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

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: 2022 Soybean ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, and HawaiiSource: USDA National Agricultural Statistics ServicePublication Date: 2022AttributesNote 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.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 In Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.

The soybean aphid (Aphis glycines) is an insect pest of cultivated soybeans (Glycine max). Several genes with resistance to A. glycines (i.e. Rag genes) have been identified in soybean. Virulent strains of soybean aphid are able to overcome the resistance and colonize soybeans having one or more Rag genes. It is important to classify virulent strains of soybean aphids in evaluating soybean lines in order to develop cultivars with durable resistance. The files presented here report the number of soybean aphids on soybean lines that differed in the Rag genes they contained. Two colonies of soybean aphid were tested. Materials and Methods Tests were conducted separately against the two soybean aphid colonies, which were maintained on soybean plants at North Central Agricultural Research Laboratory (NCARL), USDA-ARS, Brookings, South Dakota, USA, largely according to procedures described in Hesler and Tilmon (2018). The first colony was established from a single aphid collected near Volga, South Dakota, USA in 2016 and designated as ‘Volga16’ (Conzemius et al. 2019). It was reared on soybean cultivar ‘LD12R12-15805Ra’ (Rag1+Rag2 pyramid; University of Illinois, Urbana-Champaign, IL, USA). A second colony designated ‘Accrue’ was derived from a colony originally established from a single first instar isolated from aphids collected at Urbana, IL, USA, and initially reared in Urbana (‘Urbana clone’; Hill et al. 2004). This colony was established as an avirulent soybean aphid colony (Hill et al. 2004). A series of sequential colonies from the initial colony was established, in order, at The Ohio State University, Wooster, OH, USA; Iowa State University, Ames, IA, USA; South Dakota State University, Brookings, SD, USA; and finally, in 2018 at NCARL. Although established as an ostensibly avirulent colony derived from the ‘Urbana clone’ colony, it was unexpectedly virulent against a known resistant accession, LD05R-16137 (containing Rag1), in initial screening tests. Two separate no-choice tests were run for each soybean aphid colony. Each test consisted of seven soybean lines. Six had one or more Rag genes: 19APH18 (Rag1), 19APH25 (Rag2), 19INC (Rag3), 19APH29 (Rag4), 19APH30 (Rag6), 19APH09Rag12 (a Rag1+Rag2 pyramid); and ‘Titan,’ an aphid-susceptible soybean cultivar (Diers et al. 1999). Two-week-old, unifoliate-stage soybean plants growing in plastic pots (6 cm top diameter, 4 cm bottom diameter, 5.7 cm height) were each infested with 10 apterous adult soybean aphids and covered with a clear plastic, ventilated, cylindrical tube. After 20 days in an environmental chamber, the shoots of test plants were clipped at soil level, placed individually in sealable plastic bags, and stored in a freezer. Plants were removed over the next few days, and the aphids on them were counted. The data are contained in separate files—one for each of two soybean aphid colonies. Resources in this dataset:Resource Title: Number of Soybean Aphids Accrue Colony vs Rag Lines. File Name: Number of Soybean Aphids Accrue Colony vs Rag Lines.xlsxResource Description: Number of Accrue colony soybean aphids per plant on various Rag soybean lines from no-choice laboratory tests.Resource Title: Number of Soybean Aphids Volga16 Colony vs Rag Lines. File Name: Number of Soybean Aphids Volga16 Colony vs Rag Lines.xlsx

Soybeans rose to 1,015.50 USd/Bu on July 11, 2025, up 0.30% from the previous day. Over the past month, Soybeans's price has fallen 2.57%, and is down 7.94% 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 July 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).

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

Intensive cropland agriculture commonly increases streamwater solute concentrations and export from small watersheds. In recent decades, the lowland tropics have become the world's largest and most important region of cropland expansion. Although the effects of intensive cropland agriculture on streamwater chemistry and watershed export have been widely studied in temperate regions, their effects in tropical regions are poorly understood. We sampled seven headwater streams draining watersheds in forest (n=3) or soybeans (n=4) to examine the effects of soybean cropping on stream solute concentrations and watershed export in a region of rapid soybean expansion in the Brazilian state of Mato Grosso. We measured stream flows and concentrations of NO3-, PO43-, SO42-, Cl-, NH4+, Ca2+, Mg2+, Na+, K+, Al3+, Fe3+ and dissolved organic carbon (DOC) biweekly to monthly to determine solute export. We also measured stormflows and stormflow solute concentrations in a subset of watersheds (2 forest, 2 soybean) during 2 to 3 storms, and solutes and δ18O in groundwater, rainwater and throughfall to characterize watershed flowpaths. Concentrations of all solutes except K+ varied seasonally in streamwater, but only Fe3+ concentrations differed between land uses. The highest streamwater and rainwater solute concentrations occurred during the peak season of wildfires in Mato Grosso, suggesting that regional changes in atmospheric composition and deposition influence seasonal stream solute concentrations. Despite no concentration differences between forest and soybean land uses, annual export of NH4+, PO43-, Ca2+, Fe3+, Na+, SO42-, DOC and TSS were significantly higher from soybean than forest watersheds (5.6-fold mean increase). This increase largely reflected a 4.3-fold increase in water export from soybean watersheds. Despite this increase, total solute export per unit watershed area (i.e. yield) remained low for all watersheds (<1 kg NO3- N/ha/yr, <2.1 kg NH4+-N/ha/yr, <0.2 kg PO43--P/ha/yr, <1.5 kg Ca2+/ha/yr). Responses of both streamflows and solute concentrations to crop agriculture appear to be controlled by high soil hydraulic conductivity, groundwater-dominated hydrologic flowpaths on deep soils, and the absence of nitrogen fertilization. To date, these factors have buffered streams from the large increases in solute concentrations that often accompany intensive croplands in other locations.

Basis reflects both local and global supply and demand forces. It is calculated as the difference between the local cash price and the futures price. It affects when and where many grain producers and shippers buy and sell grain. Many factors affect basis—such as local supplies, storage and transportation availability, and global demand—and they interact in complex ways. How changes in basis manifest in transportation is likewise complex and not always direct. For instance, an increase in current demand will drive cash prices up relative to future prices, and increase basis. At the same time, grain will enter the transportation system to fulfill that demand. However, grain supplies also affect basis, but will have the opposite effect on transportation. During harvest, the increase in the supply of grain pushes down cash prices relative to futures prices, and basis weakens, but the demand for transportation increases to move the supplies.

For more information on how basis is linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6), and links below for research on the topic.

This data has corn, soybean, and wheat basis for a variety of locations. These include origins—such as Iowa, Minnesota, Nebraska, and many others—and destinations, such as the Pacific Northwest, Louisiana Gulf, Texas Gulf, and Atlantic Coast.

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain price spreads (https://agtransport.usda.gov/d/an4w-mnp7). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price).

The cash price comes from the grain prices dataset and the futures price comes from the appropriate futures market, which is Chicago Board of Trade (CME Group) for corn, soybeans, and soft red winter wheat; Kansas City Board of Trade (CME Group) for hard red winter wheat; and the Minneapolis Grain Exchange for hard red spring wheat.