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.

This dataset includes soil health, crop biomass, and crop yield data for a 13-year corn stover harvest trial in central Iowa. Following the release in 2005 of the Billion Ton Study assessment of biofuel sources, several soil health assessments associated with harvesting corn stover were initiated across ARS locations to help provide industry guidelines for sustainable stover harvest. This dataset is from a trial conducted by the National Laboratory for Agriculture and Environment from 2007-2021 at the Iowa State University Ag Engineering and Agronomy farm. Management factors evaluated in the trial included the following.

Stover harvest rate at three levels: No, moderate (3.5 ± 1.1 Mg ha-1 yr-1), or high (5.0 ± 1.7 Mg ha-1 yr-1) stover harvest rates. No-till versus chisel-plow tillage. Originally, the 3 stover harvest rates were evaluated in a complete factorial design with tillage system. However, the no-till, no-harvest system performed poorly in continuous corn and was discontinued in 2012 due to lack of producer interest. Cropping sequence. In addition to evaluating continuous corn for all stover harvest rates and tillage systems, a corn-alfalfa rotation, and a corn-soybean-wheat rotation with winter cover crops were evaluated in a subset of the tillage and stover harvest rate treatments. One-time additions of biochar in 2013 at rates of either 9 Mg/ha or 30 Mg/ha were evaluated in a continuous corn cropping system.

The dataset includes: 1) Crop biomass and yields for all crop phases in every year. 2) Soil organic carbon, total carbon, total nitrogen, and pH to 120 cm depth in 2012, 2016, and 2017. Soil cores from 2005 (pre-study) were also sampled to 90 cm depth. 3) Soil chemistry sampled to 15 cm depth every 1-2 years from 2007 to 2017. 4) Soil strength and compaction was assessed to 60 cm depth in April 2021. These data have been presented in several manuscripts, including Phillips et al. (in review), O'Brien et al. (2020), and Obrycki et al. (2018). Resources in this dataset:Resource Title: R Script for Phillips et al. 2022. File Name: Field 70-71 Analysis Script_AgDataCommons.RResource Description: This R script includes analysis and figures for Phillips et al. "Thirteen-year Stover Harvest and Tillage Effects on Soil Compaction in Iowa". It focuses primarily on the soil compaction and strength data found in "Field 70-71 ConeIndex_BulkDensityDepths_2021". It also includes analysis of corn yields from "Field 70-71 CornYield_2008-2021" and weather conditions from "PRISM_MayTemps" and "Rainfall_AEA".Resource Software Recommended: R version 4.1.3 or higher,url: https://cran.r-project.org/bin/windows/base/ Resource Title: Field 70-71 ConeIndex_BulkDensityDepths_2021. File Name: Field 70-71 ConeIndex_BulkDensityDepths_2021.csvResource Description: This dataset provides an assessment of soil strength (penetration resistance) and soil compaction (bulk density) to 60 cm depth, in continuous corn plots. Penetration resistance was measured in most-trafficked and least-trafficked areas of the plots to assess compaction from increased traffic associated with stover harvest. This spreadsheet also has associated data, including soil water, carbon, and organic matter content. Data were collected in April 2021 and are described in Phillips et al. (in review, 2022).Resource Title: Field 70-71 CornYield_2008-2021. File Name: Field 70-71 CornYield_2008-2021_ForR.csvResource Description: This dataset provides corn stover biomass and grain yields from 2008-2021. Note that this dataset is just for corn, which were presented in Phillips et al., 2022. Yields for all crop phases, including soybeans, wheat, alfalfa, and winter cover crops, are in the file "Field 70-71 Crop Yield File 2008-2020".Resource Title: PRISM_MayTemps. File Name: PRISM_MayTemps.csvResource Description: Average May temperatures during the study period, obtained from interpolation of regional weather stations using the PRISM climate model (https://prism.oregonstate.edu/). These data were used to evaluate how spring temperatures may have impacted corn establishment.Resource Title: Rainfall_AEA. File Name: Rainfall_AEA.csvResource Description: Daily rainfall for the study location, 2008-2021. Data were obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/rainfall/). Title: Field 70-71 Plot Status 2007-2021. File Name: Field 70-71 Plot Status 2007-2021.xlsxResource Description: This file contains descriptions of experimental treatments and diagrams of plot layouts as they were modified through several phases of the trial. Also includes an image of plot locations relative to NRCS soil survey map units.Resource Title: Field 70-71 Deep Soil Cores 2012-2017. File Name: Field 70-71 Deep Soil Cores 2012-2017.xlsxResource Description: Soil carbon, nitrogen, organic matter, and pH to 120 cm depth in 2012, 2016, and 2017.Resource Title: Field 70-71 Baseline Deep Soil Cores 2005. File Name: Field 70-71 Baseline Deep Soil Cores 2005.csvResource Description: Baseline soil carbon, nitrogen, and pH data from an earlier trial in 2005, prior to stover trial establishment.Resource Title: Field 70-71 Crop Yield File 2008-2020. File Name: Field 70-71 Crop Yield File 2008-2020.xlsxResource Description: Yields for all crops in all cropping sequences, 2008-2020. Some of the crop sequences have not been summarized in publications.Resource Title: Field 70-71 Surface Soil Test Data 2007-2021. File Name: Field 70-71 Surface Soil Test Data 2007-2021.xlsxResource Description: Soil chemistry data, 0-15 cm, collect near-annually from 2007 to 2021. Most analyses were performed by Harris Laboratories (now AgSource) in Lincoln, Nebraska, USA. Resource Title: Iowa Stover Harvest Trial Data Dictionary. File Name: Field 70-71 Data Dictionary.xlsxResource Description: Data dictionary for all data files.

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.

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).

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.

ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI.

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).

This data set contains a single data file (.csv format) that provides gridded values of net primary productivity (NPP) for cropland in eight counties in the central United States for the year 1992 and estimates of interannual cropland NPP in Iowa for years from 1982 through 1996. The data file also includes climate, soil texture, and land cover data for each 0.5 degree grid cell.

The magnitude and interannual variation in NPP was estimated using crop area and yield data from the U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). The major harvested commodities were corn, soybean, sorghum, sunflower, oats, barley, wheat, and hay. Total NPP estimates include both above- and below-ground components.

County-level NPP in 1992 ranged from 195 to 760 gC/m2/year. The area of highest NPP, ranging from 650 to 760 gC/m2/year, was found in a band extending across Iowa, through northern Illinois, Indiana, and southwestern Ohio. Areas of moderate NPP, from 550 to 650 gC/m2/year, occurred mostly in Michigan and Wisconsin, while large areas of low NPP, from 200 to 550 gC/m2/year, occurred in North Dakota, southern Illinois, and Minnesota. The area of highest production was also the area with the largest proportion of land sown with corn and soybean. NPP for counties in Iowa varied among years (1982-1996) by a factor of 2, with the lowest NPP in 1983 (which had an unusually wet spring), in 1988 (which was a drought year), and in 1993 (which experienced floods).

Revision Notes: The documentation for this data set has been modified, and the data files have been reformatted. The data files have been checked for accuracy and the contents are identical to those originally published in 2001.

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.

Corn fell to 393.37 USd/BU on July 14, 2025, down 0.66% from the previous day. Over the past month, Corn's price has fallen 9.52%, and is down 2.69% 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 July of 2025.

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

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.

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

As part of the US government’s Feed the Future initiative that aims to address global hunger and food security issues in sub-Saharan Africa, the US Agency for International Development is supporting three multi-stakeholder agricultural research projects under Africa Research In Sustainable Intensification for the Next Generation (Africa RISING - AR) program. The overall aim of the program is to transform agricultural systems through sustainable intensification projects in Ghana, Ethiopia, Tanzania, Malawi, Mali, and (potentially) Zambia. In West Africa, IITA works with multi-disciplinary R4D partners in selected communities located in Northern Ghana and Southern Mali. More particularly, in Northern Ghana three regions were chosen for the study: the Northern, Upper-East and Upper-West regions. These areas cover both maize-based and rice-vegetables-based systems and therefore allow to address the production constraints characterizing both realities7. As IFPRI (2012) highlights, the northern regions of Ghana are characterized by small land holdings and low input - low output farming systems, which adversely impact food security. In particular, they are subject to a seasonal cycle of food insecurity of three to seven months for cereals (i.e., maize, millet and sorghum) and four to seven months for legumes (i.e., groundnuts, cowpeas, and soybeans). These crops in the savannahs are often produced in a continuous monoculture, steadily depleting soil natural resources and causing the yields per unit area to fall to very low levels. The poverty profile of Ghana identifies the three northern regions as the poorest and most hunger-stricken areas in the country. Gender inequalities are also apparent in these regions, since women have limited access to resources and therefore limited capacity to generate income on their own.