The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Soybean productionProjection: Web Mercator Auxiliary SphereGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Soybeans - Acres HarvestedSoybeans - Operations with Area Harvested - Area Harvested: (1.0 to 24.9 Acres)Soybeans - Operations with Area Harvested - Area Harvested: (25.0 to 99.9 Acres)Soybeans - Operations with Area Harvested - Area Harvested: (100 to 249 Acres)Soybeans - Operations with Area Harvested - Area Harvested: (250 to 499 Acres)Soybeans - Operations with Area Harvested - Area Harvested: (500 to 999 Acres)Soybeans - Operations with Area Harvested - Area Harvested: (1,000 or More Acres)Soybeans - Operations with Area HarvestedSoybeans - Operations with SalesSoybeans - Production, Measured in BushelsSoybeans - Sales, Measured in US DollarsSoybeans, Irrigated - Acres HarvestedSoybeans, Irrigated - Operations with Area Harvested Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This raster dataset represents total soybean crop production in metric tons. Harvested area in hectares was multiplied by yield per hectare to create this data set. Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.

Grain Stocks Soy in the United States decreased to 1.02 Billion Bushels in the second quarter of 2025 from 1.91 Billion Bushels in the first quarter of 2025. This dataset provides - United States Quarterly Grain Stocks - Soy- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Global Soybeans Production by Country, 2023 Discover more data with ReportLinker!

Soybeans fell to 1,038.75 USd/Bu on September 18, 2025, down 0.48% from the previous day. Over the past month, Soybeans's price has risen 2.54%, and is up 2.52% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Soybeans - values, historical data, forecasts and news - updated on September of 2025.

Sulfur (S) fertilization in soybean (Glycine max (L.) Merr.) production was investigated across 50 research sites in northeastern Louisiana during the 2023 and 2024 growing seasons. The objective of the study was to assess soybean yield response to six rates of S fertilizer (0, 11, 22, 34, 45, and 67 kg S ha-1), particularly in the context of declining atmospheric sulfur dioxide (SO₂) deposition following the implementation of the U.S. Clean Air Act (US-EPA, 2024). This work represents one of the most comprehensive datasets ever compiled in the United States for developing soil-test-based S fertilizer recommendations for soybean production.Of the 50 trials, 21 were conducted at the Macon Ridge Research Station (MRRS) in Franklin Parish, which features Gigger-Gilbert silt loam soil, and 29 were conducted at the Northeast Research Station (NERS) in Tensas Parish, characterized by Commerce silt loam soils (NRCS, 2024). Trials were laid out in a randomized complete block design with 4-5 replications per treatment. In 2023, fertilizer-S treatments included Sul4r-Plus (23% Ca, 17% S) and K-Mag (22% K₂O, 11% Mg, 21% S), while in 2024, Sul4r-Plus and gypsum (23% Ca, 17% S) were used. Each plot consisted of four rows, 10.67 m in length, with row spacing of 1.02 m at MRRS and 0.97 m at NERS.Soil samples were collected before planting from untreated control plots at two depths: 0–15 cm (10–12 cores) and 0–30 cm (8–10 cores) using a 2.2 cm diameter AMS soil probe from the top of the middle two seedbeds. Samples were air-dried for five days at 45°C, ground to pass through a 2 mm sieve, and analyzed by Waters Agricultural Laboratories in Vicksburg, Mississippi. Nutrient analysis was performed using Mehlich-3 extractant (Helmke & Sparks, 1996), while soil pH and organic matter content were determined following Sikora and Kissel (2014) and Schulte and Hopkins (1996), respectively. Cation exchange capacity (CEC) was estimated using methods outlined by Maguire and Heckendorn (2015).Fertilizer treatments were applied on the seedbed surface at or before planting. Soybeans were seeded between mid- to late May at a rate of 321,000 seeds ha-1, following corn (Zea mays L.), cotton (Gossypium hirsutum L.), or soybean. All sites were conventionally tilled and furrow irrigated. Lime and additional fertilizers were applied based on LSU AgCenter soil-test-based recommendations (Parvej, 2021, 2024), with lime incorporated only at sites with soil pH below 6.0. Standard irrigation, pest, and weed management practices were followed in accordance with LSU AgCenter Extension guidelines (Padgett et al., 2024; Stephenson et al., 2024; Villegas & Towles, 2023). Soybean was harvested at physiological maturity (R8 stage, as per Fehr & Caviness, 1977), and yield was calculated based on a uniform grain moisture content of 13% (130 g H₂O kg-1).This 50-site-year dataset offers a unique and robust foundation for establishing science-based, soil-test-calibrated S fertilizer recommendations for soybean production. It serves as a valuable resource for producers, agronomists, and consultants aiming to fine-tune S fertilization strategies, enhance productivity, and reduce unnecessary input costs. This dataset is part of the article submitted to the Soil Science Society of America Journal (Moni et al., 2025).

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

Authors:

Ignacio Ciampitti1, Luiz Henrique Moro Rosso1, Emmanuela van Versendaal1, James Kimball1, and Eric Adee1

1 Department of Agronomy, Kansas State University

This dataset contains information on soybean planting dates and maturity groups for 2 years (2018, 2019) and 2 locations in Kansas (Ottawa, Topeka), presenting management and yield information.

For more information related to this dataset or codes, please contact the corresponding author at: ciampitti@ksu.edu

For cite the dataset, please use: Ciampitti, I., Moro Rosso, L.H, van Versendaal, E., Kimball, J., Adee, E. Soybean planting date x maturity group in Kansas. figshare 10.6084/m9.figshare.20018015 (2022).

USA Cropland is a time enabled imagery layer of the USDA CropScape Cropland Data Layers dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.Why USA Cropland masks out NLCD land cover in its default templateUSDA CropScape Cropland Data Layers, by default as downloaded from USDA, fill in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 1/27/2021This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Pop or Orn Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Dbl Crop WinWht/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Misc Vegs & Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonag/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Dbl Crop WinWht/Corn226,Dbl Crop Oats/Corn227,Lettuce228,Dbl Crop Triticale/Corn229,Pumpkins230,Dbl Crop Lettuce/Durum Wht231,Dbl Crop Lettuce/Cantaloupe232,Dbl Crop Lettuce/Cotton233,Dbl Crop Lettuce/Barley234,Dbl Crop Durum Wht/Sorghum235,Dbl Crop Barley/Sorghum236,Dbl Crop WinWht/Sorghum237,Dbl Crop Barley/Corn238,Dbl Crop WinWht/Cotton239,Dbl Crop Soybeans/Cotton240,Dbl Crop Soybeans/Oats241,Dbl Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Dbl Crop Barley/Soybeans

Cost and return estimates are reported for the United States and major production regions for corn, soybeans, wheat, cotton, grain sorghum, rice, peanuts, oats, barley, milk, hogs, and cow-calf. The series of commodity cost and return estimates for the U.S. and regions is divided into two categories: Recent and Historical estimates. Recent estimates date back to the point of the most recent major revision in accounting methods, account format, and regional definitions for each commodity. Historical estimates date back to when the series began. Cost-of-Production Forecasts are also available for major U.S. field crops. Organic Costs and Returns for corn, milk, wheat, and soybeans are also available.

Corn fell to 426.04 USd/BU on September 18, 2025, down 0.17% from the previous day. Over the past month, Corn's price has risen 12.26%, and is up 5.00% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on September of 2025.

USA Cropland is a time-enabled imagery layer of the USDA Cropland Data Layer dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.The USDA is now serving the Cropland Data Layer in their own application called CropCros which allows selection and display of a single product or growing season. This application will eventually replace their popular CropScape application.This dataset is GDA compliant. Compliancy information can be found here.Why USA Cropland masks out NLCD land cover in its default templateUSDA Cropland Data Layer, by default as downloaded from USDA, fills in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 2/2/2022This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Popcorn or Ornamental Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Double Crop Winter Wheat/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Miscellaneous Vegetables and Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonagricultural/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Double Crop Winter Wheat/Corn226,Double Crop Oats/Corn227,Lettuce228,Double Crop Triticale/Corn229,Pumpkins230,Double Crop Lettuce/Durum Wheat231,Double Crop Lettuce/Cantaloupe232,Double Crop Lettuce/Cotton233,Double Crop Lettuce/Barley234,Double Crop Durum Wheat/Sorghum235,Double Crop Barley/Sorghum236,Double Crop Winter Wheat/Sorghum237,Double Crop Barley/Corn238,Double Crop Winter Wheat/Cotton239,Double Crop Soybeans/Cotton240,Double Crop Soybeans/Oats241,Double Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Double Crop Barley/Soybeans

On-Farm Residue Removal Study for Resilient Economic Agricultural Practices in Morris, Minnesota Interest in harvesting crop residues for energy has waxed and waned since the oil embargo of 1973. Since the at least the late 1990’s interest has been renewed due to concern of peak oil, highly volatile natural gas prices, replacing fossil fuel with renewable sources and a push for energy independence. The studies conducted on harvesting crop residues during the 1970’s and1980’s focused primarily on erosion risk and nutrient removal as a result early estimates of residue availability focused on erosion control (Perlack et al., 2005). More recently, the focus has expanded to also address harvest impacts on soil organic matter and other constraints (Wilhelm et al., 2007; Wilhelm et al., 2010). In West Central Minnesota, crop residues have been proposed a replacement for natural gas (Archer and Johnson, 2012) while nationally residues are also be considered for cellulosic ethanol production (US DOE, 2011). The objective of the on-farm study was to assess the impact of residue harvest on working farms with different management systems and soils. Indicators of erosion risk, soil organic matter, and crop productivity is response to grain plus cob, or grain plus stover compared to grain only harvest. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/fe5f312c-e9ad-4485-b5f9-7897f5bcd9f6

Abstract: The objective of this study was to analyze the bilateral trade between Brazil and the main actors in international commercialization of soybeans - Argentina, the United States and China - aiming to verify how the practice of certain intervention actions influence the trade dynamics of this commodity. In this sense, scenarios were simulated based on the commercial practices adopted by these countries, and the equilibrium results were obtained through the Global Trade Analysis Project (GTAP) and analyzed on the basis of Game Theory. These results were used to construct the matrix of payoffs, associated to the commercial strategies of countries considered as players. The results showed that policies to encourage production are effective ways for governments to make countries more competitive and to obtain commercial gains from or in conjunction with others. It was noted that China could become a major competitor among the soybean exporters, depending on the policy adopted by the government and by Chinese institutions. It was concluded that market access is the main source of trade gains for the products of soybean complex, and that the elimination of tariffs on imports of these products brings significant gains to Brazil, the United States and to Argentina, as well as ways to subsidize production and/or exports.

This data study contains data on pigeonpea and soybean intensification through phosphorous fertilization. It contains data on cropping system, phosphorous level, treatments and variety of pigeonpea, and yields among many others. About the project Project title: AfricaRISING - Sustainable Intensification of Maize-Legume-Livestock Integrated Farming Systems in East and Southern Africa Project abstract The Malawi project has local theme 'Agro-ecological intensification in Malawi through action research with smallholder farmers' with a lot of emphasis on co-learning with farmers and other stakeholders.The purpose of the Africa RISING Malawi component is to enhance farmer knowledge and support sustainable intensification (SI) pathways for productivity gains in maize-legume diversified systems, that also integrates livestock-related enterprises such as improved fodder for intensified dairy production. The project is setting up a research approach that systematically assesses SI best-bet options that appropriately respond to the needs of resource-poor farmers - particularly female headed households. Building on successful examples of participatory action research and experiences from biophysical research on smallholder farms in Malawi over the past two decades, the research team has begun taping into these products of agricultural research to move towards more sustainable smallholder production systems. We envisage that farm-scale production strategies employed by different farm/farmer typologies will be further distilled through scenario analyses using farming systems simulation modeling approaches. The project works with an alliance of actors (agro-dealers, extension services, NGOs, local government structures, etc) as R4D platforms for the two districts. Project website: http://africa-rising.net Project start date: 01/01/2012 Project end date : 09/30/2016

Among abiotic stresses to agricultural crops, drought stress is the most prolific and has worldwide detrimental impacts. The soybean (Glycine max) is one of the most important sources of nutrition to both livestock and humans. Different plant introductions (PI) of soybeans have been identified to have different drought tolerance levels. Here, two soybean lines, Pana (drought sensitive) and PI 567731 (drought tolerant) were selected to identify chemical compounds and pathways which could be targets for metabolomic analysis induced by abiotic stress. Extracts from the two lines are analyzed by direct infusion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. The high mass resolution and accuracy of the method allows for identification of ions from hundreds of different compounds in each cultivar. The exact m/z of these species were filtered through SoyCyc and the Human Metabolome Database to identify possible molecular formulas of the ions. Next, the exact m/z values are converted into Kendrick masses and their Kendrick mass defects (KMD) computed, which are then sorted from high to low KMD. This latter process assists in identifying many additional molecular formulas, and is noted to be particularly useful in identifying formulas whose mass difference corresponds to two hydrogen atoms. In this study, more than 460 ionic formulas are identified in Pana, and more than 340 ionic formulas are identified in PI 567731, with many of these formulas reported from soybean for the first time. Using the SoyCyc matches, the metabolic pathways from each cultivar are compared, providing for lists of molecular targets available to profile effects of abiotic stress on these soybean cultivars. Key metabolites include chlorophylls, pheophytins, mono- and diacylglycerols, cycloeucalenone, squalene, and plastoquinones and involve pathways which include the anabolism and catabolism of chlorophyll, glycolipid desaturartion, and biosynthesis of phytosterols, plant sterols, and carotenoids. Methods Direct infusion ESI FT-ICR mass spectrometry was conducted using three replicates from each cultivar; the time-domain data was converted to m/z domain data prior to processing to identify features in the mass spectra. Direct infusion ESI-FT-ICR data sets were processed as follows using Bruker Daltonics (Bremen, Germany) Data Analysis 4.0 software. Software was instructed to find all peaks with a signal-to-noise ratio > 3 to produce a peak list. Next, the peak list was subjected to the deconvolution process such that isotopic envelopes were determined, and each individual ionic species was then grouped as part of the given isotopic cluster. A threshold of 0.1% peak area relative to the most intense peak (m/z 1073.506 in each cultivar list, corresponding to ion C67H94NaN4O6) was used. The peak list was reduced to the monoisotopic isotope of each isotopic cluster, and this was the m/z value used in compiling lists for each cultivar. After compilation of the m/z list for each cultivar, it was first passed through the SoyCyc database of metabolites (https://soycyc.soybase.org/); matches of either protonated, sodiated, or potassiated ions to the known metabolites within 3 ppm mass error was considered a confirmation of the ionic formula. Each list was then filtered through HMDB to discover matches to either protonated, sodiated, or potassiated ions in the database. For endogenous compounds, the 3 ppm mass error was again used to constitute a match. For non-natural compounds, however, a stricter limit of 1 ppm was used to constitute a match between the database and the m/z list. To further annotate the m/z with ionic formulas, each list was converted to the corresponding Kendrick mass and KMD calculated for each ion; ions were then sorted by KMD and plotted as nominal Kendrick mass vs. KMD to assist in identification of ionic formulas to those m/z which did not yet have one. Final lists of ionic formulas from each cultivar were then recorded and compared. For those m/z values which matched entries in the SoyCyc database, an examination of the metabolic pathways involved was also performed to obtain context on how the cultivars might respond to drought at a molecular level. Note: the absence of an annotated peak in the list does not mean that metabolite is not present; rather, the metabolite is not detected with an abundance greater than 0.1% within the restrictive mass accuracy window employed. Metabolites from each cultivar identified in SoyCyc were the inputs into the Pathway Covering tool (https://pmn.plantcyc.org/cmpd-pwy-coverage.shtml) using a constant cost function; the tool then computed a minimal-cost set of metabolic pathways for Glycine max from each cultivar’s data set. For this analysis, Pathway Tools version 26.0 [42] was used employing data identified within the SoyCyc 10.0.2 database.

Estimated areas, production, yield, average farm price and total farm value of principal field crops.

ABSTRACT Management of agricultural production systems interferes with greenhouse gases (GHG) emissions, thereby altering physical, chemical, and biological attributes of soil; therefore, it is important to understand the relationship between soil attributes and GHG emissions. This study evaluated GHG emissions and their relationship with soil attributes in off-season soybean, maize, brachiaria and eucalyptus production systems. The experiment was carried out in Brejo, Maranhão, Brazil, with soybean ( Glycine max ), maize ( Zea mays ), brachiaria ( Urochloa ruzizienses ), and eucalyptus ( Eucalyptus grandis ). Fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were evaluated using air samples analyzed by gas chromatography. Soil attributes were ammonium and nitrate contents, total organic carbon, moisture, pH, density, total porosity, and water-filled pore space. N2O flux was 287.1 µg m-2 h -1 for eucalyptus cultivation, while areas cultivated with soybeans, maize and brachiaria had influxes of 46.7, 7.2, and 13.17 µg m-2 h-1, respectively. In the off-season, the highest emissions of N2O and CO2 were measured in eucalyptus areas due to soil moisture and porosity conditions provided by accumulation of litter on the soil surface.

This data study contains data on the intensification of production of soyabean, groundnut, cowpea and maize. About the project Project title: Malawi: Grain Legumes Productivity in Different Crop Arrangements Project abstract The Malawi project has local theme 'Agro-ecological intensification in Malawi through action research with smallholder farmers' with a lot of emphasis on co-learning with farmers and other stakeholders.The purpose of the Africa RISING Malawi component is to enhance farmer knowledge and support sustainable intensification (SI) pathways for productivity gains in maize-legume diversified systems, that also integrates livestock-related enterprises such as improved fodder for intensified dairy production. The project is setting up a research approach that systematically assesses SI best-bet options that appropriately respond to the needs of resource-poor farmers - particularly female headed households. Building on successful examples of participatory action research and experiences from biophysical research on smallholder farms in Malawi over the past 2 decades, the research team has begun taping into these products of agricultural research to move towards more sustainable smallholder production systems. We envisage that farm-scale production strategies employed by different farm/farmer typologies will be further distilled through scenario analyses using farming systems simulation modeling approaches. The project works with an alliance of actors, (agro-dealers, extension services, NGOs, local government structures, etc) as R4D platforms for the two districts. Project website: http://africa-rising.net Project start date: 01/01/2012 Project end date : 30/9/2016

This data product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat).

Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat

Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.)

This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page.

Spreadsheet Models

For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for:

1. the national-level season-average price received by farmers and
2. the implied counter-cyclical payment rate.

Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.