General description and operation of the agro-environmental system: Crop management modeling. [Virginia

NASA Technical Reports Server (NTRS)

Gross, E.; Scott, J. H., Jr.

1981-01-01

Input for a data management system to provide farmers with information to improve crop management practices in Virginia requires monitoring of control crops at field stations, crop surveys derived from remotely sensed aircraft data, meteorological data from synchronous satellites, and details of local agricultural conditions. Presently models are under development for determining pest problems, water balance in the soil, stages of plant maturity, and optimum planting date. The status of the Cerospora leafspot model for peanut crop management is considered. Other models under development planned relate to Cylindtocladium Blackrot and Sclerotinia blight of peanuts, cyst nematode (Globerdena solanacearum) of tobacco, and red crown rot of soybeans. A software for program for estimating precipitation and solar radiation on a statewise basis is also being developed.

Perennial crop phase effects on soil fertility

USDA-ARS?s Scientific Manuscript database

There is a need to develop agricultural management systems that enhance soil fertility and reduce reliance on external inputs. Perennial phases in crop rotations are effective at restoring soil fertility, though little information exists in the northern Great Plains regarding soil-based outcomes re...

Remote sensing-based Information for crop monitoring: contribution of SAR and Moderate resolution optical data on Asian rice production

NASA Astrophysics Data System (ADS)

Boschetti, Mirco; Holectz, Francesco; Manfron, Giacinto; Collivignarelli, Francesco; Nelson, Andrew

2013-04-01

Updated information on crop typology and status are strongly required to support suitable action to better manage agriculture production and reduce food insecurity. In this field, remote sensing has been demonstrated to be a suitable tool to monitor crop condition however rarely the tested system became really operative. The ones today available, such as the European Commission MARS, are mainly based on the analysis of NDVI time series and required ancillary external information like crop mask to interpret the seasonal signal. This condition is not always guarantied worldwide reducing the potentiality of the remote sensing monitoring. Moreover in tropical countries cloud contamination strongly reduce the possibility of using optical remote sensing data for crop monitoring. In this framework we focused our analysis on the rice production monitoring in Asian tropical area. Rice is in fact the staple food for half of the world population (FAO 2004), in Asia almost 90% of the world's rice is produced and consumed and Rice and poverty often coincide. In this contest the production of reliable rice production information is of extreme interest. We tried to address two important issue in terms of required geospatial information for crop monitoring: rice crop detection (rice map) and seasonal dynamics analysis (phenology). We use both SAR and Optical data in order to exploit the potential complementarity of this system. Multi-temporal ASAR Wide Swath data are in fact the best option to deal with cloud contamination. SAR can easily penetrate the clouds providing information on the surface target. Temporal analysis of archive ASAR data allowed to derived accurate map, at 100m spatial resolution, of permanent rice cultivated areas. On the other and high frequency revisiting optical data, in this case MODIS, have been used to extract seasonal information for the year under analysis. MOD09A1 Surface Reflectance 8-Day L3 Global 500m have been exploited to derive time series of Vegetation Index. A temporal smoothing procedure based on Savitzky-Golay polynomial filter function was applied to the original 8-day composite VI data (EVI and NDVI) in order to eliminate spurious data which affect the time series and to produce an interpolated VI temporal profile. Finally within the area previously identify as rice by SAR analysis phenological estimation have been conducted. Crop growth minima and maxima, respectively indicator of rice transplanting and heading, have been identify from the derivative analysis time series. This procedure was tested in Bangladesh for the year 2011. Results showed that the combined use of both data typology represents the more suitable multisource framework to provide reliable information on rice crop growth. Preliminary maps analysis reveals how SAR rice detection was in agreement with local information and phenology extracted by MODIS data provided spatially distributed data comparable with local knowledge of crop calendar.

Analysis of data systems requirements for global crop production forecasting in the 1985 time frame

NASA Technical Reports Server (NTRS)

Downs, S. W.; Larsen, P. A.; Gerstner, D. A.

1978-01-01

Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process.

Compatibility of switchgrass as an energy crop in farming systems of the southeastern USA

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

Bransby, D.I.; Rodriguez-Kabana, R.; Sladden, S.E.

1993-12-31

The objective of this paper is to examine the compatibility of switchgrass as an energy crop in farming systems in the southeastern USA, relative to other regions. In particular, the issues addressed are (1) competition between switchgrass as an energy crop and existing farm enterprises, based primarily on economic returns, (2) complementarity between switchgrass and existing farm enterprises, and (3) environmental benefits. Because projected economic returns for switchgrass as an energy crop are highest in the Southeast, and returns from forestry and beef pastures (the major existing enterprises) are low, there is a very strong economic incentive in this region.more » In contrast, based on current information, economic viability of switchgrass as an energy crop in other regions appears doubtful. In addition, switchgrass in the southeastern USA would complement forage-livestock production, row crop production and wildlife and would provide several additional environmental benefits. It is concluded that the southeastern USA offers the greatest opportunity for developing switchgrass as an economically viable energy crop.« less

Relevance of Crop Biology for Environmental Risk Assessment of Genetically Modified Crops in Africa.

PubMed

Akinbo, Olalekan; Hancock, James F; Makinde, Diran

2015-01-01

Knowledge about the crop biology of economic crops in Africa is needed for regulators to accurately review dossiers and conduct comprehensive environmental risk assessments (ERAs). This information allows regulators to decide whether biotech crops present a risk to biodiversity, since crossing between domesticated crops and their wild relatives could affect the adaptations of the wild species. The criteria that should be used in the evaluation of African crops for ERA include growth habit, center of origin, center of genetic diversity, proximity of wild relatives, inter-fertility, mode of pollen dispersal, length of pollen viability, mating system, invasiveness, weediness, mode of propagation, mode of seed dispersal, and length of seed dormancy. In this paper, we discuss the crops being genetic engineered in Africa and describe the crop biology of those with native relatives.

Developing and establishing bee species as crop pollinators: the example of Osmia spp. (Hymenoptera: Megachilidae) and fruit trees.

PubMed

Bosch, J; Bosch, J; Kemp, W P

2002-02-01

The development of a bee species as a new crop pollinator starts with the identification of a pollination-limited crop production deficit and the selection of one or more candidate pollinator species. The process continues with a series of studies on the developmental biology, pollinating efficacy, nesting behaviour, preference for different nesting substrates, and population dynamics of the candidate pollinator. Parallel studies investigate the biology of parasites, predators and pathogens. The information gained in these studies is combined with information on the reproductive biology of the crop to design a management system. Complete management systems should provide guidelines on rearing and releasing methods, bee densities required for adequate pollination, nesting materials, and control against parasites, predators and pathogens. Management systems should also provide methods to ensure a reliable pollinator supply. Pilot tests on a commercial scale are then conducted to test and eventually refine the management system. The process culminates with the delivery of a viable system to manage and sustain the new pollinator on a commercial scale. The process is illustrated by the development of three mason bees, Osmia cornifrons (Radoszkowski), O. lignaria Say and O. cornuta (Latreille) as orchard pollinators in Japan, the USA and Europe, respectively.

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.

Effect of crop rotation on soil nutrient balance and weediness in soddy podzolic organic farming fields

NASA Astrophysics Data System (ADS)

Zarina, Livija; Zarina, Liga

2017-04-01

The nutrient balance in different crop rotations under organic cropping system has been investigated in Latvia at the Institute of Agricultural Resources and Economics since 2006. Latvia is located in a humid and moderate climatic region where the rainfall exceeds evaporation (soil moisture coefficient > 1) and the soil moisture regime is characteristic with percolation. The average annual precipitation is 670-850 mm. The average temperature varies from -6.7° C in January to 16.5 °C in July. The growing season is 175 - 185 days. The most widespread are podzolic soils and mainly they are present in agricultural fields in all regions of Latvia. In a wider sense the goal of the soil management in organic farming is a creation of the biologically active flora and fauna in the soil by maintaining a high level of soil organic matter which is good for crops nutrient balance. Crop rotation is a central component of organic farming systems and has many benefits, including growth of soil microbial activity, which may increase nutrient availability. The aim of the present study was to calculate nutrient balance for each crop in the rotations and average in each rotation. Taking into account that crop rotations can limit build-up of weeds, additionally within the ERA-net CORE Organic Plus transnational programs supported project PRODIVA the information required for a better utilization of crop diversification for weed management in North European organic arable cropping systems was summarized. It was found that the nutrient balance was influenced by nutrients uptake by biomass of growing crops in crop rotation. The number of weeds in the organic farming fields with crop rotation is dependent on the cultivated crops and the succession of crops in the crop rotation.

The GRIN-Taxonomy crop wild relative inventory

USDA-ARS?s Scientific Manuscript database

In order to provide an informational tool for assessing and prioritizing germplasm needs for ex situ conservation in the U.S. National Plant Germplasm System (NPGS), the USDA Agricultural Research Service in 2008 initiated a project to identify wild relatives (CWR) of major and minor crops. Each cro...

Linking pesticides and human health: a geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure.

PubMed

VoPham, Trang; Wilson, John P; Ruddell, Darren; Rashed, Tarek; Brooks, Maria M; Yuan, Jian-Min; Talbott, Evelyn O; Chang, Chung-Chou H; Weissfeld, Joel L

2015-08-01

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.

Combining Remote Sensing imagery of both fine and coarse spatial resolution to Estimate Crop Evapotranspiration and quantifying its Influence on Crop Growth Monitoring.

NASA Astrophysics Data System (ADS)

Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio; Duveiller, Gregory; Piccard, Isabelle; de Wit, Allard; Tychon, Bernard; Bakary, Djaby; Defourny, Pierre

2010-05-01

This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. Evapotranspiration (ET) is a valuable parameter in the crop monitoring context since it provides information on the plant water stress status, which strongly influences crop development and, by extension, crop yield. To assess crop evapotranspiration over the GLOBAM study areas (300x300 km sites in Northern Europe and Central Ethiopia), a Soil-Vegetation-Atmosphere Transfer (SVAT) model forced with remote sensing and numerical weather prediction data has been used. This model runs at pre-operational level in the framework of the EUMETSAT LSA-SAF (Land Surface Analysis Satellite Application Facility) using SEVIRI and ECMWF data, as well as the ECOCLIMAP database to characterize the vegetation. The model generates ET images at the Meteosat Second Generation (MSG) spatial resolution (3 km at subsatellite point),with a temporal resolution of 30 min and monitors the entire MSG disk which covers Europe, Africa and part of Sud America . The SVAT model was run for 2007 using two approaches. The first approach is at the standard pre-operational mode. The second incorporates remote sensing information at various spatial resolutions going from LANDSAT (30m) to SEVIRI (3-5 km) passing by AWIFS (56m) and MODIS (250m). Fine spatial resolution data consists of crop type classification which enable to identify areas where pure crop specific MODIS time series can be compiled and used to derive Leaf Area Index estimations for the most important crops (wheat and maize). The use of this information allowed to characterize the type of vegetation and its state of development in a more accurate way than using the ECOCLIMAP database. Finally, the CASA method was applied using the evapotranspiration images with FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) images from LSA-SAF to obtain Dry Matter Productivity (DMP) and crop yield. The potential of using evapotranspiration obtained from remote sensing in crop growth modeling is studied and discussed. Results of comparing the evapotranspiration obtained with ground truth data are shown as well as the influence of using high resolution information to characterize the vegetation in the evapotranspiration estimation. The values of DMP and yield obtained with the CASA method are compared with those obtained using crop growth modeling and field data, showing the potential of using this simplified remote sensing method for crop monitoring and yield forecasting. This methodology could be applied in an operative way to the entire MSG disk, allowing the continuous crop growth monitoring.

The status of parametric studies in radar agriculture

NASA Technical Reports Server (NTRS)

Morain, S. A.

1972-01-01

Outlined is an information system based on the use of remote sensor data and the design, testing, and implementation of interpretation keys for agriculture. The task of crop identification from radar imagery emphasizes dichotomous keys and the effects of frequency, angular and other microwave dependencies of crops for use in discrimination. A mosaic is formulated from imagery and used to study acres in wheat for spread of circular irrigation, spread of crops, and other phenomena.

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

DTIC Science & Technology

1993-01-01

during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on...well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote - sensing techniques with crop

Utilization of GIS/GPS-Based Information Technology in Commercial Crop Decision Making in California, Washington, Oregon, Idaho, and Arizona

PubMed Central

Thomas, C. S.; Skinner, P. W.; Fox, A. D.; Greer, C. A.; Gubler, W. D.

2002-01-01

Ground-based weather, plant-stage measurements, and remote imagery were geo-referenced in geographic information system (GIS) software using an integrated approach to determine insect and disease risk and crop cultural requirements. Weather forecasts and disease weather forecasts for agricultural areas were constructed with elevation, weather, and satellite data. Models for 6 insect pests and 12 diseases of various crops were calculated and presented daily in georeferenced maps for agricultural areas in northern California and Washington. Grape harvest dates and yields also were predicted with high accuracy. The data generated from the GIS global positioning system (GPS) analyses were used to make management decisions over a large number of acres in California, Washington, Oregon, Idaho, and Arizona. Information was distributed daily over the Internet as regional weather, insect, and disease risk maps as industry-sponsored or subscription-based products. Use of GIS/GPS technology for semi-automated data analysis is discussed. PMID:19265934

Using the GRIN-Global System to Identify Useful Plant Genetic Resources & Information

USDA-ARS?s Scientific Manuscript database

The GRIN-Global (GG) System has been developed to provide the world's crop genebanks and plant genetic resource (PGR) users with a powerful, flexible, easy-to-use PGR information management system. Developed jointly by the USDA Agricultural Research Service, Bioversity International and the Global C...

Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

NASA Astrophysics Data System (ADS)

Gunda, T.; Bazuin, J. T.; Nay, J.; Yeung, K. L.

2017-03-01

Access to seasonal climate forecasts can benefit farmers by allowing them to make more informed decisions about their farming practices. However, it is unclear whether farmers realize these benefits when crop choices available to farmers have different and variable costs and returns; multiple countries have programs that incentivize production of certain crops while other crops are subject to market fluctuations. We hypothesize that the benefits of forecasts on farmer livelihoods will be moderated by the combined impact of differing crop economics and changing climate. Drawing upon methods and insights from both physical and social sciences, we develop a model of farmer decision-making to evaluate this hypothesis. The model dynamics are explored using empirical data from Sri Lanka; primary sources include survey and interview information as well as game-based experiments conducted with farmers in the field. Our simulations show that a farmer using seasonal forecasts has more diversified crop selections, which drive increases in average agricultural income. Increases in income are particularly notable under a drier climate scenario, when a farmer using seasonal forecasts is more likely to plant onions, a crop with higher possible returns. Our results indicate that, when water resources are scarce (i.e. drier climate scenario), farmer incomes could become stratified, potentially compounding existing disparities in farmers’ financial and technical abilities to use forecasts to inform their crop selections. This analysis highlights that while programs that promote production of certain crops may ensure food security in the short-term, the long-term implications of these dynamics need careful evaluation.

A New Strategy in Observer Modeling for Greenhouse Cucumber Seedling Growth

PubMed Central

Qiu, Quan; Zheng, Chenfei; Wang, Wenping; Qiao, Xiaojun; Bai, He; Yu, Jingquan; Shi, Kai

2017-01-01

State observer is an essential component in computerized control loops for greenhouse-crop systems. However, the current accomplishments of observer modeling for greenhouse-crop systems mainly focus on mass/energy balance, ignoring physiological responses of crops. As a result, state observers for crop physiological responses are rarely developed, and control operations are typically made based on experience rather than actual crop requirements. In addition, existing observer models require a large number of parameters, leading to heavy computational load and poor application feasibility. To address these problems, we present a new state observer modeling strategy that takes both environmental information and crop physiological responses into consideration during the observer modeling process. Using greenhouse cucumber seedlings as an instance, we sample 10 physiological parameters of cucumber seedlings at different time point during the exponential growth stage, and employ them to build growth state observers together with 8 environmental parameters. Support vector machine (SVM) acts as the mathematical tool for observer modeling. Canonical correlation analysis (CCA) is used to select the dominant environmental and physiological parameters in the modeling process. With the dominant parameters, simplified observer models are built and tested. We conduct contrast experiments with different input parameter combinations on simplified and un-simplified observers. Experimental results indicate that physiological information can improve the prediction accuracies of the growth state observers. Furthermore, the simplified observer models can give equivalent or even better performance than the un-simplified ones, which verifies the feasibility of CCA. The current study can enable state observers to reflect crop requirements and make them feasible for applications with simplified shapes, which is significant for developing intelligent greenhouse control systems for modern greenhouse production. PMID:28848565

A New Strategy in Observer Modeling for Greenhouse Cucumber Seedling Growth.

PubMed

Qiu, Quan; Zheng, Chenfei; Wang, Wenping; Qiao, Xiaojun; Bai, He; Yu, Jingquan; Shi, Kai

2017-01-01

State observer is an essential component in computerized control loops for greenhouse-crop systems. However, the current accomplishments of observer modeling for greenhouse-crop systems mainly focus on mass/energy balance, ignoring physiological responses of crops. As a result, state observers for crop physiological responses are rarely developed, and control operations are typically made based on experience rather than actual crop requirements. In addition, existing observer models require a large number of parameters, leading to heavy computational load and poor application feasibility. To address these problems, we present a new state observer modeling strategy that takes both environmental information and crop physiological responses into consideration during the observer modeling process. Using greenhouse cucumber seedlings as an instance, we sample 10 physiological parameters of cucumber seedlings at different time point during the exponential growth stage, and employ them to build growth state observers together with 8 environmental parameters. Support vector machine (SVM) acts as the mathematical tool for observer modeling. Canonical correlation analysis (CCA) is used to select the dominant environmental and physiological parameters in the modeling process. With the dominant parameters, simplified observer models are built and tested. We conduct contrast experiments with different input parameter combinations on simplified and un-simplified observers. Experimental results indicate that physiological information can improve the prediction accuracies of the growth state observers. Furthermore, the simplified observer models can give equivalent or even better performance than the un-simplified ones, which verifies the feasibility of CCA. The current study can enable state observers to reflect crop requirements and make them feasible for applications with simplified shapes, which is significant for developing intelligent greenhouse control systems for modern greenhouse production.

Implementation of Sentinel-2 Data in the M4Land System for the Generation of Continuous Information Products in Agriculture

NASA Astrophysics Data System (ADS)

Klug, P.; Schlenz, F.; Hank, T.; Migdall, S.; Weiß, I.; Danner, M.; Bach, H.; Mauser, W.

2016-08-01

The analysis system developed in the frame of the M4Land project (Model based, Multi-temporal, Multi scale and Multi sensorial retrieval of continuous land management information) has proven its capabilities of classifying crop type and creating products on the intensity of agricultural production using optical remote sensing data from Landsat and RapidEye. In this study, Sentinel-2 data is used for the first time together with Landsat 7 ETM+ and 8 OLI data within the M4Land analysis system to derive continuously crop type and the agricultural intensity of fields in an area north of Munich, Germany and the year 2015.

Machine-assisted analysis of Landsat data in the study of crop-soils relationships

USGS Publications Warehouse

Draeger, William C.

1976-01-01

To date, relatively few studies have dealt with crop-soil interactions as they affect the appearance of agricultural areas on Landsat imagery, and hence crop and soil classification or the analysis of agricultural land use.The Image 100, a computer-based data analysis system which allows an interpreter to interact directly and rapidly with Landsat computer compatible tape data, provided a tool to assist in the evaluation of the extent and significance of these interactions. Used with timely and accurate ground data, the system made possible a determination of the variability in crop spectral appearance, from soil type to soil type, as recorded on Landsat data. Information was provided in the form of spectral distribution histrograms for each crop-soil class on each Landsat band. Several crop categories in a test area in  rookings County, South Dakota, were classified using training fields that were selected to be representative of each major crop-soil class. Accuracies in each case, on a total acreage basis, were greater than 90 percent.

A Decade of Carbon Flux Measurements with Annual and Perennial Crop Rotations on the Canadian Prairies

NASA Astrophysics Data System (ADS)

Amiro, B. D.; Tenuta, M.; Gao, X.; Gervais, M.

2016-12-01

The Fluxnet database has over 100 cropland sites, some of which have long-term (over a decade) measurements. Carbon neutrality is one goal of sustainable agriculture, although measurements over many annual cropping systems have indicated that soil carbon is often lost. Croplands are complex systems because the CO2 exchange depends on the type of crop, soil, weather, and management decisions such as planting date, nutrient fertilization and pest management strategy. Crop rotations are often used to decrease pest pressure, and can range from a simple 2-crop system, to have 4 or more crops in series. Carbon dioxide exchange has been measured using the flux-gradient technique since 2006 in agricultural systems in Manitoba, Canada. Two cropping systems are being followed: one that is a rotation of annual crops (corn, faba bean, spring wheat, rapeseed, barley, spring wheat, corn, soybean, spring wheat, soybean); and the other with a perennial phase of alfalfa/grass in years 3 to 6. Net ecosystem production ranged from a gain of 330 g C m-2 y-1 in corn to a loss of 75 g C m-2 y-1 in a poor spring-wheat crop. Over a decade, net ecosystem production for the annual cropping system was not significantly different from zero (carbon neutral), but the addition of the perennial phase increased the sink to 130 g C m-2 y-1. Once harvest removals were included, there was a net loss of carbon ranging from 77 g C m-2 y-1 in the annual system to 52 g C m-2 y-1 in the annual-perennial system; but neither of these were significantly different from zero. Termination of the perennial phase of the rotation only caused short-term increases in respiration. We conclude that both these systems were close to carbon-neutral over a decade even though they were tilled with a short growing season (90 to 130 days). We discuss the need for more datasets on agricultural systems to inform management options to increase the soil carbon sink.

Application of Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region

NASA Astrophysics Data System (ADS)

Jayasinghe, S.; Dutta, R.; Basnayake, S. B.; Granger, S. L.; Andreadis, K. M.; Das, N.; Markert, K. N.; Cutter, P. G.; Towashiraporn, P.; Anderson, E.

2017-12-01

The Lower Mekong Region has been experiencing frequent and prolonged droughts resulting in severe damage to agricultural production leading to food insecurity and impacts on livelihoods of the farming communities. Climate variability further complicates the situation by making drought harder to forecast. The Regional Drought and Crop Yield Information System (RDCYIS), developed by SERVIR-Mekong, helps decision makers to take effective measures through monitoring, analyzing and forecasting of drought conditions and providing early warnings to farmers to make adjustments to cropping calendars. The RDCYIS is built on regionally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. The RHEAS allows ingestion of numerus freely available earth observation and ground observation data to generate and customize drought related indices, variables and crop yield information for better decision making. The Lower Mekong region has experienced severe drought in 2016 encompassing the region's worst drought in 90 years. This paper presents the simulation of the 2016 drought event using RDCYIS based on its hindcast and forecast capabilities. The regionally calibrated RDCYIS can help capture salient features of drought through a variety of drought indices, soil variables, energy balance variables and water balance variables. The RDCYIS is capable of assimilating soil moisture data from different satellite products and perform ensemble runs to further reduce the uncertainty of it outputs. The calibrated results have correlation coefficient around 0.73 and NSE between 0.4-0.5. Based on the acceptable results of the retrospective runs, the system has the potential to generate reliable drought monitoring and forecasting information to improve decision-makings at operational, technological and institutional level of mandated institutes of lower Mekong countries. This is turn would help countries to prepare for and respond to drought situations by taking short and long-term risk mitigation measures such as adjusting cropping calendars, rainwater harvesting, and so on.

Development of a decision support system for small reservoir irrigation systems in rainfed and drought prone areas.

PubMed

Balderama, Orlando F

2010-01-01

An integrated computer program called Cropping System and Water Management Model (CSWM) with a three-step feature (expert system-simulation-optimization) was developed to address a range of decision support for rainfed farming, i.e. crop selection, scheduling and optimisation. The system was used for agricultural planning with emphasis on sustainable agriculture in the rainfed areas through the use of small farm reservoirs for increased production and resource conservation and management. The application of the model was carried out using crop, soil, and climate and water resource data from the Philippines. Primarily, four sets of data representing the different rainfall classification of the country were collected, analysed, and used as input in the model. Simulations were also done on date of planting, probabilities of wet and dry period and with various capacities of the water reservoir used for supplemental irrigation. Through the analysis, useful information was obtained to determine suitable crops in the region, cropping schedule and pattern appropriate to the specific climate conditions. In addition, optimisation of the use of the land and water resources can be achieved in areas partly irrigated by small reservoirs.

Proceedings of Plenary Session: The LACIE Symposium

NASA Technical Reports Server (NTRS)

1978-01-01

A technology assessment of the LACIE data processing and information systems was discussed during the Large Area Crop Inventory Experiment Symposium. Crop inventories of wheat yield in the United States as well as several other nations (such as the U.S.S.R., Canada, etc.) were discussed, along with the methodology involved in acquiring this data.

A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. A thermal-based scheme, called the Two-Source Energy Balance (TSEB) model, solves for the soil/substrate and canopy temp...

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

A satellite-driven, client-server hydro-economic model prototype for agricultural water management

NASA Astrophysics Data System (ADS)

Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

2017-04-01

Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because this product is only available for the conterminous United States, the framework is currently only applicable in this region. To obtain information on crop phenology, productivity and transpiration at adequate spatial and temporal frequencies we blend high spatial resolution Landsat information with high temporal fidelity MODIS imagery. The result is a 30 m, 8-day fused dataset of crop greenness that is subsequently transformed into productivity and transpiration by adapting existing forest productivity and transpiration algorithms for agricultural applications. To ensure all involved agencies work with identical information and that end-users are sheltered from the computational burden of storing and processing remote sensing data, this modeling framework is integrated in a client-server architecture based on the Hydra platform (www.hydraplatform.org). Assimilation and processing of resource-intensive remote sensing information, as well as hydrologic and other ancillary data, occur on the server side. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. This architecture ensures that all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Identification of agricultural crops by computer processing of ERTS MSS data

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cipra, J. E.

1973-01-01

Quantitative evaluation of computer-processed ERTS MSS data classifications has shown that major crop species (corn and soybeans) can be accurately identified. The classifications of satellite data over a 2000 square mile area not only covered more than 100 times the area previously covered using aircraft, but also yielded improved results through the use of temporal and spatial data in addition to the spectral information. Furthermore, training sets could be extended over far larger areas than was ever possible with aircraft scanner data. And, preliminary comparisons of acreage estimates from ERTS data and ground-based systems agreed well. The results demonstrate the potential utility of this technology for obtaining crop production information.

A center for commercial development of space: Real-time satellite mapping. Remote sensing-based agricultural information expert system

NASA Technical Reports Server (NTRS)

Hadipriono, Fabian C.; Diaz, Carlos F.; Merritt, Earl S.

1989-01-01

The research project results in a powerful yet user friendly CROPCAST expert system for use by a client to determine the crop yield production of a certain crop field. The study is based on the facts that heuristic assessment and decision making in agriculture are significant and dominate much of agribusiness. Transfer of the expert knowledge concerning remote sensing based crop yield production into a specific expert system is the key program in this study. A knowledge base consisting of a root frame, CROP-YIELD-FORECAST, and four subframes, namely, SATELLITE, PLANT-PHYSIOLOGY, GROUND, and MODEL were developed to accommodate the production rules obtained from the domain expert. The expert system shell Personal Consultant Plus version 4.0. was used for this purpose. An external geographic program was integrated to the system. This project is the first part of a completely built expert system. The study reveals that much effort was given to the development of the rules. Such effort is inevitable if workable, efficient, and accurate rules are desired. Furthermore, abundant help statements and graphics were included. Internal and external display routines add to the visual capability of the system. The work results in a useful tool for the client for making decisions on crop yield production.

Precision Farming and Precision Pest Management: The Power of New Crop Production Technologies

PubMed Central

Strickland, R. Mack; Ess, Daniel R.; Parsons, Samuel D.

1998-01-01

The use of new technologies including Geographic Information Systems (GIS), the Global Positioning System (GPS), Variable Rate Technology (VRT), and Remote Sensing (RS) is gaining acceptance in the present high-technology, precision agricultural industry. GIS provides the ability to link multiple data values for the same geo-referenced location, and provides the user with a graphical visualization of such data. When GIS is coupled with GPS and RS, management decisions can be applied in a more precise "micro-managed" manner by using VRT techniques. Such technology holds the potential to reduce agricultural crop production costs as well as crop and environmental damage. PMID:19274236

Using a geographic information system and scanning technology to create high-resolution land-use data sets

USGS Publications Warehouse

Harvey, Craig A.; Kolpin, Dana W.; Battaglin, William A.

1996-01-01

A geographic information system (GIS) procedure was developed to compile low-altitude aerial photography, digitized data, and land-use data from U.S. Department of Agriculture Consolidated Farm Service Agency (CFSA) offices into a high-resolution (approximately 5 meters) land-use GIS data set. The aerial photography consisted of 35-mm slides which were scanned into tagged information file format (TIFF) images. These TIFF images were then imported into the GIS where they were registered into a geographically referenced coordinate system. Boundaries between land use were delineated from these GIS data sets using on-screen digitizing techniques. Crop types were determined using information obtained from the U.S. Department of Agriculture CFSA offices. Crop information not supplied by the CFSA was attributed by manual classification procedures. Automated methods to provide delineation of the field boundaries and land-use classification were investigated. It was determined that using these data sources, automated methods were less efficient and accurate than manual methods of delineating field boundaries and classifying land use.

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

USGS Publications Warehouse

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

1997-01-01

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

The crop growth research chamber

NASA Technical Reports Server (NTRS)

Wagenbach, Kimberly

1993-01-01

The Crop Growth Research Chamber (CGRC) has been defined by CELSS principle investigators and science advisory panels as a necessary ground-based tool in the development of a regenerative life support system. The focus of CGRC research will be on the biomass production component of the CELSS system. The ground-based Crop Growth Research Chamber is for the study of plant growth and development under stringently controlled environments isolated from the external environment. The chamber has importance in three areas of CELSS activities: (1) crop research; (2) system control and integration, and (3) flight hardware design and experimentation. The laboratory size of the CGRC will be small enough to allow duplication of the unit, the conducting of controlled experiments, and replication of experiments, but large enough to provide information representative of larger plant communities. Experiments will focus on plant growth in a wide variety of environments and the effects of those environments on plant production of food, water, oxygen, toxins, and microbes. To study these effects in a closed system, tight control of the environment is necessary.

Agricultural land cover mapping in the context of a geographically referenced digital information system. [Carroll, Macon, and Gentry Counties, Missouri

NASA Technical Reports Server (NTRS)

Stoner, E. R.

1982-01-01

The introduction of soil map information to the land cover mapping process can improve discrimination of land cover types and reduce confusion among crop types that may be caused by soil-specific management practices and background reflectance characteristics. Multiple dates of LANDSAT MSS digital were analyzed for three study areas in northern Missouri to produce cover types for major agricultural land cover classes. Digital data bases were then developed by adding ancillary data such as digitized soil and transportation network information to the LANDSAT-derived cover type map. Procedures were developed to manipulate the data base parameters to extract information applicable to user requirements. An agricultural information system combining such data can be used to determine the productive capacity of land to grow crops, fertilizer needs, chemical weed control rates, irrigation suitability, and trafficability of soil for planting.

Legume Information System (LegumeInfo.org): a key component of a set of federated data resources for the legume family

USDA-ARS?s Scientific Manuscript database

The Legume Information System (LIS), at http://legumeinfo.org, is a genomic data portal (GDP) for the legume family. LIS provides access to genetic and genomic information for major crop and model legumes. With more than two-dozen domesticated legume species, there are numerous specialists working o...

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Introducing the Brassica Information Portal: Towards integrating genotypic and phenotypic Brassica crop data

PubMed Central

Eckes, Annemarie H.; Gubała, Tomasz; Nowakowski, Piotr; Szymczyszyn, Tomasz; Wells, Rachel; Irwin, Judith A.; Horro, Carlos; Hancock, John M.; King, Graham; Dyer, Sarah C.; Jurkowski, Wiktor

2017-01-01

The Brassica Information Portal (BIP) is a centralised repository for brassica phenotypic data. The site hosts trait data associated with brassica research and breeding experiments conducted on brassica crops, that are used as oilseeds, vegetables, livestock forage and fodder and for biofuels. A key feature is the explicit management of meta-data describing the provenance and relationships between experimental plant materials, as well as trial design and trait descriptors. BIP is an open access and open source project, built on the schema of CropStoreDB, and as such can provide trait data management strategies for any crop data. A new user interface and programmatic submission/retrieval system helps to simplify data access for researchers, breeders and other end-users. BIP opens up the opportunity to apply integrative, cross-project analyses to data generated by the Brassica Research Community. Here, we present a short description of the current status of the repository. PMID:28529710

Pathogens and fecal indicators in waste stabilization pond systems with direct reuse for irrigation: Fate and transport in water, soil and crops.

PubMed

Verbyla, M E; Iriarte, M M; Mercado Guzmán, A; Coronado, O; Almanza, M; Mihelcic, J R

2016-05-01

Wastewater use for irrigation is expanding globally, and information about the fate and transport of pathogens in wastewater systems is needed to complete microbial risk assessments and develop policies to protect public health. The lack of maintenance for wastewater treatment facilities in low-income areas and developing countries results in sludge accumulation and compromised performance over time, creating uncertainty about the contamination of soil and crops. The fate and transport of pathogens and fecal indicators was evaluated in waste stabilization ponds with direct reuse for irrigation, using two systems in Bolivia as case studies. Results were compared with models from the literature that have been recommended for design. The removal of Escherichia coli in both systems was adequately predicted by a previously-published dispersed flow model, despite more than 10years of sludge accumulation. However, a design equation for helminth egg removal overestimated the observed removal, suggesting that this equation may not be appropriate for systems with accumulated sludge. To assess the contamination of soil and crops, ratios were calculated of the pathogen and fecal indicator concentrations in soil or on crops to their respective concentrations in irrigation water (termed soil-water and crop-water ratios). Ratios were similar within each group of microorganisms but differed between microorganism groups, and were generally below 0.1mLg(-1) for coliphage, between 1 and 100mLg(-1) for Giardia and Cryptosporidium, and between 100 and 1000mLg(-1) for helminth eggs. This information can be used for microbial risk assessments to develop safe water reuse policies in support of the United Nations' 2030 Sustainable Development Agenda. Copyright © 2016 Elsevier B.V. All rights reserved.

Pest management in Douglas-fir seed orchards: a microcomputer decision method

Treesearch

James B. Hoy; Michael I. Haverty

1988-01-01

The computer program described provides a Douglas-fir seed orchard manager (user) with a quantitative method for making insect pest management decisions on a desk-top computer. The decision system uses site-specific information such as estimates of seed crop size, insect attack rates, insecticide efficacy and application costs, weather, and crop value. At sites where...

Using remote sensing to calculate plant available nitrogen needed by crops on swine factory farm sprayfields in North Carolina

NASA Astrophysics Data System (ADS)

Christenson, Elizabeth; Serre, Marc

2015-10-01

North Carolina (NC) is the second largest producer of hogs in the United States with Duplin county, NC having the densest population of hogs in the world. In NC, liquid swine manure is generally stored in open-air lagoons and sprayed onto sprayfields with sprinkler systems to be used as fertilizer for crops. Swine factory farms, termed concentrated animal feeding operations (CAFOs), are regulated by the Department of Environment and Natural Resources (DENR) based on nutrient management plans (NMPs) having balanced plant available nitrogen (PAN). The estimated PAN in liquid manure being sprayed must be less than the estimated PAN needed crops during irrigation. Estimates for PAN needed by crops are dependent on crop and soil types. Objectives of this research were to develop a new, time-efficient method to identify PAN needed by crops on Duplin county sprayfields for years 2010-2014. Using remote sensing data instead of NMP data to identify PAN needed by crops allowed calendar year identification of which crops were grown on sprayfields instead of a five-year range of values. Although permitted data have more detailed crop information than remotely sensed data, identification of PAN needed by crops using remotely sensed data is more time efficient, internally consistent, easily publically accessible, and has the ability to identify annual changes in PAN on sprayfields. Once PAN needed by crops is known, remote sensing can be used to quantify PAN at other spatial scales, such as sub-watershed levels, and can be used to inform targeted water quality monitoring of swine CAFOs.

A management information system to study space diets

NASA Technical Reports Server (NTRS)

Kang, Sukwon; Both, A. J.; Janes, H. W. (Principal Investigator)

2002-01-01

A management information system (MIS), including a database management system (DBMS) and a decision support system (DSS), was developed to dynamically analyze the variable nutritional content of foods grown and prepared in an Advanced Life Support System (ALSS) such as required for long-duration space missions. The DBMS was designed around the known nutritional content of a list of candidate crops and their prepared foods. The DSS was designed to determine the composition of the daily crew diet based on crop and nutritional information stored in the DBMS. Each of the selected food items was assumed to be harvested from a yet-to-be designed ALSS biomass production subsystem and further prepared in accompanying food preparation subsystems. The developed DBMS allows for the analysis of the nutrient composition of a sample 20-day diet for future Advanced Life Support missions and is able to determine the required quantities of food needed to satisfy the crew's daily consumption. In addition, based on published crop growth rates, the DBMS was able to calculate the required size of the biomass production area needed to satisfy the daily food requirements for the crew. Results from this study can be used to help design future ALSS for which the integration of various subsystems (e.g., biomass production, food preparation and consumption, and waste processing) is paramount for the success of the mission.

A management information system to study space diets.

PubMed

Kang, Sukwon; Both, A J

2002-01-01

A management information system (MIS), including a database management system (DBMS) and a decision support system (DSS), was developed to dynamically analyze the variable nutritional content of foods grown and prepared in an Advanced Life Support System (ALSS) such as required for long-duration space missions. The DBMS was designed around the known nutritional content of a list of candidate crops and their prepared foods. The DSS was designed to determine the composition of the daily crew diet based on crop and nutritional information stored in the DBMS. Each of the selected food items was assumed to be harvested from a yet-to-be designed ALSS biomass production subsystem and further prepared in accompanying food preparation subsystems. The developed DBMS allows for the analysis of the nutrient composition of a sample 20-day diet for future Advanced Life Support missions and is able to determine the required quantities of food needed to satisfy the crew's daily consumption. In addition, based on published crop growth rates, the DBMS was able to calculate the required size of the biomass production area needed to satisfy the daily food requirements for the crew. Results from this study can be used to help design future ALSS for which the integration of various subsystems (e.g., biomass production, food preparation and consumption, and waste processing) is paramount for the success of the mission.

Exploring U.S Cropland - A Web Service based Cropland Data Layer Visualization, Dissemination and Querying System (Invited)

NASA Astrophysics Data System (ADS)

Yang, Z.; Han, W.; di, L.

2010-12-01

The National Agricultural Statistics Service (NASS) of the USDA produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, U.S. crop specific land cover classification. These digital data layers are widely used for a variety of applications by universities, research institutions, government agencies, and private industry in climate change studies, environmental ecosystem studies, bioenergy production & transportation planning, environmental health research and agricultural production decision making. The CDL is also used internally by NASS for crop acreage and yield estimation. Like most geospatial data products, the CDL product is only available by CD/DVD delivery or online bulk file downloading via the National Research Conservation Research (NRCS) Geospatial Data Gateway (external users) or in a printed paper map format. There is no online geospatial information access and dissemination, no crop visualization & browsing, no geospatial query capability, nor online analytics. To facilitate the application of this data layer and to help disseminating the data, a web-service based CDL interactive map visualization, dissemination, querying system is proposed. It uses Web service based service oriented architecture, adopts open standard geospatial information science technology and OGC specifications and standards, and re-uses functions/algorithms from GeoBrain Technology (George Mason University developed). This system provides capabilities of on-line geospatial crop information access, query and on-line analytics via interactive maps. It disseminates all data to the decision makers and users via real time retrieval, processing and publishing over the web through standards-based geospatial web services. A CDL region of interest can also be exported directly to Google Earth for mashup or downloaded for use with other desktop application. This web service based system greatly improves equal-accessibility, interoperability, usability, and data visualization, facilitates crop geospatial information usage, and enables US cropland online exploring capability without any client-side software installation. It also greatly reduces the need for paper map and analysis report printing and media usages, and thus enhances low-carbon Agro-geoinformation dissemination for decision support.

Are We on the Right Track: Can Our Understanding of Abscission in Model Systems Promote or Derail Making Improvements in Less Studied Crops?

PubMed Central

Patterson, Sara E.; Bolivar-Medina, Jenny L.; Falbel, Tanya G.; Hedtcke, Janet L.; Nevarez-McBride, Danielle; Maule, Andrew F.; Zalapa, Juan E.

2016-01-01

As the world population grows and resources and climate conditions change, crop improvement continues to be one of the most important challenges for agriculturalists. The yield and quality of many crops is affected by abscission or shattering, and environmental stresses often hasten or alter the abscission process. Understanding this process can not only lead to genetic improvement, but also changes in cultural practices and management that will contribute to higher yields, improved quality and greater sustainability. As plant scientists, we have learned significant amounts about this process through the study of model plants such as Arabidopsis, tomato, rice, and maize. While these model systems have provided significant valuable information, we are sometimes challenged to use this knowledge effectively as variables including the economic value of the crop, the uniformity of the crop, ploidy levels, flowering and crossing mechanisms, ethylene responses, cultural requirements, responses to changes in environment, and cellular and tissue specific morphological differences can significantly influence outcomes. The value of genomic resources for lesser-studied crops such as cranberries and grapes and the orphan crop fonio will also be considered. PMID:26858730

Are We on the Right Track: Can Our Understanding of Abscission in Model Systems Promote or Derail Making Improvements in Less Studied Crops?

PubMed

Patterson, Sara E; Bolivar-Medina, Jenny L; Falbel, Tanya G; Hedtcke, Janet L; Nevarez-McBride, Danielle; Maule, Andrew F; Zalapa, Juan E

2015-01-01

As the world population grows and resources and climate conditions change, crop improvement continues to be one of the most important challenges for agriculturalists. The yield and quality of many crops is affected by abscission or shattering, and environmental stresses often hasten or alter the abscission process. Understanding this process can not only lead to genetic improvement, but also changes in cultural practices and management that will contribute to higher yields, improved quality and greater sustainability. As plant scientists, we have learned significant amounts about this process through the study of model plants such as Arabidopsis, tomato, rice, and maize. While these model systems have provided significant valuable information, we are sometimes challenged to use this knowledge effectively as variables including the economic value of the crop, the uniformity of the crop, ploidy levels, flowering and crossing mechanisms, ethylene responses, cultural requirements, responses to changes in environment, and cellular and tissue specific morphological differences can significantly influence outcomes. The value of genomic resources for lesser-studied crops such as cranberries and grapes and the orphan crop fonio will also be considered.

Prioritizing stream types according to their potential risk to receive crop plant material--A GIS-based procedure to assist in the risk assessment of genetically modified crops and systemic insecticide residues.

PubMed

Bundschuh, Rebecca; Kuhn, Ulrike; Bundschuh, Mirco; Naegele, Caroline; Elsaesser, David; Schlechtriemen, Ulrich; Oehen, Bernadette; Hilbeck, Angelika; Otto, Mathias; Schulz, Ralf; Hofmann, Frieder

2016-03-15

Crop plant residues may enter aquatic ecosystems via wind deposition or surface runoff. In the case of genetically modified crops or crops treated with systemic pesticides, these materials may contain insecticidal Bt toxins or pesticides that potentially affect aquatic life. However, the particular exposure pattern of aquatic ecosystems (i.e., via plant material) is not properly reflected in current risk assessment schemes, which primarily focus on waterborne toxicity and not on plant material as the route of uptake. To assist in risk assessment, the present study proposes a prioritization procedure of stream types based on the freshwater network and crop-specific cultivation data using maize in Germany as a model system. To identify stream types with a high probability of receiving crop materials, we developed a formalized, criteria-based and thus transparent procedure that considers the exposure-related parameters, ecological status--an estimate of the diversity and potential vulnerability of local communities towards anthropogenic stress--and availability of uncontaminated reference sections. By applying the procedure to maize, ten stream types out of 38 are expected to be the most relevant if the ecological effects from plant-incorporated pesticides need to be evaluated. This information is an important first step to identifying habitats within these stream types with a high probability of receiving crop plant material at a more local scale, including accumulation areas. Moreover, the prioritization procedure developed in the present study may support the selection of aquatic species for ecotoxicological testing based on their probability of occurrence in stream types having a higher chance of exposure. Finally, this procedure can be adapted to any geographical region or crop of interest and is, therefore, a valuable tool for a site-specific risk assessment of crop plants carrying systemic pesticides or novel proteins, such as insecticidal Bt toxins, expressed in genetically modified crops. Copyright © 2015 Elsevier B.V. All rights reserved.

Sustainable harvest: managing plasticity for resilient crops

PubMed Central

Bloomfield, Justin A; Rose, Terry J; King, Graham J

2014-01-01

Maintaining crop production to feed a growing world population is a major challenge for this period of rapid global climate change. No consistent conceptual or experimental framework for crop plants integrates information at the levels of genome regulation, metabolism, physiology and response to growing environment. An important role for plasticity in plants is assisting in homeostasis in response to variable environmental conditions. Here, we outline how plant plasticity is facilitated by epigenetic processes that modulate chromatin through dynamic changes in DNA methylation, histone variants, small RNAs and transposable elements. We present examples of plant plasticity in the context of epigenetic regulation of developmental phases and transitions and map these onto the key stages of crop establishment, growth, floral initiation, pollination, seed set and maturation of harvestable product. In particular, we consider how feedback loops of environmental signals and plant nutrition affect plant ontogeny. Recent advances in understanding epigenetic processes enable us to take a fresh look at the crosstalk between regulatory systems that confer plasticity in the context of crop development. We propose that these insights into genotype × environment (G × E) interaction should underpin development of new crop management strategies, both in terms of information-led agronomy and in recognizing the role of epigenetic variation in crop breeding. PMID:24891039

The biogeochemistry of bioenergy landscapes: carbon, nitrogen, and water considerations.

PubMed

Robertson, G Philip; Hamilton, Stephen K; Del Grosso, Stephen J; Parton, William J

2011-06-01

The biogeochemical liabilities of grain-based crop production for bioenergy are no different from those of grain-based food production: excessive nitrate leakage, soil carbon and phosphorus loss, nitrous oxide production, and attenuated methane uptake. Contingent problems are well known, increasingly well documented, and recalcitrant: freshwater and coastal marine eutrophication, groundwater pollution, soil organic matter loss, and a warming atmosphere. The conversion of marginal lands not now farmed to annual grain production, including the repatriation of Conservation Reserve Program (CRP) and other conservation set-aside lands, will further exacerbate the biogeochemical imbalance of these landscapes, as could pressure to further simplify crop rotations. The expected emergence of biorefinery and combustion facilities that accept cellulosic materials offers an alternative outcome: agricultural landscapes that accumulate soil carbon, that conserve nitrogen and phosphorus, and that emit relatively small amounts of nitrous oxide to the atmosphere. Fields in these landscapes are planted to perennial crops that require less fertilizer, that retain sediments and nutrients that could otherwise be transported to groundwater and streams, and that accumulate carbon in both soil organic matter and roots. If mixed-species assemblages, they additionally provide biodiversity services. Biogeochemical responses of these systems fall chiefly into two areas: carbon neutrality and water and nutrient conservation. Fluxes must be measured and understood in proposed cropping systems sufficient to inform models that will predict biogeochemical behavior at field, landscape, and regional scales. Because tradeoffs are inherent to these systems, a systems approach is imperative, and because potential biofuel cropping systems and their environmental contexts are complex and cannot be exhaustively tested, modeling will be instructive. Modeling alternative biofuel cropping systems converted from different starting points, for example, suggests that converting CRP to corn ethanol production under conventional tillage results in substantially increased net greenhouse gas (GHG) emissions that can be only partly mitigated with no-till management. Alternatively, conversion of existing cropland or prairie to switchgrass production results in a net GHG sink. Outcomes and policy must be informed by science that adequately quantifies the true biogeochemical costs and advantages of alternative systems.

The beginnings of crop phosphoproteomics: exploring early warning systems of stress

PubMed Central

Rampitsch, Christof; Bykova, Natalia V.

2012-01-01

This review examines why a knowledge of plant protein phosphorylation events is important in devising strategies to protect crops from both biotic and abiotic stresses, and why proteomics should be included when studying stress pathways. Most of the achievements in elucidating phospho-signaling pathways in biotic and abiotic stress are reported from model systems: while these are discussed, this review attempts mainly to focus on work done with crops, with examples of achievements reported from rice, maize, wheat, grape, Brassica, tomato, and soy bean after cold acclimation, hormonal and oxidative hydrogen peroxide treatment, salt stress, mechanical wounding, or pathogen challenge. The challenges that remain to transfer this information into a format that can be used to protect crops against biotic and abiotic stresses are enormous. The tremendous increase in the speed and ease of DNA sequencing is poised to reveal the whole genomes of many crop species in the near future, which will facilitate phosphoproteomics and phosphogenomics research. PMID:22783265

Fusarium and mycotoxin spectra in Swiss barley are affected by various cropping techniques.

PubMed

Schöneberg, Torsten; Martin, Charlotte; Wettstein, Felix E; Bucheli, Thomas D; Mascher, Fabio; Bertossa, Mario; Musa, Tomke; Keller, Beat; Vogelgsang, Susanne

2016-10-01

Fusarium head blight is one of the most important cereal diseases worldwide. Cereals differ in terms of the main occurring Fusarium species and the infection is influenced by various factors, such as weather and cropping measures. Little is known about Fusarium species in barley in Switzerland, hence harvest samples from growers were collected in 2013 and 2014, along with information on respective cropping factors. The incidence of different Fusarium species was obtained by using a seed health test and mycotoxins were quantified by LC-MS/MS. With these techniques, the most dominant species, F. graminearum, and the most prominent mycotoxin, deoxynivalenol (DON), were identified. Between the three main Swiss cropping systems, Organic, Extenso and Proof of ecological performance, we observed differences with the lowest incidence and toxin accumulation in organically cultivated barley. Hence, we hypothesise that this finding was based on an array of growing techniques within a given cropping system. We observed that barley samples from fields with maize as previous crop had a substantially higher F. graminearum incidence and elevated DON accumulation compared with other previous crops. Furthermore, the use of reduced tillage led to a higher disease incidence and toxin content compared with samples from ploughed fields. Further factors increasing Fusarium infection were high nitrogen fertilisation as well as the application of fungicides and growth regulators. Results from the current study can be used to develop optimised cropping systems that reduce the risks of mycotoxin contamination.

Fusarium and mycotoxin spectra in Swiss barley are affected by various cropping techniques

PubMed Central

Schöneberg, Torsten; Martin, Charlotte; Wettstein, Felix E.; Bucheli, Thomas D.; Mascher, Fabio; Bertossa, Mario; Musa, Tomke; Keller, Beat; Vogelgsang, Susanne

2016-01-01

ABSTRACT Fusarium head blight is one of the most important cereal diseases worldwide. Cereals differ in terms of the main occurring Fusarium species and the infection is influenced by various factors, such as weather and cropping measures. Little is known about Fusarium species in barley in Switzerland, hence harvest samples from growers were collected in 2013 and 2014, along with information on respective cropping factors. The incidence of different Fusarium species was obtained by using a seed health test and mycotoxins were quantified by LC-MS/MS. With these techniques, the most dominant species, F. graminearum, and the most prominent mycotoxin, deoxynivalenol (DON), were identified. Between the three main Swiss cropping systems, Organic, Extenso and Proof of ecological performance, we observed differences with the lowest incidence and toxin accumulation in organically cultivated barley. Hence, we hypothesise that this finding was based on an array of growing techniques within a given cropping system. We observed that barley samples from fields with maize as previous crop had a substantially higher F. graminearum incidence and elevated DON accumulation compared with other previous crops. Furthermore, the use of reduced tillage led to a higher disease incidence and toxin content compared with samples from ploughed fields. Further factors increasing Fusarium infection were high nitrogen fertilisation as well as the application of fungicides and growth regulators. Results from the current study can be used to develop optimised cropping systems that reduce the risks of mycotoxin contamination. PMID:27491813

VegScape: U.S. Crop Condition Monitoring Service

NASA Astrophysics Data System (ADS)

mueller, R.; Yang, Z.; Di, L.

2013-12-01

Since 1995, the US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has provided qualitative biweekly vegetation condition indices to USDA policymakers and the public on a weekly basis during the growing season. Vegetation indices have proven useful for assessing crop condition and identifying the areal extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. With growing emphasis on more extreme weather events and food security issues rising to the forefront of national interest, a new vegetation condition monitoring system was developed. The new vegetation condition portal named VegScape was initiated at the start of the 2013 growing season. VegScape delivers web mapping service based interactive vegetation indices. Users can use an interactive map to explore, query and disseminate current crop conditions. Vegetation indices like Normal Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and mean, median, and ratio comparisons to prior years can be constructed for analytical purposes and on-demand crop statistics. The NASA MODIS satellite with 250 meter (15 acres) resolution and thirteen years of data history provides improved spatial and temporal resolutions and delivers improved detailed timely (i.e., daily) crop specific condition and dynamics. VegScape thus provides supplemental information to support NASS' weekly crop reports. VegScape delivers an agricultural cultivated crop mask and the most recent Cropland Data Layer (CDL) product to exploit the agricultural domain and visualize prior years' planted crops. Additionally, the data can be directly exported to Google Earth for web mashups or delivered via web mapping services for uses in other applications. VegScape supports the ethos of data democracy by providing free and open access to digital geospatial data layers using open geospatial standards, thereby supporting transparent and collaborative government initiatives. NASS developed VegScape in cooperation with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. VegScape Ratio to Median NDVI

Modeling crop residue burning experiments to evaluate smoke emissions and plume transport.

PubMed

Zhou, Luxi; Baker, Kirk R; Napelenok, Sergey L; Pouliot, George; Elleman, Robert; O'Neill, Susan M; Urbanski, Shawn P; Wong, David C

2018-06-15

Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment in the Pacific Northwest focused on cropland residue burning was used to evaluate model performance in capturing surface and aloft impacts from the burning events. The Community Multiscale Air Quality (CMAQ) model was used to simulate multiple crop residue burns with 2 km grid spacing using field-specific information and also more general assumptions traditionally used to support National Emission Inventory based assessments. Field study specific information, which includes area burned, fuel consumption, and combustion completeness, resulted in increased biomass consumption by 123 tons (60% increase) on average compared to consumption estimated with default methods in the National Emission Inventory (NEI) process. Buoyancy heat flux, a key parameter for model predicted fire plume rise, estimated from fuel loading obtained from field measurements can be 30% to 200% more than when estimated using default field information. The increased buoyancy heat flux resulted in higher plume rise by 30% to 80%. This evaluation indicates that the regulatory air quality modeling system can replicate intensity and transport (horizontal and vertical) features for crop residue burning in this region when region-specific information is used to inform emissions and plume rise calculations. Further, previous vertical emissions allocation treatment of putting all cropland residue burning in the surface layer does not compare well with measured plume structure and these types of burns should be modeled more similarly to prescribed fires such that plume rise is based on an estimate of buoyancy. Copyright © 2018 Elsevier B.V. All rights reserved.

A Proprietary Information Dissemination and Education System.

ERIC Educational Resources Information Center

Rollins, Timothy J.; Golden, Kerry

1994-01-01

In focus group interviews, 14 Pennsylvania Crop Management Association technicians identified their primary role as information providers and consultants, felt the need for better communication skills and training as nonformal adult educators, considered human resources the most valuable information sources, and believed farmers participated only…

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

NASA Astrophysics Data System (ADS)

Shang, J.

2015-12-01

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

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System.

PubMed

Ni, Jun; Yao, Lili; Zhang, Jingchao; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

2017-03-03

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R²) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

PubMed Central

Ni, Jun; Yao, Lili; Zhang, Jingchao; Cao, Weixing; Zhu, Yan; Tai, Xiuxiang

2017-01-01

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively. PMID:28273815

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.

Mind the Roots: Phenotyping Below-Ground Crop Diversity and Its Influence on Final Yield

NASA Astrophysics Data System (ADS)

Nieters, C.; Guadagno, C. R.; Lemli, S.; Hosseini, A.; Ewers, B. E.

2017-12-01

Changes in global climate patterns and water regimes are having profound impacts on worldwide crop production. An ever-growing population paired with increasing temperatures and unpredictable periods of severe drought call for accurate modeling of future crop yield. Although novel approaches are being developed in high-throughput, above-ground image phenotyping, the below-ground plant system is still poorly phenotyped. Collection of plant root morphology and hydraulics are needed to inform mathematical models to reliably estimate yields of crops grown in sub-optimal conditions. We used Brassica rapa to inform our model as it is a globally cultivated crop with several functionally diverse cultivars. Specifically, we use 7 different accessions from oilseed (R500 and Yellow Sarson), leafy type (Pac choi and Chinese cabbage), a vegetable turnip, and two Wisconsin Fast Plants (Imb211 and Fast Plant self-compatible), which have shorter life cycles and potentially large differences in allocation to roots. Bi-weekly, we harvested above and below-ground biomass to compare the varieties in terms of carbon allocation throughout their life cycle. Using WinRhizo software, we analyzed root system length and surface area to compare and contrast root morphology among cultivars. Our results confirm that root structural characteristics are crucial to explain plant water use and carbon allocation. The root:shoot ratio reveals a significant (p < 0.01) difference among crop accession. To validate the procedure across different varieties and life stages we also compared surface area results from the image-based technology to dry biomass finding a strong linear relationship (R2= 0.85). To assess the influence of a diverse above-ground morphology on the root system we also measured above-ground anatomical and physiological traits such as gas exchange, chlorophyll content, and chlorophyll a fluorescence. A thorough analysis of the root system will clarify carbon dynamics and hydraulics at the whole-plant level, improving final yield predictions.

From LACIE to GEOGLAM: Integrating Earth Observations into Operational Agricultural Monitoring Systems

NASA Astrophysics Data System (ADS)

Becker-Reshef, I.; Justice, C. O.

2012-12-01

Earth observation data, owing to their synoptic, timely and repetitive coverage, have long been recognized as an indispensible tool for agricultural monitoring at local to global scales. Research and development over the past several decades in the field of agricultural remote sensing has led to considerable capacity for crop monitoring within the current operational monitoring systems. These systems are relied upon nationally and internationally to provide crop outlooks and production forecasts as the growing season progresses. This talk will discuss the legacy and current state of operational agricultural monitoring using earth observations. In the US, the National Aeronautics and Space Administration (NASA) and the US Department of Agriculture (USDA) have been collaborating to monitor global agriculture from space since the 1970s. In 1974, the USDA, NASA and National Oceanic and Atmospheric Administration (NOAA) initiated the Large Area Crop Inventory Experiment (LACIE) which demonstrated that earth observations could provide vital information on crop production, with unprecedented accuracy and timeliness, prior to harvest. This experiment spurred many agencies and researchers around the world to further develop and evaluate remote sensing technologies for timely, large area, crop monitoring. The USDA and NASA continue to closely collaborate. More recently they jointly initiated the Global Agricultural Monitoring Project (GLAM) to enhance the agricultural monitoring and the crop-production estimation capabilities of the USDA Foreign Agricultural Service by using the new generation of NASA satellite observations including from MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments. Internationally, in response to the growing calls for improved agricultural information, the Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative which was adopted by the G20 as part of the action plan on food price volatility and agriculture. The goal of GEOGLAM is to enhance agricultural production estimates through leveraging advances in the research domain and in satellite technologies, and integrating these into the existing operational monitoring systems.

Varying geospatial analyses to assess climate risk and adaptive capacity in a hotter, drier Southwestern United States

NASA Astrophysics Data System (ADS)

Elias, E.; Reyes, J. J.; Steele, C. M.; Rango, A.

2017-12-01

Assessing vulnerability of agricultural systems to climate variability and change is vital in securing food systems and sustaining rural livelihoods. Farmers, ranchers, and forest landowners rely on science-based, decision-relevant, and localized information to maintain production, ecological viability, and economic returns. This contribution synthesizes a collection of research on the future of agricultural production in the American Southwest (SW). Research was based on a variety of geospatial methodologies and datasets to assess the vulnerability of rangelands and livestock, field crops, specialty crops, and forests in the SW to climate-risk and change. This collection emerged from the development of regional vulnerability assessments for agricultural climate-risk by the U.S. Department of Agriculture (USDA) Climate Hub Network, established to deliver science-based information and technologies to enable climate-informed decision-making. Authors defined vulnerability differently based on their agricultural system of interest, although each primarily focuses on biophysical systems. We found that an inconsistent framework for vulnerability and climate risk was necessary to adequately capture the diversity, variability, and heterogeneity of SW landscapes, peoples, and agriculture. Through the diversity of research questions and methodologies, this collection of articles provides valuable information on various aspects of SW vulnerability. All articles relied on geographic information systems technology, with highly variable levels of complexity. Agricultural articles used National Agricultural Statistics Service data, either as tabular county level summaries or through the CropScape cropland raster datasets. Most relied on modeled historic and future climate information, but with differing assumptions regarding spatial resolution and temporal framework. We assert that it is essential to evaluate climate risk using a variety of complementary methodologies and perspectives. In addition, we found that spatial analysis supports informed adaptation, within and outside the SW United States. The persistence and adaptive capacity of agriculture in the water-limited Southwest serves as an instructive example and may offer solutions to reduce future climate risk.

Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

2014-12-01

The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are associated to derive food production estimates. Based on trends analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. CropWatch bulletin can be downloaded from the CropWatch website at http://www.cropwatch.com.cn.

Site-specific nutrient management systems

USDA-ARS?s Scientific Manuscript database

Site-specific nutrient management systems were created to manage for spatial and temporal variability in biophysical factors that determine the availability and demand of crop nutrients. These systems differ among geographical regions in the information utilized and way they operate to accomplish th...

Greenhouse irrigation control system design based on ZigBee and fuzzy PID technology

NASA Astrophysics Data System (ADS)

Zhou, Bing; Yang, Qiliang; Liu, Kenan; Li, Peiqing; Zhang, Jing; Wang, Qijian

In order to achieve the water demand information accurately detect of the greenhouse crop and its precision irrigation automatic control, this article has designed a set of the irrigated control system based on ZigBee and fuzzy PID technology, which composed by the soil water potential sensor, CC2530F256 wireless microprocessor, IAR Embedded Workbench software development platform. And the time of Irrigation as the output .while the amount of soil water potential and crop growth cycle as the input. The article depended on Greenhouse-grown Jatropha to verify the object, the results show that the system can irrigate timely and appropriately according to the soil water potential and water demend of the different stages of Jatropha growth , which basically meet the design requirements. Therefore, the system has broad application prospects in the amount of greenhouse crop of fine control irrigation.

DNA-informed breeding of rosaceous crops: promises, progress and prospects

PubMed Central

Peace, Cameron P

2017-01-01

Crops of the Rosaceae family provide valuable contributions to rural economies and human health and enjoyment. Sustained solutions to production challenges and market demands can be met with genetically improved new cultivars. Traditional rosaceous crop breeding is expensive and time-consuming and would benefit from improvements in efficiency and accuracy. Use of DNA information is becoming conventional in rosaceous crop breeding, contributing to many decisions and operations, but only after past decades of solved challenges and generation of sufficient resources. Successes in deployment of DNA-based knowledge and tools have arisen when the ‘chasm’ between genomics discoveries and practical application is bridged systematically. Key steps are establishing breeder desire for use of DNA information, adapting tools to local breeding utility, identifying efficient application schemes, accessing effective services in DNA-based diagnostics and gaining experience in integrating DNA information into breeding operations and decisions. DNA-informed germplasm characterization for revealing identity and relatedness has benefitted many programs and provides a compelling entry point to reaping benefits of genomics research. DNA-informed germplasm evaluation for predicting trait performance has enabled effective reallocation of breeding resources when applied in pioneering programs. DNA-based diagnostics is now expanding from specific loci to genome-wide considerations. Realizing the full potential of this expansion will require improved accuracy of predictions, multi-trait DNA profiling capabilities, streamlined breeding information management systems, strategies that overcome plant-based features that limit breeding progress and widespread training of current and future breeding personnel and allied scientists. PMID:28326185

Rice crop risk map in Babahoyo canton (Ecuador)

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

The Potential Role of Neglected and Underutilised Crop Species as Future Crops under Water Scarce Conditions in Sub-Saharan Africa

PubMed Central

Chivenge, Pauline; Mabhaudhi, Tafadzwanashe; Modi, Albert T.; Mafongoya, Paramu

2015-01-01

Modern agricultural systems that promote cultivation of a very limited number of crop species have relegated indigenous crops to the status of neglected and underutilised crop species (NUCS). The complex interactions of water scarcity associated with climate change and variability in sub-Saharan Africa (SSA), and population pressure require innovative strategies to address food insecurity and undernourishment. Current research efforts have identified NUCS as having potential to reduce food and nutrition insecurity, particularly for resource poor households in SSA. This is because of their adaptability to low input agricultural systems and nutritional composition. However, what is required to promote NUCS is scientific research including agronomy, breeding, post-harvest handling and value addition, and linking farmers to markets. Among the essential knowledge base is reliable information about water utilisation by NUCS with potential for commercialisation. This commentary identifies and characterises NUCS with agronomic potential in SSA, especially in the semi-arid areas taking into consideration inter alia: (i) what can grow under water-scarce conditions, (ii) water requirements, and (iii) water productivity. Several representative leafy vegetables, tuber crops, cereal crops and grain legumes were identified as fitting the NUCS category. Agro-biodiversity remains essential for sustainable agriculture. PMID:26016431

Evaporation from irrigated crops: Its measurement, modeling and estimation from remotely sensed data

NASA Astrophysics Data System (ADS)

Garatuza-Payan, Jaime

The research described in this dissertation is predicated on the hypothesis that remotely sensed information from climatological satellites can be used to estimate the actual evapotranspiration from agricultural crops to improve irrigation scheduling and water use efficiency. The goal of the enabling research program described here was to facilitate and demonstrate the potential use of satellite data for the rapid and routine estimation of water use by irrigated crops in the Yaqui Valley irrigation scheme, an extensive irrigated area in Sonora, Mexico. The approach taken was first, to measure and model the evapotranspiration and crop factors for wheat and cotton, the most common irrigated crops in the Yaqui Valley scheme. Second, to develop and test a high-resolution (4 km x 4 km) method for determining cloud cover and solar radiation from GOES satellite data. Then third, to demonstrate the application of satellite data to calculate the actual evaporation for sample crops in the Yaqui Valley scheme by combining estimates of potential rate with relevant crop factors and information on crop management. Results show that it is feasible to provide routine estimates of evaporation for the most common crops in the Yaqui Valley irrigation scheme from satellite data. Accordingly, a system to provide such estimates has been established and the Water Users Association, the entity responsible for water distribution in Yaqui Valley, can now use them to decide whether specific fields need irrigation. A Web site (teka-pucem.itson.mx) is also being created which will allow individual farmers to have direct access to the evaporation estimates via the Internet.

Method for estimating pesticide use for county areas of the conterminous United States

USGS Publications Warehouse

Thelin, Gail P.; Gianessi, Leonard P.

2000-01-01

Information on the amount and distribution of pesticide compounds used throughout the United States is essential to evaluate the relation between water quality and pesticide use. This information is the basis of the U.S. Geological Survey?s National Water-Quality Assessment (NAWQA) Program studies of the effects of pesticides on water quality in 57 major hydrologic systems, or study units, located throughout the conterminous United States. To support these studies, a method was devised to estimate county pesticide use for the conterminous United States by combining (1) state-level information on pesticide use rates available from the National Center for Food and Agricultural Policy, and (2) county-level information on harvested crop acreage from the Census of Agriculture. The average annual pesticide use, the total amount of pesticides applied (in pounds), and the corresponding area treated (in acres) were compiled for the 208 pesticide compounds that are applied to crops in the conterminous United States. Pesticide use was ranked by compound and crop on the basis of the amount of each compound applied to 86 selected crops. Tabular summaries of pesticide use for NAWQA study units and for the Nation were prepared, along with maps that show the distribution of selected pesticides to agricultural land.

Web-based information system design of agricultural management towards self-sufficiency local food in North Aceh

NASA Astrophysics Data System (ADS)

Salahuddin; Husaini; Anwar

2018-01-01

The agricultural sector, especially food crops and horticulture, is one of the sectors driving regional economic pillars in Aceh Utara Regency of Aceh Province. Some agricultural products and food crops that become excellent products in North Aceh regency are: rice, corn, peanuts, long beans, cassava and soybeans. The Local Government of North Aceh Regency has not been optimal in empowering and maximizing the potential of agriculture resources. One of the obstacles is caused by the North Aceh Regency Government does not have an adequate database and web information system/GIS (Geographic Information System) for data management of agricultural centre in North Aceh Regency. This research is expected to assist local government of North Aceh Regency in managing agriculture sector to realize local food independence the region in supporting national food security program. The method in this research is using waterfall method for designing and making information system by conducting sequential process starting from data collection stage, requirement analysis, design, coding, testing and implementation system. The result of this research is a web-based information system for the management of agriculture superior agricultural product centre in North Aceh. This application provides information mapping the location of agricultural superior product producers and mapping of potential locations for the development of certain commodities in North Aceh Regency region in realizing food self-sufficiency in the region.

Development of low-altitude remote sensing systems for crop production management

USDA-ARS?s Scientific Manuscript database

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field. Precision agriculture is built on agricultural mechanization and state-of-the-art technologies of geographical information systems (GIS), global positioning systems (G...

Using a Decision Support System to Optimize Production of Agricultural Crop Residue Biofeedstock

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

Reed L. Hoskinson; Ronald C. Rope; Raymond K. Fink

2007-04-01

For several years the Idaho National Laboratory (INL) has been developing a Decision Support System for Agriculture (DSS4Ag) which determines the economically optimum recipe of various fertilizers to apply at each site in a field to produce a crop, based on the existing soil fertility at each site, as well as historic production information and current prices of fertilizers and the forecast market price of the crop at harvest, for growing a crop such as wheat, potatoes, corn, or cotton. In support of the growing interest in agricultural crop residues as a bioenergy feedstock, we have extended the capability ofmore » the DSS4Ag to develop a variable-rate fertilizer recipe for the simultaneous economically optimum production of both grain and straw, and have been conducting field research to test this new DSS4Ag. In this paper we report the results of two years of field research testing and enhancing the DSS4Ag’s ability to economically optimize the fertilization for the simultaneous production of both grain and its straw, where the straw is an agricultural crop residue that can be used as a biofeedstock.« less

The Potential of Small Satellites for Crop Monitoring in Emerging Economies

NASA Astrophysics Data System (ADS)

Bydekerke, L.; Meuleman, K.

2008-08-01

The use of low resolution data for monitoring of the overall vegetation condition and crops is nowadays wide spread in emerging economies. Various initiatives, global and local, have promoted the use of this type of imagery for assessing the progress of the growing season since the eighties. The normalized difference vegetation Index (NDVI), from various sensors with 250m to 8 km resolution, are used to identify potential anomalies in vegetation development which, in combination with other data, are used to identify emerging crisis situations in crop development and production before harvest time. Satellite data is analyzed by specialized centers and crop / vegetation assessments are summarized into bulletins, which are then used for communication with non-remote sensing specialists at the policy level. Satellite data is currently provided by large expensive space infrastructures and centrally distributed to the users. In this paper the current flow of information from satellite to information for agriculture is analyzed and the potential contribution of low cost small satellite in addressing the needs of the users is discussed. Two scenario's are presented: i. a centralized system whereby a few institutes have access to data generated by small satellites which process and analyze the data for use by analysts; ii. a decentralized system whereby a variety of users have direct access to data generated by small satellites who are capable of extracting, processing and analyzing information relevant for crop monitoring. The work shows that with affordable space infrastructure, as small satellites, the second scenario may become possible, but the complexity and the cost of the ground segment service remain limiting factors. Expertise and knowledge for processing, analysis and maintenance of IT/infrastructure is currently not enough, specifically in Institutions whose mandate is dealing with crop monitoring, such as the Ministries of Agriculture. However, in the short term, a limited number of specialized centers, can play a key role in gradually facilitating the integration of remote sensing information into the daily workflow, and gradually optimizing costs and efforts. The potential use of future small satellite missions such as e.g. SPOT-Vegetation continuity mission (Proba-V) is also addressed.

Application of modified VICAR/IBIS GIS to analysis of July 1991 Flevoland AIRSAR data

NASA Technical Reports Server (NTRS)

Norikane, L.; Broek, B.; Freeman, A.

1992-01-01

Three overflights of the Flevoland calibration/agricultural site were made by the JPL Airborne Synthetic Aperture Radar (AIRSAR) on 3, 12, and 28 July 1991 as part of MAC-Europe '92. A polygon map was generated at TNO-FEL which overlayed the slant range projected July 3 data set. Each polygon was identified by a sequence of points and a crop label. The polygon map was composed of 452 uniquely identified polygons and 15 different crop types. Analysis of the data was done using our modified Video Image Communication and Retrieval/Image Based Information System Geographic Information System (VICAR/IBIS GIS). This GIS is an extension of the VICAR/IBIS GIS first developed by Bryant in the 1970's which is itself an extension of the VICAR image processing system also developed at JPL.

Using knowledge of agricultural practices to enhance through-the-season interpretation of Landsat data

NASA Technical Reports Server (NTRS)

Malila, W. A.; Pestre, C. R.

1984-01-01

Landsat data contain features that can be interpreted to produce information about crops, in support of crop estimation procedures. This paper considers ways in which detailed knowledge of agricultural practices and events might increase and improve the utilization of Landsat data in both the predictive and observational or measurement components of such procedures. Landsat observables related to agricultural practices and events throughout the cropping season are listed. Agricultural fields are identified as the preferred observational units for incorporating refined agricultural understanding, such as crop rotation patterns, into machine procedures. Uses of Landsat data from both prior seasons and the current season are considered, as is use of predictive models of crop appearance. The investigation of knowledge engineering systems tailored to through-the-season estimation problems is recommended for long range development.

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.

Identifying Decision Support Tools to Bridge Climate and Agricultural Needs in the Midwest

NASA Astrophysics Data System (ADS)

Hall, B. L.; Kluck, D. R.; Hatfield, J.; Black, C.; Kellner, O.; Woloszyn, M.; Timlin, M. S.

2015-12-01

Climate monitoring tools designed to help stakeholders reduce climate impacts have been developed for the primary Midwest field crops of corn and soybean. However, the region also produces vital livestock and specialty crops that currently lack similar climate monitoring and projection tools. In autumn 2015, the National Oceanic and Atmospheric Administration's (NOAA's) National Integrated Drought Information System (NIDIS) and Midwestern Regional Climate Center (MRCC) partnered with the US Department of Agriculture's Midwest Climate Hub to convene agriculture stakeholders, climate scientists, and climate service specialists to discuss climate impacts and needs for these two, often under-represented, sectors. The goals of this workshop were to (1) identify climate impacts that specialty crops and livestock producers face within the Midwest, (2) develop an understanding of the types of climate and weather information and tools currently available in the Midwest that could be applied to decision making, and (3) discover the types of climate and weather information and tools needed to address concerns of specialty crop and livestock commodities across the Midwest. This presentation will discuss the workshop and provide highlights of the outcomes that developed into strategic plans for the future to better serve these sectors of agriculture in the Midwest.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

Cereal Crop Proteomics: Systemic Analysis of Crop Drought Stress Responses Towards Marker-Assisted Selection Breeding

PubMed Central

Ghatak, Arindam; Chaturvedi, Palak; Weckwerth, Wolfram

2017-01-01

Sustainable crop production is the major challenge in the current global climate change scenario. Drought stress is one of the most critical abiotic factors which negatively impact crop productivity. In recent years, knowledge about molecular regulation has been generated to understand drought stress responses. For example, information obtained by transcriptome analysis has enhanced our knowledge and facilitated the identification of candidate genes which can be utilized for plant breeding. On the other hand, it becomes more and more evident that the translational and post-translational machinery plays a major role in stress adaptation, especially for immediate molecular processes during stress adaptation. Therefore, it is essential to measure protein levels and post-translational protein modifications to reveal information about stress inducible signal perception and transduction, translational activity and induced protein levels. This information cannot be revealed by genomic or transcriptomic analysis. Eventually, these processes will provide more direct insight into stress perception then genetic markers and might build a complementary basis for future marker-assisted selection of drought resistance. In this review, we survey the role of proteomic studies to illustrate their applications in crop stress adaptation analysis with respect to productivity. Cereal crops such as wheat, rice, maize, barley, sorghum and pearl millet are discussed in detail. We provide a comprehensive and comparative overview of all detected protein changes involved in drought stress in these crops and have summarized existing knowledge into a proposed scheme of drought response. Based on a recent proteome study of pearl millet under drought stress we compare our findings with wheat proteomes and another recent study which defined genetic marker in pearl millet. PMID:28626463

Coupling sensing to crop models for closed-loop plant production in advanced life support systems

NASA Astrophysics Data System (ADS)

Cavazzoni, James; Ling, Peter P.

1999-01-01

We present a conceptual framework for coupling sensing to crop models for closed-loop analysis of plant production for NASA's program in advanced life support. Crop status may be monitored through non-destructive observations, while models may be independently applied to crop production planning and decision support. To achieve coupling, environmental variables and observations are linked to mode inputs and outputs, and monitoring results compared with model predictions of plant growth and development. The information thus provided may be useful in diagnosing problems with the plant growth system, or as a feedback to the model for evaluation of plant scheduling and potential yield. In this paper, we demonstrate this coupling using machine vision sensing of canopy height and top projected canopy area, and the CROPGRO crop growth model. Model simulations and scenarios are used for illustration. We also compare model predictions of the machine vision variables with data from soybean experiments conducted at New Jersey Agriculture Experiment Station Horticulture Greenhouse Facility, Rutgers University. Model simulations produce reasonable agreement with the available data, supporting our illustration.

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.

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.

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation.

PubMed

Villarrubia, Gabriel; Paz, Juan F De; Iglesia, Daniel H De La; Bajo, Javier

2017-08-02

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

PubMed Central

Villarrubia, Gabriel; De Paz, Juan F.; De La Iglesia, Daniel H.; Bajo, Javier

2017-01-01

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device. PMID:28767089

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

7 CFR 1218.4 - Crop year.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE BLUEBERRY PROMOTION, RESEARCH, AND INFORMATION ORDER Blueberry Promotion, Research, and Information Order Definitions § 1218.4 Crop year. Crop...

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-05-11

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

GIS based evaluation of crop suitability for agricultural sustainability around Kolaghat thermal power plant, India.

PubMed

Adak, Subhas; Adhikari, Kalyan; Brahmachari, Koushik

2016-09-01

Fly ash exhaust from Kolaghat thermal power plant, West Bengal, India,?? affects the areas within the radius of 3 - 4 km. Land information system indicated that surface texture within 4 km was silty loam and clay content increased with increase of distance. Soil pH was alkaline (7.58-8.01) in affected circles, whereas soil was acidic (5.95-6.41) in rest of block. Organic carbon (OC) is roving from 0.36 to 0.64% in the nearer circles which is lesser from others. The present Crop suitability analysis revealed that 96.98 % area was suitable (S1) for maize, sesame, jute, whereas these were cultivated in less than 1% of land. Flowers are the best suitable (S1) in 88.9 % but it was grown in 6.02 % area.? The present rice area within 4 km of KTPP is showing moderately suitable (S2) and S1 for the rest. Wheat is moderately suitable (S2) in the almost all the circles.? Cultivation of vegetable crops is limited in the affected circles while the highly suitable (S1) comprises 67.49 % for the remaining areas though it covered only 6.01 % of the block.? This evaluation precisely improves more than 300% from the earlier cropping intensity of 177.95 %. Suitability based land use allocation serves as stepping stone to promote agricultural sustainability. Geographic information system (GIS) model has been developed to assess site specific crop suitability for sustainable agricultural planning.

Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq.

PubMed

Qader, Sarchil Hama; Dash, Jadunandan; Atkinson, Peter M

2018-02-01

Crop production and yield estimation using remotely sensed data have been studied widely, but such information is generally scarce in arid and semi-arid regions. In these regions, inter-annual variation in climatic factors (such as rainfall) combined with anthropogenic factors (such as civil war) pose major risks to food security. Thus, an operational crop production estimation and forecasting system is required to help decision-makers to make early estimates of potential food availability. Data from NASA's MODIS with official crop statistics were combined to develop an empirical regression-based model to forecast winter wheat and barley production in Iraq. The study explores remotely sensed indices representing crop productivity over the crop growing season to find the optimal correlation with crop production. The potential of three different remotely sensed indices, and information related to the phenology of crops, for forecasting crop production at the governorate level was tested and their results were validated using the leave-one-year-out approach. Despite testing several methodological approaches, and extensive spatio-temporal analysis, this paper depicts the difficulty in estimating crop yield on an annual base using current satellite low-resolution data. However, more precise estimates of crop production were possible. The result of the current research implies that the date of the maximum vegetation index (VI) offered the most accurate forecast of crop production with an average R 2 =0.70 compared to the date of MODIS EVI (Avg R 2 =0.68) and a NPP (Avg R 2 =0.66). When winter wheat and barley production were forecasted using NDVI, EVI and NPP and compared to official statistics, the relative error ranged from -20 to 20%, -45 to 28% and -48 to 22%, respectively. The research indicated that remotely sensed indices could characterize and forecast crop production more accurately than simple cropping area, which was treated as a null model against which to evaluate the proposed approach. Copyright © 2017 Elsevier B.V. All rights reserved.

7 CFR 1221.6 - Crop year.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 10 2010-01-01 2010-01-01 false Crop year. 1221.6 Section 1221.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... INFORMATION ORDER Sorghum Promotion, Research, and Information Order Definitions § 1221.6 Crop year. Crop year...

7 CFR 1221.6 - Crop year.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 10 2011-01-01 2011-01-01 false Crop year. 1221.6 Section 1221.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (MARKETING AGREEMENTS... INFORMATION ORDER Sorghum Promotion, Research, and Information Order Definitions § 1221.6 Crop year. Crop year...

Remote Sensing Data Fusion to Detect Illicit Crops and Unauthorized Airstrips

NASA Astrophysics Data System (ADS)

Pena, J. A.; Yumin, T.; Liu, H.; Zhao, B.; Garcia, J. A.; Pinto, J.

2018-04-01

Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic information. This methodology emerges as a complementary and effective strategy to control and eradicate illicit crops.

Crop Frequency Mapping for Land Use Intensity Estimation During Three Decades

NASA Astrophysics Data System (ADS)

Schmidt, Michael; Tindall, Dan

2016-08-01

Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, food security and sustainability of agricultural practices. Such spatial datasets are likely to support decisions on natural resource management, planning and policy. The complete Landsat Time Series (LTS) archive for 23 Landsat footprints in western Queensland from 1987 to 2015 was used in a multi-temporal mapping approach. Spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by on ground training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons for each year were summarised, and combined with various vegetation indices and band ratios computed from a mid-season spectral-composite image. All available temporal information was spatially aggregated to the scale of image segments in the mid- season composite for each growing season and used to train a random forest classifier for a Crop and No- Crop classification. Validation revealed that the predictive accuracy varied by growing season and region to be within k = 0.88 to 0.97 and are thus suitable for mapping current and historic cropping activity. Crop frequency maps were produced for all regions at different time intervals. The crop frequency maps were validated separately with a historic crop information time series. Different land use intensities and conversions e.g. from agricultural to pastures are apparent and potential drivers of these conversions are discussed.

Crop and cattle production responses to tillage and cover crop management in an integrated crop-livestock system in the southeastern USA

USDA-ARS?s Scientific Manuscript database

Integrated crop-livestock systems can help achieve greater environmental quality from disparate crop and livestock systems by recycling nutrients and taking advantage of synergies between systems. We investigated crop and animal production responses in integrated crop-livestock systems with two typ...

Nitrogen and phosphorus exports from high rainfall zone cropping in Australia: issues and opportunities for research.

PubMed

Mathers, Nicole J; Nash, David M; Gangaiya, Philomena

2007-01-01

Cropping is one of the many industries contributing to the excessive loading of nitrogen (N) and phosphorus (P) to rivers and lakes in Australia. Nitrogen and P exports from cropping systems have not been systematically investigated to the same extent as those from other agricultural sectors, such as dairy pastures. Therefore, this review relies heavily on information derived from agronomy and other fundamental studies on soil-nutrient interactions to determine the potential for nutrient export from high rainfall zone (HRZ) cropping. There is a great deal of variation in environmental and management strategies across cropping in the HRZ, which suggests that nutrient exports could occur under a range of scenarios. The potential for exports is therefore discussed within a conceptual framework of nutrient sources, mechanisms for mobilization, and transport pathways in HRZ cropping. Transport refers to nutrient movement by flowing water after it has been mobilized, and export refers to the transfer of nutrients from one landscape compartment (e.g., a soil) to another (e.g., a stream or lake). The transport of nutrients from HRZ cropping can occur through surface and/or subsurface pathways depending on factors such as landform and infiltration and nutrient sorption characteristics of the soil profile. Surface pathways are likely to be more significant for phosphorus. For N, subsurface movement is likely to be as significant as surface movement because nitrates are generally not bound by most soils. Information about mechanisms of nutrient mobilization is essential for developing management strategies to control nutrient exports from HRZ cropping.

Mapping marginal croplands suitable for cellulosic feedstock crops in the Great Plains, United States

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2016-01-01

Growing cellulosic feedstock crops (e.g., switchgrass) for biofuel is more environmentally sustainable than corn-based ethanol. Specifically, this practice can reduce soil erosion and water quality impairment from pesticides and fertilizer, improve ecosystem services and sustainability (e.g., serve as carbon sinks), and minimize impacts on global food supplies. The main goal of this study was to identify high-risk marginal croplands that are potentially suitable for growing cellulosic feedstock crops (e.g., switchgrass) in the US Great Plains (GP). Satellite-derived growing season Normalized Difference Vegetation Index, a switchgrass biomass productivity map obtained from a previous study, US Geological Survey (USGS) irrigation and crop masks, and US Department of Agriculture (USDA) crop indemnity maps for the GP were used in this study. Our hypothesis was that croplands with relatively low crop yield but high productivity potential for switchgrass may be suitable for converting to switchgrass. Areas with relatively low crop indemnity (crop indemnity <$2 157 068) were excluded from the suitable areas based on low probability of crop failures. Results show that approximately 650 000 ha of marginal croplands in the GP are potentially suitable for switchgrass development. The total estimated switchgrass biomass productivity gain from these suitable areas is about 5.9 million metric tons. Switchgrass can be cultivated in either lowland or upland regions in the GP depending on the local soil and environmental conditions. This study improves our understanding of ecosystem services and the sustainability of cropland systems in the GP. Results from this study provide useful information to land managers for making informed decisions regarding switchgrass development in the GP.

A distribution benefits model for improved information on worldwide crop production. Volume 1: Model structure and application to wheat

NASA Technical Reports Server (NTRS)

Andrews, J.

1976-01-01

The improved model is suitable for the study of benefits of worldwide information on a variety of crops. Application to the previously studied case of worldwide wheat production shows that about $108 million per year of distribution benefits to the United States would be achieved by a satellite-based wheat information system meeting the goals of LACIE. The model also indicates that improved information alone will not change world stock levels unless production itself is stabilized. The United States benefits mentioned above are associated with the reduction of price fluctuations within the year and the more effective use of international trade to balance supply and demand. Price fluctuations from year to year would be reduced only if production variability were itself reduced.

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

PubMed

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

2017-01-01

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

GEOGLAM Crop Assessment Tool: Adapting from global agricultural monitoring to food security monitoring

NASA Astrophysics Data System (ADS)

Humber, M. L.; Becker-Reshef, I.; Nordling, J.; Barker, B.; McGaughey, K.

2014-12-01

The GEOGLAM Crop Monitor's Crop Assessment Tool was released in August 2013 in support of the GEOGLAM Crop Monitor's objective to develop transparent, timely crop condition assessments in primary agricultural production areas, highlighting potential hotspots of stress/bumper crops. The Crop Assessment Tool allows users to view satellite derived products, best available crop masks, and crop calendars (created in collaboration with GEOGLAM Crop Monitor partners), then in turn submit crop assessment entries detailing the crop's condition, drivers, impacts, trends, and other information. Although the Crop Assessment Tool was originally intended to collect data on major crop production at the global scale, the types of data collected are also relevant to the food security and rangelands monitoring communities. In line with the GEOGLAM Countries at Risk philosophy of "foster[ing] the coordination of product delivery and capacity building efforts for national and regional organizations, and the development of harmonized methods and tools", a modified version of the Crop Assessment Tool is being developed for the USAID Famine Early Warning Systems Network (FEWS NET). As a member of the Countries at Risk component of GEOGLAM, FEWS NET provides agricultural monitoring, timely food security assessments, and early warnings of potential significant food shortages focusing specifically on countries at risk of food security emergencies. While the FEWS NET adaptation of the Crop Assessment Tool focuses on crop production in the context of food security rather than large scale production, the data collected is nearly identical to the data collected by the Crop Monitor. If combined, the countries monitored by FEWS NET and GEOGLAM Crop Monitor would encompass over 90 countries representing the most important regions for crop production and food security.

WheatGenome.info: an integrated database and portal for wheat genome information.

PubMed

Lai, Kaitao; Berkman, Paul J; Lorenc, Michal Tadeusz; Duran, Chris; Smits, Lars; Manoli, Sahana; Stiller, Jiri; Edwards, David

2012-02-01

Bread wheat (Triticum aestivum) is one of the most important crop plants, globally providing staple food for a large proportion of the human population. However, improvement of this crop has been limited due to its large and complex genome. Advances in genomics are supporting wheat crop improvement. We provide a variety of web-based systems hosting wheat genome and genomic data to support wheat research and crop improvement. WheatGenome.info is an integrated database resource which includes multiple web-based applications. These include a GBrowse2-based wheat genome viewer with BLAST search portal, TAGdb for searching wheat second-generation genome sequence data, wheat autoSNPdb, links to wheat genetic maps using CMap and CMap3D, and a wheat genome Wiki to allow interaction between diverse wheat genome sequencing activities. This system includes links to a variety of wheat genome resources hosted at other research organizations. This integrated database aims to accelerate wheat genome research and is freely accessible via the web interface at http://www.wheatgenome.info/.

PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions.

PubMed

Yue, Junyang; Zhang, Danfeng; Ban, Rongjun; Ma, Xiaojing; Chen, Danyang; Li, Guangwei; Liu, Jia; Wisniewski, Michael; Droby, Samir; Liu, Yongsheng

2017-01-01

Penicillium expansum , the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium -crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein-protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium -Crop Protein-Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen-plant systems and available domain-domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. : http://bdg.hfut.edu.cn/pcppi/index.html. © The Author(s) 2017. Published by Oxford University Press.

Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system

NASA Astrophysics Data System (ADS)

Hu, Shun; Shi, Liangsheng; Zha, Yuanyuan; Williams, Mathew; Lin, Lin

2017-12-01

Improvements to agricultural water and crop managements require detailed information on crop and soil states, and their evolution. Data assimilation provides an attractive way of obtaining these information by integrating measurements with model in a sequential manner. However, data assimilation for soil-water-atmosphere-plant (SWAP) system is still lack of comprehensive exploration due to a large number of variables and parameters in the system. In this study, simultaneous state-parameter estimation using ensemble Kalman filter (EnKF) was employed to evaluate the data assimilation performance and provide advice on measurement design for SWAP system. The results demonstrated that a proper selection of state vector is critical to effective data assimilation. Especially, updating the development stage was able to avoid the negative effect of ;phenological shift;, which was caused by the contrasted phenological stage in different ensemble members. Simultaneous state-parameter estimation (SSPE) assimilation strategy outperformed updating-state-only (USO) assimilation strategy because of its ability to alleviate the inconsistency between model variables and parameters. However, the performance of SSPE assimilation strategy could deteriorate with an increasing number of uncertain parameters as a result of soil stratification and limited knowledge on crop parameters. In addition to the most easily available surface soil moisture (SSM) and leaf area index (LAI) measurements, deep soil moisture, grain yield or other auxiliary data were required to provide sufficient constraints on parameter estimation and to assure the data assimilation performance. This study provides an insight into the response of soil moisture and grain yield to data assimilation in SWAP system and is helpful for soil moisture movement and crop growth modeling and measurement design in practice.

A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

Program review presentation to Level 1, Interagency Coordination Committee

NASA Technical Reports Server (NTRS)

1982-01-01

Progress in the development of crop inventory technology is reported. Specific topics include the results of a thematic mapper analysis, variable selection studies/early season estimator improvements, the agricultural information system simulator, large unit proportion estimation, and development of common features for multi-satellite information extraction.

When should irrigators invest in more water-efficient technologies as an adaptation to climate change?

NASA Astrophysics Data System (ADS)

Malek, K.; Adam, J. C.; Stockle, C.; Brady, M.; Yoder, J.

2015-12-01

The western US is expected to experience more frequent droughts with higher magnitudes and persistence due to the climate change, with potentially large impacts on agricultural productivity and the economy. Irrigated farmers have many options for minimizing drought impacts including changing crops, engaging in water markets, and switching irrigation technologies. Switching to more efficient irrigation technologies, which increase water availability in the crop root zone through reduction of irrigation losses, receives significant attention because of the promise of maintaining current production with less. However, more efficient irrigation systems are almost always more capital-intensive adaptation strategy particularly compared to changing crops or trading water. A farmer's decision to switch will depend on how much money they project to save from reducing drought damages. The objective of this study is to explore when (and under what climate change scenarios) it makes sense economically for farmers to invest in a new irrigation system. This study was performed over the Yakima River Basin (YRB) in Washington State, although the tools and information gained from this study are transferable to other watersheds in the western US. We used VIC-CropSyst, a large-scale grid-based modeling framework that simulates hydrological processes while mechanistically capturing crop water use, growth and development. The water flows simulated by VIC-CropSyst were used to run the RiverWare river system and water management model (YAK-RW), which simulates river processes and calculates regional water availability for agricultural use each day (i.e., the prorationing ratio). An automated computational platform has been developed and programed to perform the economic analysis for each grid cell, crop types and future climate projections separately, which allows us to explore whether or not implementing a new irrigation system is economically viable. Results of this study indicate that climate change could justify the investment in new irrigation systems during this century, but the timing of a farmer's response is likely to depend on a variety of factors, including changes in the frequency and magnitude of drought events, current irrigation systems, climatological characteristics within the basin, and crop type.

Diversified cropping systems support greater microbial cycling and retention of carbon and nitrogen

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

King, Alison E.; Hofmockel, Kirsten S.

2017-03-01

Diversifying biologically simple cropping systems often entails altering other management practices, such as tillage regime or nitrogen (N) source. We hypothesized that the interaction of crop rotation, N source, and tillage in diversified cropping systems would promote microbially-mediated soil C and N cycling while attenuating inorganic N pools. We studied a cropping systems trial in its 10th year in Iowa, USA, which tested a 2-yr cropping system of corn (Zea mays L.)/soybean [Glycine max (L.) Merr.] managed with conventional fertilizer N inputs and conservation tillage, a 3-yr cropping system of corn/soybean/small grain + red clover (Trifolium pratense L.), and amore » 4-yr cropping system of corn/soybean/small grain + alfalfa (Medicago sativa L.)/alfalfa. Three year and 4-yr cropping systems were managed with composted manure, reduced N fertilizer inputs, and periodic moldboard ploughing. We assayed soil microbial biomass carbon (MBC) and N (MBN), soil extractable NH4 and NO3, gross proteolytic activity of native soil, and potential activity of six hydrolytic enzymes eight times during the growing season. At the 0-20cm depth, native protease activity in the 4-yr cropping system was greater than in the 2-yr cropping system by a factor of 7.9, whereas dissolved inorganic N pools did not differ between cropping systems (P = 0.292). At the 0-20cm depth, MBC and MBN the 4-yr cropping system exceeded those in the 2-yr cropping system by factors of 1.51 and 1.57. Our findings suggest that diversified crop cropping systems, even when periodically moldboard ploughed, support higher levels of microbial biomass, greater production of bioavailable N from SOM, and a deeper microbially active layer than less diverse cropping systems.« less

Monitoring Global Crop Condition Indicators Using a Web-Based Visualization Tool

Treesearch

Bob Tetrault; Bob Baldwin

2006-01-01

Global crop condition information for major agricultural regions in the world can be monitored using the web-based application called Crop Explorer. With this application, U.S. and international producers, traders, researchers, and the public can access remote sensing information used by agricultural economists and scientists who predict crop production worldwide. For...

Application of SAR remote sensing and crop modeling for operational rice crop monitoring in South and South East Asian Countries

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Maunahan, A. A.; Gatti, L.; Quicho, E. D.; Pazhanivelan, S.; Campos-Taberner, M.; Collivignarelli, F.; Haro, J. G.; Intrman, A.; Phuong, D.; Boschetti, M.; Prasadini, P.; Busetto, L.; Minh, V. Q.; Tuan, V. Q.

2017-12-01

This study uses multi-temporal SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations in South and South Asian countries and assimilate the information into ORYZA Crop Growth Simulation Model (CGSM) to monitor rice yield. The study demonstrates examples of operational application of this rice monitoring system in: (1) detecting drought impact on rice planting in Central Thailand and Tamil Nadu, India, (2) mapping heat stress impact on rice yield in Andhra Pradesh, India, and (3) generating historical rice yield data for districts in Red River Delta, Vietnam.

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

Safety assessment and public concerns for genetically modified food products: the Japanese experience.

PubMed

Hino, Akihiro

2002-01-01

The recombinant DNA (rDNA) technique is expected to bring about great progress in the improvement of breeding technology and the development of new plant varieties showing high quality and high yield, such as those with excellent pest and disease resistance, those with environmental stress tolerance, and so forth. In the United States and Canada, many genetically modified (GM) crop plants were commercialized as early as 1994. In Japan, 35 transgenic crop plants, such as herbicide tolerant soybean, cotton, and canola, and insect-resistant corn, cotton, and potatos, were authorized and considered marketable until April 2001. The general public, however, is not familiar with rDNA technology, and some people seem to feel uncomfortable with biotechnology, frequently because of the difficulty of the technology and lacking of sufficient information. New labeling systems were initiated in April 2001 in Japan to provide information regarding the use of GM crops as raw material.

Economic evaluation: Concepts, selected studies, system costs, and a proposed program

NASA Technical Reports Server (NTRS)

Osterhoudt, F. H. (Principal Investigator)

1979-01-01

The more usual approaches to valuing crop information are reviewed and an integrated approach is recommended. Problems associated with implementation are examined. What has already been accomplished in the economic evaluation of LACIE-type information is reported including various studies of benefits. The costs of the existing and proposed systems are considered. A method and approach is proposed for further studies.

Spider fauna of semiarid eastern Colorado agroecosystems: diversity, abundance, and effects of crop intensification.

PubMed

Kerzicnik, Lauren M; Peairs, Frank B; Cushing, Paula E; Draney, Michael L; Merrill, Scott C

2013-02-01

Spiders are critical predators in agroecosystems. Crop management practices can influence predator density and diversity, which, in turn, can influence pest management strategies. Crop intensification is a sustainable agricultural technique that can enhance crop production although optimizing soil moisture. To date, there is no information on how crop intensification affects natural enemy populations, particularly spiders. This study had two objectives: to characterize the abundance and diversity of spiders in eastern Colorado agroecosystems, and to test the hypothesis that spider diversity and density would be higher in wheat (Triticum aestivum L.) in crop-intensified rotations compared with wheat in conventional rotations. We collected spiders through pitfall, vacuum, and lookdown sampling from 2002 to 2007 to test these objectives. Over 11,000 spiders in 19 families from 119 species were captured from all sampling techniques. Interestingly, the hunting spider guild represented 89% of the spider fauna captured from all sites with the families Gnaphosidae and Lycosidae representing 75% of these spiders. Compared with European agroecosystems, these agroecosystems had greater diversity, which can be beneficial for the biological control of pests. Overall, spider densities were low in these semiarid cropping systems, and crop intensification effects on spider densities were not evident at this scale.

Tracking big and small agriculture with new satellite sensors

NASA Astrophysics Data System (ADS)

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

2017-12-01

New sensors from both the public and private sector are opening up exciting possibilities for monitoring agriculture and its use of water. This talk will present selected examples from recent work using data from Planet's Planetscope and Skysat sensors as well as Sentinel-1 and Sentinel-2 missions that are part of Europe's Copernicus program. Among other things, these satellites are now helping to track crop types and productivity for fields in rainfed cropping systems of East Africa and irrigated systems in South Asia. This information should contribute to understanding land and water use decisions throughout the world.

[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.

Comparison of Satellite-based Basal and Adjusted Evapotranspiration for Several California Crops

NASA Astrophysics Data System (ADS)

Johnson, L.; Lund, C.; Melton, F. S.

2013-12-01

There is a continuing need to develop new sources of information on agricultural crop water consumption in the arid Western U.S. Pursuant to the California Water Conservation Act of 2009, for instance, the stakeholder community has developed a set of quantitative indicators involving measurement of evapotranspiration (ET) or crop consumptive use (Calif. Dept. Water Resources, 2012). Fraction of reference ET (or, crop coefficients) can be estimated from a biophysical description of the crop canopy involving green fractional cover (Fc) and height as per the FAO-56 practice standard of Allen et al. (1998). The current study involved 19 fields in California's San Joaquin Valley and Central Coast during 2011-12, growing a variety of specialty and commodity crops: lettuce, raisin, tomato, almond, melon, winegrape, garlic, peach, orange, cotton, corn and wheat. Most crops were on surface or subsurface drip, though micro-jet, sprinkler and flood were represented as well. Fc was retrospectively estimated every 8-16 days by optical satellite data and interpolated to a daily timestep. Crop height was derived as a capped linear function of Fc using published guideline maxima. These variables were used to generate daily basal crop coefficients (Kcb) per field through most or all of each respective growth cycle by the density coefficient approach of Allen & Pereira (2009). A soil water balance model for both topsoil and root zone, based on FAO-56 and using on-site measurements of applied irrigation and precipitation, was used to develop daily soil evaporation and crop water stress coefficients (Ke, Ks). Key meteorological variables (wind speed, relative humidity) were extracted from the California Irrigation Management Information System (CIMIS) for climate correction. Basal crop ET (ETcb) was then derived from Kcb using CIMIS reference ET. Adjusted crop ET (ETc_adj) was estimated by the dual coefficient approach involving Kcb, Ke, and incorporating Ks. Cumulative ETc_adj throughout each monitoring period was lower than cumulative ETb for most crops, indicating that effect of water stress tended to exceed that of soil evaporation relative to basal conditions. We present results from the analysis and discuss implications for operational use of satellite-based Kcb and ETcb estimates for agricultural water resource management.

Combining spatial and spectral information to improve crop/weed discrimination algorithms

NASA Astrophysics Data System (ADS)

Yan, L.; Jones, G.; Villette, S.; Paoli, J. N.; Gée, C.

2012-01-01

Reduction of herbicide spraying is an important key to environmentally and economically improve weed management. To achieve this, remote sensors such as imaging systems are commonly used to detect weed plants. We developed spatial algorithms that detect the crop rows to discriminate crop from weeds. These algorithms have been thoroughly tested and provide robust and accurate results without learning process but their detection is limited to inter-row areas. Crop/Weed discrimination using spectral information is able to detect intra-row weeds but generally needs a prior learning process. We propose a method based on spatial and spectral information to enhance the discrimination and overcome the limitations of both algorithms. The classification from the spatial algorithm is used to build the training set for the spectral discrimination method. With this approach we are able to improve the range of weed detection in the entire field (inter and intra-row). To test the efficiency of these algorithms, a relevant database of virtual images issued from SimAField model has been used and combined to LOPEX93 spectral database. The developed method based is evaluated and compared with the initial method in this paper and shows an important enhancement from 86% of weed detection to more than 95%.

Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone.

PubMed

García-Tejero, Iván Francisco; Ortega-Arévalo, Carlos José; Iglesias-Contreras, Manuel; Moreno, José Manuel; Souza, Luciene; Tavira, Simón Cuadros; Durán-Zuazo, Víctor Hugo

2018-03-31

Different tools are being implemented in order to improve the water management in agricultural irrigated areas of semiarid environments. Thermography has been progressively introduced as a promising technique for irrigation scheduling and the assessing of crop-water status, especially when deficit irrigation is being implemented. However, an important limitation is related to the cost of the actual cameras, this being a severe limitation to its practical usage by farmers and technicians. This work evaluates the potential and the robustness of a thermal imaging camera that is connected to smartphone (Flir One) recently developed by Flir Systems Inc. as a first step to assess the crop water status. The trial was developed in mature almond ( Prunus dulcis Mill.) trees that are subjected to different irrigation treatments. Thermal information obtained by the Flir One camera was deal with the thermal information obtained with a conventional Thermal Camera (Flir SC660) with a high resolution, and subsequently, confronted with other related plant physiological parameters (leaf water potential, Ψ leaf , and stomatal conductance, g s ). Thermal imaging camera connected to smartphone provided useful information in estimating the crop-water status in almond trees, being a potential promising tool to accelerate the monitoring process and thereby enhance water-stress management of almond orchards.

Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone

PubMed Central

García-Tejero, Iván Francisco; Ortega-Arévalo, Carlos José; Iglesias-Contreras, Manuel; Moreno, José Manuel; Souza, Luciene; Tavira, Simón Cuadros; Durán-Zuazo, Víctor Hugo

2018-01-01

Different tools are being implemented in order to improve the water management in agricultural irrigated areas of semiarid environments. Thermography has been progressively introduced as a promising technique for irrigation scheduling and the assessing of crop-water status, especially when deficit irrigation is being implemented. However, an important limitation is related to the cost of the actual cameras, this being a severe limitation to its practical usage by farmers and technicians. This work evaluates the potential and the robustness of a thermal imaging camera that is connected to smartphone (Flir One) recently developed by Flir Systems Inc. as a first step to assess the crop water status. The trial was developed in mature almond (Prunus dulcis Mill.) trees that are subjected to different irrigation treatments. Thermal information obtained by the Flir One camera was deal with the thermal information obtained with a conventional Thermal Camera (Flir SC660) with a high resolution, and subsequently, confronted with other related plant physiological parameters (leaf water potential, Ψleaf, and stomatal conductance, gs). Thermal imaging camera connected to smartphone provided useful information in estimating the crop-water status in almond trees, being a potential promising tool to accelerate the monitoring process and thereby enhance water-stress management of almond orchards. PMID:29614740

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.

Enhancing the USDA Global Crop Assessment Decision Support System Using SMAP Soil Moisture Data

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mladenova, I. E.; Crow, W. T.; Reynolds, C. A.

2016-12-01

The Foreign Agricultural Services (FAS) is a subdivision of U.S. Department of Agriculture (USDA) that is in charge with providing information on current and expected crop supply and demand estimates. Knowledge of the amount of water in the root zone is an essential source of information for the crop analysts as it governs the crop development and crop growth, which in turn determine the end-of-season yields. USDA FAS currently relies on root zone soil moisture (RZSM) estimates generated using the modified two-layer Palmer Model (PM). PM is a simple water-balance hydrologic model that is driven by daily precipitation observations and minimum and maximum temperature data. These forcing data are based on ground meteorological station measurements from the World Meteorological Organization (WMO), and gridded weather data from the former U.S. Air Force Weather Agency (AFWA), currently called U.S. Air Force 557th Weather Wing. The PM was extended by adding a data assimilation (DA) unit that provides the opportunity to routinely ingest satellite-based soil moisture observations. This allows us to adjust for precipitation-related inaccuracies and enhance the quality of the PM soil moisture estimates. The current operational DA system is based on a 1-D Ensample Kalman Filter approach and relies on observations obtained from the Soil Moisture Ocean Salinity Mission (SMOS). Our talk will demonstrate the value of assimilating two satellite products (i.e. a passive and active) and discuss work that is done in preparation for ingesting soil moisture observations from the Soil Moisture Active Passive (SMAP) mission.

Cryopreserved storage of clonal germplasm in the USDA National Plant Germplasm System

USDA-ARS?s Scientific Manuscript database

The U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), National Plant Germplasm System (NPGS) plant collections are a critical source of genetic diversity for breeding and selection of improved crops, including vegetatively propagated plants. Information on these collections is...

Towards a Quantitative Use of Satellite Remote Sensing in Crop Growth Models for Large Scale Agricultural Production Estimate (Invited)

NASA Astrophysics Data System (ADS)

Defourny, P.

2013-12-01

The development of better agricultural monitoring capabilities is clearly considered as a critical step for strengthening food production information and market transparency thanks to timely information about crop status, crop area and yield forecasts. The documentation of global production will contribute to tackle price volatility by allowing local, national and international operators to make decisions and anticipate market trends with reduced uncertainty. Several operational agricultural monitoring systems are currently operating at national and international scales. Most are based on the methods derived from the pioneering experiences completed some decades ago, and use remote sensing to qualitatively compare one year to the others to estimate the risks of deviation from a normal year. The GEO Agricultural Monitoring Community of Practice described the current monitoring capabilities at the national and global levels. An overall diagram summarized the diverse relationships between satellite EO and agriculture information. There is now a large gap between the current operational large scale systems and the scientific state of the art in crop remote sensing, probably because the latter mainly focused on local studies. The poor availability of suitable in-situ and satellite data over extended areas hampers large scale demonstrations preventing the much needed up scaling research effort. For the cropland extent, this paper reports a recent research achievement using the full ENVISAT MERIS 300 m archive in the context of the ESA Climate Change Initiative. A flexible combination of classification methods depending to the region of the world allows mapping the land cover as well as the global croplands at 300 m for the period 2008 2012. This wall to wall product is then compared with regards to the FP 7-Geoland 2 results obtained using as Landsat-based sampling strategy over the IGADD countries. On the other hand, the vegetation indices and the biophysical variables such the Green Area Index (GAI), fAPAR and fcover usually retrieved from MODIS, MERIS, SPOT-Vegetation described the quality of the green vegetation development. The GLOBAM (Belgium) and EU FP-7 MOCCCASIN projects (Russia) improved the standard products and were demonstrated over large scale. The GAI retrieved from MODIS time series using a purity index criterion depicted successfully the inter-annual variability. Furthermore, the quantitative assimilation of these GAI time series into a crop growth model improved the yield estimate over years. These results showed that the GAI assimilation works best at the district or provincial level. In the context of the GEO Ag., the Joint Experiment of Crop Assessment and Monitoring (JECAM) was designed to enable the global agricultural monitoring community to compare such methods and results over a variety of regional cropping systems. For a network of test sites around the world, satellite and field measurements are currently collected and will be made available for collaborative effort. This experiment should facilitate international standards for data products and reporting, eventually supporting the development of a global system of systems for agricultural crop assessment and monitoring.

Direct and indirect impacts of crop-livestock organization on mixed crop-livestock systems sustainability: a model-based study.

PubMed

Sneessens, I; Veysset, P; Benoit, M; Lamadon, A; Brunschwig, G

2016-11-01

Crop-livestock production is claimed more sustainable than specialized production systems. However, the presence of controversial studies suggests that there must be conditions of mixing crop and livestock productions to allow for higher sustainable performances. Whereas previous studies focused on the impact of crop-livestock interactions on performances, we posit here that crop-livestock organization is a key determinant of farming system sustainability. Crop-livestock organization refers to the percentage of the agricultural area that is dedicated to each production. Our objective is to investigate if crop-livestock organization has both a direct and an indirect impact on mixed crop-livestock (MC-L) sustainability. In that objective, we build a whole-farm model parametrized on representative French sheep and crop farming systems in plain areas (Vienne, France). This model permits simulating contrasted MC-L systems and their subsequent sustainability through the following indicators of performance: farm income, production, N balance, greenhouse gas (GHG) emissions (/kg product) and MJ consumption (/kg product). Two MC-L systems were simulated with contrasted crop-livestock organizations (MC20-L80: 20% of crops; MC80-L20: 80% of crops). A first scenario - constraining no crop-livestock interactions in both MC-L systems - permits highlighting that crop-livestock organization has a significant direct impact on performances that implies trade-offs between objectives of sustainability. Indeed, the MC80-L20 system is showing higher performances for farm income (+44%), livestock production (+18%) and crop GHG emissions (-14%) whereas the MC20-L80 system has a better N balance (-53%) and a lower livestock MJ consumption (-9%). A second scenario - allowing for crop-livestock interactions in both MC20-L80 and MC80-L20 systems - stated that crop-livestock organization has a significant indirect impact on performances. Indeed, even if crop-livestock interactions permit improving performances, crop-livestock organization influences the capacity of MC-L systems to benefit from crop-livestock interactions. As a consequence, we observed a decreasing performance trade-off between MC-L systems for farm income (-4%) and crop GHG emissions (-10%) whereas the gap increases for nitrogen balance (+23%), livestock production (+6%) - MJ consumption (+16%) - GHG emissions (+5%) and crop MJ consumption (+5%). However, the indirect impact of crop-livestock organization doesn't reverse the trend of trade-offs between objectives of sustainability determined by the direct impact of crop-livestock organization. As a conclusion, crop-livestock organization is a key factor that has to be taken into account when studying the sustainability of mixed crop-livestock systems.

An Updated Decision Support Interface: A Tool for Remote Monitoring of Crop Growing Conditions

NASA Astrophysics Data System (ADS)

Husak, G. J.; Budde, M. E.; Rowland, J.; Verdin, J. P.; Funk, C. C.; Landsfeld, M. F.

2014-12-01

Remote sensing of agroclimatological variables to monitor food production conditions is a critical component of the Famine Early Warning Systems Network portfolio of tools for assessing food security in the developing world. The Decision Support Interface (DSI) seeks to integrate a number of remotely sensed and modeled variables to create a single, simplified portal for analysis of crop growing conditions. The DSI has been reformulated to incorporate more variables and give the user more freedom in exploring the available data. This refinement seeks to transition the DSI from a "first glance" agroclimatic indicator to one better suited for the differentiation of drought events. The DSI performs analysis of variables over primary agricultural zones at the first sub-national administrative level. It uses the spatially averaged rainfall, normalized difference vegetation index (NDVI), water requirement satisfaction index (WRSI), and actual evapotranspiration (ETa) to identify potential hazards to food security. Presenting this information in a web-based client gives food security analysts and decision makers a lightweight portal for information on crop growing conditions in the region. The crop zones used for the aggregation contain timing information which is critical to the DSI presentation. Rainfall and ETa are accumulated from different points in the crop phenology to identify season-long deficits in rainfall or transpiration that adversely affect the crop-growing conditions. Furthermore, the NDVI and WRSI serve as their own seasonal accumulated measures of growing conditions by capturing vegetation vigor or actual evapotranspiration deficits. The DSI is currently active for major growing regions of sub-Saharan Africa, with intention of expanding to other areas over the coming years.

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

PubMed

Kumar, Manoj

2016-08-01

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

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

PubMed

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

2017-01-01

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

From Crop Domestication to Super-domestication

PubMed Central

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

2007-01-01

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

Incorporation of Monitoring Systems to Model Irrigated Cotton at a Landscape Level

USDA-ARS?s Scientific Manuscript database

Advances in computer speed, industry IT core capabilities, and available soils and weather information have resulted in the need for “cropping system models” that address in detail the spatial and temporal water, energy and carbon balance of the system at a landscape scale. Many of these models have...

Seeking Energy System Pathways to Reduce Ozone Damage to Ecosystems through Adjoint-based Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Capps, S. L.; Pinder, R. W.; Loughlin, D. H.; Bash, J. O.; Turner, M. D.; Henze, D. K.; Percell, P.; Zhao, S.; Russell, M. G.; Hakami, A.

2014-12-01

Tropospheric ozone (O3) affects the productivity of ecosystems in addition to degrading human health. Concentrations of this pollutant are significantly influenced by precursor gas emissions, many of which emanate from energy production and use processes. Energy system optimization models could inform policy decisions that are intended to reduce these harmful effects if the contribution of precursor gas emissions to human health and ecosystem degradation could be elucidated. Nevertheless, determining the degree to which precursor gas emissions harm ecosystems and human health is challenging because of the photochemical production of ozone and the distinct mechanisms by which ozone causes harm to different crops, tree species, and humans. Here, the adjoint of a regional chemical transport model is employed to efficiently calculate the relative influences of ozone precursor gas emissions on ecosystem and human health degradation, which informs an energy system optimization. Specifically, for the summer of 2007 the Community Multiscale Air Quality (CMAQ) model adjoint is used to calculate the location- and sector-specific influences of precursor gas emissions on potential productivity losses for the major crops and sensitive tree species as well as human mortality attributable to chronic ozone exposure in the continental U.S. The atmospheric concentrations are evaluated with 12-km horizontal resolution with crop production and timber biomass data gridded similarly. These location-specific factors inform the energy production and use technologies selected in the MARKet ALlocation (MARKAL) model.

Modeling salt movement and halophytic crop growth on marginal lands with the APEX model

NASA Astrophysics Data System (ADS)

Goehring, N.; Saito, L.; Verburg, P.; Jeong, J.; Garrett, A.

2016-12-01

Saline soils negatively impact crop productivity in nearly 20% of irrigated agricultural lands worldwide. At these saline sites, cultivation of highly salt-tolerant plants, known as halophytes, may increase productivity compared to conventional salt-sensitive crops (i.e., glycophytes), thereby increasing the economic potential of marginal lands. Through a variety of mechanisms, halophytes are more effective than glycophytes at excluding, accumulating, and secreting salts from their tissues. Each mechanism can have a different impact on the salt balance in the plant-soil-water system. To date, little information is available to understand the long-term impacts of halophyte cultivation on environmental quality. This project utilizes the Agricultural Policy/Environmental Extender (APEX) model, developed by the US Department of Agriculture, to model the growth and production of two halophytic crops. The crops being modeled include quinoa (Chenopodium quinoa), which has utilities for human consumption and forage, and AC Saltlander green wheatgrass (Elymus hoffmannii), which has forage utility. APEX simulates salt movement between soil layers and accounts for the salt balance in the plant-soil-water system, including salinity in irrigation water and crop-specific salt uptake. Key crop growth parameters in APEX are derived from experimental growth data obtained under non-stressed conditions. Data from greenhouse and field experiments in which quinoa and AC Saltlander were grown under various soil salinity and irrigation salinity treatments are being used to parameterize, calibrate, and test the model. This presentation will discuss progress on crop parameterization and completed model runs under different salt-affected soil and irrigation conditions.

Satellite Estimation of Fractional Cover in Several California Specialty Crops

NASA Technical Reports Server (NTRS)

Johnson, Lee; Cahn, Michael; Rosevelt, Carolyn; Guzman, Alberto; Farrara, Barry; Melton, Forrest S.

2016-01-01

Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

Satellite Estimation of Fractional Cover in Several California Specialty Crops

NASA Astrophysics Data System (ADS)

Johnson, L.; Cahn, M.; Rosevelt, C.; Guzman, A.; Lockhart, T.; Farrara, B.; Melton, F. S.

2016-12-01

Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

Information Technology Supports Integration of Satellite Imagery with Irrigation Management in California's Central Valley

USDA-ARS?s Scientific Manuscript database

Remotely sensed data can potentially be used to develop crop coefficient estimates over large areas and make irrigation scheduling more practical, convenient, and accurate. A demonstration system is being developed under NASA's Terrestrial Observation and Prediction System (TOPS) to automatically r...

Data Requirements to Assess Department of Defense (DOD) Investments in Law Enforcement in Southwest Asia

DTIC Science & Technology

2011-09-01

form similar organizational structures—loosely-connected webs of small, specialized cells, etc.28 Illicit networks form organizational structures...Activities SIMCI Sistema Integrado de Monitoreo de Cultivos Ilícitos (Integrated Crops Monitoring System) STRIDE System To Retrieve Information from Drug

AIRBORNE REMOTELY SENSED INFORMATION FOR PESTICIDAL TRANSGENIC CROPS: HOW SPECTRAL IMAGING MAY PLAY A ROLE

EPA Science Inventory

The importance of sustainability to Bt crops. Resistance management as a sustainability strategy. The importance of data quality to sustainability and regulation of Bt Crops. Where information improvement can be useful across the globe.

Rainwater harvesting potential for farming system development in a hilly watershed of Bangladesh

NASA Astrophysics Data System (ADS)

Tariqul Islam, Md.; Mohabbat Ullah, Md.; Mostofa Amin, M. G.; Hossain, Sahadat

2017-09-01

Water resources management is an important part in farming system development. Agriculture in Chittagong Hill Tracts of Bangladesh is predominantly rainfed with an average 2210 mm monsoonal rain, but rainfall during dry winter period (December-February) is inadequate for winter crop production. The natural soil water content (as low as 7 %) of hillslope and hilltop during the dry season is not suitable for shallow-rooted crop cultivation. A study was conducted to investigate the potential of monsoonal rainwater harvesting and its impact on local cropping system development. Irrigation facilities provided by the managed rainwater harvesting reservoir increased research site's cropping intensity from 155 to 300 %. Both gravity flow irrigation of valley land and low lift pumping to hillslope and hilltop from rainwater harvesting reservoir were much more economical compared to forced mode pumping of groundwater because of the installation and annual operating cost of groundwater pumping. To abstract 7548 m3 of water, equivalent to the storage capacity of the studied reservoirs, from aquifer required 2174 kWh energy. The improved water supply system enabled triple cropping system for valley land and permanent horticultural intervention at hilltop and hillslope. The perennial vegetation in hilltop and hillslope would also conserve soil moisture. Water productivity and benefit-cost ratio analysis show that vegetables and fruit production were more profitable than rice cultivation under irrigation with harvested rainwater. Moreover, the reservoir showed potentiality of integrated farming in such adverse area by facilitating fish production. The study provides water resource managers and government officials working with similar problems with valuable information for formulation of plan, policy, and strategy.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Imputing historical statistics, soils information, and other land-use data to crop area

NASA Technical Reports Server (NTRS)

Perry, C. R., Jr.; Willis, R. W.; Lautenschlager, L.

1982-01-01

In foreign crop condition monitoring, satellite acquired imagery is routinely used. To facilitate interpretation of this imagery, it is advantageous to have estimates of the crop types and their extent for small area units, i.e., grid cells on a map represent, at 60 deg latitude, an area nominally 25 by 25 nautical miles in size. The feasibility of imputing historical crop statistics, soils information, and other ancillary data to crop area for a province in Argentina is studied.

Can Climate Information be relevant to decision making for Agriculture on the 1-10 year timescale? Case studies from southern Africa

NASA Astrophysics Data System (ADS)

Fujisawa, Mariko

2016-04-01

Climate forecasts have been developed to assist decision making in sectors averse to, and affected by, climate risks, and agriculture is one of those. In agriculture and food security, climate information is now used on a range of timescales, from days (weather), months (seasonal outlooks) to decades (climate change scenarios). Former researchers have shown that when seasonal climate forecast information was provided to farmers prior to decision making, farmers adapted by changing their choice of planting seeds and timing or area planted. However, it is not always clear that the end-users' needs for climate information are met and there might be a large gap between information supplied and needed. It has been pointed out that even when forecasts were available, they were often not utilized by farmers and extension services because of lack of trust in the forecast or the forecasts did not reach the targeted farmers. Many studies have focused on the use of either seasonal forecasts or longer term climate change prediction, but little research has been done on the medium term, that is, 1 to 10 year future climate information. The agriculture and food system sector is one potential user of medium term information, as land use policy and cropping systems selection may fall into this time scale and may affect farmers' decision making process. Assuming that reliable information is provided and it is utilized by farmers for decision making, it might contribute to resilient farming and indeed to longer term food security. To this end, we try to determine the effect of medium term climate information on farmers' strategic decision making process. We explored the end-users' needs for climate information and especially the possible role of medium term information in agricultural system, by conducting interview surveys with farmers and agricultural experts. In this study, the cases of apple production in South Africa, maize production in Malawi and rice production in Tanzania will be presented. With case studies of various crops, we also aim to identify what climatic factors and timescale of prediction may be critical to what crop types of farmers, which may be of value to climate prediction community to further develop climate prediction useful for agricultural system.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Does cattle grazing of dual-purpose wheat accelerate the rate of stubble decomposition and nutrients released

USDA-ARS?s Scientific Manuscript database

Decomposition and nutrient release of winter annual forages in integrated crop-livestock systems could be affected by the resultant alterations in structure and quality of residues caused by grazing, but little information is available to test this hypothesis. Information on residue dynamics is need...

Developing an automatic classification system of vegetation anomalies for early warning with the ASAP (Anomaly hot Spots of Agricultural Production) system

NASA Astrophysics Data System (ADS)

Meroni, M.; Rembold, F.; Urbano, F.; Lemoine, G.

2016-12-01

Anomaly maps and time profiles of remote sensing derived indicators relevant to monitor crop and vegetation stress can be accessed online thanks to a rapidly growing number of web based portals. However, timely and systematic global analysis and coherent interpretation of such information, as it is needed for example for SDG 2 related monitoring, remains challenging. With the ASAP system (Anomaly hot Spots of Agricultural Production) we propose a two-step analysis to provide monthly warning of production deficits in water-limited agriculture worldwide. The first step is fully automated and aims at classifying each administrative unit (1st sub-national level) into a number of possible warning levels, ranging from "none" to "watch" and up to "extended alarm". The second step involves the verification of the automatic warnings and integration into a short national level analysis by agricultural analysts. In this paper we describe the methodological development of the automatic vegetation anomaly classification system. Warnings are triggered only during the crop growing season, defined by a remote sensing based phenology. The classification takes into consideration the fraction of the agricultural and rangelands area for each administrative unit that is affected by a severe anomaly of two rainfall-based indicators (the Standardized Precipitation Index (SPI), computed at 1 and 3-month scale) and one biophysical indicator (the cumulative NDVI from the start of the growing season). The severity of the warning thus depends on the timing, the nature and the number of indicators for which an anomaly is detected. The prototype system is using global NDVI images of the METOP sensor, while a second version is being developed based on 1km Modis NDVI with temporal smoothing and near real time filtering. Also a specific water balance model is under development to include agriculture water stress information in addition to the SPI. The monthly warning classification and crop condition assessment will be made available on a website and will strengthen the JRC support to information products based on consensus assessment such as the GEOGLAM Crop Monitor for Early Warning.

Soil and Land Resources Information System (SLISYS-Tarim) for Sustainable Management of River Oases along the Tarim River, China

NASA Astrophysics Data System (ADS)

Othmanli, Hussein; Zhao, Chengyi; Stahr, Karl

2017-04-01

The Tarim River Basin is the largest continental basin in China. The region has extremely continental desert climate characterized by little rainfall <50 mm/a and high potential evaporation >3000 mm/a. The climate change is affecting severely the basin causing soil salinization, water shortage, and regression in crop production. Therefore, a Soil and Land Resources Information System (SLISYS-Tarim) for the regional simulation of crop yield production in the basin was developed. The SLISYS-Tarim consists of a database and an agro-ecological simulation model EPIC (Environmental Policy Integrated Climate). The database comprises relational tables including information about soils, terrain conditions, land use, and climate. The soil data implicate information of 50 soil profiles which were dug, analyzed, described and classified in order to characterize the soils in the region. DEM data were integrated with geological maps to build a digital terrain structure. Remote sensing data of Landsat images were applied for soil mapping, and for land use and land cover classification. An additional database for climate data, land management and crop information were linked to the system, too. Construction of the SLISYS-Tarim database was accomplished by integrating and overlaying the recommended thematic maps within environment of the geographic information system (GIS) to meet the data standard of the global and national SOTER digital database. This database forms appropriate input- and output data for the crop modelling with the EPIC model at various scales in the Tarim Basin. The EPIC model was run for simulating cotton production under a constructed scenario characterizing the current management practices, soil properties and climate conditions. For the EPIC model calibration, some parameters were adjusted so that the modeled cotton yield fits to the measured yield on the filed scale. The validation of the modeling results was achieved in a later step based on remote sensing data. The simulated cotton yield varied according to field management, soil type and salinity level, where soil salinity was the main limiting factor. Furthermore, the calibrated and validated EPIC model was run under several scenarios of climate conditions and land management practices to estimate the effect of climate change on cotton production and sustainability of agriculture systems in the basin. The application of SLISYS-Tarim showed that this database can be a suitable framework for storage and retrieval of soil and terrain data at various scales. The simulation with the EPIC model can assess the impact of climate change and management strategies. Therefore, SLISYS-Tarim can be a good tool for regional planning and serve the decision support system on regional and national scale.

Analysis on the electromagnetic scattering properties of crops at multi-band

NASA Astrophysics Data System (ADS)

Wu, Tao; Wu, Zhensen; Liu, Xiaoyi

2014-12-01

The vector radiative transfer (VRT) theory for active microwave remote sensing and Rayleigh-Gans approximation (GRG) are applied in the study, and an iterative algorithm is used to solve the RT equations, thus we obtain the zeroorder and first-order equation for numerical results. The Michigan Microwave Canopy Scattering (MIMICS) model is simplified to adapt to the crop model, by analyzing body-surface bistatic scattering and backscattering properties between a layer of soybean or wheat consisting of stems and leaves and different underlying soil surface at multi-band (i.e. P, L, S, X, Ku-band), we obtain microwave scattering mechanisms of crop components and the effect of underlying ground on total crop scattering. Stem and leaf are regard as a needle and a circular disk, respectively. The final results are compared with some literature data to verify our calculating method, numerical results show multi-band crop microwave scattering properties differ from scattering angle, azimuth angle and moisture of vegetation and soil, which offer the part needed information for the design of future bistatic radar systems for crop sensing applications.

Geospatial approaches to characterizing agriculture in the Chincoteague Bay subbasin.

PubMed

Kutz, Frederick W; Morgan, John M; Monn, Jeremy; Petrey, Chad P

2012-01-01

Most agricultural information is reported by government sources on a state or county basis. The purpose of this study was to demonstrate use of geospatial data, the 2002 Agricultural Cropland Data Layer (CDL) for the mid-Atlantic region, to characterize agricultural, environmental, and other scientific parameters for the Chincoteague Bay subbasin using geographic information systems. This study demonstrated that agriculture can be characterized accurately on subbasin and subwatershed bases, thus complimenting various assessment technologies. Approximately 28% of the dry land of the subbasin was cropland. Field corn was the largest crop. Soybeans, either singly or double-cropped with wheat, were the second most predominant crop. Although the subbasin is relatively small, cropping practices in the northern part were different from those in the southern portion. Other crops, such as fresh vegetables and vegetables grown for processing, were less than 10% of the total cropland. A conservative approximation of the total pesticide usage in the subbasin in 2002 was over 277,000 lbs of active ingredients. Herbicides represented the most frequently used pesticides in the subbasin, both in number (17) and in total active ingredients (over 261,000 lbs). Ten insecticides predominated in the watershed, while only small quantities of three fungicides were used. Total pesticide usage and intensity were estimated using the CDL. Nutrient inputs to cropland from animal manure, chemical fertilizer, and atmospheric deposition were modeled at over 30 million pounds of nitrogen and over 7 million pounds of phosphorous. Crops under conservation tillage had the largest input of both nutrients.

Surveying Rubisco Diversity and Temperature Response to Improve Crop Photosynthetic Efficiency.

PubMed

Orr, Douglas J; Alcântara, André; Kapralov, Maxim V; Andralojc, P John; Carmo-Silva, Elizabete; Parry, Martin A J

2016-10-01

The threat to global food security of stagnating yields and population growth makes increasing crop productivity a critical goal over the coming decades. One key target for improving crop productivity and yields is increasing the efficiency of photosynthesis. Central to photosynthesis is Rubisco, which is a critical but often rate-limiting component. Here, we present full Rubisco catalytic properties measured at three temperatures for 75 plants species representing both crops and undomesticated plants from diverse climates. Some newly characterized Rubiscos were naturally "better" compared to crop enzymes and have the potential to improve crop photosynthetic efficiency. The temperature response of the various catalytic parameters was largely consistent across the diverse range of species, though absolute values showed significant variation in Rubisco catalysis, even between closely related species. An analysis of residue differences among the species characterized identified a number of candidate amino acid substitutions that will aid in advancing engineering of improved Rubisco in crop systems. This study provides new insights on the range of Rubisco catalysis and temperature response present in nature, and provides new information to include in models from leaf to canopy and ecosystem scale. © 2016 American Society of Plant Biologists. All Rights Reserved.

Fertilizer Emission Scenario Tool for crop management system scenarios

EPA Pesticide Factsheets

The Fertilizer Emission Scenario Tool for CMAQ is a high-end computer interface that simulates daily fertilizer application information for any gridded domain. It integrates the Weather Research and Forecasting model and CMAQ.

Development of a Global Agricultural Hotspot Detection and Early Warning System

NASA Astrophysics Data System (ADS)

Lemoine, G.; Rembold, F.; Urbano, F.; Csak, G.

2015-12-01

The number of web based platforms for crop monitoring has grown rapidly over the last years and anomaly maps and time profiles of remote sensing derived indicators can be accessed online thanks to a number of web based portals. However, while these systems make available a large amount of crop monitoring data to the agriculture and food security analysts, there is no global platform which provides agricultural production hotspot warning in a highly automatic and timely manner. Therefore a web based system providing timely warning evidence as maps and short narratives is currently under development by the Joint Research Centre. The system (called "HotSpot Detection System of Agriculture Production Anomalies", HSDS) will focus on water limited agricultural systems worldwide. The automatic analysis of relevant meteorological and vegetation indicators at selected administrative units (Gaul 1 level) will trigger warning messages for the areas where anomalous conditions are observed. The level of warning (ranging from "watch" to "alert") will depend on the nature and number of indicators for which an anomaly is detected. Information regarding the extent of the agricultural areas concerned by the anomaly and the progress of the agricultural season will complement the warning label. In addition, we are testing supplementary detailed information from other sources for the areas triggering a warning. These regard the automatic web-based and food security-tailored analysis of media (using the JRC Media Monitor semantic search engine) and the automatic detection of active crop area using Sentinel 1, upcoming Sentinel-2 and Landsat 8 imagery processed in Google Earth Engine. The basic processing will be fully automated and updated every 10 days exploiting low resolution rainfall estimates and satellite vegetation indices. Maps, trend graphs and statistics accompanied by short narratives edited by a team of crop monitoring experts, will be made available on the website on a monthly basis.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Candidate Species Selection: Cultural and Photosynthetic Aspects

NASA Technical Reports Server (NTRS)

Mitchell, C. A.

1982-01-01

Cultural information is provided for a data base that will be used to select candidate crop species for a controlled ecological life support system (CELSS). Lists of food crops which will satisfy most nutritional requirements of humans and also fit within the scope of cultural restrictions that logically would apply to a closed, regenerating system were generated. Cultural and environmental conditions that will allow the most rapid production of edible biomass from candidate species in the shortest possible time are identified. Cultivars which are most productive in terms of edible biomass production by (CE) conditions, and which respond to the ever-closed approach to optimization realized by each shortened production cycle are selected. The experimental approach with lettuce was to grow the crop hydroponically in a growth chamber and to manipulate such variables as light level and duration, day/night temperature, and nutrient form and level in the solution culture.

Geostatistics, remote sensing and precision farming.

PubMed

Mulla, D J

1997-01-01

Precision farming is possible today because of advances in farming technology, procedures for mapping and interpolating spatial patterns, and geographic information systems for overlaying and interpreting several soil, landscape and crop attributes. The key component of precision farming is the map showing spatial patterns in field characteristics. Obtaining information for this map is often achieved by soil sampling. This approach, however, can be cost-prohibitive for grain crops. Soil sampling strategies can be simplified by use of auxiliary data provided by satellite or aerial photo imagery. This paper describes geostatistical methods for estimating spatial patterns in soil organic matter, soil test phosphorus and wheat grain yield from a combination of Thematic Mapper imaging and soil sampling.

Kasza: design of a closed water system for the greenhouse horticulture.

PubMed

van der Velde, Raphaël T; Voogt, Wim; Pickhardt, Pieter W

2008-01-01

The need for a closed and sustainable water system in greenhouse areas is stimulated by the implementation in the Netherlands of the European Framework Directive. The Dutch national project Kasza: Design of a Closed Water System for the Greenhouse Horticulture will provide information how the water system in a greenhouse horticulture area can be closed. In this paper the conceptual design of two systems to close the water cycle in a greenhouse area is described. The first system with reverse osmosis system can be used in areas where desalination is required in order to be able to use the recycle water for irrigation of all crops. The second system with advanced oxidation using UV and peroxide can be applied in areas with more salt tolerant crops and good (low sodium) water sources for irrigation. Both systems are financially feasible in new greenhouse areas with substantial available recycle water. (c) IWA Publishing 2008.

How well do meteorological indicators represent agricultural and forest drought across Europe?

NASA Astrophysics Data System (ADS)

Bachmair, S.; Tanguy, M.; Hannaford, J.; Stahl, K.

2018-03-01

Drought monitoring and early warning (M&EW) systems are an important component of agriculture/silviculture drought risk assessment. Many operational information systems rely mostly on meteorological indicators, and a few incorporate vegetation state information. However, the relationships between meteorological drought indicators and agricultural/silvicultural drought impacts vary across Europe. The details of this variability have not been elucidated sufficiently on a continental scale in Europe to inform drought risk management at administrative scales. The objective of this study is to fill this gap and evaluate how useful the variety of meteorological indicators are to assess agricultural/silvicultural drought across Europe. The first part of the analysis systematically linked meteorological drought indicators to remote sensing based vegetation indices (VIs) for Europe at NUTs3 administrative regions scale using correlation analysis for crops and forests. In a second step, a stepwise multiple linear regression model was deployed to identify variables explaining the spatial differences observed. Finally, corn crop yield in Germany was chosen as a case study to verify VIs’ representativeness of agricultural drought impacts. Results show that short accumulation periods of SPI and SPEI are best linked to crop vegetation stress in most cases, which further validates the use of SPI3 in existing operational drought monitors. However, large regional differences in correlations are also revealed. Climate (temperature and precipitation) explained the largest proportion of variance, suggesting that meteorological indices are less informative of agricultural/silvicultural drought in colder/wetter parts of Europe. These findings provide important context for interpreting meteorological indices on widely used national to continental M&EW systems, leading to a better understanding of where/when such M&EW tools can be indicative of likely agricultural stress and impacts.

NASA crop calendars: Wheat, barley, oats, rye, sorghum, soybeans, corn

NASA Technical Reports Server (NTRS)

Stuckey, M. R.; Anderson, E. N.

1975-01-01

Crop calenders used to determine when Earth Resources Technology Satellite ERTS data would provide the most accurate wheat acreage information and to minimize the amount of ground verified information needed are presented. Since barley, oats, and rye are considered 'confusion crops, i.e., hard to differentiate from wheat in ERTS imagery, specific dates are estimated for these crops in the following stages of development: (1) seed-bed operation, (2) planting or seeding, (3) intermediate growth, (4) dormancy, (5) development of crop to full ground cover, (6) heading or tasseling, and flowering, (7) harvesting, and (8) posting-harvest operations. Dormancy dates are included for fall-snow crops. A synopsis is given of each states' growing conditions, special cropping practices, and other characteristics which are helpful in identifying crops from ERTS imagery.

Observing Crop-Height Dynamics Using a UAV

NASA Astrophysics Data System (ADS)

Ziliani, M. G.; Parkes, S. D.; McCabe, M.

2017-12-01

Retrieval of vegetation height during a growing season is a key indicator for monitoring crop status, offering insight to the forecast yield relative to previous planting cycles. Improvement in Unmanned Aerial Vehicle (UAV) technologies, supported by advances in computer vision and photogrammetry software, has enabled retrieval of crop heights with much higher spatial resolution and coverage. These methodologies retrieve a Digital Surface Map (DSM), which combine terrain and crop elements to obtain a Crop Surface Map (CSM). Here we describe an automated method for deriving high resolution CSMs from a DSM, using RGB imagery from a UAV platform. Importantly, the approach does not require the need for a digital terrain map (DTM). The method involves distinguishing between vegetation and bare-ground cover pixels, using vegetation index maps from the RGB orthomosaic derived from the same flight as the DSM. We show that the absolute crop height can be extracted to within several centimeters, exploiting the data captured from a single UAV flight. In addition, the method is applied across five surveys during a maize growing cycle and compared against a terrain map constructed from a baseline UAV survey undertaken prior to crop growth. Results show that the approach is able to reproduce the observed spatial variability of the crop height within the maize field throughout the duration of the growing season. This is particularly valuable since it may be employed to detect intra-field problems (i.e. fertilizer variability, inefficiency in the irrigation system, salinity etc.) at different stages of the season, from which remedial action can be initiated to mitigate against yield loss. The method also demonstrates that UAV imagery combined with commercial photogrammetry software can determine a CSM from a single flight without the requirement of a prior DTM. This, together with the dynamic crop height estimation, provide useful information with which to inform precision agricultural management at the local scale.

Drought Information Supported by Citizen Scientists (DISCS)

NASA Astrophysics Data System (ADS)

Molthan, A.; Maskey, M.; Hain, C.; Meyer, P.; Nair, U. S.; Handyside, C. T.; White, K.; Amin, M.

2017-12-01

Each year, drought impacts various regions of the United States on time scales of weeks, months, seasons, or years, which in turn leads to a need to document these impacts and inform key decisions on land management, use of water resources, and disaster response. Mapping impacts allows decision-makers to understand potential damage to agriculture and loss of production, to communicate and document drought impacts on crop yields, and to inform water management decisions. Current efforts to collect this information includes parsing of media reports, collaborations with local extension offices, and partnerships with the National Weather Service cooperative observer network. As part of a NASA Citizen Science for Earth Systems proposal award, a research and applications team from Marshall Space Flight Center, the University of Alabama in Huntsville, and collaborators within the NWS have developed a prototype smartphone application focused on the collection of citizen science observations of crop health and drought impacts, along with development of innovative low-cost soil moisture sensors to supplement subjective assessments of local soil moisture conditions. Observations provided by citizen scientists include crop type and health, phase of growth, soil moisture conditions, irrigation status, along with an optional photo and comment to provide visual confirmation and other details. In exchange for their participation, users of the app also have access to unique land surface modeling data sets produced at MSFC such as the NASA Land Information System soil moisture and climatology/percentile products from the Short-term Prediction Research and Transition (SPoRT) Center, assessments of vegetation health and stress from NASA and NOAA remote sensing platforms (e.g. MODIS/VIIRS), outputs from a crop stress model developed at the University of Alabama in Huntsville, recent rainfall estimates from the NOAA/NWS network of ground-based weather radars, and other observations made by their fellow citizen scientists. This presentation will highlight development of the application, data collected to date, feedback from participants, and opportunities to use the collected information in support of addressing science questions such as verification and validation of modeling and remote sensing data sets.

Assessment of Land Degradation and Greening in Ken River Basin of Central India

NASA Astrophysics Data System (ADS)

Pandey, Ashish; Palmate, Santosh S.

2017-04-01

Natural systems have significant impact of land degradation on biodiversity loss, food and water insecurity. To achieve the sustainable development goals, advances in remote sensing and geographical information systems (GIS) are progressively utilized to combat climate change, land degradation and poverty issues of developing country. The Ken River Basin (KRB) has dominating land cover pattern of agriculture and forest area. Nowadays, this pattern is affected due to climate change and anthropogenic activity like deforestation. In this study, land degradation and greening status of KRB of Central India during the years 2001 to 2013 have been assessed using MODIS land cover (MCD12Q1) data sets. International Geosphere Biosphere Programme (IGBP) land cover data has been extracted from the MCD12Q1 data product. Multiple rasters of MODIS landcover were analyzed and compared for assigning unique combination of land cover dynamics employing ArcGIS software. Result reveals that 14.38% natural vegetation was degraded, and crop land and woody savannas were greened by 9.68% to 6.94% respectively. Natural vegetation degradation have been observed in the upper KRB area, and resulted to increase in crop land (3418.87 km2) and woody savannas (1242.23 km2) area. Due to transition of 1043.6 km2 area of deciduous broadleaf forest to woody savannas greening was also observed. Moreover, both crop land and woody savannas showed inter-transitions of 669.31 km2 into crop land to woody savannas, and 874.09 km2 into woody savannas to crop land. The present analysis reveals that natural vegetation has more land conversions into woody savannas and crop land in the KRB area. Further, Spatial change analysis shows that land degradation and greening has occurred mostly in the upper part of the KRB. The study reveals that the land transition information can be useful for proper planning and management of natural resources.

Meteorological risks are drivers of environmental innovation in agro-ecosystem management

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van de Vijver, Hans; Vanwindekens, Frédéric; de Frutos Cachorro, Julia; Verspecht, Ann; Planchon, Viviane; Buyse, Jeroen

2017-04-01

Agricultural crop production is to a great extent determined by weather conditions. The research hypothesis is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management. The methodology comprised five major parts: the hazard, its impact on different agro-ecosystems, vulnerability, risk management and risk communication. Generalized Extreme Value (GEV) theory was used to model annual maxima of meteorological variables based on a location-, scale- and shape-parameter that determine the center of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Spatial interpolation of GEV-derived return levels resulted in spatial temperature extremes, precipitation deficits and wet periods. The temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was realised using a bio-physically based modelling framework that couples phenology, a soil water balance and crop growth. 20-year return values for drought and waterlogging during different crop stages were related to arable yields. The method helped quantify agricultural production risks and rate both weather and crop-based agricultural insurance. The spatial extent of vulnerability is developed on different layers of geo-information to include meteorology, soil-landscapes, crop cover and management. Vulnerability of agroecosystems was mapped based on rules set by experts' knowledge and implemented by Fuzzy Inference System modelling and Geographical Information System tools. The approach was applied for cropland vulnerability to heavy rain and grassland vulnerability to drought. The level of vulnerability and resilience of an agro-ecosystem was also determined by risk management which differed across sectors and farm types. A calibrated agro-economic model demonstrated a marked influence of climate adapted land allocation and crop management on individual utility. The "chain of risk" approach allowed for investigating the hypothesis that meteorological risks act as drivers for agricultural innovation. Risk types were quantified in terms of probability and distribution, and further distinguished according to production type. Examples of strategies and options were provided at field, farm and policy level using different modelling methods.

Alabama Cooperative Extension System - ACES.edu

Science.gov Websites

-Related Information Agriculture Aquaculture & Seafood Production Business Management Crop Production - Food, Fiber, Ornamentals & Turf Food Safety Livestock & Poultry Precision Agriculture Insects ; Youth Agriculture Disasters Economic Development Family & Health General Extension Home & Garden

The design of composite monitoring scheme for multilevel information in crop early diseases

NASA Astrophysics Data System (ADS)

Zhang, Yan; Meng, Qinglong; Shang, Jing

2018-02-01

It is difficult to monitor and predict the crops early diseases in that the crop disease monitoring is usually monitored by visible light images and the availabilities in early warning are poor at present. The features of common nondestructive testing technology applied to the crop diseases were analyzed in this paper. Based on the changeable characteristics of the virus from the incubation period to the onset period of crop activities, the multilevel composite information monitoring scheme were designed by applying infrared thermal imaging, visible near infrared hyperspectral imaging, micro-imaging technology to the monitoring of multilevel information of crop disease infection comprehensively. The early warning process and key monitoring parameters of compound monitoring scheme are given by taking the temperature, color, structure and texture of crops as the key monitoring characteristics of disease. With overcoming the deficiency that the conventional monitoring scheme is only suitable for the observation of diseases with naked eyes, the monitoring and early warning of the incubation and early onset of the infection crops can be realized by the composite monitoring program as mentioned in this paper.

Stimulating innovation for global monitoring of agriculture and its impact on the environment in support of GEOGLAM

NASA Astrophysics Data System (ADS)

Bydekerke, Lieven; Gilliams, Sven; Gobin, Anne

2015-04-01

There is an urgent need to ensure food supply for a growing global population. To enable a sustainable growth of agricultural production, effective and timely information is required to support decision making and to improve management of agricultural resources. This requires innovative ways and monitoring methods that will not only improve short-term crop production forecasts, but also allow to assess changes in cultivation practices, agricultural areas, agriculture in general and, its impact on the environment. The G20 launched in June 2011 the "GEO Global Agricultural Monitoring initiative (GEOGLAM), requesting the GEO (Group on Earth Observations) Agricultural Community of Practice to implement GEOGLAM with the main objective to improve crop yield forecasts as an input to the Agricultural Market Information System (AMIS), in order to foster stabilisation of markets and increase transparency on agricultural production. In response to this need, the European Commission decided in 2013 to fund an international partnership to contribute to GEOGLAM and its research agenda. The resulting SIGMA project (Stimulating Innovation for Global Monitoring of Agriculture), a partnership of 23 globally distributed expert organisations, focusses on developing datasets and innovative techniques in support of agricultural monitoring and its impact on the environment in support of GEOGLAM. SIGMA has 3 generic objectives which are: (i) develop and test methods to characterise cropland and assess its changes at various scales; (ii) develop and test methods to assess changes in agricultural production levels; and; (iii) study environmental impacts of agriculture. Firstly, multi-scale remote sensing data sets, in combination with field and other ancillary data, will be used to generate an improved (global) agro-ecological zoning map and crop mask. Secondly, a combination of agro-meteorological models, satellite-based information and long-term time series will be explored to assess crop yield gaps and shifts in cultivation. The third research topic entails the development of best practices for assessing the impact of crop land and cropping system change on the environment. In support of the GEO JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative, SIGMA has selected case studies in Ukraine, Russia, Europe, Africa, Latin America and China, coinciding with the JECAM sites in these area, to explore possible methodological synergies and particularities according to different cropping systems. In combination with research conducted at regional and global scale, it is one of the goals to improve the understanding of dynamics, interactions and validity of the developed methods at the various scales. In addition, specific activities will be dedicated to raising awareness and strengthening capacity for what concerns agro-environmental monitoring, data accessibility and interoperability in line with the GEOSS Data-core principles. The SIGMA project will also anticipate on the availability of the SENTINEL satellites for agricultural applications as open-data in the near future. References http://proba-v.vgt.vito.be/ http://www.geoglam-sigma.info/

Environmental health impacts of feeding crops to farmed fish.

PubMed

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

2016-05-01

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

A Spatial Allocation Procedure to Downscale Regional Crop Production Estimates from an Integrated Assessment Model

NASA Astrophysics Data System (ADS)

Moulds, S.; Djordjevic, S.; Savic, D.

2017-12-01

The Global Change Assessment Model (GCAM), an integrated assessment model, provides insight into the interactions and feedbacks between physical and human systems. The land system component of GCAM, which simulates land use activities and the production of major crops, produces output at the subregional level which must be spatially downscaled in order to use with gridded impact assessment models. However, existing downscaling routines typically consider cropland as a homogeneous class and do not provide information about land use intensity or specific management practices such as irrigation and multiple cropping. This paper presents a spatial allocation procedure to downscale crop production data from GCAM to a spatial grid, producing a time series of maps which show the spatial distribution of specific crops (e.g. rice, wheat, maize) at four input levels (subsistence, low input rainfed, high input rainfed and high input irrigated). The model algorithm is constrained by available cropland at each time point and therefore implicitly balances extensification and intensification processes in order to meet global food demand. It utilises a stochastic approach such that an increase in production of a particular crop is more likely to occur in grid cells with a high biophysical suitability and neighbourhood influence, while a fall in production will occur more often in cells with lower suitability. User-supplied rules define the order in which specific crops are downscaled as well as allowable transitions. A regional case study demonstrates the ability of the model to reproduce historical trends in India by comparing the model output with district-level agricultural inventory data. Lastly, the model is used to predict the spatial distribution of crops globally under various GCAM scenarios.

Agricultural pesticide emissions associated with common crops in the United States

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

Benjey, W.G.

Annual emissions for the year 1987 from the application of agricultural pesticides have been estimated by crop type by county for the United States using a geographic information system. The emissions estimates are based upon computed volatilization rates accounting for the properties of each pesticide, evaporation rates, mode of application (surface or soil incorporation) and percent of interception by leaves. Key pesticide properties include the Henry's Law constant, half-life in soil and the organic carbon partitioning coefficient. The volatilization rates are multiplied by the amount of pesticide applied by crop acreage in each county as determined from agricultural census andmore » pesticide sales data. The geographic distribution of the dominant emissions, such as atrazine and diazinon, etc. are presented by crop type and state. For a given pesticide, the geographic variability is controlled principally by amount applied and water availability as reflected in evaporation rates.« less

An energy balance approach for mapping crop waterstress and yield impacts over the Czech Republic

USDA-ARS?s Scientific Manuscript database

There is a growing demand for timely, spatially distributed information regarding crop condition and water use to inform agricultural decision making and yield forecasting efforts. Remote sensing of land-surface temperature has proven valuable for mapping evapotranspiration (ET) and crop stress from...

Dynamic cropping systems: Holistic approach for dryland agricultural systems in the northern Great Plains of North America

USDA-ARS?s Scientific Manuscript database

Cropping systems over the past century have developed greater crop specialization, more effectively conserve our soil and water resources, and are more resilient. The purpose of this chapter is to discuss the evolution of cropping systems in the Northern Great Plains and provide an approach to crop...

The use of automated weather stations for irrigation management in the Jordan Valley

USDA-ARS?s Scientific Manuscript database

We discuss an irrigation management information system approach developed by NCARE researchers with the help of USDA-ARS. The system is capable of providing farmers with online crop water requirements based on automated meteorological data published on the internet (www.ncare.gov.jo/imis, and www.m...

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.

Biology Notes.

ERIC Educational Resources Information Center

School Science Review, 1982

1982-01-01

Presents procedures, exercises, demonstrations, and information on a variety of biology topics including labeling systems, biological indicators of stream pollution, growth of lichens, reproductive capacity of bulbous buttercups, a straw balance to measure transpiration, interaction of fungi, osmosis, and nitrogen fixation and crop production. (DC)

Insect Resistance

USDA-ARS?s Scientific Manuscript database

Insect pests exhibit a diverse array of genetic-based responses when interacting with crop systems; these changes can be in response to pathogens, symbiotic microbes, host plants, chemicals, and the environment. Agricultural research has for decades focused on gathering crucial information on the bi...

The southern Brazilian grassland biome: soil carbon stocks, fluxes of greenhouse gases and some options for mitigation.

PubMed

Pillar, V D; Tornquist, C G; Bayer, C

2012-08-01

The southern Brazilian grassland biome contains highly diverse natural ecosystems that have been used for centuries for grazing livestock and that also provide other important environmental services. Here we outline the main factors controlling ecosystem processes, review and discuss the available data on soil carbon stocks and greenhouse gases emissions from soils, and suggest opportunities for mitigation of climatic change. The research on carbon and greenhouse gases emissions in these ecosystems is recent and the results are still fragmented. The available data indicate that the southern Brazilian natural grassland ecosystems under adequate management contain important stocks of organic carbon in the soil, and therefore their conservation is relevant for the mitigation of climate change. Furthermore, these ecosystems show a great and rapid loss of soil organic carbon when converted to crops based on conventional tillage practices. However, in the already converted areas there is potential to mitigate greenhouse gas emissions by using cropping systems based on no soil tillage and cover-crops, and the effect is mainly related to the potential of these crop systems to accumulate soil organic carbon in the soil at rates that surpass the increased soil nitrous oxide emissions. Further modelling with these results associated with geographic information systems could generate regional estimates of carbon balance.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

PubMed Central

Pathak, Rajesh Kumar; Gupta, Sanjay Mohan; Gaur, Vikram Singh; Pandey, Dinesh

2015-01-01

Abstract In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes. PMID:26484978

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

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

Hilker, Frank M.; Allen, Linda J. S.; Bokil, Vrushali A.

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with largemore » holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.« less

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya

DOE PAGES

Hilker, Frank M.; Allen, Linda J. S.; Bokil, Vrushali A.; ...

2017-08-01

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with largemore » holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Finally, our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty.« less

Remote sensing applications for sustainable agriculture in South Africa (Conference Presentation)

NASA Astrophysics Data System (ADS)

Jarmain, Caren; Van Niekerk, Adriaan; Goudriaan, Ruben

2016-10-01

Agriculture contributes greatly to the economy of South Africa (SA), through job creation and produce exports. SA is classified as a semi-arid country and due to its low rainfall, fierce competition exists for the available water resources. Balancing the need for water resources on the one hand, with the importance of agricultural production on the other, is often challenging. A lot of emphasis is placed on prudent water management and enhanced crop water use efficiency. Suitable information and tools are key in empowering both water resources managers and (crop) producers for sustainable agricultural production. Information and tools available at frequent intervals throughout the production season and at a range of levels - from the field to the catchment and for the entire country - has become essential. The frequency and availability of remote sensing data, developments in algorithms to produce information related to the water cycle and crop growth and hence the actual information sets produced over time, makes for fitting solutions. Though much progress has been made over the past years to integrate these spatial data products into water management and agricultural systems, it is likely still in its infancy. In the paper, some flagship projects related to sustainable agriculture and water management - both research and applied - are showcased.

iPot: Improved potato monitoring in Belgium using remote sensing and crop growth modelling

NASA Astrophysics Data System (ADS)

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

2016-04-01

Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these. The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market. The iPot project, financed by the Belgian Science Policy Office (Belspo), aims at providing the Belgian potato processing sector, represented by Belgapom, with near real time information on field condition (weather-soil), crop development and yield estimates, derived from a combination of satellite images and crop growth models. During the cropping season regular UAV flights (RGB, 3x3 cm) and high resolution satellite images (DMC/Deimos, 22m pixel size) were combined to elucidate crop phenology and performance at variety trials. UAV images were processed using a K-means clustering algorithm to classify the crop according to its greenness at 5m resolution. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) on the DMC images. Both DMC and UAV-based cover maps showed similar patterns, and helped detect different crop stages during the season. A wide spread field monitoring campaign with crop observations and measurements allowed for further calibration of the satellite image derived vegetation indices. Curve fitting techniques and phenological models were developed and compared with the vegetation indices during the season, both at trials and farmers' fields. Understanding and predicting crop phenology and canopy development is important for timely crop management and ultimately for yield estimates. An intuitive web-based geo-information platform is developed to allow both the industry and the research centres to access, analyse and combine the data with their own field observations for improved decision-making.

The value of seasonal forecasting and crop mix adaptation to climate variability for agriculture under climate change

NASA Astrophysics Data System (ADS)

Choi, H. S.; Schneider, U.; Schmid, E.; Held, H.

2012-04-01

Changes to climate variability and frequency of extreme weather events are expected to impose damages to the agricultural sector. Seasonal forecasting and long range prediction skills have received attention as an option to adapt to climate change because seasonal climate and yield predictions could improve farmers' management decisions. The value of seasonal forecasting skill is assessed with a crop mix adaptation option in Spain where drought conditions are prevalent. Yield impacts of climate are simulated for six crops (wheat, barely, cotton, potato, corn and rice) with the EPIC (Environmental Policy Integrated Climate) model. Daily weather data over the period 1961 to 1990 are used and are generated by the regional climate model REMO as reference period for climate projection. Climate information and its consequent yield variability information are given to the stochastic agricultural sector model to calculate the value of climate information in the agricultural market. Expected consumers' market surplus and producers' revenue is compared with and without employing climate forecast information. We find that seasonal forecasting benefits not only consumers but also producers if the latter adopt a strategic crop mix. This mix differs from historical crop mixes by having higher shares of crops which fare relatively well under climate change. The corresponding value of information is highly sensitive to farmers' crop mix choices.

Exploring the Influence of Smallholders' Perceptions Regarding Water Availability on Crop Choice and Water Allocation Through Socio-Hydrological Modeling

NASA Astrophysics Data System (ADS)

Kuil, L.; Evans, T.; McCord, P. F.; Salinas, J. L.; Blöschl, G.

2018-04-01

While it is known that farmers adopt different decision-making behaviors to cope with stresses, it remains challenging to capture this diversity in formal model frameworks that are used to advance theory and inform policy. Guided by cognitive theory and the theory of bounded rationality, this research develops a novel, socio-hydrological model framework that can explore how a farmer's perception of water availability impacts crop choice and water allocation. The model is informed by a rich empirical data set at the household level collected during 2013 in Kenya's Upper Ewaso Ng'iro basin that shows that the crop type cultivated is correlated with water availability. The model is able to simulate this pattern and shows that near-optimal or "satisficing" crop patterns can emerge also when farmers were to make use of simple decision rules and have diverse perceptions on water availability. By focusing on farmer decision making it also captures the rebound effect, i.e., as additional water becomes available through the improvement of crop efficiencies it will be reallocated on the farm instead of flowing downstream, as a farmer will adjust his (her) water allocation and crop pattern to the new water conditions. This study is valuable as it is consistent with the theory of bounded rationality, and thus offers an alternative, descriptive model in addition to normative models. The framework can be used to understand the potential impact of climate change on the socio-hydrological system, to simulate and test various assumptions regarding farmer behavior and to evaluate policy interventions.

Crop classification using multidate/multifrequency radar data. [Colby, Kansas

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Shanmugam, K. S.; Narayanan, V.; Dobson, C.

1981-01-01

Both C- and L-band radar data acquired over a test site near Colby, Kansas during the summer of 1978 were used to identify three types of vegetation cover and bare soil. The effects of frequency, polarization, and the look angle on the overall accuracy of recognizing the four types of ground cover were analyzed. In addition, multidate data were used to study the improvement in recognition accuracy possible with the addition of temporal information. The soil moisture conditions had changed considerably during the temporal sequence of the data; hence, the effects of soil moisture on the ability to discriminate between cover types were also analyzed. The results provide useful information needed for selecting the parameters of a radar system for monitoring crops.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

New Microwave-Based Missions Applications for Rainfed Crops Characterization

NASA Astrophysics Data System (ADS)

Sánchez, N.; Lopez-Sanchez, J. M.; Arias-Pérez, B.; Valcarce-Diñeiro, R.; Martínez-Fernández, J.; Calvo-Heras, J. M.; Camps, A.; González-Zamora, A.; Vicente-Guijalba, F.

2016-06-01

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

Influence of precipitation and crop germination on resource selection by mule deer (Odocoileus hemionus) in southwest Colorado

USGS Publications Warehouse

Carrollo, Emily M.; Johnson, Heather E.; Fischer, Justin W.; Hammond, Matthew; Dorsey, Patricia D.; Anderson, Charles; Vercauteren, Kurt C.; Walter, W. David

2017-01-01

Mule deer (Odocoileus hemionus) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km2, and for two summer seasons that ranged between 5.51 and 6.24 km2. Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

Influence of Precipitation and Crop Germination on Resource Selection by Mule Deer (Odocoileus hemionus) in Southwest Colorado.

PubMed

Carrollo, Emily M; Johnson, Heather E; Fischer, Justin W; Hammond, Matthew; Dorsey, Patricia D; Anderson, Charles; Vercauteren, Kurt C; Walter, W David

2017-11-09

Mule deer (Odocoileus hemionus) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km 2 , and for two summer seasons that ranged between 5.51 and 6.24 km 2 . Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

7 CFR 1219.5 - Crop year.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ORDERS; MISCELLANEOUS COMMODITIES), DEPARTMENT OF AGRICULTURE HASS AVOCADO PROMOTION, RESEARCH, AND INFORMATION Hass Avocado Promotion, Research, and Information Order Definitions § 1219.5 Crop year...

A bioenergy feedstock/vegetable double-cropping system

USDA-ARS?s Scientific Manuscript database

Certain warm-season vegetable crops may lend themselves to bioenergy double-cropping systems, which involve growing a winter annual bioenergy feedstock crop followed by a summer annual crop. The objective of the study was to compare crop productivity and weed communities in different pumpkin product...

Using NASA UAVSAR Datasets to Link Soil Moisture to Crop Conditions

NASA Astrophysics Data System (ADS)

Davitt, A. W. D.; McDonald, K. C.; Azarderakhsh, M.; Winter, J.

2015-12-01

California and The Central Valley are experiencing one of that region's worst, persistent droughts, which represents the continuation of a prolonged drought that started in the early 2000's. Due to the continued drought, many agricultural regions in The Central Valley have been experiencing water shortages, negatively impacting agricultural production and the socio-economics of the region. Due to these impacts, there has been an increased incentive to find new ways to conserve water for use in irrigation. Recent advances in remote sensing techniques provide the ability for end users to better understand field conditions so they may make more informed decisions on irrigation timing and amounts. However, a good understanding of soil moisture and its role in crop health and yield is lacking to support informed water management decisions. Though known to be important, a robust understanding of the role of the spatio-temporal patterns in soil moisture linked to crop health is lacking. Remote sensing platforms such as NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provide the capacity to obtain within-field measurements to estimate within-field and field-to-field variability in soil moisture. UAVSAR radar images acquired from 2010 to 2014 for Yolo County, California are being examined to determine the suitability of high resolution (field scale) multi-temporal L-band radar backscatter imagery for soil moisture assessment and crop conditions through the growing season. By using such data and linking to in-situ meteorology measurements, modeling (MIMICS), and other remote sensing derived datasets (Sentinel, Landsat, MODIS, and TOPS-SIMS), an integrated monitoring system can potentially support the assessment of agricultural field conditions. This allows growers to optimize the use of limited water supplies through informed water management practices, potentially improving crop conditions and yield in a water stressed region.

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

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 ...

Incorporating a Constrained Optimization Algorithm into Remote- Sensing/Precision Agriculture Methodology

NASA Astrophysics Data System (ADS)

Morgenthaler, George; Khatib, Nader; Kim, Byoungsoo

with information to improve their crop's vigor has been a major topic of interest. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, the efficiency of farming must increase to meet future food requirements and to make farming a sustainable occupation for the farmer. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The goal is to increase farm revenue by increasing crop yield and decreasing applications of costly chemical and water treatments. In addition, this methodology will decrease the environmental costs of farming, i.e., reduce air, soil, and water pollution. Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now available. Commercial satellite systems can image (multi-spectral) the Earth with a resolution of approximately 2.5 m. Variable precision dispensing systems using GPS are available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been formulated. Personal computers and internet access are in place in most farm homes and can provide a mechanism to periodically disseminate, e.g. bi-weekly, advice on what quantities of water and chemicals are needed in individual regions of the field. What is missing is a model that fuses the disparate sources of information on the current states of the crop and soil, and the remaining resource levels available with the decisions farmers are required to make. This must be a product that is easy for the farmer to understand and to implement. A "Constrained Optimization Feed-back Control Model" to fill this void will be presented. The objective function of the model will be used to maximize the farmer's profit by increasing yields while decreasing environmental costs and decreasing application of costly treatments. This model will incorporate information from remote sensing, in-situ weather sources, soil measurements, crop models, and tacit farmer knowledge of the relative productivity of the selected control regions of the farm to provide incremental advice throughout the growing season on water and chemical treatments. Genetic and meta-heuristic algorithms will be used to solve the constrained optimization problem that possesses complex constraints and a non-linear objective function. *

Geographic information systems in corn rootworm management

USDA-ARS?s Scientific Manuscript database

Corn rootworms (Diabrotica spp. Coleoptera: Chrysomelidae) are serious pests of corn (Zea mays) in the United States and Europe. Control measures for corn rootworms (CRW) were historically based upon chemical pesticides and crop rotation. Pesticide use created environmental and economic concerns. In...

Sugarcane Crop Extraction Using Object-Oriented Method from ZY-3 High Resolution Satellite Tlc Image

NASA Astrophysics Data System (ADS)

Luo, H.; Ling, Z. Y.; Shao, G. Z.; Huang, Y.; He, Y. Q.; Ning, W. Y.; Zhong, Z.

2018-04-01

Sugarcane is one of the most important crops in Guangxi, China. As the development of satellite remote sensing technology, more remotely sensed images can be used for monitoring sugarcane crop. With the help of Three Line Camera (TLC) images, wide coverage and stereoscopic mapping ability, Chinese ZY-3 high resolution stereoscopic mapping satellite is useful in attaining more information for sugarcane crop monitoring, such as spectral, shape, texture difference between forward, nadir and backward images. Digital surface model (DSM) derived from ZY-3 TLC images are also able to provide height information for sugarcane crop. In this study, we make attempt to extract sugarcane crop from ZY-3 images, which are acquired in harvest period. Ortho-rectified TLC images, fused image, DSM are processed for our extraction. Then Object-oriented method is used in image segmentation, example collection, and feature extraction. The results of our study show that with the help of ZY-3 TLC image, the information of sugarcane crop in harvest time can be automatic extracted, with an overall accuracy of about 85.3 %.

Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species?

PubMed Central

Archontoulis, S. V.; Yin, X.; Vos, J.; Danalatos, N. G.; Struik, P. C.

2012-01-01

Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C3 leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO2 at the stomatal cavity (An–Ci), the model was parameterized by analysing the photosynthesis response to incident light intensity (An–Iinc). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from An–Ci or from An–Iinc data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored An–Iinc data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model. PMID:22021569

The agricultural features of Nizhegorodskaya gubernia (province) in the XIX century

NASA Astrophysics Data System (ADS)

Kirillova, Vasilisa

2017-04-01

One of the main conditions for the sustainable development of any country is the food security of the population, based on the development of agriculture. This condition can be realized through the efficient use of the productive capacity of agriculture, and above all natural resources. From 1882 to 1887 in the Nizhegorodskaya gubernia (province) complex physiographic (landscape) researches were conducted by V.V. Dokuchaev and his followers. This investigation was focused on studying the relationship between the soils and the environment, having no parallel either in Russia or abroad, and received evaluation of the soils was the first experience of such a scale and nature. Reports of the expedition were presented in 14 volumes on the natural science of the study, and 11 volumes of economic statistics. Natural science volume includes descriptions of irrigation and hydrography, geology, soil and vegetation in uezds (districts). Economic volume represent a set of common data on the situation of the peasant economy, they contain information about the number of arable land, including fertilized, hayfields, forests, manure stocks, livestock, harvest volumes, proportions of cultivated crops. The aim of this research was to study the list and structure of crops cultivated in the Nizhegorodskaya gubernia in the XIX century and their compliance with the soil and climatic conditions. From the materials of the expedition reports for the eight districts of the Nizhegorodskaya gubernia was compiled a list of crops and crop area information which was introduced in the GIS (MapInfo). Geographic information systems were used to visualize the collected material in the form of maps, cartograms and charts. For the conformity assessment of soil and climatic conditions of the studied area of selected crops a map "Agricultural zoning of Russia for optimal crop growing" by I.I. Karmanov and D.S. Bulgakov (National Soil Atlas, 2011) was applied. According to this map the Nizhegorodskaya gubernia is located in three agro-climatic areas: (№9) European Southern taiga, sod-podzolic (humus soils of Opol'e), rye, barley, oat, potato and forage (corn silage), (number 11) North-steppe (ETP), gray forest soils with patches of chernozems, winter-wheat-rye, barley, oat, potato with corn silage, (№12) forest-steppe (ETP), leached and podzolized chernozems with gray forest soils , winter-wheat-rye, barley, oat, potato with sugar beet and corn for silage. Analysis of digitized information on cultivated crops of Nizhegorodskaya gubernia in the XIX century and agro-climatic characteristics of areas has shown that the list of selected crops in general corresponds to the recommendations by present-day scientists, but has its own characteristics. In reporting materials there is no information about the cultivation of crops such as winter wheat, sugar beet and corn. Potatoes and barley are cultivated in small quantities, their place is taken lentil, millet and spelt, which in today's recommendations are not mentioned.

Research in biomass production and utilization: Systems simulation and analysis

NASA Astrophysics Data System (ADS)

Bennett, Albert Stewart

There is considerable public interest in developing a sustainable biobased economy that favors support of family farms and rural communities and also promotes the development of biorenewable energy resources. This study focuses on a number of questions related to the development and exploration of new pathways that can potentially move us toward a more sustainable biobased economy. These include issues related to biomass fuels for drying grain, economies-of-scale, new biomass harvest systems, sugar-to-ethanol crop alternatives for the Upper Midwest U.S., biomass transportation, post-harvest biomass processing and double cropping production scenarios designed to maximize biomass feedstock production. The first section of this study considers post-harvest drying of shelled corn grain both at farm-scale and at larger community-scaled installations. Currently, drying of shelled corn requires large amounts of fossil fuel energy. To address future energy concerns, this study evaluates the potential use of combined heat and power systems that use the combustion of corn stover to produce steam for drying and to generate electricity for fans, augers, and control components. Because of the large capital requirements for solid fuel boilers and steam turbines/engines, both farm-scale and larger grain elevator-scaled systems benefit by sharing boiler and power infrastructure with other processes. The second and third sections evaluate sweet sorghum as a possible "sugarcane-like" crop that can be grown in the Upper Midwest. Various harvest systems are considered including a prototype mobile juice harvester, a hypothetical one-pass unit that separates grain heads from chopped stalks and traditional forage/silage harvesters. Also evaluated were post-harvest transportation, storage and processing costs and their influence on the possible use of sweet sorghum as a supplemental feedstock for existing dry-grind ethanol plants located in the Upper Midwest. Results show that the concept of a mobile juice harvester is not economically viable due to low sugar recovery. The addition of front-end stalk processing/pressing equipment into existing ethanol facilities was found to be economically viable when combined with the plants' use of residuals as a natural gas fuel replacement. Because of high loss of fermentable carbohydrates during ensilage, storage of sweet sorghum in bunkers was not found to be economically viable. The fourth section looks at double cropping winter triticale with late-planted summer corn and compares these scenarios to traditional single cropped corn. Double cropping systems show particular promise for co-production of grain and biomass feedstocks and potentially can allow for greater utilization of grain crop residues. However, additional costs and risks associated with producing two crops instead of one could make biomass-double crops less attractive for producers despite productivity advantages. Detailed evaluation and comparisons show double cropped triticale-corn to be at a significant economic disadvantage relative to single crop corn. The cost benefits associated with using less equipment combined with availability of risk mitigating crop insurance and government subsidies will likely limit farmer interest and clearly indicate that traditional single-crop corn will provide greater financial returns to management. To evaluate the various sweet sorghum, single crop corn and double cropped triticale-corn production scenarios, a detailed but generic model was developed. The primary goal of this generic approach was to develop a modeling foundation that can be rapidly adapted, by an experienced user, to describe new and existing biomass and crop production scenarios that may be of interest to researchers. The foundation model allows input of management practices, crop production characteristics and utilizes standardized machinery performance and cost information, including farm-owned machinery and implements, and machinery and farm production operations provided by custom operators. (Abstract shortened by UMI.)

Soil quality and the solar corridor crop system

USDA-ARS?s Scientific Manuscript database

The solar corridor crop system (SCCS) is designed for improved crop productivity based on highly efficient use of solar radiation by integrating row crops with drilled or solid-seeded crops in broad strips (corridors) that also facilitate establishment of cover crops for year-round soil cover. The S...

Soil Quality and the Solar Corridor Crop System

USDA-ARS?s Scientific Manuscript database

The solar corridor crop system (SCCS) is designed for improved crop productivity based on highly efficient use of solar radiation by integrating row crops with drilled or solid-seeded crops in broad strips (corridors) that also facilitate establishment of cover crops for year-round soil cover. The S...

Fungal Plant Pathogens Associated with Emerging Crops in North America: An Emerging Challenge for Plant Health Professionals

USDA-ARS?s Scientific Manuscript database

'Emerging crops' is a term typically applied to ethnic food plants, or plants used in traditional or ethnic medicine, some of which are becoming viable niche markets in North America. Information on crop protection of these plants is often scarce to lacking. Literature providing information on diagn...

Mixed crop-livestock systems: an economic and environmental-friendly way of farming?

PubMed

Ryschawy, J; Choisis, N; Choisis, J P; Joannon, A; Gibon, A

2012-10-01

Intensification and specialisation of agriculture in developed countries enabled productivity to be improved but had detrimental impacts on the environment and threatened the economic viability of a huge number of farms. The combination of livestock and crops, which was very common in the past, is assumed to be a viable alternative to specialised livestock or cropping systems. Mixed crop-livestock systems can improve nutrient cycling while reducing chemical inputs and generate economies of scope at farm level. Most assumptions underlying these views are based on theoretical and experimental evidence. Very few assessments of their environmental and economic advantages have nevertheless been undertaken in real-world farming conditions. In this paper, we present a comparative assessment of the environmental and economic performances of mixed crop-livestock farms v. specialised farms among the farm population of the French 'Coteaux de Gascogne'. In this hilly region, half of the farms currently use a mixed crop-livestock system including beef cattle and cash crops, the remaining farms being specialised in either crops or cattle. Data were collected through an exhaustive survey of farms located in our study area. The economic performances of farming systems were assessed on 48 farms on the basis of (i) overall gross margin, (ii) production costs and (iii) analysis of the sensitivity of gross margins to fluctuations in the price of inputs and outputs. The environmental dimension was analysed through (i) characterisation of farmers' crop management practices, (ii) analysis of farm land use diversity and (iii) nitrogen farm-gate balance. Local mixed crop-livestock farms did not have significantly higher overall gross margins than specialised farms but were less sensitive than dairy and crop farms to fluctuations in the price of inputs and outputs considered. Mixed crop-livestock farms had lower costs than crop farms, while beef farms had the lowest costs as they are grass-based systems. Concerning crop management practices, our results revealed an intensification gradient from low to high input farming systems. Beyond some general trends, a wide range of management practices and levels of intensification were observed among farms with a similar production system. Mixed crop-livestock farms were very heterogeneous with respect to the use of inputs. Nevertheless, our study revealed a lower potential for nitrogen pollution in mixed crop-livestock and beef production systems than in dairy and crop farming systems. Even if a wide variability exists within system, mixed crop-livestock systems appear to be a way for an environmental and economical sustainable agriculture.

US/Canada wheat and barley crop calender exploratory experiment implementation plan

NASA Technical Reports Server (NTRS)

1980-01-01

A plan is detailed for a supplemental experiment to evaluate several crop growth stage models and crop starter models. The objective of this experiment is to provide timely information to aid in understanding crop calendars and to provide data that will allow a selection between current crop calendar models.

Survey of Crop Losses in Response to Phytoparasitic Nematodes in the United States for 1994

PubMed Central

Koenning, S. R.; Overstreet, C.; Noling, J. W.; Donald, P. A.; Becker, J. O.; Fortnum, B. A.

1999-01-01

Previous reports of crop losses to plant-parasitic nematodes have relied on published results of survey data based on certain commodities, including tobacco, peanuts, cotton, and soybean. Reports on crop-loss assessment by land-grant universities and many commodity groups generally are no longer available, with the exception of the University of Georgia, the Beltwide Cotton Conference, and selected groups concerned with soybean. The Society of Nematologists Extension Committee contacted extension personnel in 49 U.S. states for information on estimated crop losses caused by plant-parasitic nematodes in major crops for the year 1994. Included in this paper are survey results from 35 states on various crops including corn, cotton, soybean, peanut, wheat, rice, sugarcane, sorghum, tobacco, numerous vegetable crops, fruit and nut crops, and golf greens. The data are reported systematically by state and include the estimated loss, hectarage of production, source of information, nematode species or taxon when available, and crop value. The major genera of phytoparasitic nematodes reported to cause crop losses were Heterodera, Hoplolaimus, Meloidogyne, Pratylenchus, Rotylenchulus, and Xiphinema. PMID:19270925

Survey of crop losses in response to phytoparasitic nematodes in the United States for 1994.

PubMed

Koenning, S R; Overstreet, C; Noling, J W; Donald, P A; Becker, J O; Fortnum, B A

1999-12-01

Previous reports of crop losses to plant-parasitic nematodes have relied on published results of survey data based on certain commodities, including tobacco, peanuts, cotton, and soybean. Reports on crop-loss assessment by land-grant universities and many commodity groups generally are no longer available, with the exception of the University of Georgia, the Beltwide Cotton Conference, and selected groups concerned with soybean. The Society of Nematologists Extension Committee contacted extension personnel in 49 U.S. states for information on estimated crop losses caused by plant-parasitic nematodes in major crops for the year 1994. Included in this paper are survey results from 35 states on various crops including corn, cotton, soybean, peanut, wheat, rice, sugarcane, sorghum, tobacco, numerous vegetable crops, fruit and nut crops, and golf greens. The data are reported systematically by state and include the estimated loss, hectarage of production, source of information, nematode species or taxon when available, and crop value. The major genera of phytoparasitic nematodes reported to cause crop losses were Heterodera, Hoplolaimus, Meloidogyne, Pratylenchus, Rotylenchulus, and Xiphinema.

Variation of Bacterial Community Diversity in Rhizosphere Soil of Sole-Cropped versus Intercropped Wheat Field after Harvest.

PubMed

Yang, Zhenping; Yang, Wenping; Li, Shengcai; Hao, Jiaomin; Su, Zhifeng; Sun, Min; Gao, Zhiqiang; Zhang, Chunlai

2016-01-01

As the major crops in north China, spring crops are usually planted from April through May every spring and harvested in fall. Wheat is also a very common crop traditionally planted in fall or spring and harvested in summer year by year. This continuous cropping system exhibited the disadvantages of reducing the fertility of soil through decreasing microbial diversity. Thus, management of microbial diversity in the rhizosphere plays a vital role in sustainable crop production. In this study, ten common spring crops in north China were chosen sole-cropped and four were chosen intercropped with peanut in wheat fields after harvest. Denaturing gradient gel electrophoresis (DGGE) and DNA sequencing of one 16S rDNA fragment were used to analyze the bacterial diversity and species identification. DGGE profiles showed the bacterial community diversity in rhizosphere soil samples varied among various crops under different cropping systems, more diverse under intercropping system than under sole-cropping. Some intercropping-specific bands in DGGE profiles suggested that several bacterial species were stimulated by intercropping systems specifically. Furthermore, the identification of these dominant and functional bacteria by DNA sequencing indicated that intercropping systems are more beneficial to improve soil fertility. Compared to intercropping systems, we also observed changes in microbial community of rhizosphere soil under sole-crops. The rhizosphere bacterial community structure in spring crops showed a strong crop species-specific pattern. More importantly, Empedobacter brevis, a typical plant pathogen, was only found in the carrot rhizosphere, suggesting carrot should be sown prudently. In conclusion, our study demonstrated that crop species and cropping systems had significant effects on bacterial community diversity in the rhizosphere soils. We strongly suggest sorghum, glutinous millet and buckwheat could be taken into account as intercropping crops with peanut; while hulled oat, mung bean or foxtail millet could be considered for sowing in wheat fields after harvest in North China.

Research in satellite-aided crop inventory and monitoring

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Dragg, J. L.; Bizzell, R. M.; Trichel, M. C. (Principal Investigator)

1982-01-01

Automated information extraction procedures for analysis of multitemporal LANDSAT data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develope and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed.

Fungal community composition and diversity vary with soil depths and landscape position in a no-till wheat cropping system.

USDA-ARS?s Scientific Manuscript database

Fungal communities in soil are critical to plant health and ecosystem processes in agricultural systems. Although the composition of fungal communities is often related to soil edaphic characteristic and host plant identity, there is a paucity of information on how communities vary with soil depth a...

The Pilot Study of Integrating Spatial Educational Experiences (Isee) in an Undergraduate Crop Production Course

ERIC Educational Resources Information Center

Mitzman, Stephanie; Snyder, Lori Unruh; Schulze, Darrell G.; Owens, Phillip R.; Bracke, Marianne Stowell

2011-01-01

Recent National Research Council reports make compelling arguments for the need to incorporate spatial abilities and use spatial technologies throughout our educational system. We conducted a pilot study to determine the pedagogical effectiveness of teaching with geographic information systems (GIS) by using a web-based GIS tool of Indiana soils.…

Data base design for a worldwide multicrop information system

NASA Technical Reports Server (NTRS)

Driggers, W. G.; Downs, J. M.; Hickman, J. R.; Packard, R. L. (Principal Investigator)

1979-01-01

A description of the USDA Application Test System data base design approach and resources is presented. The data is described in detail by category, with emphasis on those characteristics which influenced the design most. It was concluded that the use of a generalized data base in support of crop assessment is a sound concept. The IDMS11 minicomputer base system is recommended for this purpose.

New Approaches to Capture High Frequency Agricultural Dynamics in Africa through Mobile Phones

NASA Astrophysics Data System (ADS)

Evans, T. P.; Attari, S.; Plale, B. A.; Caylor, K. K.; Estes, L. D.; Sheffield, J.

2015-12-01

Crop failure early warning systems relying on remote sensing constitute a new critical resource to assess areas where food shortages may arise, but there is a disconnect between the patterns of crop production on the ground and the environmental and decision-making dynamics that led to a particular crop production outcome. In Africa many governments use mid-growing season household surveys to get an on-the-ground assessment of current agricultural conditions. But these efforts are cost prohibitive over large scales and only offer a one-time snapshot at a particular time point. They also rely on farmers to recall past decisions and farmer recall may be imperfect when answering retrospectively on a decision made several months back (e.g. quantity of seed planted). We introduce a novel mobile-phone based approach to acquire information from farmers over large spatial extents, at high frequency at relatively low-cost compared to household survey approaches. This system makes compromises in number of questions which can feasibly be asked of a respondent (compared to household interviews), but the benefit of capturing weekly data from farmers is very exciting. We present data gathered from farmers in Kenya and Zambia to understand key dimensions of agricultural decision making such as choice of seed variety/planting date, frequency and timing of weeding/fertilizing and coping strategies such as pursuing off-farm labor. A particularly novel aspect of this work is reporting from farmers of what their expectation of end-season harvest will be on a week-by-week basis. Farmer's themselves can serve as sentinels of crop failure in this system. And farmers responses to drought are as much driven by their expectations of looming crop failure that may be different from that gleaned from remote sensing based assessment. This work is one piece of a larger design to link farmers to high-density meteorological data in Africa as an additional tool to improve crop failure early warning systems and understand adaptation to climate variability.

Impact of nowcasting on the production and processing of agricultural crops. [in the US

NASA Technical Reports Server (NTRS)

Dancer, W. S.; Tibbitts, T. W.

1973-01-01

The value was studied of improved weather information and weather forecasting to farmers, growers, and agricultural processing industries in the United States. The study was undertaken to identify the production and processing operations that could be improved with accurate and timely information on changing weather patterns. Estimates were then made of the potential savings that could be realized with accurate information about the prevailing weather and short term forecasts for up to 12 hours. This weather information has been termed nowcasting. The growing, marketing, and processing operations of the twenty most valuable crops in the United States were studied to determine those operations that are sensitive to short-term weather forecasting. Agricultural extension specialists, research scientists, growers, and representatives of processing industries were consulted and interviewed. The value of the crops included in this survey and their production levels are given. The total value for crops surveyed exceeds 24 billion dollars and represents more than 92 percent of total U.S. crop value.

Fusion of spatio-temporal UAV and proximal sensing data for an agricultural decision support system

NASA Astrophysics Data System (ADS)

Katsigiannis, P.; Galanis, G.; Dimitrakos, A.; Tsakiridis, N.; Kalopesas, C.; Alexandridis, T.; Chouzouri, A.; Patakas, A.; Zalidis, G.

2016-08-01

Over the last few years, multispectral and thermal remote sensing imagery from unmanned aerial vehicles (UAVs) has found application in agriculture and has been regarded as a means of field data collection and crop condition monitoring source. The integration of information derived from the analysis of these remotely sensed data into agricultural management applications facilitates and aids the stakeholder's decision making. Whereas agricultural decision support systems (DSS) have long been utilised in farming applications, there are still critical gaps to be addressed; as the current approach often neglects the plant's level information and lacks the robustness to account for the spatial and temporal variability of environmental parameters within agricultural systems. In this paper, we demonstrate the use of a custom built autonomous UAV platform in providing critical information for an agricultural DSS. This hexacopter UAV bears two cameras which can be triggered simultaneously and can capture both the visible, near-infrared (VNIR) and the thermal infrared (TIR) wavelengths. The platform was employed for the rapid extraction of the normalized difference vegetation index (NDVI) and the crop water stress index (CWSI) of three different plantations, namely a kiwi, a pomegranate, and a vine field. The simultaneous recording of these two complementary indices and the creation of maps was advantageous for the accurate assessment of the plantation's status. Fusion of UAV and soil scanner system products pinpointed the necessity for adjustment of the irrigation management applied. It is concluded that timely CWSI and NDVI measures retrieved for different crop growing stages can provide additional information and can serve as a tool to support the existing irrigation DSS that had so far been exclusively based on telemetry data from soil and agrometeorological sensors. Additionally, the use of the multi-sensor UAV was found to be beneficial in collecting timely, spatio-temporal information for the fusion with ground-based proximal sensing data. This research work was designed and deployed in the frame of the project "AGRO_LESS: Joint reference strategies for rural activities of reduced inputs".

Soil organic carbon sequestration and tillage systems in Mediterranean environments

NASA Astrophysics Data System (ADS)

Francaviglia, Rosa; Di Bene, Claudia; Marchetti, Alessandro; Farina, Roberta

2016-04-01

Soil carbon sequestration is of special interest in Mediterranean areas, where rainfed cropping systems are prevalent, inputs of organic matter to soils are low and mostly rely on crop residues, while losses are high due to climatic and anthropic factors such as intensive and non-conservative farming practices. The adoption of reduced or no tillage systems, characterized by a lower soil disturbance in comparison with conventional tillage, has proved to be positively effective on soil organic carbon (SOC) conservation and other physical and chemical processes, parameters or functions, e.g. erosion, compaction, ion retention and exchange, buffering capacity, water retention and aggregate stability. Moreover, soil biological and biochemical processes are usually improved by the reduction of tillage intensity. The work deals with some results available in the scientific literature, and related to field experiment on arable crops performed in Italy, Greece, Morocco and Spain. Data were organized in a dataset containing the main environmental parameters (altitude, temperature, rainfall), soil tillage system information (conventional, minimum and no-tillage), soil parameters (bulk density, pH, particle size distribution and texture), crop type, rotation, management and length of the experiment in years, initial SOCi and final SOCf stocks. Sampling sites are located between 33° 00' and 43° 32' latitude N, 2-860 m a.s.l., with mean annual temperature and rainfall in the range 10.9-19.6° C and 355-900 mm. SOC data, expressed in t C ha-1, have been evaluated both in terms of Carbon Sequestration Rate, given by [(SOCf-SOCi)/length in years], and as percentage change in comparison with the initial value [(SOCf-SOCi)/SOCi*100]. Data variability due to the different environmental, soil and crop management conditions that influence SOC sequestration and losses will be examined.

NASA's NI-SAR Observing Strategy and Data Availability for Agricultural Monitoring and Assessment

NASA Astrophysics Data System (ADS)

Siqueira, P.; Dubayah, R.; Kellndorfer, J. M.; Saatchi, S. S.; Chapman, B. D.

2014-12-01

The monitoring and characterization of global crop development by remote sensing is a complex task, in part, because of the time varying nature of the target and the diversity of crop types and agricultural practices that vary worldwide. While some of these difficulties are overcome with the availability of national and market-derived resources (e.g. publication of crop statistics by the USDA and FAO), monitoring by remote sensing has the ability of augmenting those resources to better identify changes over time, and to provide timely assessments for the current year's production. Of the remote sensing techniques that are used for agricultural applications, optical observations of NDVI from Landsat, AVHRR, MODIS and similar sensors have historically provided the majority of data that is used by the community. In addition, radiometer and radar sensors, are often used for estimating soil moisture and structural information for these agricultural regions. The combination of these remote sensing datasets and national resources constitutes the state of the art for crop monitoring and yield forecasts. To help improve these crop monitoring efforts in the future, the joint NASA-ISRO SAR mission known as NI-SAR is being planned for launch in 2020, and will have L- and S-band fully polarimetric radar systems, a fourteen day repeat period, and a swath width on the order of several hundred kilometers. To address the needs of the science and applications communities that NI-SAR will support, the systems observing strategy is currently being planned such that data rate and the system configuration will address the needs of the community. In this presentation, a description of the NI-SAR system will be given along with the currently planned observing strategy and derived products that will be relevant to the overall GEOGLAM initiative.

The Crop Risk Zones Monitoring System for resilience to drought in the Sahel

NASA Astrophysics Data System (ADS)

Vignaroli, Patrizio; Rocchi, Leandro; De Filippis, Tiziana; Tarchiani, Vieri; Bacci, Maurizio; Toscano, Piero; Pasqui, Massimiliano; Rapisardi, Elena

2016-04-01

Food security is still one of the major concerns that Sahelian populations have to face. In the Sahel, agriculture is primarily based on rainfed crops and it is often structurally inadequate to manage the climatic variability. The predominantly rainfed cropping system of Sahel region is dependent on season quality on a year-to-year basis, and susceptible to weather extremes of droughts and extreme temperatures. Low water-storage capacity and high dependence on rainfed agriculture leave the agriculture sector even more vulnerable to climate risks. Crop yields may suffer significantly with either a late onset or early cessation of the rainy season, as well as with a high frequency of damaging dry spells. Early rains at the beginning of the season are frequently followed by dry spells which may last a week or longer. As the amount of water stored in the soil at this time of the year is negligible, early planted crops can suffer water shortage stresses during a prolonged dry spell. Therefore, the choice of the sowing date is of fundamental importance for farmers. The ability to estimate effectively the onset of the season and potentially dangerous dry spells becomes therefore vital for planning rainfed agriculture practices aiming to minimize risks and maximize yields. In this context, advices to farmers are key drivers for prevention allowing a better adaptation of traditional crop calendar to climatic variability. In the Sahel, particularly in CILSS (Permanent Interstates Committee for Drought Control in the Sahel) countries, national Early Warning System (EWS) for food security are underpinned by Multidisciplinary Working Groups (MWGs) lead by National Meteorological Services (NMS). The EWSs are mainly based on tools and models utilizing numeric forecasts and satellite data to outlook and monitor the growing season. This approach is focused on the early identification of risks and on the production of information within the prescribed time period for decision-making. Since the '90s, analysis tools and models based on meteorological satellites data have been developed within different regional and national initiatives to allow near-real-time monitoring of the cropping season. The software was in general stand-alone applications, transferred to MWGs without continuous user support and updates. Currently MWGs in the Sahel do not have any working operational tool for drought risk identification and forecast, because such tools are by now obsolete from the IT perspective. The challenge and the objective of this work is to provide to MWGs and local end-users an open access/source Crop Risk Zones Monitoring System (CRZ-MS) supporting decision making for drought risk reduction and resilience improvement. A first prototype has been developed for Niger and Mali NMSs, based on a coherent Open Source web-based infrastructure to treat all input and output data in a interoperable, platform-independent and uniform way. The System architecture and functions are based on a agro-meteorological model, running in two different modes: 1) diagnostic mode for the drought monitoring during the agro-pastoral campaign allowing MWGs to identify agricultural drought risk areas in order to support decision making at local and national level in agricultural drought management. This early warning information also represents an input for estimating the nutritional food insecurity, for the identification of potentially vulnerable populations and assessing food crises risks by National EWSs put in place by CILSS with EU, FAO and WFP. 2) predictive mode for "advisory-support" activities to the farmers by the Agricultural Extension Services, in order to implement the most appropriate strategies for minimizing drought risk on crops (i.e. identification of the optimal period of sowing, choice of varieties based on the expected length of the growing season, adoption of suitable cultural practices for soil water management) and to build farmers resilience. To increase the accessibility of appropriate and targeted drought risk information, it is essential to move from generic information to specific advises for end-users at different decision-making levels, bridging the gap between available technology and local users' needs. Thus, advices to farmers are a fundamental component of prevention allowing a better country's preparedness to cope with weather variability.

2005 AG20/20 Annual Review

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; McKellip, Rodney D.

2005-01-01

Topics covered include: Implementation and Validation of Sensor-Based Site-Specific Crop Management; Enhanced Management of Agricultural Perennial Systems (EMAPS) Using GIS and Remote Sensing; Validation and Application of Geospatial Information for Early Identification of Stress in Wheat; Adapting and Validating Precision Technologies for Cotton Production in the Mid-Southern United States - 2004 Progress Report; Development of a System to Automatically Geo-Rectify Images; Economics of Precision Agriculture Technologies in Cotton Production-AG 2020 Prescription Farming Automation Algorithms; Field Testing a Sensor-Based Applicator for Nitrogen and Phosphorus Application; Early Detection of Citrus Diseases Using Machine Vision and DGPS; Remote Sensing of Citrus Tree Stress Levels and Factors; Spectral-based Nitrogen Sensing for Citrus; Characterization of Tree Canopies; In-field Sensing of Shallow Water Tables and Hydromorphic Soils with an Electromagnetic Induction Profiler; Maintaining the Competitiveness of Tree Fruit Production Through Precision Agriculture; Modeling and Visualizing Terrain and Remote Sensing Data for Research and Education in Precision Agriculture; Thematic Soil Mapping and Crop-Based Strategies for Site-Specific Management; and Crop-Based Strategies for Site-Specific Management.

An AgMIP framework for improved agricultural representation in integrated assessment models

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

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agriculturalmore » Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.« less

An AgMIP framework for improved agricultural representation in integrated assessment models

NASA Astrophysics Data System (ADS)

Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.

2017-12-01

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

Proximity to crops and residential to agricultural herbicides in Iowa

USGS Publications Warehouse

Ward, M.H.; Lubin, J.; Giglierano, J.; Colt, J.S.; Wolter, C.; Bekiroglu, N.; Camann, D.; Hartge, P.; Nuckols, J.R.

2006-01-01

Rural residents can be exposed to agricultural pesticides through the proximity of their homes to crop fields. Previously, we developed a method to create historical crop maps using a geographic information system. The aim of the present study was to determine whether crop maps are useful for predicting levels of crop herbicides in carpet dust samples from residences. From homes of participants in a case-control study of non-Hodgkin lymphoma in Iowa (1998-2000), we collected vacuum cleaner dust and measured 14 herbicides with high use on corn and soybeans in Iowa. Of 112 homes, 58% of residences had crops within 500 m of their home, an intermediate distance for primary drift from aerial and ground applications. Detection rates for herbicides ranged from 0% for metribuzin and cyanazine to 95% for 2,4-dichlorophenoxyacetic acid. Six herbicides used almost exclusively in agriculture were detected in 28% of homes. Detections and concentrations were highest in homes with an active farmer. Increasing acreage of corn and soybean fields within 750 m of homes was associated with significantly elevated odds of detecting agricultural herbicides compared with homes with no crops within 750 m (adjusted odds ratio per 10 acres = 1.06; 95% confidence interval, 1.02-1.11). Herbicide concentrations also increased significantly with increasing acreage within 750 m. We evaluated the distance of crop fields from the home at < 100, 101-250, 251-500, and 501-750 m. Including the crop buffer distance parameters in the model did not significantly improve the fit compared with a model with total acres within 750 m. Our results indicate that crop maps may be a useful method for estimating levels of herbicides in homes from nearby crop fields.

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.

Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China

DOE PAGES

Ye, Qing; Yang, Xiaoguang; Dai, Shuwei; ...

2015-06-05

Here, we discuss that rice is one of the main crops grown in southern China. Global climate change has significantly altered the local water availability and temperature regime for rice production. In this study, we explored the influence of climate change on suitable rice cropping areas, rice cropping systems and crop water requirements (CWRs) during the growing season for historical (from 1951 to 2010) and future (from 2011 to 2100) time periods. The results indicated that the land areas suitable for rice cropping systems shifted northward and westward from 1951 to 2100 but with different amplitudes.

The value of information as applied to the Landsat Follow-on benefit-cost analysis

NASA Technical Reports Server (NTRS)

Wood, D. B.

1978-01-01

An econometric model was run to compare the current forecasting system with a hypothetical (Landsat Follow-on) space-based system. The baseline current system was a hybrid of USDA SRS domestic forecasts and the best known foreign data. The space-based system improved upon the present Landsat by the higher spatial resolution capability of the thematic mapper. This satellite system is a major improvement for foreign forecasts but no better than SRS for domestic forecasts. The benefit analysis was concentrated on the use of Landsat Follow-on to forecast world wheat production. Results showed that it was possible to quantify the value of satellite information and that there are significant benefits in more timely and accurate crop condition information.

Midwestern US Farmers Perceive Crop Advisers as Conduits of Information on Agricultural Conservation Practices

NASA Astrophysics Data System (ADS)

Eanes, Francis R.; Singh, Ajay S.; Bulla, Brian R.; Ranjan, Pranay; Prokopy, Linda S.; Fales, Mary; Wickerham, Benjamin; Doran, Patrick J.

2017-11-01

Nonpoint source pollution from agricultural land uses continues to pose one of the most significant threats to water quality in the US, with measurable impacts across local, regional, and national scales. The impact and the influence of targeted conservation efforts are directly related to the degree to which farmers are familiar with and trust the entities providing the information and/or outreach. Recent research suggests that farmers consistently rank independent and retail-affiliated crop advisers as among the most trusted and influential sources for agronomic information, but little is understood about whether farmers are willing to receive advice from crop advisers on the use of practices that conserve soil and water, and, if so, whether crop advisers will be perceived as influential. We present survey data from farmers ( n = 1461) in Michigan's Saginaw Bay (Lake Huron) watershed to explore these questions. Results suggest that farmers view crop advisers as trustworthy sources of information about conservation, and influential on management practices that have large conservation implications. We discuss these results, along with perceived barriers and opportunities to crop advisers partnering with traditional conservation agencies to enhance the impact of voluntary conservation programs.

Midwestern US Farmers Perceive Crop Advisers as Conduits of Information on Agricultural Conservation Practices.

PubMed

Eanes, Francis R; Singh, Ajay S; Bulla, Brian R; Ranjan, Pranay; Prokopy, Linda S; Fales, Mary; Wickerham, Benjamin; Doran, Patrick J

2017-11-01

Nonpoint source pollution from agricultural land uses continues to pose one of the most significant threats to water quality in the US, with measurable impacts across local, regional, and national scales. The impact and the influence of targeted conservation efforts are directly related to the degree to which farmers are familiar with and trust the entities providing the information and/or outreach. Recent research suggests that farmers consistently rank independent and retail-affiliated crop advisers as among the most trusted and influential sources for agronomic information, but little is understood about whether farmers are willing to receive advice from crop advisers on the use of practices that conserve soil and water, and, if so, whether crop advisers will be perceived as influential. We present survey data from farmers (n = 1461) in Michigan's Saginaw Bay (Lake Huron) watershed to explore these questions. Results suggest that farmers view crop advisers as trustworthy sources of information about conservation, and influential on management practices that have large conservation implications. We discuss these results, along with perceived barriers and opportunities to crop advisers partnering with traditional conservation agencies to enhance the impact of voluntary conservation programs.

The century experiment: the first twenty years of UC Davis' Mediterranean agroecological experiment.

PubMed

Wolf, Kristina M; Torbert, Emma E; Bryant, Dennis; Burger, Martin; Denison, R Ford; Herrera, Israel; Hopmans, Jan; Horwath, Will; Kaffka, Stephen; Kong, Angela Y Y; Norris, R F; Six, Johan; Tomich, Thomas P; Scow, Kate M

2018-02-01

The Century Experiment at the Russell Ranch Sustainable Agriculture Facility at the University of California, Davis provides long-term agroecological data from row crop systems in California's Central Valley starting in 1993. The Century Experiment was initially designed to study the effects of a gradient of water and nitrogen availability on soil properties and crop performance in ten different cropping systems to measure tradeoffs and synergies between agricultural productivity and sustainability. Currently systems include 11 different cropping systems-consisting of four different crops and a cover crop mixture-and one native grass system. This paper describes the long-term core data from the Century Experiment from 1993-2014, including crop yields and biomass, crop elemental contents, aerial-photo-based Normalized Difference Vegetation Index data, soil properties, weather, chemical constituents in irrigation water, winter weed populations, and operational data including fertilizer and pesticide application amounts and dates, planting dates, planting quantity and crop variety, and harvest dates. This data set represents the only known long-term set of data characterizing food production and sustainability in irrigated and rainfed Mediterranean annual cropping systems. There are no copyright restrictions associated with the use of this dataset. © 2018 by the Ecological Society of America.

Cover crops as a gateway to greater conservation in Iowa?: Integrating crop models, field trials, economics and farmer perspectives regarding soil resilience in light of climate change

NASA Astrophysics Data System (ADS)

Roesch-McNally, G. E.; Basche, A.; Tyndall, J.; Arbuckle, J. G.; Miguez, F.; Bowman, T.

2014-12-01

Scientists predict a number of climate changes for the US Midwest with expected declines in crop productivity as well as eco-hydrological impacts. More frequent extreme rain events particularly in the spring may well increase saturated soils thus complicating agronomic interests and also exacerbate watershed scale impairments (e.g., sediment, nutrient loss). In order to build more resilient production systems in light of climate change, farmers will increasingly need to implement conservation practices (singularly or more likely in combination) that enable farmers to manage profitable businesses yet mitigate consequential environmental impacts that have both in-field and off-farm implications. Cover crops are empirically known to promote many aspects of soil and water health yet even the most aggressive recent estimates show that only 1-2% of the total acreage in Iowa have been planted to cover crops. In order to better understand why farmers are reluctant to adopt cover crops across Iowa we combined agronomic and financial data from long-term field trials, working farm trials and model simulations so as to present comprehensive data-driven information to farmers in focus group discussions in order to understand existing barriers, perceived benefits and responses to the information presented. Four focus groups (n=29) were conducted across Iowa in four geographic regions. Focus group discussions help explore the nuance of farmers' responses to modeling outputs and their real-life agronomic realities, thus shedding light on the social and psychological barriers with cover crop utilization. Among the key insights gained, comprehensive data-driven research can influence farmer perspectives on potential cover crop impacts to cash crop yields, experienced costs are potentially quite variable, and having field/farm benefits articulated in economic terms are extremely important when farmers weigh the opportunity costs associated with adopting new practices. Our work represents multidisciplinary collaboration necessary to gain greater understanding of what it will take for farmers to cover the ground to prevent erosion and nutrient losses in the context of a changing climate.

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

Zhang, Xuesong; Izaurralde, Roberto C.; Manowitz, David H.

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatially-explicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems tomore » each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000–2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies.« less

GMOs in animal agriculture: time to consider both costs and benefits in regulatory evaluations.

PubMed

Van Eenennaam, Alison L

2013-09-25

In 2012, genetically engineered (GE) crops were grown by 17.3 million farmers on over 170 million hectares. Over 70% of harvested GE biomass is fed to food producing animals, making them the major consumers of GE crops for the past 15 plus years. Prior to commercialization, GE crops go through an extensive regulatory evaluation. Over one hundred regulatory submissions have shown compositional equivalence, and comparable levels of safety, between GE crops and their conventional counterparts. One component of regulatory compliance is whole GE food/feed animal feeding studies. Both regulatory studies and independent peer-reviewed studies have shown that GE crops can be safely used in animal feed, and rDNA fragments have never been detected in products (e.g. milk, meat, eggs) derived from animals that consumed GE feed. Despite the fact that the scientific weight of evidence from these hundreds of studies have not revealed unique risks associated with GE feed, some groups are calling for more animal feeding studies, including long-term rodent studies and studies in target livestock species for the approval of GE crops. It is an opportune time to review the results of such studies as have been done to date to evaluate the value of the additional information obtained. Requiring long-term and target animal feeding studies would sharply increase regulatory compliance costs and prolong the regulatory process associated with the commercialization of GE crops. Such costs may impede the development of feed crops with enhanced nutritional characteristics and durability, particularly in the local varieties in small and poor developing countries. More generally it is time for regulatory evaluations to more explicitly consider both the reasonable and unique risks and benefits associated with the use of both GE plants and animals in agricultural systems, and weigh them against those associated with existing systems, and those of regulatory inaction. This would represent a shift away from a GE evaluation process that currently focuses only on risk assessment and identifying ever diminishing marginal hazards, to a regulatory approach that more objectively evaluates and communicates the likely impact of approving a new GE plant or animal on agricultural production systems.

GMOs in animal agriculture: time to consider both costs and benefits in regulatory evaluations

PubMed Central

2013-01-01

In 2012, genetically engineered (GE) crops were grown by 17.3 million farmers on over 170 million hectares. Over 70% of harvested GE biomass is fed to food producing animals, making them the major consumers of GE crops for the past 15 plus years. Prior to commercialization, GE crops go through an extensive regulatory evaluation. Over one hundred regulatory submissions have shown compositional equivalence, and comparable levels of safety, between GE crops and their conventional counterparts. One component of regulatory compliance is whole GE food/feed animal feeding studies. Both regulatory studies and independent peer-reviewed studies have shown that GE crops can be safely used in animal feed, and rDNA fragments have never been detected in products (e.g. milk, meat, eggs) derived from animals that consumed GE feed. Despite the fact that the scientific weight of evidence from these hundreds of studies have not revealed unique risks associated with GE feed, some groups are calling for more animal feeding studies, including long-term rodent studies and studies in target livestock species for the approval of GE crops. It is an opportune time to review the results of such studies as have been done to date to evaluate the value of the additional information obtained. Requiring long-term and target animal feeding studies would sharply increase regulatory compliance costs and prolong the regulatory process associated with the commercialization of GE crops. Such costs may impede the development of feed crops with enhanced nutritional characteristics and durability, particularly in the local varieties in small and poor developing countries. More generally it is time for regulatory evaluations to more explicitly consider both the reasonable and unique risks and benefits associated with the use of both GE plants and animals in agricultural systems, and weigh them against those associated with existing systems, and those of regulatory inaction. This would represent a shift away from a GE evaluation process that currently focuses only on risk assessment and identifying ever diminishing marginal hazards, to a regulatory approach that more objectively evaluates and communicates the likely impact of approving a new GE plant or animal on agricultural production systems. PMID:24066781

Biotech crop planting resumes high adoption in 2016.

PubMed

Aldemita, Rhodora R; Hautea, Randy A

2018-01-02

The global area of biotech crops in 2016 increased from 179.7 million hectares to 185.1 million hectares, a 3% increase equivalent to 5.4 million hectares. Some 26 countries planted biotech crops, 19 of which were developing countries and seven were industrial. Information and data collected from various credible sources showed variations from the previous year. Fluctuations in biotech crop area (both increases and decreases) are influenced by factors including, among others, acceptance and commercialization of new products, demand for meat and livestock feeds, weather conditions, global market price, disease/pest pressure, and government's enabling policies. Countries which have increased biotech crop area in decreasing order in 2016 were Brazil, United States of America, Canada, South Africa, Australia, Bolivia, Philippines, Spain, Vietnam, Bangladesh, Colombia, Honduras, Chile, Sudan, Slovakia, and Costa Rica. Countries with decreased biotech area in decreasing order were China, India, Argentina, Paraguay, Uruguay, Mexico, Portugal, and Czech Republic, in decreasing incremental decrease in biotech area. Pakistan and Myanmar were the only countries with no change in biotech crop (cotton) planted. Information detailed in the paper including future crops and traits in each country could guide stakeholders in informed crafting of strategies and policies for increased adoption of biotech crops in the country.

Space Data for Crop Management

NASA Technical Reports Server (NTRS)

1990-01-01

CROPIX, Inc., formed in 1984 by Frank Lamb, president of the Eastern Oregon Farming Company, monitors primarily potato crops in a 20,000 square mile area of northern Oregon and central Washington. Potatoes are a high value specialty crop that can be more profitable to the farmer if he has advance knowledge of market conditions, knows when to harvest, and when to take it to market. By processing and collecting data collected by the NASA-developed Landsat Earth Resources survey satellites, Lamb is able to provide accurate information on crop acreage and conditions on a more timely basis than the routine estimates by the USDA. CROPIX uses Landsat data to make acreage estimates of crops, and to calculate a field-by-field vegetative index number. CROPIX then distributes to its customers a booklet containing color-coded maps, an inventory of crops, plus data and graphs on crop conditions and other valuable information.

Applying Customized Climate Advisory Information to Translate Extreme Rainfall Events into Farming Options in the Sudan-Sahel of West Africa

NASA Astrophysics Data System (ADS)

Salack, S.; Worou, N. O.; Sanfo, S.; Nikiema, M. P.; Boubacar, I.; Paturel, J. E.; Tondoh, E. J.

2017-12-01

In West Africa, the risk of food insecurity linked to the low productivity of small holder farming increases as a result of rainfall extremes. In its recent evolution, the rainy season in the Sudan-Sahel zone presents mixed patterns of extreme climatic events. In addition to intense rain events, the distribution of events is associated with pockets of intra-seasonal long dry spells. The negative consequences of these mixed patterns are obvious on the farm: soil water logging, erosion of arable land, dwartness and dessication of crops, and loss in production. The capacity of local farming communities to respond accordingly to rainfall extreme events is often constrained by lack of access to climate information and advisory on smart crop management practices that can help translate extreme rainfall events into farming options. The objective of this work is to expose the framework and the pre-liminary results of a scheme that customizes climate-advisory information package delivery to subsistence farmers in Bakel (Senegal), Ouahigouya & Dano (Burkina Faso) and Bolgatanga (Ghana) for sustainable family agriculture. The package is based on the provision of timely climate information (48-hours, dekadal & seasonal) embedded with smart crop management practices to explore and exploite the potential advantage of intense rainfall and extreme dry spells in millet, maize, sorghum and cowpea farming communities. It is sent via mobile phones and used on selected farms (i.e agro-climatic farm schools) on which some small on-farm infrastructure were built to alleviate negative impacts of weather. Results provide prominent insight on how co-production of weather/climate information, customized access and guidiance on its use can induce fast learning (capacity building of actors), motivation for adaptation, sustainability, potential changes in cropping system, yields and family income in the face of a rainfall extremes at local scales of Sudan-Sahel of West Africa. Keywords: Climate Information, Smart Practices, Farming Options, Agro-Climatic Farm Schools, Sudan-Sahel

AgMIP: Next Generation Models and Assessments

NASA Astrophysics Data System (ADS)

Rosenzweig, C.

2014-12-01

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

Effect of water content and organic carbon on remote sensing of crop residue cover

NASA Astrophysics Data System (ADS)

Serbin, G.; Hunt, E. R., Jr.; Daughtry, C. S. T.; McCarty, G. W.; Brown, D. J.; Doraiswamy, P. C.

2009-04-01

Crop residue cover is an important indicator of tillage method. Remote sensing of crop residue cover is an attractive and efficient method when compared with traditional ground-based methods, e.g., the line-point transect or windshield survey. A number of spectral indices have been devised for residue cover estimation. Of these, the most effective are those in the shortwave infrared portion of the spectrum, situated between 1950 and 2500 nm. These indices include the hyperspectral Cellulose Absorption Index (CAI), and advanced multispectral indices, i.e., the Lignin-Cellulose Absorption (LCA) index and the Shortwave Infrared Normalized Difference Residue Index (SINDRI), which were devised for the NASA Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Spectra of numerous soils from U.S. Corn Belt (Indiana and Iowa) were acquired under wetness conditions varying from saturation to oven-dry conditions. The behavior of soil reflectance with water content was also dependent on the soil organic carbon content (SOC) of the soils, and the location of the spectral bands relative to significant water absorptions. High-SOC soils showed the least change in spectral index values with increase in soil water content. Low-SOC soils, on the other hand, showed measurable difference. For CAI, low-SOC soils show an initial decrease in index value followed by an increase, due to the way that water content affects CAI spectral bands. Crop residue CAI values decrease with water content. For LCA, water content increases decrease crop residue index values and increase them for soils, resulting in decreased contrast. SINDRI is also affected by SOC and water content. As such, spatial information on the distribution of surface soil water content and SOC, when used in a geographic information system (GIS), will improve the accuracy of remotely-sensed crop residue cover estimates.

Catchment Area Treatment (CAT) Plan and Crop Area Optimization for Integrated Management in a Water Resource Project

NASA Astrophysics Data System (ADS)

Jaiswal, R. K.; Thomas, T.; Galkate, R. V.; Ghosh, N. C.; Singh, S.

2013-09-01

A scientifically developed catchment area treatment (CAT) plan and optimized pattern of crop areas may be the key for sustainable development of water resource, profitability in agriculture and improvement of overall economy in drought affected Bundelkhand region of Madhya Pradesh (India). In this study, an attempt has been made to develop a CAT plan using spatial variation of geology, geomorphology, soil, drainage, land use in geographical information system for selection of soil and water conservation measures and crop area optimization using linear programming for maximization of return considering water availability, area affinity, fertilizers, social and market constraints in Benisagar reservoir project of Chhatarpur district (M.P.). The scientifically developed CAT plan based on overlaying of spatial information consists of 58 mechanical measure (49 boulder bunds, 1 check dam, 7 cully plug and 1 percolation tank), 2.60 km2 land for agro forestry, 2.08 km2 land for afforestation in Benisagar dam and 67 mechanical measures (45 boulder bunds and 22 gully plugs), 7.79 km2 land for agro forestry, 5.24 km2 land for afforestation in Beniganj weir catchment with various agronomic measures for agriculture areas. The linear programming has been used for optimization of crop areas in Benisagar command for sustainable development considering various scenarios of water availability, efficiencies, affinity and fertilizers availability in the command. Considering present supply condition of water, fertilizers, area affinity and making command self sufficient in most of crops, the net benefit can be increase to Rs. 1.93 crores from 41.70 km2 irrigable area in Benisagar command by optimizing cropping pattern and reducing losses during conveyance and application of water.

Agronomic responses to late-seeded cover crops in a semiarid region

USDA-ARS?s Scientific Manuscript database

Intensification of cropping systems in the Great Plains beyond annual cropping practices may be limited by inadequate precipitation, short growing seasons, and highly variable climatic conditions. Inclusion of cover crops in dryland cropping systems may serve as an effective intensification strateg...

Soil microbiome characteristics and soilborne disease development associated with long-term potato cropping system practices

USDA-ARS?s Scientific Manuscript database

Potato cropping system practices substantially affect soil microbial communities and the development of soilborne diseases. Cropping systems incorporating soil health management practices, such as longer rotations, disease-suppressive crops, reduced tillage, and/or organic amendments can potentially...

Early forecasting of crop condition using an integrative remote sensing method for corn and soybeans in Iowa and Illinois, USA

NASA Astrophysics Data System (ADS)

Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

2017-04-01

The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotely sense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE < 15.0%) and the average accuracy of the normal/bad year classification was well (> 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remote sensing method for crop condition and crop phenology contributes to reducing the growing risk of crop production in the Earth.

Co-Adapting Water Demand and Supply to Changing Climate in Agricultural Water Systems, A Case Study in Northern Italy

NASA Astrophysics Data System (ADS)

Giuliani, M.; Li, Y.; Mainardi, M.; Arias Munoz, C.; Castelletti, A.; Gandolfi, C.

2013-12-01

Exponentially growing water demands and increasing uncertainties in the hydrologic cycle due to changes in climate and land use will challenge water resources planning and management in the next decade. Improving agricultural productivity is particularly critical, being this sector the one characterized by the highest water demand. Moreover, to meet projected growth in human population and per-capita food demand, agricultural production will have to significantly increase in the next decades, even though water availability is expected to decrease due to climate change impacts. Agricultural systems are called to adapt their strategies (e.g., changing crop patterns and the corresponding water demand, or maximizing the efficiency in the water supply modifying irrigation scheduling and adopting high efficiency irrigation techniques) in order to re-optimize the use of limited water resources. Although many studies have assessed climate change impacts on agricultural practices and water management, most of them assume few scenarios of water demand or water supply separately, while an analysis of their reciprocal feedbacks is still missing. Moreover, current practices are generally established according to historical agreements and normative constraints and, in the absence of dramatic failures, the shift toward more efficient water management is not easily achievable. In this work, we propose to activate an information loop between farmers and water managers to improve the effectiveness of agricultural water management practices by matching the needs of the farmers with the design of water supply strategies. The proposed approach is tested on a real-world case study, namely the Lake Como serving the Muzza-Bassa Lodigiana irrigation district (Italy). A distributed-parameter, dynamic model of the system allows to simulate crop growth and the final yield over a range of hydro-climatic conditions, irrigation strategies and water-related stresses. The spatial component of the model is managed by a Web GIS to support the visualization of the results and the participation of the stakeholders. The activation of the information loop allows farmers to decide the most profitable crop option on the basis of an expected water supply. Knowing the farmers decisions, the water supply strategy (i.e., the regulation of Lake Como) is then optimized with respect to the actual irrigation demand of the crops. By recursively running this procedure, the farmers and the water manager will exchange information until the system converges to an equilibrium. Our results show that the proposed co-adaptation loop is able to enhance the efficiency of agricultural water management practices and foster crop production. Moreover, the analysis of the co-evolution of the two systems under change allows to estimate the potential for the approach to mitigate climate change adverse impacts.

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

Assessing Impacts of Climate Change on Food Security Worldwide

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia E.; Antle, John; Elliott, Joshua

2015-01-01

The combination of a warming Earth and an increasing population will likely strain the world's food systems in the coming decades. Experts involved with the Agricultural Model Intercomparison and Improvement Project (AgMIP) focus on quantifying the changes through time. AgMIP, a program begun in 2010, involves about 800 climate scientists, economists, nutritionists, information technology specialists, and crop and livestock experts. In mid-September 2015, the Aspen Global Change Institute convened an AgMIP workshop to draft plans and protocols for assessing global- and regional-scale modeling of crops, livestock, economics, and nutrition across major agricultural regions worldwide. The goal of this Coordinated Global and Regional Integrated Assessments (CGRA) project is to characterize climate effects on large- and small-scale farming systems.

Analysis of Environmental Stress Factors Using an Artificial Growth System and Plant Fitness Optimization

PubMed Central

Lee, Meonghun; Yoe, Hyun

2015-01-01

The environment promotes evolution. Evolutionary processes represent environmental adaptations over long time scales; evolution of crop genomes is not inducible within the relatively short time span of a human generation. Extreme environmental conditions can accelerate evolution, but such conditions are often stress inducing and disruptive. Artificial growth systems can be used to induce and select genomic variation by changing external environmental conditions, thus, accelerating evolution. By using cloud computing and big-data analysis, we analyzed environmental stress factors for Pleurotus ostreatus by assessing, evaluating, and predicting information of the growth environment. Through the indexing of environmental stress, the growth environment can be precisely controlled and developed into a technology for improving crop quality and production. PMID:25874206

Agricultural Model for the Nile Basin Decision Support System

NASA Astrophysics Data System (ADS)

van der Bolt, Frank; Seid, Abdulkarim

2014-05-01

To analyze options for increasing food supply in the Nile basin the Nile Agricultural Model (AM) was developed. The AM includes state-of-the-art descriptions of biophysical, hydrological and economic processes and realizes a coherent and consistent integration of hydrology, agronomy and economics. The AM covers both the agro-ecological domain (water, crop productivity) and the economic domain (food supply, demand, and trade) and allows to evaluate the macro-economic and hydrological impacts of scenarios for agricultural development. Starting with the hydrological information from the NileBasin-DSS the AM calculates the available water for agriculture, the crop production and irrigation requirements with the FAO-model AquaCrop. With the global commodity trade model MAGNET scenarios for land development and conversion are evaluated. The AM predicts consequences for trade, food security and development based on soil and water availability, crop allocation, food demand and food policy. The model will be used as a decision support tool to contribute to more productive and sustainable agriculture in individual Nile countries and the whole region.

Crop diversity effects on productivity and economic returns under dryland agriculture

USDA-ARS?s Scientific Manuscript database

Increasing crop diversity has been identified as a method to improve agronomic performance of cropping systems and increase provision of ecosystem services. However, there is a need to understand the economic performance of more diverse cropping systems. Crop productivity and economic net returns we...

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 ...

Remote sensing in precision farming: real-time monitoring of water and fertilizer requirements of agricultural crops

NASA Astrophysics Data System (ADS)

Zilberman, Arkadi; Ben Asher, Jiftah; Kopeika, Norman S.

2016-10-01

The advancements in remote sensing in combination with sensor technology (both passive and active) enable growers to analyze an entire crop field as well as its local features. In particular, changes of actual evapo-transpiration (ET) as a function of water availability can be measured remotely with infrared radiometers. Detection of crop water stress and ET and combining it with the soil water flow model enable rational irrigation timing and application amounts. Nutrient deficiency, and in particular nitrogen deficiency, causes substantial crop losses. This deficiency needs to be identified immediately. A faster the detection and correction, a lesser the damage to the crop yield. In the present work, to retrieve ET a novel deterministic approach was used which is based on the remote sensing data. The algorithm can automatically provide timely valuable information on plant and soil water status, which can improve the management of irrigated crops. The solution is capable of bridging between Penman-Monteith ET model and Richards soil water flow model. This bridging can serve as a preliminary tool for expert irrigation system. To support decisions regarding fertilizers the greenness of plant canopies is assessed and quantified by using the spectral reflectance sensors and digital color imaging. Fertilization management can be provided on the basis of sampling and monitoring of crop nitrogen conditions using RS technique and translating measured N concentration in crop to kg/ha N application in the field.

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

PubMed

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

2003-01-01

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

Tolerance of interseeded annual ryegrass and red clover cover crops to residual herbicides in Mid-Atlantic corn cropping systems

USDA-ARS?s Scientific Manuscript database

In the Mid-Atlantic region, there is increasing interest in the use of relay-cropping strategies to establish cover crops in corn cropping systems. Recent studies have demonstrated the potential to establish annual ryegrass and red clover cover crops at the V5 corn growth stage using a high-clearan...

Perspectives on genetically modified crops and food detection.

PubMed

Lin, Chih-Hui; Pan, Tzu-Ming

2016-01-01

Genetically modified (GM) crops are a major product of the global food industry. From 1996 to 2014, 357 GM crops were approved and the global value of the GM crop market reached 35% of the global commercial seed market in 2014. However, the rapid growth of the GM crop-based industry has also created controversies in many regions, including the European Union, Egypt, and Taiwan. The effective detection and regulation of GM crops/foods are necessary to reduce the impact of these controversies. In this review, the status of GM crops and the technology for their detection are discussed. As the primary gap in GM crop regulation exists in the application of detection technology to field regulation, efforts should be made to develop an integrated, standardized, and high-throughput GM crop detection system. We propose the development of an integrated GM crop detection system, to be used in combination with a standardized international database, a decision support system, high-throughput DNA analysis, and automated sample processing. By integrating these technologies, we hope that the proposed GM crop detection system will provide a method to facilitate comprehensive GM crop regulation. Copyright © 2015. Published by Elsevier B.V.

Modeling Virus Coinfection to Inform Management of Maize Lethal Necrosis in Kenya.

PubMed

Hilker, Frank M; Allen, Linda J S; Bokil, Vrushali A; Briggs, Cheryl J; Feng, Zhilan; Garrett, Karen A; Gross, Louis J; Hamelin, Frédéric M; Jeger, Michael J; Manore, Carrie A; Power, Alison G; Redinbaugh, Margaret G; Rúa, Megan A; Cunniffe, Nik J

2017-10-01

Maize lethal necrosis (MLN) has emerged as a serious threat to food security in sub-Saharan Africa. MLN is caused by coinfection with two viruses, Maize chlorotic mottle virus and a potyvirus, often Sugarcane mosaic virus. To better understand the dynamics of MLN and to provide insight into disease management, we modeled the spread of the viruses causing MLN within and between growing seasons. The model allows for transmission via vectors, soil, and seed, as well as exogenous sources of infection. Following model parameterization, we predict how management affects disease prevalence and crop performance over multiple seasons. Resource-rich farmers with large holdings can achieve good control by combining clean seed and insect control. However, crop rotation is often required to effect full control. Resource-poor farmers with smaller holdings must rely on rotation and roguing, and achieve more limited control. For both types of farmer, unless management is synchronized over large areas, exogenous sources of infection can thwart control. As well as providing practical guidance, our modeling framework is potentially informative for other cropping systems in which coinfection has devastating effects. Our work also emphasizes how mathematical modeling can inform management of an emerging disease even when epidemiological information remains scanty. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

NASA Astrophysics Data System (ADS)

Brown, M. E.; Abbott, P. C.; Escobar, V. M.

2013-12-01

This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon Monitoring System (CMS). A general evaluation framework to determine the usefulness of the CMS products to various users and to the broader community interested in managing carbon is shown in Figure 2. The first step in conducting such an analysis is to develop an understanding of the history, institutions, behaviors and other factors setting the context of an application which CMS data products inform. Decision makers are identified (who may become early adopters), and the alternative decisions they might take are elaborated. Economic models informed by biophysical models would then predict the outcome of the engagement. The new information must then be linked to a revised decision, and that decision in turn must lead to better economic or social outcomes on average. The value of the information is estimated as the predicted increase in economic surplus (profit, cost, consumer welfare) or social outcome that is a direct result of that revised decision. Alternative Monte Carlo simulations would estimate averages of key outcomes under alternative circumstances, such as differing regulations or better data, hence capturing consequences of the changes induced. These approaches will be described in the context of NASA and satellite data.

Large Scale Crop Classification in Ukraine using Multi-temporal Landsat-8 Images with Missing Data

NASA Astrophysics Data System (ADS)

Kussul, N.; Skakun, S.; Shelestov, A.; Lavreniuk, M. S.

2014-12-01

At present, there are no globally available Earth observation (EO) derived products on crop maps. This issue is being addressed within the Sentinel-2 for Agriculture initiative where a number of test sites (including from JECAM) participate to provide coherent protocols and best practices for various global agriculture systems, and subsequently crop maps from Sentinel-2. One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this abstract, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using non-missing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. For this, an ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for large scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine [1-2]. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy and kappa coefficient. The obtained classification map is also validated through estimated crop and forest areas and comparison to official statistics. 1. A.Yu. Shelestov et al., "Geospatial information system for agricultural monitoring," Cybernetics Syst. Anal., vol. 49, no. 1, pp. 124-132, 2013. 2. J. Gallego et al., "Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations," J. Autom. Inform. Scie., vol. 44, no. 5, pp. 67-80, 2012.

Biosafety management and commercial use of genetically modified crops in China.

PubMed

Li, Yunhe; Peng, Yufa; Hallerman, Eric M; Wu, Kongming

2014-04-01

As a developing country with relatively limited arable land, China is making great efforts for development and use of genetically modified (GM) crops to boost agricultural productivity. Many GM crop varieties have been developed in China in recent years; in particular, China is playing a leading role in development of insect-resistant GM rice lines. To ensure the safe use of GM crops, biosafety risk assessments are required as an important part of the regulatory oversight of such products. With over 20 years of nationwide promotion of agricultural biotechnology, a relatively well-developed regulatory system for risk assessment and management of GM plants has been developed that establishes a firm basis for safe use of GM crops. So far, a total of seven GM crops involving ten events have been approved for commercial planting, and 5 GM crops with a total of 37 events have been approved for import as processing material in China. However, currently only insect-resistant Bt cotton and disease-resistant papaya have been commercially planted on a large scale. The planting of Bt cotton and disease-resistant papaya have provided efficient protection against cotton bollworms and Papaya ringspot virus (PRSV), respectively. As a consequence, chemical application to these crops has been significantly reduced, enhancing farm income while reducing human and non-target organism exposure to toxic chemicals. This article provides useful information for the colleagues, in particular for them whose mother tongue is not Chinese, to clearly understand the biosafety regulation and commercial use of genetically modified crops in China.

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.

76 FR 60810 - Agency Information Collection Activities: Notice of Intent To Renew Collection, Copies of Crop...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-30

... COMMODITY FUTURES TRADING COMMISSION Agency Information Collection Activities: Notice of Intent To Renew Collection, Copies of Crop and Market Information Reports AGENCY: Commodity Futures Trading Commission. ACTION: Notice. SUMMARY: The Commodity Futures Trading Commission (CFTC) is announcing an...

AgRISTARS: Agriculture and resources inventory surveys through aerospace remote sensing

NASA Technical Reports Server (NTRS)

1981-01-01

The major objectives and FY 1980 accomplishments are described of a long term program designed to determine the usefulness, cost, and extent to which aerospace remote sensing data can be integrated into existing or future USDA systems to improve the objectivity, reliability, timeliness, and adequacy of information. A general overview, the primary and participating agencies, and the technical highlights of each of the following projects are presented: early warning/crop condition assessment; foreign commodity production forecasting; yield model development; supporting research; soil moisture; domestic crops and land cover; renewable resources inventory; and conservation and pollution.

Genetically engineered plants in the product development pipeline in India.

PubMed

Warrier, Ranjini; Pande, Hem

2016-01-02

In order to proactively identify emerging issues that may impact the risk assessment and risk management functions of the Indian biosafety regulatory system, the Ministry of Environment, Forests and Climate Change sought to understand the nature and diversity of genetically engineered crops that may move to product commercialization within the next 10 y. This paper describes the findings from a questionnaire designed to solicit information about public and private sector research and development (R&D) activities in plant biotechnology. It is the first comprehensive overview of the R&D pipeline for GE crops in India.

Integrated crop-livestock systems and cover crop grazing in the Northern Great Plains

USDA-ARS?s Scientific Manuscript database

Integrating crops and livestock has been identified as an approach to sustainably intensify agricultural systems, increasing production while reducing the need for external inputs, building soil health, and increasing economic returns. Cover crops and grazing these cover crops are a natural fit with...

Development of a decision support system for crop disease monitoring, surveillance and prediction in Bomet county, Kenya

NASA Astrophysics Data System (ADS)

Otieno, O. M.

2015-12-01

The study proposes to use Geographic Information Systems and Remote Sensing techniques to spatially model Maize Lethal Necrosis (MLN) disease in maize growing areas in Kenya. Results from this work will be used for prediction, monitoring and to guide intervention on MLN. This will minimize maize yield losses resulting from MLN infestation and thus safeguard the livelihoods of maize farmers in Kenya. MLN was first reported in Kenya in September 2011 in Bomet county. It then subsequently spread to other parts in Kenya. Maize crops are susceptible to MLN at all growth stages. Once infected the only option left for the farmers is to burn their maize plantations. Infection rate and damage is very high affecting yields and sometimes causing complete loss of maize yield.The modelling exercise will cover the period prior to and after the incidence of MLN. Specifically, the analysis will integrate spatio-temporal information on maize phenology and field surveys with the intention of delineating the extent of MLN infestation and the degree of damage as a result of MLN. Additionally, the task will identify potential predisposing factors leading to MLN resurgence and spread and to predict potential areas where MLN is likely to spread and to estimate the potential impact of MLN on the farm holders. The area of study for this task will be Bomet County. Historical and current environmental and spatial indicators including temperature, rainfall, soil moisture, vegetation health and crop cover will be fed into a model in order to determine the main factors that aide the occurrence and the spread of MLN. Multi-spectral image processing will be used to produce indices to study maize crop health whilst image classification techniques will be used to identify crop cover clusters by differentiating the variations in spectral signatures in the area of study and hence distinguish infected, unaffected maize crops and other crop cover classes. Variables from these indicators will then be weighted in a spatial model and be used as a basis for generating site-specific MLN prediction maps that will guide policy on MLN management in Kenya. The broaderobjective is to document a model that can be up-scaled and replicated in other maize producing areas in Kenya affected by MLN.

Long-term cropping systems study

USDA-ARS?s Scientific Manuscript database

This long-term study has been conducted on the Agronomy Farm at ARDC since the early 1970’s. In the beginning, the objectives were mainly related to crop production as affected by different cropping systems. The cropping systems included in the study are Continuous Corn, Soybean, and Sorghum; 2-year...

Early Warning of El Nino Impacts on Food Security

NASA Astrophysics Data System (ADS)

Rowland, J.; Verdin, J. P.; Hillbruner, C.; Budde, M. E.

2016-12-01

Before and during the El Niño of 2015-2016, regular and frequent application of climate monitoring and seasonal forecasts enabled early warning of food insecurity in Africa, Central America, and the Caribbean. As it happened, drought associated with the quasi-El Niño of 2014 had already adversely impacted harvests in Central America, Haiti, and Southern Africa, so the effects of the El Niño of 2015-2016 were especially hard-hitting and particularly devastating to crop conditions and food security. In the case of Ethiopia, 2014 conditions were normal but there were record rainfall deficits in 2015, with consequent crop failure, inadequate forage, and sharply curtailed water availability. Combining such agro-climatological information with knowledge of household economies, livelihood systems, markets & trade, and health & nutrition, FEWS NET constructed scenarios of food insecurity eight months into the future, with monthly updates. These scenarios informed assistance programming by USAID and partners. Overall, FEWS NET estimates that at least 18 million people will be severely food insecure during 2015/16 as a direct result of the impact of El Nino on rainfall. However, in Ethiopia, the contrast with the 1982-1983 El Niño is dramatic; though the two events were climatically similar, the human impacts of the 2015-2016 El Niño are much less, thanks not only to well-functioning early warning systems and large scale emergency response, but also improved social safety nets and lack of ongoing armed conflict. In southern Africa, El Nino resulted in extensive failed crops, with some areas of South Africa and Zimbabwe having insufficient rain to plant crops. Remote sensing products provided relevant information to depict the severity of rainfall and vegetation deficits. Likewise, in Central America and the Caribbean (Hispaniola), rainfall deficits were portrayed in the perspective of 30+ years of data.

Geographic information system applied to the estimation of the plant water status

NASA Astrophysics Data System (ADS)

Castillo, Cristina; de la Rosa, Jose Mª; Temnani, Abdel; Pérez-Pastor, Alejandro

2017-04-01

The importance of Geographic Information Systems (GIS) at handling managing geospatial data is demonstrated in a large number of scientific and professionals disciplines that have an impact on the territory. Thus, in agriculture, it is a transversal tool that includes the recopilation of: (i) geographic information: soil-plant geolocated sensors in experimental fields, water and fertilizers consumption for each irrigation sector, energy consumption and digital surface models (ii) representation and analysis: obtaining temperature maps, aspect models, solar radiation, run-off and salinity, as well as hardware, software and the people who compose it, results in the optimization of resources (goods, energy and workforce) what it makes the farm more efficient and more beneficial for the environment. In addition, in this project, the use of new technologies, such as satellite imagery or drones with multispectral cameras, allow to obtain other parameters that are not observed with the naked eye, like the state of the crop in spectroradiometric terms (remote sensing), stressed crops through indexes like NDVI, that may lead to take decisions like: (i) irrigation variations (ii) early detection of fillings in droppers (iii) affected areas for a pest, helping to distribute the workforce efficiently (pesticide use in an optimal way). The main objective of GIS use in this project is to establish direct relationships between parameters taken from the soil and plant with image processing in four different crops, orange, peach, apricot trees and table grape. In this way, the leaf area index (LAI) can be calculated, assessing how different irrigation management affects: i) Control (CTL), irrigated to ensure non-limiting water conditions (120% of crop evapotranspiration) and ii) Regulated deficit irrigation (RDI) irrigated as CTL during critical periods and decreasing irrigation in non-critical periods. Acknowledgements This work has been funded by the European Union LIFE+ project IRRIMAN (LIFE13 ENV/ES/000539).

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

PubMed Central

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

1996-01-01

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

Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach

NASA Astrophysics Data System (ADS)

Sun, Liang; Anderson, Martha C.; Gao, Feng; Hain, Christopher; Alfieri, Joseph G.; Sharifi, Amirreza; McCarty, Gregory W.; Yang, Yun; Yang, Yang; Kustas, William P.; McKee, Lynn

2017-07-01

The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multisensor satellite data fusion methodology, combined with a multiscale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30 m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step - an improvement from 27% errors using standard Landsat-only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use data sets into watershed-scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

Capitalizing on deliberate, accidental, and GM-driven environmental change caused by crop modification.

PubMed

Knox, Oliver G G; Walker, Robin L; Booth, Elaine J; Hall, Clare; Crossan, Angus N; Gupta, Vadakattu V S R

2012-01-01

The transgenic traits associated with the majority of commercial genetically modified crops are focused on improving herbicide and insecticide management practices. The use of the transgenic technology in these crops and the associated chemistry has been the basis of studies that provide evidence for occasional improvement in environmental benefits due to the use of less residual herbicides, more targeted pesticides, and reduced field traffic. This is nicely exemplified through studies using Environmental Impact Quotient (EIQ) assessments. Whilst EIQ evaluations may sometimes illustrate environmental benefits they have their limitations. EIQ evaluations are not a surrogate for Environmental Risk Assessments and may not reflect real environmental interactions between crops and the environment. Addressing the impact cultivated plants have on the environment generally attracts little public attention and research funding, but the introduction of GM has facilitated an expansion of research to address potential environmental concerns from government, NGOs, industry, consumers, and growers. In this commentary, some evidence from our own research and several key papers that highlight EIQ assessments of the impact crops are having on the environment are presented. This information may be useful as an education tool on the potential benefits of GM and conventional farming. In addition, other deliberate, accidental, and GM-driven benefits derived from the examination of GM cropping systems is briefly discussed.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Multimodel ensembles of wheat growth: many models are better than one

USDA-ARS?s Scientific Manuscript database

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but suc...

Economic Benefits of Predictive Models for Pest Control in Agricultural Crops

USDA-ARS?s Scientific Manuscript database

Various forms of crop models or decision making tools for managing crops have existed for many years. The potential advantage of all of these decision making tools is that more informed and economically improved crop management or decision making is accomplished. However, examination of some of thes...

Perceptions of Crop Science Instructional Materials.

ERIC Educational Resources Information Center

Elkins, D. M.

1994-01-01

A number of crop science instructors have indicated that there is a shortage of quality, current crop/plant science teaching materials, particularly textbooks. A survey instrument was developed to solicit information from teachers about the use and adequacy of textbooks, laboratory manuals, and videotapes in crop/plant science instruction. (LZ)

Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2018-01-01

Switchgrass (Panicum virgatum) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e. it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e. NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario‐based (‘A1B’) land use and land cover map for 2050, the US Geological Survey crop type and Compound Topographic Index (CTI) maps, and long‐term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2528 km2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4232 km2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development.

Adaptation for Planting and Irrigation Decisions to Changing Monsoon Regime in Northeast India: Risk-based Hydro-economic Optimization

NASA Astrophysics Data System (ADS)

Zhu, T.; Cai, X.

2013-12-01

Delay in onset of Indian summer monsoon becomes increasingly frequent. Delayed monsoon and occasional monsoon failures seriously affect agricultural production in the northeast as well as other parts of India. In the Vaishali district of the Bihar State, Monsoon rainfall is very skewed and erratic, often concentrating in shorter durations. Farmers in Vaishali reported that delayed Monsoon affected paddy planting and, consequently delayed cropping cycle, putting crops under the risks of 'terminal heat.' Canal system in the district does not function due to lack of maintenance; irrigation relies almost entirely on groundwater. Many small farmers choose not to irrigate when monsoon onset is delayed due to high diesel price, leading to reduced production or even crop failure. Some farmers adapt to delayed onset of Monsoon by planting short-duration rice, which gives the flexibility for planting the next season crops. Other sporadic autonomous adaptation activities were observed as well, with various levels of success. Adaptation recommendations and effective policy interventions are much needed. To explore robust options to adapt to the changing Monsoon regime, we build a stochastic programming model to optimize revenues of farmer groups categorized by landholding size, subject to stochastic Monsoon onset and rainfall amount. Imperfect probabilistic long-range forecast is used to inform the model onset and rainfall amount probabilities; the 'skill' of the forecasting is measured using probabilities of correctly predicting events in the past derived through hindcasting. Crop production functions are determined using self-calibrating Positive Mathematical Programming approach. The stochastic programming model aims to emulate decision-making behaviors of representative farmer agents through making choices in adaptation, including crop mix, planting dates, irrigation, and use of weather information. A set of technological and policy intervention scenarios are tested, including irrigation subsidies, drought and heat-tolerant crop varieties, and enhancing agricultural extension. A portfolio of prioritized adaption options are recommended for the study area.

Monitoring cover crops using radar remote sensing in southern Ontario, Canada

NASA Astrophysics Data System (ADS)

Shang, J.; Huang, X.; Liu, J.; Wang, J.

2016-12-01

Information on agricultural land surface conditions is important for developing best land management practices to maintain the overall health of the fields. The climate condition supports one harvest per year for the majority of the field crops in Canada, with a relative short growing season between May and September. During the non-growing-season months (October to the following April), many fields are traditionally left bare. In more recent year, there has been an increased interest in planting cover crops. Benefits of cover crops include boosting soil organic matters, preventing soil from erosion, retaining soil moisture, and reducing surface runoff hence protecting water quality. Optical remote sensing technology has been exploited for monitoring cover crops. However limitations inherent to optical sensors such as cloud interference and signal saturation (when leaf area index is above 2.5) impeded its operational application. Radar remote sensing on the other hand is not hindered by unfavorable weather conditions, and the signal continues to be sensitive to crop growth beyond the saturation point of optical sensors. It offers a viable means for capturing timely information on field surface conditions (with or without crop cover) or crop development status. This research investigated the potential of using multi-temporal RADARSAT-2 C-band synthetic aperture radar (SAR) data collected in 2015 over multiple fields of winter wheat, corn and soybean crops in southern Ontario, Canada, to retrieve information on the presence of cover crops and their growth status. Encouraging results have been obtained. This presentation will report the methodology developed and the results obtained.

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

NASA Astrophysics Data System (ADS)

Ismaeel, A.; Zhou, Q.

2018-04-01

Accurate information of crop rotation in large basin is essential for policy decisions on land, water and nutrient resources around the world. Crop area estimation using low spatial resolution remote sensing data is challenging in a large heterogeneous basin having more than one cropping seasons. This study aims to evaluate the accuracy of two phenological datasets individually and in combined form to map crop rotations in complex irrigated Indus basin without image segmentation. Phenology information derived from Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, having 8-day temporal and 1000 m spatial resolution, was used in the analysis. An unsupervised (temporal space clustering) to supervised (area knowledge and phenology behavior) classification approach was adopted to identify 13 crop rotations. Estimated crop area was compared with reported area collected by field census. Results reveal that combined dataset (NDVI*LAI) performs better in mapping wheat-rice, wheat-cotton and wheat-fodder rotation by attaining root mean square error (RMSE) of 34.55, 16.84, 20.58 and mean absolute percentage error (MAPE) of 24.56 %, 36.82 %, 30.21 % for wheat, rice and cotton crop respectively. For sugarcane crop mapping, LAI produce good results by achieving RMSE of 8.60 and MAPE of 34.58 %, as compared to NDVI (10.08, 40.53 %) and NDVI*LAI (10.83, 39.45 %). The availability of major crop rotation statistics provides insight to develop better strategies for land, water and nutrient accounting frameworks to improve agriculture productivity.

75 FR 25832 - Commodity Credit Corporation Information Collection; Noninsured Crop Disaster Assistance Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-10

... produced for food or fiber. Additionally, NAP provides assistance for losses of floriculture, ornamental... oats and sea grass, and industrial crops. The information collected is necessary to determine whether a...

Sensitivity of an Integrated Mesoscale Atmosphere and Agriculture Land Modeling System (WRF/CMAQ-EPIC) to MODIS Vegetation and Lightning Assimilation

NASA Astrophysics Data System (ADS)

Ran, L.; Cooter, E. J.; Gilliam, R. C.; Foroutan, H.; Kang, D.; Appel, W.; Wong, D. C.; Pleim, J. E.; Benson, V.; Pouliot, G.

2017-12-01

The combined meteorology and air quality modeling system composed of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model is an important decision support tool that is used in research and regulatory decisions related to emissions, meteorology, climate, and chemical transport. The Environmental Policy Integrated Climate (EPIC) is a cropping model which has long been used in a range of applications related to soil erosion, crop productivity, climate change, and water quality around the world. We have integrated WRF/CMAQ with EPIC using the Fertilizer Emission Scenario Tool for CMAQ (FEST-C) to estimate daily soil N information with fertilization for CMAQ bi-directional ammonia flux modeling. Driven by the weather and N deposition from WRF/CMAQ, FEST-C EPIC simulations are conducted on 22 different agricultural production systems ranging from managed grass lands (e.g. hay and alfalfa) to crop lands (e.g. corn grain and soybean) with rainfed and irrigated information across any defined conterminous United States (U.S.) CMAQ domain and grid resolution. In recent years, this integrated system has been enhanced and applied in many different air quality and ecosystem assessment projects related to land-water-atmosphere interactions. These enhancements have advanced this system to become a valuable tool for integrated assessments of air, land and water quality in light of social drivers and human and ecological outcomes. This presentation will focus on evaluating the sensitivity of precipitation and N deposition in the integrated system to MODIS vegetation input and lightning assimilation and their impacts on agricultural production and fertilization. We will describe the integrated modeling system and evaluate simulated precipitation and N deposition along with other weather information (e.g. temperature, humidity) for 2011 over the conterminous U.S. at 12 km grids from a coupled WRF/CMAQ with MODIS and lightning assimilation. Simulated agricultural production and fertilization from FEST-C EPIC driven by the changed meteorology and N deposition from MODIS and lightning assimilations will be evaluated and analyzed.

Molecular and systems approaches towards drought-tolerant canola crops.

PubMed

Zhu, Mengmeng; Monroe, J Grey; Suhail, Yasir; Villiers, Florent; Mullen, Jack; Pater, Dianne; Hauser, Felix; Jeon, Byeong Wook; Bader, Joel S; Kwak, June M; Schroeder, Julian I; McKay, John K; Assmann, Sarah M

2016-06-01

1169 I. 1170 II. 1170 III. 1172 IV. 1176 V. 1181 VI. 1182 1183 References 1183 SUMMARY: Modern agriculture is facing multiple challenges including the necessity for a substantial increase in production to meet the needs of a burgeoning human population. Water shortage is a deleterious consequence of both population growth and climate change and is one of the most severe factors limiting global crop productivity. Brassica species, particularly canola varieties, are cultivated worldwide for edible oil, animal feed, and biodiesel, and suffer dramatic yield loss upon drought stress. The recent release of the Brassica napus genome supplies essential genetic information to facilitate identification of drought-related genes and provides new information for agricultural improvement in this species. Here we summarize current knowledge regarding drought responses of canola, including physiological and -omics effects of drought. We further discuss knowledge gained through translational biology based on discoveries in the closely related reference species Arabidopsis thaliana and through genetic strategies such as genome-wide association studies and analysis of natural variation. Knowledge of drought tolerance/resistance responses in canola together with research outcomes arising from new technologies and methodologies will inform novel strategies for improvement of drought tolerance and yield in this and other important crop species. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Yield and Economic Performance of Organic and Conventional Cotton-Based Farming Systems – Results from a Field Trial in India

PubMed Central

Forster, Dionys; Andres, Christian; Verma, Rajeev; Zundel, Christine; Messmer, Monika M.; Mäder, Paul

2013-01-01

The debate on the relative benefits of conventional and organic farming systems has in recent time gained significant interest. So far, global agricultural development has focused on increased productivity rather than on a holistic natural resource management for food security. Thus, developing more sustainable farming practices on a large scale is of utmost importance. However, information concerning the performance of farming systems under organic and conventional management in tropical and subtropical regions is scarce. This study presents agronomic and economic data from the conversion phase (2007–2010) of a farming systems comparison trial on a Vertisol soil in Madhya Pradesh, central India. A cotton-soybean-wheat crop rotation under biodynamic, organic and conventional (with and without Bt cotton) management was investigated. We observed a significant yield gap between organic and conventional farming systems in the 1st crop cycle (cycle 1: 2007–2008) for cotton (−29%) and wheat (−27%), whereas in the 2nd crop cycle (cycle 2: 2009–2010) cotton and wheat yields were similar in all farming systems due to lower yields in the conventional systems. In contrast, organic soybean (a nitrogen fixing leguminous plant) yields were marginally lower than conventional yields (−1% in cycle 1, −11% in cycle 2). Averaged across all crops, conventional farming systems achieved significantly higher gross margins in cycle 1 (+29%), whereas in cycle 2 gross margins in organic farming systems were significantly higher (+25%) due to lower variable production costs but similar yields. Soybean gross margin was significantly higher in the organic system (+11%) across the four harvest years compared to the conventional systems. Our results suggest that organic soybean production is a viable option for smallholder farmers under the prevailing semi-arid conditions in India. Future research needs to elucidate the long-term productivity and profitability, particularly of cotton and wheat, and the ecological impact of the different farming systems. PMID:24324659

Yield and economic performance of organic and conventional cotton-based farming systems--results from a field trial in India.

PubMed

Forster, Dionys; Andres, Christian; Verma, Rajeev; Zundel, Christine; Messmer, Monika M; Mäder, Paul

2013-01-01

The debate on the relative benefits of conventional and organic farming systems has in recent time gained significant interest. So far, global agricultural development has focused on increased productivity rather than on a holistic natural resource management for food security. Thus, developing more sustainable farming practices on a large scale is of utmost importance. However, information concerning the performance of farming systems under organic and conventional management in tropical and subtropical regions is scarce. This study presents agronomic and economic data from the conversion phase (2007-2010) of a farming systems comparison trial on a Vertisol soil in Madhya Pradesh, central India. A cotton-soybean-wheat crop rotation under biodynamic, organic and conventional (with and without Bt cotton) management was investigated. We observed a significant yield gap between organic and conventional farming systems in the 1(st) crop cycle (cycle 1: 2007-2008) for cotton (-29%) and wheat (-27%), whereas in the 2(nd) crop cycle (cycle 2: 2009-2010) cotton and wheat yields were similar in all farming systems due to lower yields in the conventional systems. In contrast, organic soybean (a nitrogen fixing leguminous plant) yields were marginally lower than conventional yields (-1% in cycle 1, -11% in cycle 2). Averaged across all crops, conventional farming systems achieved significantly higher gross margins in cycle 1 (+29%), whereas in cycle 2 gross margins in organic farming systems were significantly higher (+25%) due to lower variable production costs but similar yields. Soybean gross margin was significantly higher in the organic system (+11%) across the four harvest years compared to the conventional systems. Our results suggest that organic soybean production is a viable option for smallholder farmers under the prevailing semi-arid conditions in India. Future research needs to elucidate the long-term productivity and profitability, particularly of cotton and wheat, and the ecological impact of the different farming systems.

Plants for space plantations. [crops for closed life support systems

NASA Technical Reports Server (NTRS)

Nikishanova, T. I.

1978-01-01

Criteria for selection of candidate crops for closed life support systems are presented and discussed, and desired characteristics of candidate higher plant crops are given. Carbohydrate crops, which are most suitable, grown worldwide are listed and discussed. The sweet potato, ipomoea batatas Poir., is shown to meet the criteria to the greatest degree, and the criteria are recommended as suitable for initial evaluation of candidate higher plant crops for such systems.

SPATIAL AND TEMPORAL PATTERNS OF THE MOVEMENT OF SEASONAL AGRICULTURAL MIGRANT CHILDREN INTO WISCONSIN, EDUCATIONAL PROGRAMS FOR CHILDREN OF MIGRATORY AGRICULTURAL WORKERS IN WISCONSIN, REPORT 2.

ERIC Educational Resources Information Center

LINDSEY, HERBERT H.; AND OTHERS

USEFUL MEANS OF ANTICIPATING THE MOVEMENTS OF MIGRANT CHILDREN INCLUDE ANALYSIS OF CROPS, THE HARVESTING OF WHICH REQUIRES OUT-OF-STATE WORKERS, DISTRIBUTIONAL MAPS OF CROP ACREAGE, NORMAL TIME SCHEDULES FOR CROPS, AND INFORMATION ON AGRICULTURAL DEVELOPMENTS. SUCH INFORMATION ASSISTS IN THE PLANNING OF SCHOOL PROGRAMS. IN WISCONSIN, MOST MIGRANT…

Crop Species Diversity Changes in the United States: 1978–2012

PubMed Central

Aguilar, Jonathan; Gramig, Greta G.; Hendrickson, John R.; Archer, David W.; Forcella, Frank; Liebig, Mark A.

2015-01-01

Anecdotal accounts regarding reduced US cropping system diversity have raised concerns about negative impacts of increasingly homogeneous cropping systems. However, formal analyses to document such changes are lacking. Using US Agriculture Census data, which are collected every five years, we quantified crop species diversity from 1978 to 2012, for the contiguous US on a county level basis. We used Shannon diversity indices expressed as effective number of crop species (ENCS) to quantify crop diversity. We then evaluated changes in county-level crop diversity both nationally and for each of the eight Farm Resource Regions developed by the National Agriculture Statistics Service. During the 34 years we considered in our analyses, both national and regional ENCS changed. Nationally, crop diversity was lower in 2012 than in 1978. However, our analyses also revealed interesting trends between and within different Resource Regions. Overall, the Heartland Resource Region had the lowest crop diversity whereas the Fruitful Rim and Northern Crescent had the highest. In contrast to the other Resource Regions, the Mississippi Portal had significantly higher crop diversity in 2012 than in 1978. Also, within regions there were differences between counties in crop diversity. Spatial autocorrelation revealed clustering of low and high ENCS and this trend became stronger over time. These results show that, nationally counties have been clustering into areas of either low diversity or high diversity. Moreover, a significant trend of more counties shifting to lower rather than to higher crop diversity was detected. The clustering and shifting demonstrates a trend toward crop diversity loss and attendant homogenization of agricultural production systems, which could have far-reaching consequences for provision of ecosystem system services associated with agricultural systems as well as food system sustainability. PMID:26308552

Increasing public understanding of transgenic crops through the World Wide Web.

PubMed

Byrne, Patrick F; Namuth, Deana M; Harrington, Judy; Ward, Sarah M; Lee, Donald J; Hain, Patricia

2002-07-01

Transgenic crops among the most controversial "science and society" issues of recent years. Because of the complex techniques involved in creating these crops and the polarized debate over their risks and beliefs, a critical need has arisen for accessible and balanced information on this technology. World Wide Web sites offer several advantages for disseminating information on a fast-changing technical topic, including their global accessibility; and their ability to update information frequently, incorporate multimedia formats, and link to networks of other sites. An alliance between two complementary web sites at Colorado State University and the University of Nebraska-Lincoln takes advantage of the web environment to help fill the need for public information on crop genetic engineering. This article describes the objectives and features of each site. Viewership data and other feedback have shown these web sites to be effective means of reaching public audiences on a complex scientific topic.

A crops and soils data base for scene radiation research

NASA Technical Reports Server (NTRS)

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

1982-01-01

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

Integrated production of warm season grasses and agroforestry for biomass production

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

Samson, R.; Omielan, J.; Girouard, P.

1993-12-31

Increased research on C{sub 3} and C{sub 4} perennial biomass crops is generating a significant amount of information on the potential of these crops to produce large quantities of low cost biomass. In many parts of North America it appears that both C{sub 3} and C{sub 4} species are limited by water availability particularly on marginal soils. In much of North America, rainfall is exceeded by evaporation. High transpiration rates by fast growing trees and rainfall interception by the canopy appear to indicate that this can further exacerbate the problem of water availability. C{sub 4} perennial grasses appear to havemore » distinct advantages over C{sub 3} species planted in monoculture systems particularly on marginal soils. C{sub 4} grasses historically predominated over much of the land that is now available for biomass production because of their adaptation to low humidity environments and periods of low soil moisture. The planting of short rotation forestry (SRF) species in an energy agroforestry system is proposed as an alternative production strategy which could potentially alleviate many of the problems associated with SRF monocultures. Energy agroforestry would be complementary to both production of conventional farm crops and C{sub 4} perennial biomass crops because of beneficial microclimatic effects.« less

Connecting Digital Repeat Photography to Ecosystem Fluxes in Inland Pacific Northwest, US Cropping Systems

NASA Astrophysics Data System (ADS)

Russell, E.; Chi, J.; Waldo, S.; Pressley, S. N.; Lamb, B. K.; Pan, W.

2017-12-01

Diurnal and seasonal gas fluxes vary by crop growth stage. Digital cameras are increasingly being used to monitor inter-annual changes in vegetation phenology in a variety of ecosystems. These cameras are not designed as scientific instruments but the information they gather can add value to established measurement techniques (i.e. eddy covariance). This work combined deconstructed digital images with eddy covariance data from five agricultural sites (1 fallow, 4 cropped) in the inland Pacific Northwest, USA. The data were broken down with respect to crop stage and management activities. The fallow field highlighted the camera response to changing net radiation, illumination, and rainfall. At the cropped sites, the net ecosystem exchange, gross primary production, and evapotranspiration were correlated with the greenness and redness values derived from the images over the growing season. However, the color values do not change quickly enough to respond to day-to-day variability in the flux exchange as the two measurement types are based on different processes. The management practices and changes in phenology through the growing season were not visible within the camera data though the camera did capture the general evolution of the ecosystem fluxes.

Deriving crop calendar using NDVI time-series

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Classification of Liss IV Imagery Using Decision Tree Methods

NASA Astrophysics Data System (ADS)

Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

2016-06-01

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

USDA Foreign Agricultural Service overview for operational monitoring of current crop conditions and production forecasts.

NASA Astrophysics Data System (ADS)

Crutchfield, J.

2016-12-01

The presentation will discuss the current status of the International Production Assessment Division of the USDA ForeignAgricultural Service for operational monitoring and forecasting of current crop conditions, and anticipated productionchanges to produce monthly, multi-source consensus reports on global crop conditions including the use of Earthobservations (EO) from satellite and in situ sources.United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) International Production AssessmentDivision (IPAD) deals exclusively with global crop production forecasting and agricultural analysis in support of the USDAWorld Agricultural Outlook Board (WAOB) lockup process and contributions to the World Agricultural Supply DemandEstimates (WASE) report. Analysts are responsible for discrete regions or countries and conduct in-depth long-termresearch into national agricultural statistics, farming systems, climatic, environmental, and economic factors affectingcrop production. IPAD analysts become highly valued cross-commodity specialists over time, and are routinely soughtout for specialized analyses to support governmental studies. IPAD is responsible for grain, oilseed, and cotton analysison a global basis. IPAD is unique in the tools it uses to analyze crop conditions around the world, including customweather analysis software and databases, satellite imagery and value-added image interpretation products. It alsoincorporates all traditional agricultural intelligence resources into its forecasting program, to make the fullest use ofavailable information in its operational commodity forecasts and analysis. International travel and training play animportant role in learning about foreign agricultural production systems and in developing analyst knowledge andcapabilities.

Cover Crop Chart: An Intuitive Educational Resource for Extension Professionals

ERIC Educational Resources Information Center

Liebig, Mark A.; Johnson, Holly; Archer, David; Hendrickson, John; Nichols, Kristine; Schmer, Marty; Tanaka, Don

2013-01-01

Interest in cover crops by agricultural producers has increased the need for information regarding the suitability of crops for addressing different production and natural resource goals. To help address this need, staff at the USDA-ARS Northern Great Plains Research Laboratory developed a decision aid called the Cover Crop Chart (CCC). Visually…

A system of regional agricultural land use mapping tested against small scale Apollo 9 color infrared photography of the Imperial Valley (California)

USGS Publications Warehouse

Johnson, Claude W.; Browden, Leonard W.; Pease, Robert W.

1969-01-01

Interpretation results of the small scale ClR photography of the Imperial Valley (California) taken on March 12, 1969 by the Apollo 9 earth orbiting satellite have shown that world wide agricultural land use mapping can be accomplished from satellite ClR imagery if sufficient a priori information is available for the region being mapped. Correlation of results with actual data is encouraging although the accuracy of identification of specific crops from the single image is poor. The poor results can be partly attributed to only one image taken during mid-season when the three major crops were reflecting approximately the same and their ClR image appears to indicate the same crop type. However, some incapacity can be attributed to lack of understanding of the subtle variations of visual and infrared color reflectance of vegetation and surrounding environment. Analysis of integrated color variations of the vegetation and background environment recorded on ClR imagery is discussed. Problems associated with the color variations may be overcome by development of a semi-automatic processing system which considers individual field units or cells. Design criteria for semi-automatic processing system are outlined.

An Overview of CRISPR-Based Tools and Their Improvements: New Opportunities in Understanding Plant–Pathogen Interactions for Better Crop Protection

PubMed Central

Barakate, Abdellah; Stephens, Jennifer

2016-01-01

Modern omics platforms have made the determination of susceptible/resistance genes feasible in any species generating huge numbers of potential targets for crop protection. However, the efforts to validate these targets have been hampered by the lack of a fast, precise, and efficient gene targeting system in plants. Now, the repurposing of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system has solved this problem. CRISPR/Cas9 is the latest synthetic endonuclease that has revolutionized basic research by allowing facile genome editing in prokaryotes and eukaryotes. Gene knockout is now feasible at an unprecedented efficiency with the possibility of multiplexing several targets and even genome-wide mutagenesis screening. In a short time, this powerful tool has been engineered for an array of applications beyond gene editing. Here, we briefly describe the CRISPR/Cas9 system, its recent improvements and applications in gene manipulation and single DNA/RNA molecule analysis. We summarize a few recent tests targeting plant pathogens and discuss further potential applications in pest control and plant–pathogen interactions that will inform plant breeding for crop protection. PMID:27313592

Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2015-12-01

Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was observed in different plot levels in the paddy fields from the two images. However, no such significant variation in rice genotypes at growth level was observed. Hence, the spectral information acquired from space platform can be linearly scaled to map the variation in field levels of rice crop which will be act as an informative system for rice agriculture practice.

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.

Synthetic Aperture Radar (SAR)-based paddy rice monitoring system: Development and application in key rice producing areas in Tropical Asia

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Holecz, F.; Khan, N. I.; Barbieri, M.; Quicho, E.; Collivignarelli, F.; Maunahan, A.; Gatti, L.; Romuga, G. C.

2017-01-01

Reliable and regular rice information is essential part of many countries’ national accounting process but the existing system may not be sufficient to meet the information demand in the context of food security and policy. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland paddy rice, especially in tropical region where pervasive cloud cover in the rainy seasons limits the use of optical imagery. This study uses multi-temporal X-band and C-band SAR imagery, automated image processing, rule-based classification and field observations to classify rice in multiple locations across Tropical Asia and assimilate the information into ORYZA Crop Growth Simulation model (CGSM) to generate high resolution yield maps. The resulting cultivated rice area maps had classification accuracies above 85% and yield estimates were within 81-93% agreement against district level reported yields. The study sites capture much of the diversity in water management, crop establishment and rice maturity durations and the study demonstrates the feasibility of rice detection, yield monitoring, and damage assessment in case of climate disaster at national and supra-national scales using multi-temporal SAR imagery combined with CGSM and automated methods.

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.

Integrated weed management systems with herbicide-tolerant crops in the European Union: lessons learnt from home and abroad.

PubMed

Lamichhane, Jay Ram; Devos, Yann; Beckie, Hugh J; Owen, Micheal D K; Tillie, Pascal; Messéan, Antoine; Kudsk, Per

2017-06-01

Conventionally bred (CHT) and genetically modified herbicide-tolerant (GMHT) crops have changed weed management practices and made an important contribution to the global production of some commodity crops. However, a concern is that farm management practices associated with the cultivation of herbicide-tolerant (HT) crops further deplete farmland biodiversity and accelerate the evolution of herbicide-resistant (HR) weeds. Diversification in crop systems and weed management practices can enhance farmland biodiversity, and reduce the risk of weeds evolving herbicide resistance. Therefore, HT crops are most effective and sustainable as a component of an integrated weed management (IWM) system. IWM advocates the use of multiple effective strategies or tactics to manage weed populations in a manner that is economically and environmentally sound. In practice, however, the potential benefits of IWM with HT crops are seldom realized because a wide range of technical and socio-economic factors hamper the transition to IWM. Here, we discuss the major factors that limit the integration of HT crops and their associated farm management practices in IWM systems. Based on the experience gained in countries where CHT or GMHT crops are widely grown and the increased familiarity with their management, we propose five actions to facilitate the integration of HT crops in IWM systems within the European Union.

HERBICIDE SENSITIVITY OF ECHINOCHLOA CRUS-GALLI POPULATIONS: A COMPARISON BETWEEN CROPPING SYSTEMS.

PubMed

Claerhout, S; De Cauwer, B; Reheul, D

2014-01-01

Echinochloa crus-galli populations exhibit high morphological variability and their response to herbicides varies from field to field. Differential response to herbicides could reflect differences in selection pressure, caused by years of cropping system related herbicide usage. This study investigates the relation between herbicide sensitivity of Echinochloa crus-galli populations and the cropping system to which they were subjected. The herbicide sensitivity of Echinochloa crus-galli was evaluated for populations collected on 18 fields, representing three cropping systems, namely (1) a long-term organic cropping system, (2) a conventional cropping system with corn in crop rotation or (3) a conventional cropping system with long-term monoculture of corn. Each cropping system was represented by 6 E. crus-galli populations. All fields were located on sandy soils. Dose-response pot experiments were conducted in the greenhouse to assess the effectiveness of three foliar-applied corn herbicides: nicosulfuron (ALS-inhibitor), cycloxydim (ACCase-inhibitor) and topramezone (HPPD-inhibitor), and two soil-applied corn herbicides: S-metolachlor and dimethenamid-P (both VLCFA-inhibitors). Foliar-applied herbicides were tested at a quarter, half and full recommended doses. Soil-applied herbicides were tested within a dose range of 0-22.5 g a.i. ha(-1) for S-metolachlor and 0-45 g a.i. ha(-1) for dimethenamid-P. Foliar-applied herbicides were applied at the three true leaves stage. Soil-applied herbicides were treated immediately after sowing the radicle-emerged seeds. All experiments were performed twice. The foliage dry weight per pot was determined four weeks after treatment. Plant responses to herbicides were expressed as biomass reduction (%, relative to the untreated control). Sensitivity to foliar-applied herbicides varied among cropping systems. Compared to populations from monoculture corn fields, populations originating from organic fields were significantly more sensitive to cycloxydim, topramezone and nicosulfuron (resp. 5.3%, 5.9% and 12.3%). Populations from the conventional crop rotation system showed intermediate sensitivity levels. Contrary to foliar-applied herbicides, the effectiveness of soil-applied herbicides was not affected by cropping system. Integrated weed management may be necessary to preserve herbicide efficacy on the long term.

A Description and Source Listing of Curriculum Materials in Agricultural Education, 1970-1971.

ERIC Educational Resources Information Center

American Vocational Association, Washington, DC. Agricultural Education Div.

To provide teachers of vocational agriculture, agricultural supervisors, and agricultural teacher educators with information on current curriculum materials available to them, this annotated bibliography presents 207 references classified according to the AGDEX filing system. Topics are: (1) Field Crops, (2) Horticulture, (3) Forestry, (4) Animal…

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...

A thermal-based remote sensing modeling system for estimating evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/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...

Agriculture, forest, and range

NASA Technical Reports Server (NTRS)

1975-01-01

The findings and recommendations of the panel for developing a satellite remote-sensing global information system in the next decade are reported. User requirements were identified in five categories: (1) cultivated crops, (2) land resources, (3)water resources, (4)forest management, and (5) range management. The benefits from the applications of satellite data are discussed.

Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Franch, Belen; Vermote, Eric; Roger, Jean-Claude; Becker-Reshef, Inbal; Justice, Christopher; Kussul, Nataliia

2017-01-01

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

Modeling impacts of water and fertilizer management on the ecosystem service of rice rotated cropping system in China

NASA Astrophysics Data System (ADS)

Chen, H.; Yu, C.; Li, C.

2015-12-01

Sustainable agricultural intensification demand optimum resource managements of agro-ecosystems. Detailed information on the impacts of water use and nutrient application on agro-ecosystem services including crop yields, greenhouse gas (GHG) emissions and nitrogen (N) loss is the key to guide field managements. In this study, we use the DeNitrification-DeComposition (DNDC) model to simulate the biogeochemical processes for rice rotated cropping systems in China. We set varied scenarios of water use in more than 1600 counties, and derived optimal rates of N application for each county in accordance to water use scenarios. Our results suggest that 0.88 ± 0.33 Tg per year (mean ± standard deviation) of synthetic N could be reduced without reducing rice yields, which accounts for 15.7 ± 5.9% of current N application in China. Field managements with shallow flooding and optimal N applications could enhance ecosystem services on a national scale, leading to 34.3% reduction of GHG emissions (CH4, N2O, and CO2), 2.8% reduction of overall N loss (NH3 volatilization, denitrification and N leaching) and 1.7% increase of rice yields, as compared to current management conditions. Among provinces with major rice production, Jiangsu, Yunnan, Guizhou, and Hubei could achieve more than 40% reduction of GHG emissions under appropriate water managements, while Zhejiang, Guangdong, and Fujian could reduce more than 30% N loss with optimal N applications. Our modeling efforts suggest that China is likely to benefit from reforming water and fertilization managements for rice rotated cropping system in terms of sustainable crop yields, GHG emission mitigation and N loss reduction, and the reformation should be prioritized in the above-mentioned provinces. Keywords: water regime, nitrogen fertilization, sustainable management, ecological modeling, DNDC

Global changes in soil stocks of carbon, nitrogen, phosphorus, and sulphur as influenced by long-term agricultural production.

PubMed

Kopittke, Peter M; Dalal, Ram C; Finn, Damien; Menzies, Neal W

2017-06-01

Quantifying changes in stocks of C, N, P, and S in agricultural soils is important not only for managing these soils sustainably as required to feed a growing human population, but for C and N, they are also important for understanding fluxes of greenhouse gases from the soil environment. In a global meta-analysis, 102 studies were examined to investigate changes in soil stocks of organic C, total N, total P, and total S associated with long-term land-use changes. Conversion of native vegetation to cropping resulted in substantial losses of C (-1.6 kg m -2 , -43%), N (-0.15 kg m -2 , -42%), P (-0.029 kg m -2 , -27%), and S (-0.015 kg m -2 , -33%). The subsequent conversion of conventional cropping systems to no-till, organic agriculture, or organic amendment systems subsequently increased stocks, but the magnitude of this increase (average of +0.47 kg m -2 for C and +0.051 kg m -2 for N) was small relative to the initial decrease. We also examined the conversion of native vegetation to pasture, with changes in C (-11%), N (+4.1%), and P (+25%) generally being modest relative to changes caused by conversion to cropping. The C:N ratio remained relatively constant irrespective of changes in land use, whilst in contrast, the C:S ratio decreased by 21% in soils converted to cropping - this suggesting that biochemical mineralization is of importance for S. The data presented here will assist in the assessment of different agricultural production systems on soil stocks of C, N, P, and S - this information assisting not only in quantifying the effects of existing agricultural production on these stocks, but also allowing for informed decision-making regarding the potential effects of future land-use changes. © 2016 John Wiley & Sons Ltd.

Folksong based appraisal of bioecocultural heritage of sorghum (Sorghum bicolor (L.) Moench): A new approach in ethnobiology

PubMed Central

Mekbib, Firew

2009-01-01

Background Sorghum is one of the main staple crops for the world's poorest and most food insecure people. As Ethiopia is the centre of origin and diversity for sorghum, the crop has been cultivated for thousands of years and hence the heritage of the crop is expected to be rich. Folksong based appraisal of bioecocultural heritage has not been done before. Methods In order to assess the bioecocultural heritage of sorghum by folksongs various research methods were employed. These included focus group discussions with 360 farmers, direct on-farm participatory monitoring and observation with 120 farmers, and key informant interviews with 60 farmers and development agents. Relevant secondary data was also collected from the museum curators and historians. Results The crop is intimately associated with the life of the farmers. The association of sorghum with the farmers from seed selection to utilization is presented using folksongs. These include both tune and textual (ballad stories or poems) types. Folksongs described how farmers maintain a number of varieties on-farm for many biological, socio-economic, ecological, ethnological and cultural reasons. Farmers describe sorghum as follows: Leaf number is less than twenty; Panicle hold a thousand seeds; a clever farmer takes hold of it. In addition, they described the various farmers' varieties ethnobotanically by songs. The relative importance of sorghum vis-à-vis others crops is similarly explained in folksong terms. Conclusion The qualitative description of farmers' characterisation of the crop systems based on folksongs is a new system of appraising farmers' bioecocultural heritage. Hence, researchers, in addition to formal and quantitative descriptions, should use the folksong system for enhanced characterisation and utilization of bioecocultural heritages. In general, the salient characteristics of the folksongs used in describing the bioecocultural heritages are their oral traditions, varied function, communal or individual recreation and message transmissions. PMID:19575802

A Proteomic Workflow Using High-Throughput De Novo Sequencing Towards Complementation of Genome Information for Improved Comparative Crop Science.

PubMed

Turetschek, Reinhard; Lyon, David; Desalegn, Getinet; Kaul, Hans-Peter; Wienkoop, Stefanie

2016-01-01

The proteomic study of non-model organisms, such as many crop plants, is challenging due to the lack of comprehensive genome information. Changing environmental conditions require the study and selection of adapted cultivars. Mutations, inherent to cultivars, hamper protein identification and thus considerably complicate the qualitative and quantitative comparison in large-scale systems biology approaches. With this workflow, cultivar-specific mutations are detected from high-throughput comparative MS analyses, by extracting sequence polymorphisms with de novo sequencing. Stringent criteria are suggested to filter for confidential mutations. Subsequently, these polymorphisms complement the initially used database, which is ready to use with any preferred database search algorithm. In our example, we thereby identified 26 specific mutations in two cultivars of Pisum sativum and achieved an increased number (17 %) of peptide spectrum matches.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

Comparative Performance Analysis of a Hyper-Temporal Ndvi Analysis Approach and a Landscape-Ecological Mapping Approach

NASA Astrophysics Data System (ADS)

Ali, A.; de Bie, C. A. J. M.; Scarrott, R. G.; Ha, N. T. T.; Skidmore, A. K.

2012-07-01

Both agricultural area expansion and intensification are necessary to cope with the growing demand for food, and the growing threat of food insecurity which is rapidly engulfing poor and under-privileged sections of the global population. Therefore, it is of paramount importance to have the ability to accurately estimate crop area and spatial distribution. Remote sensing has become a valuable tool for estimating and mapping cropland areas, useful in food security monitoring. This work contributes to addressing this broad issue, focusing on the comparative performance analysis of two mapping approaches (i) a hyper-temporal Normalized Difference Vegetation Index (NDVI) analysis approach and (ii) a Landscape-ecological approach. The hyper-temporal NDVI analysis approach utilized SPOT 10-day NDVI imagery from April 1998-December 2008, whilst the Landscape-ecological approach used multitemporal Landsat-7 ETM+ imagery acquired intermittently between 1992 and 2002. Pixels in the time-series NDVI dataset were clustered using an ISODATA clustering algorithm adapted to determine the optimal number of pixel clusters to successfully generalize hyper-temporal datasets. Clusters were then characterized with crop cycle information, and flooding information to produce an NDVI unit map of rice classes with flood regime and NDVI profile information. A Landscape-ecological map was generated using a combination of digitized homogenous map units in the Landsat-7 ETM+ imagery, a Land use map 2005 of the Mekong delta, and supplementary datasets on the regions terrain, geo-morphology and flooding depths. The output maps were validated using reported crop statistics, and regression analyses were used to ascertain the relationship between land use area estimated from maps, and those reported in district crop statistics. The regression analysis showed that the hyper-temporal NDVI analysis approach explained 74% and 76% of the variability in reported crop statistics in two rice crop and three rice crop land use systems respectively. In contrast, 64% and 63% of the variability was explained respectively by the Landscape-ecological map. Overall, the results indicate the hyper-temporal NDVI analysis approach is more accurate and more useful in exploring when, why and how agricultural land use manifests itself in space and time. Furthermore, the NDVI analysis approach was found to be easier to implement, was more cost effective, and involved less subjective user intervention than the landscape-ecological approach.

Bioenergy cropping systems that incorporate native grasses stimulate growth of plant-associated soil microbes in the absence of nitrogen fertilization

DOE PAGES

Oates, Lawrence G.; Duncan, David S.; Sanford, Gregg R.; ...

2016-10-03

The choice of crops and their management can strongly influence soil microbial communities and their processes. Here, we used lipid biomarker profiling to characterize how soil microbial composition of five potential bioenergy cropping systems diverged from a common baseline five years after they were established. The cropping systems we studied included an annual system (continuous no-till corn) and four perennial crops (switchgrass, miscanthus, hybrid poplar, and restored prairie). Partial- and no-stover removal were compared for the corn system, while N-additions were compared to unfertilized plots for the perennial cropping systems. Arbuscular mycorrhizal fungi (AMF) and Gram-negative biomass was higher inmore » unfertilized perennial grass systems, especially in switchgrass and prairie. Gram-positive bacterial biomass decreased in all systems relative to baseline values in surface soils (0–10 cm), but not subsurface soils (10–25 cm). Overall microbial composition was similar between the two soil depths. Our findings demonstrate the capacity of unfertilized perennial cropping systems to recreate microbial composition found in undisturbed soil environments and indicate how strongly agroecosystem management decisions such as N addition and plant community composition can influence soil microbial assemblages.« less

Bioenergy cropping systems that incorporate native grasses stimulate growth of plant-associated soil microbes in the absence of nitrogen fertilization

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

Oates, Lawrence G.; Duncan, David S.; Sanford, Gregg R.

The choice of crops and their management can strongly influence soil microbial communities and their processes. Here, we used lipid biomarker profiling to characterize how soil microbial composition of five potential bioenergy cropping systems diverged from a common baseline five years after they were established. The cropping systems we studied included an annual system (continuous no-till corn) and four perennial crops (switchgrass, miscanthus, hybrid poplar, and restored prairie). Partial- and no-stover removal were compared for the corn system, while N-additions were compared to unfertilized plots for the perennial cropping systems. Arbuscular mycorrhizal fungi (AMF) and Gram-negative biomass was higher inmore » unfertilized perennial grass systems, especially in switchgrass and prairie. Gram-positive bacterial biomass decreased in all systems relative to baseline values in surface soils (0–10 cm), but not subsurface soils (10–25 cm). Overall microbial composition was similar between the two soil depths. Our findings demonstrate the capacity of unfertilized perennial cropping systems to recreate microbial composition found in undisturbed soil environments and indicate how strongly agroecosystem management decisions such as N addition and plant community composition can influence soil microbial assemblages.« less

The natural resources inventory system ASVT project

NASA Technical Reports Server (NTRS)

Joyce, A. T.

1979-01-01

The hardware/software and the associated procedures for a natural resource inventory and information system based on the use of LANDSAT-acquired multispectral scanner digital data is described. The system is designed to derive land cover/vegetation information from LANDSAT data and geographically reference this information for the production of various types of maps and for the compilation of acreage by land cover/vegetation category. The system also provides for data base building so that the LANDSAT-derived information can be related to information digitized from other sources (e.g., soils maps) in a geographic context in order to address specific applications. These applications include agricultural crop production estimation, erosion hazard-reforestation need assessment, whitetail deer habitat assessment, and site selection. The system is tested in demonstration areas located in the state of Mississippi, and the results of these application demonstrations are presented. A cost-efficiency comparison of producing land cover/vegetation maps and statistics with this system versus the use of small-scale aerial photography is made.

Assessment of Climate Change Impacts on Agricultural Water Demands and Crop Yields in California's Central Valley

NASA Astrophysics Data System (ADS)

Tansey, M. K.; Flores-Lopez, F.; Young, C. A.; Huntington, J. L.

2012-12-01

Long term planning for the management of California's water resources requires assessment of the effects of future climate changes on both water supply and demand. Considerable progress has been made on the evaluation of the effects of future climate changes on water supplies but less information is available with regard to water demands. Uncertainty in future climate projections increases the difficulty of assessing climate impacts and evaluating long range adaptation strategies. Compounding the uncertainty in the future climate projections is the fact that most readily available downscaled climate projections lack sufficient meteorological information to compute evapotranspiration (ET) by the widely accepted ASCE Penman-Monteith (PM) method. This study addresses potential changes in future Central Valley water demands and crop yields by examining the effects of climate change on soil evaporation, plant transpiration, growth and yield for major types of crops grown in the Central Valley of California. Five representative climate scenarios based on 112 bias corrected spatially downscaled CMIP 3 GCM climate simulations were developed using the hybrid delta ensemble method to span a wide range future climate uncertainty. Analysis of historical California Irrigation Management Information System meteorological data was combined with several meteorological estimation methods to compute future solar radiation, wind speed and dew point temperatures corresponding to the GCM projected temperatures and precipitation. Future atmospheric CO2 concentrations corresponding to the 5 representative climate projections were developed based on weighting IPCC SRES emissions scenarios. The Land, Atmosphere, and Water Simulator (LAWS) model was used to compute ET and yield changes in the early, middle and late 21st century for 24 representative agricultural crops grown in the Sacramento, San Joaquin and Tulare Lake basins. Study results indicate that changes in ET and yield vary between crops due to plant specific sensitivities to temperature, solar radiation and the vapor pressure deficits. Shifts in the growth period to earlier in the year, shortened growth period for annual crops as well as extended fall growth can also exert important influences. Projected increases in CO2 concentrations in the late 21st century exert very significant influences on ET and yield for many crops. To characterize potential impacts and the range of uncertainty, changes in total agricultural water demands and yields were computed assuming that current crop types and acreages in 21 Central Valley regional planning areas remained constant throughout the 21st century for each of the 5 representative future climate scenarios.

Productivity and nutrient cycling in bioenergy cropping systems

NASA Astrophysics Data System (ADS)

Heggenstaller, Andrew Howard

One of the greatest obstacles confronting large-scale biomass production for energy applications is the development of cropping systems that balance the need for increased productive capacity with the maintenance of other critical ecosystem functions including nutrient cycling and retention. To address questions of productivity and nutrient dynamics in bioenergy cropping systems, we conducted two sets of field experiments during 2005-2007, investigating annual and perennial cropping systems designed to generate biomass energy feedstocks. In the first experiment we evaluated productivity and crop and soil nutrient dynamics in three prototypical bioenergy double-crop systems, and in a conventionally managed sole-crop corn system. Double-cropping systems included fall-seeded forage triticale (x Triticosecale Wittmack), succeeded by one of three summer-adapted crops: corn (Zea mays L.), sorghum-sudangrass [Sorghum bicolor (L.) Moench], or sunn hemp (Crotalaria juncea L.). Total dry matter production was greater for triticale/corn and triticale/sorghum-sudangrass compared to sole-crop corn. Functional growth analysis revealed that photosynthetic duration was more important than photosynthetic efficiency in determining biomass productivity of sole-crop corn and double-crop triticale/corn, and that greater yield in the tiritcale/corn system was the outcome of photosynthesis occurring over an extended duration. Increased growth duration in double-crop systems was also associated with reductions in potentially leachable soil nitrogen relative to sole-crop corn. However, nutrient removal in harvested biomass was also greater in the double-crop systems, indicating that over the long-term, double-cropping would mandate increased fertilizer inputs. In a second experiment we assessed the effects of N fertilization on biomass and nutrient partitioning between aboveground and belowground crop components, and on carbon storage by four perennial, warm-season grasses: big bluestem (Andropogon geradii Vitman), switchgrass (Panicum virgatum L.), indiangrass [ Sorghastrum nutans (L.) Nash], and eastern gamagrass (Tripsacum dactyloides L.). Generally, the optimum rate of fertilization for biomass yield by the grasses was 140 kg N ha-1. Nitrogen inputs also had pronounced but grass-specific effects on biomass and nutrient partitioning, and on carbon storage. For big bluestem and switchgrass, 140 kg N ha -1. maximized root biomass, favored allocation of nutrients to roots over shoots, and led to net increases in carbon storage over the study duration. In contrast, for indiangrass and eastern gamagrass, root biomass and root nutrient allocation were generally adversely affected by N fertilization and carbon storage increased only with 0 or 65 kg N ha-1. For all grasses, 220 kg N ha -1 tended to shift allocation of nutrients to shoots over roots and resulted in no net increase in carbon storage. Optimal nitrogen management strategies for perennial, warm-season grass energy crops should take into consideration the effects of N on biomass yield as well as factors such as nutrient and carbon balance that will also impact economic feasibility and environmental sustainability.

Does grazing of cover crops impact biologically active soil C and N fractions under inversion and no tillage management

USDA-ARS?s Scientific Manuscript database

Cover crops are a key component of conservation cropping systems. They can also be a key component of integrated crop-livestock systems by offering high-quality forage during short periods between cash crops. The impact of cattle grazing on biologically active soil C and N fractions has not receiv...

Evaluating an ensemble classification approach for crop diversity verification in Danish greening subsidy control

NASA Astrophysics Data System (ADS)

Chellasamy, Menaka; Ferré, Ty Paul Andrew; Greve, Mogens Humlekrog

2016-07-01

Beginning in 2015, Danish farmers are obliged to meet specific crop diversification rules based on total land area and number of crops cultivated to be eligible for new greening subsidies. Hence, there is a need for the Danish government to extend their subsidy control system to verify farmers' declarations to warrant greening payments under the new crop diversification rules. Remote Sensing (RS) technology has been used since 1992 to control farmers' subsidies in Denmark. However, a proper RS-based approach is yet to be finalised to validate new crop diversity requirements designed for assessing compliance under the recent subsidy scheme (2014-2020); This study uses an ensemble classification approach (proposed by the authors in previous studies) for validating the crop diversity requirements of the new rules. The approach uses a neural network ensemble classification system with bi-temporal (spring and early summer) WorldView-2 imagery (WV2) and includes the following steps: (1) automatic computation of pixel-based prediction probabilities using multiple neural networks; (2) quantification of the classification uncertainty using Endorsement Theory (ET); (3) discrimination of crop pixels and validation of the crop diversification rules at farm level; and (4) identification of farmers who are violating the requirements for greening subsidies. The prediction probabilities are computed by a neural network ensemble supplied with training samples selected automatically using farmers declared parcels (field vectors containing crop information and the field boundary of each crop). Crop discrimination is performed by considering a set of conclusions derived from individual neural networks based on ET. Verification of the diversification rules is performed by incorporating pixel-based classification uncertainty or confidence intervals with the class labels at the farmer level. The proposed approach was tested with WV2 imagery acquired in 2011 for a study area in Vennebjerg, Denmark, containing 132 farmers, 1258 fields, and 18 crops. The classification results obtained show an overall accuracy of 90.2%. The RS-based results suggest that 36 farmers did not follow the crop diversification rules that would qualify for the greening subsidies. When compared to the farmers' reported crop mixes, irrespective of the rule, the RS results indicate that false crop declarations were made by 8 farmers, covering 15 fields. If the farmers' reports had been submitted for the new greening subsidies, 3 farmers would have made a false claim; while remaining 5 farmers obey the rules of required crop proportion even though they have submitted the false crop code due to their small holding size. The RS results would have supported 96 farmers for greening subsidy claims, with no instances of suggesting a greening subsidy for a holding that the farmer did not report as meeting the required conditions. These results suggest that the proposed RS based method shows great promise for validating the new greening subsidies in Denmark.

Maximizing the potential of cropping systems for nematode management.

PubMed

Noe, J P; Sasser, J N; Imbriani, J L

1991-07-01

Quantitative techniques were used to analyze and determine optimal potential profitability of 3-year rotations of cotton, Gossypium hirsutum cv. Coker 315, and soybean, Glycine max cv. Centennial, with increasing population densities of Hoplolaimus columbus. Data collected from naturally infested on-farm research plots were combined with economic information to construct a microcomputer spreadsheet analysis of the cropping system. Nonlinear mathematical functions were fitted to field data to represent damage functions and population dynamic curves. Maximum yield losses due to H. columbus were estimated to be 20% on cotton and 42% on soybean. Maximum at-harvest population densities were calculated to be 182/100 cm(3) soil for cotton and 149/100 cm(3) soil for soybean. Projected net incomes ranged from a $17.74/ha net loss for the soybean-cotton-soybean sequence to a net profit of $46.80/ha for the cotton-soybean-cotton sequence. The relative profitability of various rotations changed as nematode densities increased, indicating economic thresholds for recommending alternative crop sequences. The utility and power of quantitative optimization was demonstrated for comparisons of rotations under different economic assumptions and with other management alternatives.

Agroecology of corn production in Tlaxcala, Mexico

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

Altieri, M.A.; Trujillo, J.

1987-06-01

The primary components of Tlaxcalan corn agriculture are described, including cropping patterns employed, resource management strategies, and interactions of human and biological factors. Tlaxcalan farmers grow corn in an array of polyculture and agroforestry designs that result in a series of ecological processes important for insect pest and soil fertility management. Measurements derived from a few selected fields show that trees integrated into cropping systems modify the aerial and soil environment of associated understory corn plants, influencing their growth and yields. With decreasing distance from trees, surface concentrations of most soil nutrients increase. Certain tree species affect corn yields moremore » than others. Arthropod abundance also varies depending on their degree of association with one or more of the vegetational components of the system. Densities of predators and the corn pest Macrodactylus sp. depend greatly on the presence and phenology of adjacent alfalfa strips. Although the data were derived from nonreplicated fields, they nevertheless point out some important trends, information that can be used to design new crop association that will achieve sustained soil fertility and low pest potentials.« less

Procedure to select test organisms for environmental risk assessment of genetically modified crops in aquatic systems.

PubMed

Hilbeck, Angelika; Bundschuh, Rebecca; Bundschuh, Mirco; Hofmann, Frieder; Oehen, Bernadette; Otto, Mathias; Schulz, Ralf; Trtikova, Miluse

2017-11-01

For a long time, the environmental risk assessment (ERA) of genetically modified (GM) crops focused mainly on terrestrial ecosystems. This changed when it was scientifically established that aquatic ecosystems are exposed to GM crop residues that may negatively affect aquatic species. To assist the risk assessment process, we present a tool to identify ecologically relevant species usable in tiered testing prior to authorization or for biological monitoring in the field. The tool is derived from a selection procedure for terrestrial ecosystems with substantial but necessary changes to adequately consider the differences in the type of ecosystems. By using available information from the Water Framework Directive (2000/60/EC), the procedure can draw upon existing biological data on aquatic systems. The proposed procedure for aquatic ecosystems was tested for the first time during an expert workshop in 2013, using the cultivation of Bacillus thuringiensis (Bt) maize as the GM crop and 1 stream type as the receiving environment in the model system. During this workshop, species executing important ecological functions in aquatic environments were identified in a stepwise procedure according to predefined ecological criteria. By doing so, we demonstrated that the procedure is practicable with regard to its goal: From the initial long list of 141 potentially exposed aquatic species, 7 species and 1 genus were identified as the most suitable candidates for nontarget testing programs. Integr Environ Assess Manag 2017;13:974-979. © 2017 SETAC. © 2017 SETAC.

Unveiled Risks in a Telecoupled Arena Dependent on Agricultural Commodity Trade

NASA Astrophysics Data System (ADS)

Batistella, M.; Silva, R. F. B. D.; Liu, J.; Moran, E. F.; Torres, S.; Dou, Y.

2017-12-01

In less than five years, a new field of interdisciplinary research has been widely developed within the coupled human-natural systems (CHANS) community. Based on well-known concepts acting over long distances, such as teleconnections for biophysical sciences and globalization for human sciences, the telecoupling framework states that long distant CHANS may be integrated through flows of capital, information, and matter, shaping environmental and socioeconomic changes within local systems. An international project including researchers from Brazil, China, United Kingdom (UK), and United States of America (USA) is using such a framework to study the flows of agricultural commodities between sending systems such as Brazil and the USA, and receiving systems such as China with soybean trade as a case in point. Many findings have already shown that these dynamics are intrinsically connected and changes in environmental conditions in one system may affect economic conditions in other systems. In the case of the double-crop practice (i.e., soybean followed by maize, two crops year round) used in some regions of Brazil, maize production is strongly connected with soybean production through the supply chain, logistics, producer regions, and farmers. Consequently, maize has been displaced to second-crop status, making it more vulnerable to rain shortfalls. The use of the telecoupling framework has also unveiled a possible threat for soybean production in the long run if it only relies on international demand and inputs. The telecoupled soybean production system has put some Brazilian farmers at economic risk, and is pushing them towards diversification and alternative production practices (e.g., non-GMO crops, biological control, in-farm seed production) to reduce their risk. This, in turn, may affect the demand-consumption relationship among traditional trading partners. With this paper, we also highlight the role of cascading effects and spillover systems of telecoupling in shaping system interconnections and sustainability.

Biometry and diversity of Arabica coffee genotypes cultivated in a high density plant system.

PubMed

Rodrigues, W N; Tomaz, M A; Ferrão, M A G; Martins, L D; Colodetti, T V; Brinate, S V B; Amaral, J F T; Sobreira, F M; Apostólico, M A

2016-02-11

The present study was developed to respond to the need for an increase in crop yield in the mountain region of Caparaó (southern Espírito Santo State, Brazil), an area of traditional coffee production. This study aimed to analyze the diversity and characterize the crop yield of genotypes of Coffea arabica L. with potential for cultivation in high plant density systems. In addition, it also aimed to quantify the expression of agronomic traits in this cultivation system and provide information on the genotypes with the highest cultivation potential in the studied region. The experiment followed a randomized block design with 16 genotypes, four repetitions, and six plants per experimental plot. Plant spacing was 2.00 x 0.60 m, with a total of 8333 plants per hectare, representing a high-density cultivation system. Coffee plants were cultivated until the start of their reproductive phenological cycles and were evaluated along four complete reproductive cycles. Genotypes with high crop yield and beverage quality, short canopy, and rust resistance were selected. C. arabica genotypes showed variability in almost all characteristics. It was possible to identify different responses among genotypes grown in a high plant density cultivation system. Although the chlorophyll a content was similar among genotypes, the genotypes Acauã, Araponga MG1, Sacramento MG1, Tupi, and Catuaí IAC 44 showed a higher chlorophyll b content than the other genotypes. Among these, Sacramento MG1 also showed high leafiness and growth of vegetative structures, whereas Araponga MG1, Pau-Brasil MG1, and Tupi showed high fruit production. In addition, Araponga MG1 had also a higher and more stable crop yield over the years.

A blended approach to analyze staple and high-value crops using remote sensing with radiative transfer and crop models.

NASA Astrophysics Data System (ADS)

Davitt, A. W. D.; Winter, J.; McDonald, K. C.; Escobar, V. M.; Steiner, N.

2017-12-01

The monitoring of staple and high-value crops is important for maintaining food security. The recent launch of numerous remote sensing satellites has created the ability to monitor vast amounts of crop lands, continuously and in a timely manner. This monitoring provides users with a wealth of information on various crop types over different regions of the world. However, a challenge still remains on how to best quantify and interpret the crop and surface characteristics that are measured by visible, near-infrared, and active and passive microwave radar. Currently, two NASA funded projects are examining the ability to monitor different types of crops in California with different remote sensing platforms. The goal of both projects is to develop a cost-effective monitoring tool for use by vineyard and crop managers. The first project is designed to examine the capability to monitor vineyard water management and soil moisture in Sonoma County using Soil Moisture Active Passive (SMAP), Sentinel-1A and -2, and Landsat-8. The combined mission products create thorough and robust measurements of surface and vineyard characteristics that can potentially improve the ability to monitor vineyard health. Incorporating the Michigan Microwave Canopy Scattering (MIMICS), a radiative transfer model, enables us to better understand surface and vineyard features that influence radar measurements from Sentinel-1A. The second project is a blended approach to analyze corn, rice, and wheat growth using Sentinel-1A products with Decision Support System for Agrotechnology Transfer (DSSAT) and MIMICS models. This project aims to characterize the crop structures that influence Sentinel-1A radar measurements. Preliminary results have revealed the corn, rice, and wheat structures that influence radar measurements during a growing season. The potential of this monitoring tool can be used for maintaining food security. This includes supporting sustainable irrigation practices, identifying crop health and yield across and within fields, and improving the identification of crop areas ready for harvest.

Agricultural irrigated land-use inventory for Polk County, Florida, 2016

USGS Publications Warehouse

Marella, Richard L.; Berry, Darbi; Dixon, Joann F.

2017-08-16

An accurate inventory of irrigated crop acreage is not available at the level of resolution needed to better estimate agricultural water use or to project future water demands in many Florida counties. A detailed digital map and summary of irrigated acreage was developed for Polk County, Florida, during the 2016 growing season. This cooperative project between the U.S. Geological Survey and the Office of Agricultural Water Policy of the Florida Department of Agriculture and Consumer Services is part of an effort to improve estimates of water use and projections of future demands across all counties in the State. The irrigated areas were delineated by using land-use data provided by the Florida Department of Agriculture and Consumer Services, along with information obtained from the South and Southwest Florida Water Management Districts consumptive water-use permits. Delineations were field verified between April and December 2016. Attribute data such as crop type, primary water source, and type of irrigation system were assigned to the irrigated areas.The results of this inventory and field verification indicate that during the 2016 growing seasons (spring, summer, fall, and winter), an estimated 88,652 acres were irrigated within Polk County. Of the total field-verified crops, 83,995 acres were in citrus; 2,893 acres were in other non-citrus fruit crops (blueberries, grapes, peaches, and strawberries); 621 acres were in row crops (primarily beans and watermelons); 1,117 acres were in nursery (container and tree farms) and sod production; and 26 acres were in field crops including hay and pasture. Of the total inventoried irrigated acreage within Polk County, 98 percent (86,566 acres) was in the Southwest Florida Water Management District, and the remaining 2 percent (2,086 acres) was in the South Florida Water Management District.About 85,788 acres (96.8 percent of the acreage inventoried) were irrigated by a microirrigation system, including drip, bubblers, and spray emitters. The remaining 3.2 percent of the irrigated acreage was irrigated by a sprinkler system (2,360 acres) or subsurface flood systems (504 acres). Groundwater was the primary source of water used on irrigated acreage (88 percent, or 78,050 acres); the remaining 10,602 acres (12 percent) used groundwater combined with surface water as the irrigation source.The irrigated acreage estimated by the U.S. Geological Survey (USGS) for this 2016 inventory (88,652 acres) is about 11 percent higher than the 79,869 acres estimated by the U.S. Department of Agriculture (USDA) for 2012. Citrus and pasture in Polk County show the biggest difference in irrigated acreage between the USGS and USDA totals. Irrigated citrus acreage inventoried in 2016 by the USGS totaled 83,996 acres, whereas the USDA reported 78,305 acres of citrus in 2012. The USGS identified 6 acres of irrigated pasture and 20 acres of hay, whereas the USDA reported 6,631 acres of irrigated pasture and 1,349 acres of hay for 2012. In general, differences between the 2016 USGS field-verified acreage totals and acreage published by the USDA for 2012 could be due to (1) irrigated acreage for some specific crops increased or decreased substantially during the 4-year interval between 2012 and 2016 because of production or economic changes, (2) the assumption that if an irrigation system was present, it was used in 2016, when in fact some landowners may not have used their irrigation systems during this growing period even if they had a crop in the field, or (3) the amount of irrigated acreage published by the USDA for selected crops may be underestimated as a result of how information is obtained and formulated by the agency during census compilations.

The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

PubMed

Liu, Naisen; Cao, Weixing; Zhu, Yan; Zhang, Jingchao; Pang, Fangrong; Ni, Jun

2015-11-11

Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key indexes for WSN applications. The differences in crop conditions are influenced by the spatial distribution of soil nutrients. If the nutrients are distributed evenly, the crop conditions are expected to be approximately uniform with little difference; on the contrary, there will be great differences in crop conditions. In accordance with the differences in the spatial distribution of soil information in farmland, fuzzy c-means clustering was applied to divide the farmland into several areas, where the soil fertility of each area is nearly uniform. Then the crop growth information in the area could be monitored with complete coverage by deploying a sensor node there, which could greatly decrease the deployed sensor nodes. Moreover, in order to accurately judge the optimal cluster number of fuzzy c-means clustering, a discriminant function for Normalized Intra-Cluster Coefficient of Variation (NICCV) was established. The sensitivity analysis indicates that NICCV is insensitive to the fuzzy weighting exponent, but it shows a strong sensitivity to the number of clusters.

Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information

NASA Astrophysics Data System (ADS)

Peng, Hong-Gang; Wang, Jian-Qiang

2017-11-01

In recent years, sustainable energy crop has become an important energy development strategy topic in many countries. Selecting the most sustainable energy crop is a significant problem that must be addressed during any biofuel production process. The focus of this study is the development of an innovative multi-criteria decision-making (MCDM) method to handle sustainable energy crop selection problems. Given that various uncertain data are encountered in the evaluation of sustainable energy crops, linguistic intuitionistic fuzzy numbers (LIFNs) are introduced to present the information necessary to the evaluation process. Processing qualitative concepts requires the effective support of reliable tools; then, a cloud model can be used to deal with linguistic intuitionistic information. First, LIFNs are converted and a novel concept of linguistic intuitionistic cloud (LIC) is proposed. The operations, score function and similarity measurement of the LICs are defined. Subsequently, the linguistic intuitionistic cloud density-prioritised weighted Heronian mean operator is developed, which served as the basis for the construction of an applicable MCDM model for sustainable energy crop selection. Finally, an illustrative example is provided to demonstrate the proposed method, and its feasibility and validity are further verified by comparing it with other existing methods.

Sequential use of the STICS crop model and of the MACRO pesticide fate model to simulate pesticides leaching in cropping systems.

PubMed

Lammoglia, Sabine-Karen; Moeys, Julien; Barriuso, Enrique; Larsbo, Mats; Marín-Benito, Jesús-María; Justes, Eric; Alletto, Lionel; Ubertosi, Marjorie; Nicolardot, Bernard; Munier-Jolain, Nicolas; Mamy, Laure

2017-03-01

The current challenge in sustainable agriculture is to introduce new cropping systems to reduce pesticides use in order to reduce ground and surface water contamination. However, it is difficult to carry out in situ experiments to assess the environmental impacts of pesticide use for all possible combinations of climate, crop, and soils; therefore, in silico tools are necessary. The objective of this work was to assess pesticides leaching in cropping systems coupling the performances of a crop model (STICS) and of a pesticide fate model (MACRO). STICS-MACRO has the advantage of being able to simulate pesticides fate in complex cropping systems and to consider some agricultural practices such as fertilization, mulch, or crop residues management, which cannot be accounted for with MACRO. The performance of STICS-MACRO was tested, without calibration, from measurements done in two French experimental sites with contrasted soil and climate properties. The prediction of water percolation and pesticides concentrations with STICS-MACRO was satisfactory, but it varied with the pedoclimatic context. The performance of STICS-MACRO was shown to be similar or better than that of MACRO. The improvement of the simulation of crop growth allowed better estimate of crop transpiration therefore of water balance. It also allowed better estimate of pesticide interception by the crop which was found to be crucial for the prediction of pesticides concentrations in water. STICS-MACRO is a new promising tool to improve the assessment of the environmental risks of pesticides used in cropping systems.

Diet of generalist predators reflects effects of cropping period and farming system on extra- and intraguild prey.

PubMed

Roubinet, Eve; Birkhofer, Klaus; Malsher, Gerard; Staudacher, Karin; Ekbom, Barbara; Traugott, Michael; Jonsson, Mattias

2017-06-01

The suppression of agricultural pests by natural enemies, including generalist arthropod predators, is an economically important regulating ecosystem service. Besides pests, generalist predators may also consume non-pest extraguild and intraguild prey, which can affect their impact on pest populations. This may either reduce the impact of generalist predators on pest populations, because they are diverted from pest predation, or increase it, as it helps them survive periods of low pest availability. However, the availability of pest prey and alternative, non-pest prey can vary over the crop growing season and between farming systems, potentially affecting predator-prey interactions and the levels of biological control. We have limited information about how farming systems and environmental variation over the crop growing season influence predator diets. This limits our ability to predict the importance of generalist predators as natural enemies of agricultural pests. Here we utilize molecular gut content analyses to assess detection frequencies of extra- and intraguild prey DNA in generalist predator communities in replicated organically and conventionally managed cereal fields at two key periods of the cropping season for aphid biological control. This is done in order to understand how farming system, crop season, prey availability and predator community composition determine the composition of predator diets. Aphid pests and decomposers (springtails) were equally important prey for generalist predators early in the growing season. Later in the season, the importance of aphid prey increased with increasing aphid densities while springtail predation rates were positively correlated to abundance of this prey at both early and late crop growth stages. Intraguild predation was unidirectional: carabids fed on spiders, whereas spiders rarely fed on carabids. Carabids had higher detection frequencies for the two most common spider families in organically compared to conventionally managed fields. Our study documents that predation by generalist predator communities on aphid pests increases with pest numbers independently of their generally widespread consumption of alternative, non-pest prey. Therefore, conservation strategies in agricultural fields could promote biological control services by promoting high levels of alternative non-pest prey for generalist predator communities. © 2017 by the Ecological Society of America.

Indigenous knowledge, use and on-farm management of enset (Ensete ventricosum (Welw.) Cheesman) diversity in Wolaita, Southern Ethiopia

PubMed Central

2014-01-01

Background Ensete ventricosum (Welw.) Cheesman is a major food security crop in Southern Ethiopia, where it was originally domesticated and during millennia became pivotal crop around which an entire farming system has developed. Although its cultivation is highly localized, the enset-based farming system provides sustenance to more than 20 million people. Precise ethnobotanical information of intra-specific enset diversity and local knowledge on how communities maintain, manage and benefit from enset genetic resources is imperative for the promotion, conservation and improvement of this crop and its farming system. Methods This study was conducted in Southern Ethiopia among the Wolaita 'enset culture' community. The research sample consisted of 270 households from 12 Kebeles (villages) representing three agro-ecological ranges. By establishing Participatory Rural Appraisal (PRA) based interactions and applying ethnobotanical interviewing methods of free-listing and open-ended questionnaires, information on the use and management of enset diversity, and its associated folk-biosystematics, food traditions and material culture was collected and analyzed. Results While enset agriculture is seen as cultural heritage and identity for the Wolaita, enset intra-specific diversity holds scenic, prestige and symbolic values for the household. In the present study we recorded 67 enset landraces under cultivation, and through a comprehensive literature review we identified 28 landraces reported from other areas of Wolaita, but not encountered in our survey. Landraces, identified using 11 descriptors primarily related to agro-morphological traits, are named after perceived places of origin, agro-morphological characteristics and cooking quality attributes. Folk classification of enset is based on its domestication status, 'gender', agro-ecological adaptability and landrace suitability for different food and other uses (fiber, feed, medicinal). Enset as a food crop is used to prepare 10 different dishes in Wolaita, 8 of which are exclusively prepared using enset, and their consumption ranges from daily staple to specialty food in festive occasions and ceremonies. On-farm landrace diversity and richness is guided by household needs; its dynamics is managed through regular propagation, harvesting restrain, control of landrace composition and arrangement in the enset homegardens. Conclusions This study reported on the knowledge system, socio-cultural process and community practices that drive the maintenance of intra-specific on-farm enset diversity in Wolaita, Southern Ethiopia. The information is crucial for developing community based complementary in situ and ex situ conservation strategies to foster conservation of enset genetic resources and associated indigenous knowledge system. PMID:24885715

Representing Extremes in Agricultural Models

NASA Technical Reports Server (NTRS)

Ruane, Alex

2015-01-01

AgMIP and related projects are conducting several activities to understand and improve crop model response to extreme events. This involves crop model studies as well as the generation of climate datasets and scenarios more capable of capturing extremes. Models are typically less responsive to extreme events than we observe, and miss several forms of extreme events. Models also can capture interactive effects between climate change and climate extremes. Additional work is needed to understand response of markets and economic systems to food shocks. AgMIP is planning a Coordinated Global and Regional Assessment of Climate Change Impacts on Agricultural Production and Food Security with an aim to inform the IPCC Sixth Assessment Report.

On estimating the economic value of insectivorous bats: Prospects and priorities for biologists

USGS Publications Warehouse

Boyles, Justin G.; Sole, Catherine L.; Cryan, Paul M.; McCracken, Gary F.

2013-01-01

Bats are among the most economically important nondomesticated mammals in the world. They are well-known pollinators and seed dispersers, but crop pest suppression is probably the most valuable ecosystem service provided by bats. Scientific literature and popular media often include reports of crop pests in the diet of bats and anecdotal or extrapolated estimates of how many insects are eaten by bats. However, quantitative estimates of the ecosystem services provided by bats in agricultural systems are rare, and the few estimates that are available are limited to a single cotton-dominated system in Texas. Despite the tremendous value for conservation and economic security of such information, surprisingly few scientific efforts have been dedicated to quantifying the economic value of bats. Here, we outline the types of information needed to better quantify the value of bats in agricultural ecosystems. Because of the complexity of the ecosystems involved, creative experimental design and innovative new methods will help advance our knowledge in this area. Experiments involving bats in agricultural systems may be needed sooner than later, before population declines associated with white-nose syndrome and wind turbines potentially render them impossible.

Risk Assessment and Stewardship of Bt Crops

EPA Science Inventory

Registration of Bt crops as part of the FIFRA requirements involves the assessment of environmental risk associated with the new crop variety. The assessment analysis stipulates that the seed producer provide clear and unambiguous information relating to certain risk categories a...

Mobile Phenotyping System Using an Aeromotively Stabilized Cable-Driven Robot

NASA Astrophysics Data System (ADS)

Newman, M. B.; Zygielbaum, A. I.

2017-12-01

Agricultural researchers are constantly attempting to generate superior agricultural crops. Whether this means creating crops with greater yield, crops that are more resilient to disease, or crops that can tolerate harsh environments with fewer failures, test plots of these experimental crops must be studied in real-world environments with minimal invasion to determine how they will perform in full-scale agricultural settings. To monitor these crops without interfering with their natural growth, a noninvasive sensor system has been implemented. This system, instituted by the College of Agricultural Sciences and Natural Resources at the University of Nebraska - Lincoln (UNL), uses a system of poles, cables, and winches to support and maneuver a sensor platform above the crops at an outdoor phenotyping site. In this work, we improve upon the UNL outdoor phenotyping system presenting the concept design for a mobile, cable-driven phenotyping system as opposed to a permanent phenotyping facility. One major challenge in large-scale, cable-driven robots is stability of the end-effector. As a result, this mobile system seeks to use a novel method of end-effector stabilization using an onboard rotor drive system, herein referred to as the Instrument Platform Aeromotive Stabilization System (IPASS). A prototype system is developed and analyzed to determine the viability of IPASS.

Diversifying crop rotations with pulses enhances system productivity

PubMed Central

Gan, Yantai; Hamel, Chantal; O’Donovan, John T.; Cutforth, Herb; Zentner, Robert P.; Campbell, Con A.; Niu, Yining; Poppy, Lee

2015-01-01

Agriculture in rainfed dry areas is often challenged by inadequate water and nutrient supplies. Summerfallowing has been used to conserve rainwater and promote the release of nitrogen via the N mineralization of soil organic matter. However, summerfallowing leaves land without any crops planted for one entire growing season, creating lost production opportunity. Additionally, summerfallowing has serious environmental consequences. It is unknown whether alternative systems can be developed to retain the beneficial features of summerfallowing with little or no environmental impact. Here, we show that diversifying cropping systems with pulse crops can enhance soil water conservation, improve soil N availability, and increase system productivity. A 3-yr cropping sequence study, repeated for five cycles in Saskatchewan from 2005 to 2011, shows that both pulse- and summerfallow-based systems enhances soil N availability, but the pulse system employs biological fixation of atmospheric N2, whereas the summerfallow-system relies on ‘mining’ soil N with depleting soil organic matter. In a 3-yr cropping cycle, the pulse system increased total grain production by 35.5%, improved protein yield by 50.9%, and enhanced fertilizer-N use efficiency by 33.0% over the summerfallow system. Diversifying cropping systems with pulses can serve as an effective alternative to summerfallowing in rainfed dry areas. PMID:26424172

Exploring the Potential of TanDEM-X Data in Rice Monitoring

NASA Astrophysics Data System (ADS)

Erten, E.

2015-12-01

In this work, phenological parameters such as growth stage, calendar estimation, crop density and yield estimation for rice fields are estimated employing TanDEM-X data. Currently, crop monitoring is country-dependent. Most countries have databases based on cadastral information and annual farmer inputs. Inaccuracies are coming from wrong or missing farmer declarations and/or coarsely updated cadastral boundary definitions. This leads to inefficient regulation of the market, frauds as well as to ecological risks. An accurate crop calendar is also missing, since farmers provide estimations in advance and there is no efficient way to know the growth status over large plantations. SAR data is of particular interest for these purposes. The proposed method includes two step approach including field detection and phenological state estimation. In the context of precise farming it is substantial to define field borders which are usually changing every cultivation period. Linking the SAR inherit properties to transplanting practice such as irrigation, the spatial database of rice-planted agricultural crops can be updated. Boundaries of agricultural fields will be defined in the database, and assignments of crops and sowing dates will be continuously updated by our monitoring system considering that sowing practice variously changes depending on the field owner decision. To define and segment rice crops, the system will make use of the fact that rice fields are characterized as flooded parcels separated by path networks composed by soil or rare grass. This natural segmentation is well detectable by inspecting low amplitude and coherence values of bistatic acquisitions. Once the field borders are defined, the phenology estimation of crops monitored at any time is the key point of monitoring. In this aspect the wavelength and the polarization option of TanDEM-X are enough to characterize the small phenological changes. The combination of bistatic interferometry and Radiative Transfer Theory (RTT) with different polarization provides a realistic description of plants including their full morphology (stalks, tillers, leaves and panicles).

Mapping and monitoring of crop intensity, calendar and irrigation using multi-temporal MODIS data

NASA Astrophysics Data System (ADS)

Xiao, X.; Boes, S.; Mulukutla, G.; Proussevitch, A.; Routhier, M.

2005-12-01

Agriculture is the most extensive land use and water use on the Earth. Because of the diverse range of natural environments and human needs, agriculture is also the most complicated land use and water use system, which poses an enormous challenge to the scientific community, the public and decision-makers. Updated and geo-referenced information on crop intensity (number of crops per year), calendar (planting date, harvesting date) and irrigation is critically needed to better understand the impacts of agriculture on biogeochemical cycles (e.g., carbon, nitrogen, trace gases), water and climate dynamics. Here we present an effort to develop a novel approach for mapping and monitoring crop intensity, calendar and irrigation, using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) image data. Our algorithm employed three vegetation indices that are sensitive to the seasonal dynamics of leaf area index, light absorption by leaf chlorophyll and land surface water content. Our objective is to generate geospatial databases of crop intensity, calendar and irrigation at 500-m spatial resolution and at 8-day temporal resolution. In this presentation, we report a preliminary geospatial dataset of paddy rice crop intensity, calendar and irrigation in Asia, which is developed from the 8-day composite images of MODIS in 2002. The resultant dataset could be used in many applications, including hydrological and climate modeling.

Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

NASA Astrophysics Data System (ADS)

Multsch, S.; Exbrayat, J.-F.; Kirby, M.; Viney, N. R.; Frede, H.-G.; Breuer, L.

2015-04-01

Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray-Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.

Production versus environmental impact trade-offs for Swiss cropping systems: a model-based approach

NASA Astrophysics Data System (ADS)

Necpalova, Magdalena; Lee, Juhwan; Six, Johan

2017-04-01

There is a growing need to improve sustainability of agricultural systems. The key focus remains on optimizing current production systems in order to deliver food security at low environmental costs. It is therefore essential to identify and evaluate agricultural management practices for their potential to maintain or increase productivity and mitigate climate change and N pollution. Previous research on Swiss cropping systems has been concentrated on increasing crop productivity and soil fertility. Thus, relatively little is known about management effects on net soil greenhouse gas (GHG) emissions and environmental N losses in the long-term. The aim of this study was to extrapolate findings from Swiss long-term field experiments and to evaluate the system-level sustainability of a wide range of cropping systems under conditions beyond field experimentation by comparing their crop productivity and impacts on soil carbon, net soil GHG emissions, NO3 leaching and soil N balance over 30 years. The DayCent model was previously parameterized for common Swiss crops and crop-specific management practices and evaluated for productivity, soil carbon dynamics and N2O emissions from Swiss cropping systems. Based on a prediction uncertainty criterion for crop productivity and soil carbon (rRMSE<0.3), in total 39 cropping systems were selected. Each system was evaluated under soil and climate conditions representative of Therwil, Frick, Reckenholz and Changins sites with four replications. Soil inputs were sampled from normal probability distributions defined by available site-specific data using the Latin hypercube sampling method. Net soil GHG emissions were derived from changes in soil carbon, N2O emissions and CH4 oxidation and the annual net global warming potential (GWP) was calculated using IPCC (2014). For statistical analyses, the systems were grouped into the following categories: (a) farming system: organic (ORG), integrated (IN) and mineral (MIN); (b) tillage: conventional (CT), reduced (RT) and no-till (NT); (c) cover cropping: no cover cropping (NCC), winter cover cropping (CC) and winter green manuring (GM). The productivity of Swiss cropping systems was mainly driven by total N inputs to the systems. The GWP of systems ranged from -450 to 1309 kg CO2 eq ha-1 yr-1. All studied systems, except for ORG-RT-GM systems, acted as a source of net soil GHG emissions with the relative contribution of soil N2O emissions to GWP of more than 60%. The GWP of systems with CT decreased consistently with increasing use of organic manures (MIN>IN>ORG). NT relative to RT management showed to be more effective in reducing GWP from MIN systems due to reduced soil N2O emissions and positive effects on soil C sequestration. GM relative to CC management was shown to be more effective in mitigating NO3 leaching and overall N losses from MIN systems; particularly in combination with NT management. GM management also increased soil N balance of MIN and ORG systems relative to CC management, which caused an additional N removal through CC harvest. Our results suggest that there is a substantial potential for improvement and optimizing the sustainability of Swiss cropping systems across sites especially in the context of climate change mitigation and adaptation.

FCDD: A Database for Fruit Crops Diseases.

PubMed

Chauhan, Rupal; Jasrai, Yogesh; Pandya, Himanshu; Chaudhari, Suman; Samota, Chand Mal

2014-01-01

Fruit Crops Diseases Database (FCDD) requires a number of biotechnology and bioinformatics tools. The FCDD is a unique bioinformatics resource that compiles information about 162 details on fruit crops diseases, diseases type, its causal organism, images, symptoms and their control. The FCDD contains 171 phytochemicals from 25 fruits, their 2D images and their 20 possible sequences. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, textbooks and scientific journals. FCDD is fully searchable and supports extensive text search. The main focus of the FCDD is on providing possible information of fruit crops diseases, which will help in discovery of potential drugs from one of the common bioresource-fruits. The database was developed using MySQL. The database interface is developed in PHP, HTML and JAVA. FCDD is freely available. http://www.fruitcropsdd.com/

Systems biology approach in plant abiotic stresses.

PubMed

Mohanta, Tapan Kumar; Bashir, Tufail; Hashem, Abeer; Abd Allah, Elsayed Fathi

2017-12-01

Plant abiotic stresses are the major constraint on plant growth and development, causing enormous crop losses across the world. Plants have unique features to defend themselves against these challenging adverse stress conditions. They modulate their phenotypes upon changes in physiological, biochemical, molecular and genetic information, thus making them tolerant against abiotic stresses. It is of paramount importance to determine the stress-tolerant traits of a diverse range of genotypes of plant species and integrate those traits for crop improvement. Stress-tolerant traits can be identified by conducting genome-wide analysis of stress-tolerant genotypes through the highly advanced structural and functional genomics approach. Specifically, whole-genome sequencing, development of molecular markers, genome-wide association studies and comparative analysis of interaction networks between tolerant and susceptible crop varieties grown under stress conditions can greatly facilitate discovery of novel agronomic traits that protect plants against abiotic stresses. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Analysis of Biomass Feedstock Availability and Variability for the Peace River Region of Alberta, Canada

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

Stephen, Jamie; Sokhansanj, Shahabaddine; Bi, X.T.

2009-11-01

Biorefineries or other biomass-dependent facilities require a predictable, dependable feedstock supplied over many years to justify capital investments. Determining inter-year variability in biomass availability is essential to quantifying the feedstock supply risk. Using a geographic information system (GIS) and historic crop yield data, average production was estimated for 10 sites in the Peace River region of Alberta, Canada. Four high-yielding potential sites were investigated for variability over a 20 year time-frame (1980 2000). The range of availability was large, from double the average in maximum years to nothing in minimum years. Biomass availability is a function of grain yield, themore » biomass to grain ratio, the cropping frequency, and residue retention rate to ensure future crop productivity. Storage strategies must be implemented and alternate feedstock sources identified to supply biomass processing facilities in low-yield years.« less

Current situation of pests targeted by Bt crops in Latin America.

PubMed

Blanco, C A; Chiaravalle, W; Dalla-Rizza, M; Farias, J R; García-Degano, M F; Gastaminza, G; Mota-Sánchez, D; Murúa, M G; Omoto, C; Pieralisi, B K; Rodríguez, J; Rodríguez-Maciel, J C; Terán-Santofimio, H; Terán-Vargas, A P; Valencia, S J; Willink, E

2016-06-01

Transgenic crops producing Bacillus thuringiensis- (Bt) insecticidal proteins (Bt crops) have provided useful pest management tools to growers for the past 20 years. Planting Bt crops has reduced the use of synthetic insecticides on cotton, maize and soybean fields in 11 countries throughout Latin America. One of the threats that could jeopardize the sustainability of Bt crops is the development of resistance by targeted pests. Governments of many countries require vigilance in measuring changes in Bt-susceptibility in order to proactively implement corrective measures before Bt-resistance is widespread, thus prolonging the usefulness of Bt crops. A pragmatic approach to obtain information on the effectiveness of Bt-crops is directly asking growers, crop consultants and academics about Bt-resistance problems in agricultural fields, first-hand information that not necessarily relies on susceptibility screens performed in laboratories. This type of information is presented in this report. Problematic pests of cotton and soybeans in five Latin American countries currently are effectively controlled by Bt crops. Growers that plant conventional (non-Bt) cotton or soybeans have to spray synthetic insecticides against multiple pests that otherwise are controlled by these Bt crops. A similar situation has been observed in six Latin American countries where Bt maize is planted. No synthetic insecticide applications are used to control corn pests because they are controlled by Bt maize, with the exception of Spodoptera frugiperda. While this insect in some countries is still effectively controlled by Bt maize, in others resistance has evolved and necessitates supplemental insecticide applications and/or the use of Bt maize cultivars that express multiple Bt proteins. Partial control of S. frugiperda in certain countries is due to its natural tolerance to the Bt bacterium. Of the 31 pests targeted and controlled by Bt crops in Latin America, only S. frugiperda has shown tolerance to certain Bt proteins in growers' fields, the most reliable indication of the status of Bt-susceptibility in most of the American continent. Copyright © 2016 Elsevier Inc. All rights reserved.

Definition and feasibility of isolation distances for transgenic maize cultivation.

PubMed

Sanvido, Olivier; Widmer, Franco; Winzeler, Michael; Streit, Bernhard; Szerencsits, Erich; Bigler, Franz

2008-06-01

A major concern related to the adoption of genetically modified (GM) crops in agricultural systems is the possibility of unwanted GM inputs into non-GM crop production systems. Given the increasing commercial cultivation of GM crops in the European Union (EU), there is an urgent need to define measures to prevent mixing of GM with non-GM products during crop production. Cross-fertilization is one of the various mechanisms that could lead to GM-inputs into non-GM crop systems. Isolation distances between GM and non-GM fields are widely accepted to be an effective measure to reduce these inputs. However, the question of adequate isolation distances between GM and non-GM maize is still subject of controversy both amongst scientists and regulators. As several European countries have proposed largely differing isolation distances for maize ranging from 25 to 800 m, there is a need for scientific criteria when using cross-fertilization data of maize to define isolation distances between GM and non-GM maize. We have reviewed existing cross-fertilization studies in maize, established relevant criteria for the evaluation of these studies and applied these criteria to define science-based isolation distances. To keep GM-inputs in the final product well below the 0.9% threshold defined by the EU, isolation distances of 20 m for silage and 50 m for grain maize, respectively, are proposed. An evaluation using statistical data on maize acreage and an aerial photographs assessment of a typical agricultural landscape by means of Geographic Information Systems (GIS) showed that spatial resources would allow applying the defined isolation distances for the cultivation of GM maize in the majority of the cases under actual Swiss agricultural conditions. The here developed approach, using defined criteria to consider the agricultural context of maize cultivation, may be of assistance for the analysis of cross-fertilization data in other countries.

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...

Intensifying a semi-arid dryland crop rotation by replacing fallow with pea

USDA-ARS?s Scientific Manuscript database

Increasing dryland cropping system intensity in the semi-arid central Great Plains by reducing frequency of fallow can add diversity to cropping systems and decrease erosion potential. However elimination of the periodic fallow phase has been shown to reduce yields of subsequent crops in this region...

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...

The Asia-RiCE activity with data cube

NASA Astrophysics Data System (ADS)

Oyoshi, K.; Sobue, S.; LE Toan, T.; Lam, N. D.

2017-12-01

The Asia-RiCE initiative (http://www.asia-rice.org) has been organized to enhance rice production estimates through the use of Earth observation satellites data, and seeks to ensure that Asian rice crops are appropriately represented within GEO Global Agriculture Monitoring (GEO-GLAM) to support FAO Agriculture Market Information System (FAO-AMIS). Asia-RiCE is composed of national teams that are actively contributing to the Crop Monitor for AMIS and developing technical demonstrations of rice crop monitoring activities using both Synthetic Aperture Radar (SAR) data (Radarsat-2 from 2013; Sentinel-1 and ALOS-2 from 2015.From 2016 after the successful rice crop area and growing estimation using SAR in a technical demonstration site (provincial level), wall-to-wall (national scale) excurse as phase 2 has been implemented in Vietnam and Indonesia in cooperation with ministry of agriculture and space agencies. This paper reports this year activity of 2017 accomplishment and way forward, especially for analysis ready data (ARD) definition of SAR to ingest to CEOS data cube to provide national scale service in Vietnam and Indonesia.

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

NASA Astrophysics Data System (ADS)

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

2012-10-01

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

Soil total carbon and nitrogen and crop yields after eight years of tillage, crop rotation, and cultural practice

USDA-ARS?s Scientific Manuscript database

Information on the long-term effect of management practices on soil C and N stocks is lacking. An experiment was conducted from 2004 to 2011 in the northern Great Plains, USA to examine the effects of tillage, crop rotation, and cultural practice on annualized crop biomass (stems + leaves) residue r...

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.

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

NASA Astrophysics Data System (ADS)

Chen, Bin

2018-04-01

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

Earth benefits of interdisciplinary CELSS-related research by the NSCORT in Bioregenerative Life Support

NASA Technical Reports Server (NTRS)

Mitchell, C.; Sherman, L.; Nielsen, S.; Nelson, P.; Trumbo, P.; Hodges, T.; Hasegawa, P.; Bressan, R.; Ladisch, M.; Auslander, D.

1996-01-01

Earth benefits of research from the NSCORT in Bioregenerative Life Support will include the following: development of active control mechanisms for light, CO2, and temperature to maximize photosynthesis of crop plants during important phases of crop development; automation of crop culture systems; creation of novel culture systems for optimum productivity; creation of value-added crops with superior nutritional, yield, and waste-process characteristics; environmental control of food and toxicant composition of crops; new process technologies and novel food products for safe, nutritious, palatable vegetarian diets; creation of menus for healthful vegetarian diets with psychological acceptability; enzymatic procedures to degrade recalcitrant crop residues occurring in municipal waste; control-system strategies to ensure sustainabilty of a CELSS that will enable management of diverse complex systems on Earth.

Earth benefits of interdisciplinary celss-related research by the NSCORT in Bioregenerative Life Support

NASA Astrophysics Data System (ADS)

Mitchell, C.; Sherman, L.; Nielsen, S.; Nelson, P.; Trumbo, P.; Hodges, T.; Hasegawa, P.; Bressan, R.; Ladisch, M.; Auslander, D.

Earth benefits of research from the NSCORT in Bioregenerative Life Support will include the following: development of active control mechanisms for light, CO_2, and temperature to maximize photosynthesis of crop plants during important phases of crop development; automation of crop culture systems; creation of novel culture systems for optimum productivity; creation of value-added crops with superior nutritional, yield, and waste-process characteristics; environmental control of food and toxicant composition of crops; new process technologies and novel food products for safe, nutritious, palatable vegetarian diets; creation of menus for healthful vegetarian diets with psychological acceptability; enzymatic procedures to degrade recalcitrant crop residues occurring in municipal waste; control-system strategies to ensure sustainability of a CELSS that will enable management of diverse complex systems on Earth.

Earth benefits of interdisciplinary CELSS-related research by the NSCORT in Bioregenerative Life Support.

PubMed

Mitchell, C; Sherman, L; Nielsen, S; Nelson, P; Trumbo, P; Hodges, T; Hasegawa, P; Bressan, R; Ladisch, M; Auslander, D

1996-01-01

Earth benefits of research from the NSCORT in Bioregenerative Life Support will include the following: development of active control mechanisms for light, CO2, and temperature to maximize photosynthesis of crop plants during important phases of crop development; automation of crop culture systems; creation of novel culture systems for optimum productivity; creation of value-added crops with superior nutritional, yield, and waste-process characteristics; environmental control of food and toxicant composition of crops; new process technologies and novel food products for safe, nutritious, palatable vegetarian diets; creation of menus for healthful vegetarian diets with psychological acceptability; enzymatic procedures to degrade recalcitrant crop residues occurring in municipal waste; control-system strategies to ensure sustainabilty of a CELSS that will enable management of diverse complex systems on Earth.

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...

Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

USGS Publications Warehouse

Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

2016-01-01

Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.

Using dual-purpose crops in sheep-grazing systems.

PubMed

Dove, Hugh; Kirkegaard, John

2014-05-01

The utilisation of dual-purpose crops, especially wheat and canola grown for forage and grain production in sheep-grazing systems, is reviewed. When sown early and grazed in winter before stem elongation, later-maturing wheat and canola crops can be grazed with little impact on grain yield. Recent research has sought to develop crop- and grazing-management strategies for dual-purpose crops. Aspects examined have been grazing effects on crop growth, recovery and yield development along with an understanding of the grazing value of the crop fodder, its implications for animal nutrition and grazing management to maximise live-weight gain. By alleviating the winter 'feed gap', the increase in winter stocking rate afforded by grazing crops allows crop and livestock production to be increased simultaneously on the same farm. Integration of dual-purpose wheat with canola on mixed farms provides further systems advantages related to widened operational windows, weed and disease control and risk management. Dual-purpose crops are an innovation that has potential to assist in addressing the global food-security challenge. © 2013 Society of Chemical Industry.

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Beneficial reuse and sustainability: the fate of organic compounds in land-applied waste.

PubMed

Overcash, Michael; Sims, Ronald C; Sims, Judith L; Nieman, J Karl C

2005-01-01

Land application systems, also referred to as beneficial reuse systems, are engineered systems that have defined and permitted application areas based on site and waste characteristics to determine the land area size requirement. These terrestrial systems have orders of magnitude greater microbial capability and residence time to achieve decomposition and assimilation compared with aquatic systems. In this paper we focus on current information and information needs related to terrestrial fate pathways in land treatment systems. Attention is given to conventional organic chemicals as well as new estrogenic and pharmaceutical chemicals of commerce. Specific terrestrial fate pathways addressed include: decomposition, bound residue formation, leaching, runoff, and crop uptake. Molecular decomposition and formation of bound residues provide the basis for the design and regulation of land treatment systems. These mechanisms allow for assimilation of wastes and nondegradation of the environment and accomplish the goal of sustainable land use. Bound residues that are biologically produced are relatively immobile, degrade at rates similar to natural soil materials, and should present a significantly reduced risk to the environment as opposed to parent contaminants. With regard to leaching and runoff pathways, no comprehensive summary or mathematical model of organic chemical migration from land treatment systems has been developed. For the crop uptake pathway, a critical need exists to develop information for nonagricultural chemicals and to address full-scale performance and monitoring at more land application sites. The limited technology choices for treatment of biosolids, liquids, and other wastes implies that acceptance of some risks and occurrence of some benefits will continue to characterize land application practices that contribute directly to the goal of beneficial reuse and sustainability.

Crop water use measurement using a weighing lysimeter at the Dayr Alla Research Station in the Jordan Valley, Jordan

USDA-ARS?s Scientific Manuscript database

Since 2003, a regional project funded by USDA-ARS-OIRP has focused on improving irrigation scheduling in Jordan, Palestine and Israel. The Middle Eastern Regional Irrigation Management Information Systems (MERMIS) project involves cooperators from Palestine, Jordan, Israel and the United States, all...

The effects of anaerobic soil disinfestation on weed and nematode control, fruit yield and quality of Florida fresh-market tomato

USDA-ARS?s Scientific Manuscript database

Anaerobic soil disinfestation (ASD) is considered a promising sustainable alternative to chemical soil fumigation (CSF), and has been shown to be effective against soil-borne diseases, plant-parasitic nematodes, and weeds in several crop production systems. Nevertheless, limited information is avail...

A Description and Source Listing of Curriculum Materials in Agricultural Education, 1969-1970.

ERIC Educational Resources Information Center

American Vocational Association, Washington, DC. Agricultural Education Div.

The purpose of this annotated bibliography is to provide teachers of vocational agriculture, agricultural supervisors, and agricultural teacher educators with information on current curriculum materials available to them. Classified according to the AGDEX filing system, the 163 references are grouped under the headings: (1) Field Crops, (2)…

GRL-FLUXNET: A network of eddy covariance systems in the southern great plains

USDA-ARS?s Scientific Manuscript database

Information on exchange of energy, carbon dioxide (CO2), and water vapor (H2O) for major terrestrial ecosystems is vital to quantify carbon and water budgets to develop, evaluate, and enhance hydrologic and crop simulation models and to better understand the potential of terrestrial ecosystems to mi...

Reliability-Productivity Curve, a Tool for Adaptation Measures Identification

NASA Astrophysics Data System (ADS)

Chávez-Jiménez, A.; Granados, A.; Garrote, L. M.

2015-12-01

Due to climate change effects, water scarcity problems would intensify in several regions. These problems are going to impact negatively in the water low-priority demands, since these will be reduced in favor of those with high-priority. An example would be the reduction of agriculture water resources in favor of the urban ones. Then, it is important the evaluation of adaptation measures for a better water resources management. An important tool to face this challenge is the economic valuation of the water demands' impact within a water resources system. In agriculture this valuation is usually performed through the water productivity evaluation. The water productivity evaluation requires detailed information regarding the different crops like the applied technology, the agricultural supplies management, the water availability, etc. This is a restriction for an evaluation at basin scale due to the difficulty of gathers this level of detailed information. Besides, only the water availability is taken into account, but not the period when the water is distributed (i.e. water resources reliability). Water resources reliability is one of the most important variables in water resources management. This research proposes a methodology to determine the agriculture water productivity, using as variables the crops information, the crops price, the water resources availability, and the water resources reliability, at a basin scale. This methodology would allow identifying general water resources adaptation measures, providing the basis for further detailed studies in critical regions.

Greenhouse gas emissions from traditional and biofuels cropping systems

USDA-ARS?s Scientific Manuscript database

Cropping systems can have a tremendous effect on the greenhouse gas emissions from soils. The objectives of this study were to compare greenhouse gas emissions from traditional (continuous corn or corn/soybean rotation) and biomass (miscanthus, sorghum, switchgrass) cropping systems. Biomass croppin...

A Systematic Review of Perennial Staple Crops Literature Using Topic Modeling and Bibliometric Analysis

PubMed Central

2016-01-01

Research on perennial staple crops has increased in the past ten years due to their potential to improve ecosystem services in agricultural systems. However, multiple past breeding efforts as well as research on traditional ratoon systems mean there is already a broad body of literature on perennial crops. In this review, we compare the development of research on perennial staple crops, including wheat, rice, rye, sorghum, and pigeon pea. We utilized the advanced search capabilities of Web of Science, Scopus, ScienceDirect, and Agricola to gather a library of 914 articles published from 1930 to the present. We analyzed the metadata in the entire library and in collections of literature on each crop to understand trends in research and publishing. In addition, we applied topic modeling to the article abstracts, a type of text analysis that identifies frequently co-occurring terms and latent topics. We found: 1.) Research on perennials is increasing overall, but individual crops have each seen periods of heightened interest and research activity; 2.) Specialist journals play an important role in supporting early research efforts. Research often begins within communities of specialists or breeders for the individual crop before transitioning to a more general scientific audience; 3.) Existing perennial agricultural systems and their domesticated crop material, such as ratoon rice systems, can provide a useful foundation for breeding efforts, accelerating the development of truly perennial crops and farming systems; 4.) Primary research is lacking for crops that are produced on a smaller scale globally, such as pigeon pea and sorghum, and on the ecosystem service benefits of perennial agricultural systems. PMID:27213283

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

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

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi

2014-04-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m -2 yr -1 and total NPP in the range of 318–490more » Tg C yr -1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m -2 yr -1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m -2 yr -1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. Finally, we suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.« less

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

USGS Publications Warehouse

Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi; Bliss, Norman B.; Young, Claudia J.; West, Tristram O.; Ogle, Stephen M.

2014-01-01

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m−2 yr−1and total NPP in the range of 318–490 Tg C yr−1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m−2 yr−1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m−2 yr−1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

[The public perception of information about the potential risks of genetically modified crops in the food chain].

PubMed

Furnival, Ariadne Chloë; Pinheiro, Sônia Maria

2008-01-01

At a time when genetically modified (GM) crops are entering the Brazilian food chain, we present the findings of a study that makes use of a qualitative technique involving focal groups to look into the public's interpretation of the information available about this biotechnological innovation. This methodology produced results that revealed the interconnections drawn by the research subjects between this form of biotechnology, changes to the environment, and food production in general. The mistrust expressed about GM crops was particularly attributed by the participants to the non-availability of comprehensible information in the mass media or on product labels.

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.

GBIS: the information system of the German Genebank

PubMed Central

Oppermann, Markus; Weise, Stephan; Dittmann, Claudia; Knüpffer, Helmut

2015-01-01

The German Federal ex situ Genebank of Agricultural and Horticultural Crop Species is the largest collection of its kind in the countries of the European Union and amongst the 10 largest collections worldwide. Beside its enormous scientific value as a safeguard of plant biodiversity, the plant genetic resources maintained are also of high importance for breeders to provide new impulses. The complex processes of managing such a collection are supported by the Genebank Information System (GBIS). GBIS is an important source of information for researchers and plant breeders, e.g. for identifying appropriate germplasm for breeding purposes. In addition, the access to genebank material as a sovereign task is also of high interest to the general public. Moreover, GBIS acts as a data source for global information systems, such as the Global Biodiversity Information Facility (GBIF) or the European Search Catalogue for Plant Genetic Resources (EURISCO). Database URL: http://gbis.ipk-gatersleben.de/ PMID:25953079

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

Application of remote sensing to estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Kiefer, R. W.

1980-01-01

A variety of remote sensing data sources and interpretation techniques has been tested in a 6136 hectare watershed with agricultural, forest and urban land cover to determine the relative utility of alternative aerial photographic data sources for gathering the desired land use/land cover data. The principal photographic data sources are high altitude 9 x 9 inch color infrared photos at 1:120,000 and 1:60,000 and multi-date medium altitude color and color infrared photos at 1:60,000. Principal data for estimating soil erosion potential include precipitation, soil, slope, crop, crop practice, and land use/land cover data derived from topographic maps, soil maps, and remote sensing. A computer-based geographic information system organized on a one-hectare grid cell basis is used to store and quantify the information collected using different data sources and interpretation techniques. Research results are compared with traditional Universal Soil Loss Equation field survey methods.

Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

NASA Astrophysics Data System (ADS)

Ovando, Gustavo; Sayago, Silvina; Bocco, Mónica

2018-04-01

Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006-2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.

Soil greenhouse gas emissions affected by irrigation, tillage, crop rotation, and nitrogen fertilization.

PubMed

Sainju, Upendra M; Stevens, William B; Caesar-Tonthat, Thecan; Liebig, Mark A

2012-01-01

Management practices, such as irrigation, tillage, cropping system, and N fertilization, may influence soil greenhouse gas (GHG) emissions. We quantified the effects of irrigation, tillage, crop rotation, and N fertilization on soil CO, NO, and CH emissions from March to November, 2008 to 2011 in a Lihen sandy loam in western North Dakota. Treatments were two irrigation practices (irrigated and nonirrigated) and five cropping systems (conventional-tilled malt barley [ L.] with N fertilizer [CT-N], conventional-tilled malt barley with no N fertilizer [CT-C], no-tilled malt barley-pea [ L.] with N fertilizer [NT-PN], no-tilled malt barley with N fertilizer [NT-N], and no-tilled malt barley with no N fertilizer [NT-C]). The GHG fluxes varied with date of sampling and peaked immediately after precipitation, irrigation, and/or N fertilization events during increased soil temperature. Both CO and NO fluxes were greater in CT-N under the irrigated condition, but CH uptake was greater in NT-PN under the nonirrigated condition than in other treatments. Although tillage and N fertilization increased CO and NO fluxes by 8 to 30%, N fertilization and monocropping reduced CH uptake by 39 to 40%. The NT-PN, regardless of irrigation, might mitigate GHG emissions by reducing CO and NO emissions and increasing CH uptake relative to other treatments. To account for global warming potential for such a practice, information on productions associated with CO emissions along with NO and CH fluxes is needed. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Reconciling pesticide reduction with economic and environmental sustainability in arable farming.

PubMed

Lechenet, Martin; Bretagnolle, Vincent; Bockstaller, Christian; Boissinot, François; Petit, Marie-Sophie; Petit, Sandrine; Munier-Jolain, Nicolas M

2014-01-01

Reducing pesticide use is one of the high-priority targets in the quest for a sustainable agriculture. Until now, most studies dealing with pesticide use reduction have compared a limited number of experimental prototypes. Here we assessed the sustainability of 48 arable cropping systems from two major agricultural regions of France, including conventional, integrated and organic systems, with a wide range of pesticide use intensities and management (crop rotation, soil tillage, cultivars, fertilization, etc.). We assessed cropping system sustainability using a set of economic, environmental and social indicators. We failed to detect any positive correlation between pesticide use intensity and both productivity (when organic farms were excluded) and profitability. In addition, there was no relationship between pesticide use and workload. We found that crop rotation diversity was higher in cropping systems with low pesticide use, which would support the important role of crop rotation diversity in integrated and organic strategies. In comparison to conventional systems, integrated strategies showed a decrease in the use of both pesticides and nitrogen fertilizers, they consumed less energy and were frequently more energy efficient. Integrated systems therefore appeared as the best compromise in sustainability trade-offs. Our results could be used to re-design current cropping systems, by promoting diversified crop rotations and the combination of a wide range of available techniques contributing to pest management.

Reconciling Pesticide Reduction with Economic and Environmental Sustainability in Arable Farming

PubMed Central

Lechenet, Martin; Bretagnolle, Vincent; Bockstaller, Christian; Boissinot, François; Petit, Marie-Sophie; Petit, Sandrine; Munier-Jolain, Nicolas M.

2014-01-01

Reducing pesticide use is one of the high-priority targets in the quest for a sustainable agriculture. Until now, most studies dealing with pesticide use reduction have compared a limited number of experimental prototypes. Here we assessed the sustainability of 48 arable cropping systems from two major agricultural regions of France, including conventional, integrated and organic systems, with a wide range of pesticide use intensities and management (crop rotation, soil tillage, cultivars, fertilization, etc.). We assessed cropping system sustainability using a set of economic, environmental and social indicators. We failed to detect any positive correlation between pesticide use intensity and both productivity (when organic farms were excluded) and profitability. In addition, there was no relationship between pesticide use and workload. We found that crop rotation diversity was higher in cropping systems with low pesticide use, which would support the important role of crop rotation diversity in integrated and organic strategies. In comparison to conventional systems, integrated strategies showed a decrease in the use of both pesticides and nitrogen fertilizers, they consumed less energy and were frequently more energy efficient. Integrated systems therefore appeared as the best compromise in sustainability trade-offs. Our results could be used to re-design current cropping systems, by promoting diversified crop rotations and the combination of a wide range of available techniques contributing to pest management. PMID:24887494

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

NASA Astrophysics Data System (ADS)

Manfron, Giacinto; Delmotte, Sylvestre; Busetto, Lorenzo; Hossard, Laure; Ranghetti, Luigi; Brivio, Pietro Alessandro; Boschetti, Mirco

2017-05-01

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002-2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.

Crop and livestock enterprise integration: Effects of annual crops used for fall forage production on livestock productivity

USDA-ARS?s Scientific Manuscript database

Diversification of farm enterprises is important to maintain sustainable production systems. Systems that integrate crops and livestock may prove beneficial to each enterprise. Our objectives were to determine the effects of annual crops grazed in the fall and early-winter period on cow and calf gro...

Long-term impacts of cropping systems and landscape positions on grain crop production on claypan soils

USDA-ARS?s Scientific Manuscript database

Sustainable grain crop production on vulnerable claypan soils requires improved knowledge of long-term impacts of conservation cropping systems (CS) with reduced inputs. Therefore, effects of CS and landscape positions (LP) on corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and wheat (Triticum...

Cover Crop Chart: An intuitive educational resource for extension professionals

USDA-ARS?s Scientific Manuscript database

Interest in cover crops by agricultural producers has increased the need for information regarding the suitability of crops for addressing different production and natural resource goals. To help address this need, staff at the USDA Agricultural Research Service Northern Great Plains Research Labor...

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.

Characteristics of nitrogen balance in open-air and greenhouse vegetable cropping systems of China.

PubMed

Ti, Chaopu; Luo, Yongxia; Yan, Xiaoyuan

2015-12-01

Nitrogen (N) loss from vegetable cropping systems has become a significant environmental issue in China. In this study, estimation of N balances in both open-air and greenhouse vegetable cropping systems in China was established. Results showed that the total N input in open-air and greenhouse vegetable cropping systems in 2010 was 5.44 and 2.60 Tg, respectively. Chemical fertilizer N input in the two cropping systems was 201 kg N ha(-1) per season (open-air) and 478 kg N ha(-1) per season (greenhouse). The N use efficiency (NUE) was 25.9 ± 13.3 and 19.7 ± 9.4% for open-air and greenhouse vegetable cropping systems, respectively, significantly lower than that of maize, wheat, and rice. Approximately 30.6% of total N input was accumulated in soils and 0.8% was lost by ammonia volatilization in greenhouse vegetable system, while N accumulation and ammonia volatilization accounted for 19.1 and 11.1%, respectively, of total N input in open-air vegetable systems.

The Role of Crop Systems Simulation in Agriculture and Environment

USDA-ARS?s Scientific Manuscript database

Over the past 30 to 40 years, simulation of crop systems has advanced from a neophyte science with inadequate computing power into a robust and increasingly accepted science supported by improved software, languages, development tools, and computer capabilities. Crop system simulators contain mathe...

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

NASA Astrophysics Data System (ADS)

Brown, Robert Douglas

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

Predictive spatial modeling of narcotic crop growth patterns

USGS Publications Warehouse

Waltz, Frederick A.; Moore, D.G.

1986-01-01

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement. The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization. The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

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.

Intraspecific variation of host plant and locality influence the lepidopteran-parasitoid system of Brassica oleracea crops.

PubMed

Santolamazza-Carbone, S; Velasco, P; Selfa, J; Soengas, P; Cartea, M E

2013-06-01

The aim of the study was to investigate the attractiveness to herbivores and parasitoids of two cultivars of Brassica oleracea L., namely, B. oleracea variety acephala (kale) and B. oleracea variety capitata (cabbage), that exhibit differences of morphological and biochemical traits. To this end, field samplings were replicated at seven localities in Galicia (northwestern Spain). Three specialist and three generalist lepidopteran species were sampled. In total, 7,050 parasitoids were obtained, belonging to 18 genera and 22 species. The results showed that 1) parasitism rate and parasitoid species richness changed with locality and was higher in cabbage, although this crop had lower herbivore abundance; 2) the proportion of specialist herbivores was higher in cabbage crops, whereas generalists dominated in kale crops; 3) the abundance of the parasitoids Telenomus sp. (Hymenoptera, Scelionidae), Cotesia glomerata L. (Hymenoptera: Braconidae), and Diadegma fenestrale (Holmgren) (Hymenoptera: Ichneumonidae) was higher in kale crops; and 4) parasitism rate of Pieris rapae larvae and pupae and Mamestra brassicae eggs were higher in kale crops. In contrast with the notion that plant structural complexity provides physical refuge to the hosts and can interfere with parasitoid foraging, parasitism rate was higher on cabbage plants, which form heads of overlapped leaves. Possibly, different chemical profiles of cultivars also influenced the host-parasitoid relationship. These results suggest that top-down and bottom-up forces may enhance cabbage crops to better control herbivore pressure during the studied season. In Spain, information on natural occurring parasitoid guilds of Brassica crops is still scarce. The data provided here also represent a critical first step for conservation biological control plans of these cultivations.

Crops in silico: A community wide multi-scale computational modeling framework of plant canopies

NASA Astrophysics Data System (ADS)

Srinivasan, V.; Christensen, A.; Borkiewic, K.; Yiwen, X.; Ellis, A.; Panneerselvam, B.; Kannan, K.; Shrivastava, S.; Cox, D.; Hart, J.; Marshall-Colon, A.; Long, S.

2016-12-01

Current crop models predict a looming gap between supply and demand for primary foodstuffs over the next 100 years. While significant yield increases were achieved in major food crops during the early years of the green revolution, the current rates of yield increases are insufficient to meet future projected food demand. Furthermore, with projected reduction in arable land, decrease in water availability, and increasing impacts of climate change on future food production, innovative technologies are required to sustainably improve crop yield. To meet these challenges, we are developing Crops in silico (Cis), a biologically informed, multi-scale, computational modeling framework that can facilitate whole plant simulations of crop systems. The Cis framework is capable of linking models of gene networks, protein synthesis, metabolic pathways, physiology, growth, and development in order to investigate crop response to different climate scenarios and resource constraints. This modeling framework will provide the mechanistic details to generate testable hypotheses toward accelerating directed breeding and engineering efforts to increase future food security. A primary objective for building such a framework is to create synergy among an inter-connected community of biologists and modelers to create a realistic virtual plant. This framework advantageously casts the detailed mechanistic understanding of individual plant processes across various scales in a common scalable framework that makes use of current advances in high performance and parallel computing. We are currently designing a user friendly interface that will make this tool equally accessible to biologists and computer scientists. Critically, this framework will provide the community with much needed tools for guiding future crop breeding and engineering, understanding the emergent implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment.

The Challenges in the Development of a Long Duration Space Mission Food System

NASA Technical Reports Server (NTRS)

Perchonok, Michele H.; Swango, Beverly; Toerne, Mary E.; Russo, Dane M. (Technical Monitor)

2001-01-01

The Advanced Food System at Johnson Space Center/NASA will be responsible for supplying food to the crew for long duration exploratory missions. These missions require development of both a Transit Food System and of a Planetary Food System. The Transit Food System will consist of pre-packaged food of extended shelf life. It will be supplemented with salad crops that will be consumed fresh. The challenge is to develop a food system with a shelf life of 3 - 5 years that will use minimal power and create minimal waste from the food packaging. The Planetary Food System will allow for food processing of crops grown on the planetary surface due to the presence of some gravitational force. Crops will be processed to final products to provide a nutritious and acceptable diet for the crew. The food system must be flexible due to crop variation, availability, and shelf life. Crew meals, based on thesc: crops, must be nutritious, high quality, safe, and contain variety. The Advanced Food System becomes a fulcrum creating the right connection from crops to crew meals while dealing with issues of integration within a closed self-regenerative system (e.g., safety, waste production, volumes, water usage, etc.).