The US soybean farming industry is navigating significant changes in the current period, with soybean prices determining the initial rise and recent decline in industry performance. These prices have been influenced by several key factors, including the growing demand for biofuels and mixed consumer perceptions regarding soy products. The demand for soybean oil in biofuel production surged due to supportive policies like the Renewable Fuel Standard and rising crude oil prices, creating a lucrative market for soybean producers. However, subsequent drops in fertilizer and crude oil prices, paired with record-high soybean production, have sharply dropped soybean prices, bringing revenue and profit down with them as farmers struggle to balance costs with lower incomes. Industry has shrunk a compound annual growth rate (CAGR) of 2.6%, with a decrease of 8.7% in 2025, reaching an estimated $44.2 billion. US soybean exports are facing mounting challenges due to competitive pressures abroad and quickly evolving trade policy. Brazil’s increased production and improved export infrastructure have strengthened its position as a major supplier, particularly to China, which is reducing its reliance on US soybeans. This shift threatens US exports and compels American farmers to reassess their strategies, focusing on market diversification and emphasizing quality and sustainability to remain competitive. Rising geopolitical tensions and newly imposed tariffs, such as those affecting key markets like the EU, Canada and China, have further complicated trade, impacting US farmers' access and pricing power in these vital markets. Through the end of 2025, soybean prices are initially projected to decline due to increased production and growing global supplies. However, as climate change impacts crop yields through extreme weather and pest challenges and supplies become limited prices will be pushed upward alongside rising global demand. Subsidies will continue to play a vital role in supporting farmer incomes amids these fluctuations, providing some stability to an otherwise highly volatile industry. However, the industry faces significant uncertainty due to the ongoing USDA funding freeze is creating significant uncertainty, particularly where government support and subsidies are concerned. This freeze is affecting a wide range of agricultural programs including conservation efforts, market development, research and technical assistance. Over the next five years, the industry is expected to grow at a CAGR of 1.3%, with revenues reaching $47.1 billion by the end of 2030.

View monthly updates and historical trends for US Soybean Farm Price Received. from United States. Source: US Department of Agriculture. Track economic da…

This statistic shows the ten U.S. states in soybean production from 2019 to 2024. Illinois topped the list in 2024, with almost *** million bushels produced that year. Soybean industry Soybeans are among the major agricultural crops planted in the United States, behind only corn. They belong to the oilseed crops category and most U.S. soybeans are planted in May and early June and are harvested in late September and October. Production practices show that U.S. farmers commonly grow soybeans in crop rotation with corn. More than 80 percent of soybeans are cultivated in the upper Midwest. The Unites States counted Illinois, Iowa, and Minnesota as their leading soybean producing states as of 2022. Historical data shows that large-scale soybean production did not begin until the 20th century in the United States. However, recent statistics illustrate that the acreage of the dominant oilseed crop has expanded rapidly. The expansion of soybean acreage was favored by several factors including low production costs and a greater number of 50-50 corn-soybean rotations. Furthermore, soybeans were one of the first crop types that achieved commercial success as bioengineered crops. The first genetically modified (GM) soybeans were planted in the United States in 1996. They possess a gene that confers herbicide resistance.The usage of soybeans ranges from the animal food industry over human consumption to non-food products. The highest percentage of soybeans goes to the animal feed industry. The products intended for human consumption include products such as soy milk, soy flour or tofu.

In 2024, an estimated 4.36 billion bushels of soybeans (or soya beans) were produced in the United States, a significant increase compared to the previous year. Soya beans in the U.S. Alongside the production volume of soybeans, the production value increased in the United States in 2020: the production value of soybeans stood at about 36.8 billion U.S. dollars in 2018 and increased to roughly 57.5 billion U.S. dollars three years later. The states that produced the highest volume of soybeans in 2022 were Illinois, Iowa, and Minnesota, respectively. Leading soybean producers worldwide Since the marketing year of 2012/2013, the United States and Brazil have been the leading producers of soybeans worldwide. Producing about 139 million metric tons of it, Brazil was clearly in the lead in 2021/2022. Other noteworthy countries in terms of soybean production included Argentina, China, and India.

United States Long Term Projections: Soybeans: Soybean Price, Farm data was reported at 10.450 USD/Bushel in 2034. This stayed constant from the previous number of 10.450 USD/Bushel for 2033. United States Long Term Projections: Soybeans: Soybean Price, Farm data is updated yearly, averaging 10.400 USD/Bushel from Dec 2022 (Median) to 2034, with 13 observations. The data reached an all-time high of 14.200 USD/Bushel in 2022 and a record low of 10.000 USD/Bushel in 2027. United States Long Term Projections: Soybeans: Soybean Price, Farm data remains active status in CEIC and is reported by U.S. Department of Agriculture. The data is categorized under Global Database’s United States – Table US.RI010: Agricultural Projections: Soybeans and Products.

This statistic shows the leading countries in soybean production worldwide from 2012/13 to 2024/25. From 2015/16 to 2018/2019, the United States was the leading global producer of soybeans with a production volume of ****** million metric tons in 2018/2019. As of 2019, Brazil overtook the United States as the leading soybean-producing country with a production volume of some *** million metric tons in 2023/24. Soybean production Soybeans are among the major agricultural crops sown in the United States, behind only corn. They belong to the oilseed crops category, and the majority of U.S. soybeans are planted in May and early June and are harvested in late September and October. Production practices show that U.S. farmers commonly cultivate soybeans in crop rotation with corn. More than ** percent of soybeans are grown in the upper Midwest. The United States reported Illinois, Iowa, and Minnesota as their leading soybean producing states in 2022. Historical data demonstrates that large-scale soybean production did not commence until the 20th century in the United States. However, the latest statistics illustrate that the acreage of the dominant oilseed crop has expanded rapidly. The certain increase of soybean acreage was supported by several factors, including low production costs and a greater number of 50-50 corn-soybean rotations. Furthermore, soybeans were one of the first crop types that accomplished commercial success as bioengineered crops. The first genetically modified (GM) soybeans were cultivated in the United States in 1996. They possess a gene that confers herbicide resistance.The usage of soybeans ranges from the animal food industry over human consumption to non-food products. The highest percentage of soybeans goes to the animal feed industry. The product portfolio intended for human consumption include products such as soy milk, soy flour or tofu.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Soybeans (WPU01830131) from Jan 1947 to Aug 2025 about beans, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

What? A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021. Why? Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders. How? Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. What is 'new' or corrected in version 2.2? Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. Added: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell. CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

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.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

This is the AmeriFlux version of the carbon flux data for the site US-VT2 Vermillion Tributary Paired Cropland – Site 2 (Corn/Soy; Cover Crops). Site Description - US-VT2 is located on flat, actively managed farmland operated by working farmers, following a conventional no-till corn–soybean rotation in the U.S. Midwest that uses cover crops. US-VT2 is one of two paired working farm sites on the same property; both are managed using similar conventional practices, with the key difference being that the paired site (US-VT1) does not incorporate cover crops into its rotation. This paired design enables direct site-to-site comparisons to assess the impacts of cover cropping on carbon, water, and energy fluxes.

Soybean (Glycine max) is an important leguminous crop that is grown throughout the United States and around the world. In 2016, soybean was valued at $41 billion USD in the United States alone. Increasingly, soybean farmers are adopting alternative management strategies to improve the sustainability and profitability of their crop. Various benefits have been demonstrated for alternative management systems, but their effects on soybean-associated microbial communities are not well-understood. In order to better understand the impact of crop management systems on the soybean-associated microbiome, we employed DNA amplicon sequencing of the Internal Transcribed Spacer (ITS) region and 16S rRNA genes to analyze fungal and prokaryotic communities associated with soil, roots, stems, and leaves. Soybean plants were sampled from replicated fields under long-term conventional, no-till, and organic management systems at three time points throughout the growing season. Results indicated that sample origin was the main driver of beta diversity in soybean-associated microbial communities, but management regime and plant growth stage were also significant factors. Similarly, differences in alpha diversity are driven by compartment and sample origin. Overall, the organic management system had lower fungal and bacterial Shannon diversity. In prokaryotic communities, aboveground tissues were dominated by Sphingomonas and Methylobacterium while belowground samples were dominated by Bradyrhizobium and Sphingomonas. Aboveground fungal communities were dominated by Davidiella across all management systems, while belowground samples were dominated by Fusarium and Mortierella. Specific taxa including potential plant beneficials such as Mortierella were indicator species of the conventional and organic management systems. No-till management increased the abundance of groups known to contain plant beneficial organisms such as Bradyrhizobium and Glomeromycotina. Network analyses show different highly connected hub taxa were present in each management system. Overall, this research demonstrates how specific long-term cropping management systems alter microbial communities and how those communities change throughout the growth of soybean.

Geographically specific cradle to farm-gate life cycle analysis data for carinata, camelina, canola, and soybean production for USDA crop management zones (CMZ 01-06 and CMZ18) in South Dakota, North Dakota and Montana for no-till agricultural production data from 2010 through 2016. Dataset development was funded by a DOT Regional SunGrant and the State of South Dakota. Resources in this dataset:Resource Title: LCA model inventory and outputs with Monte Carlo uncertainty results. File Name: SI Moeller 2017 NGP Oilseeds.docxResource Description: This MS Word document contains the oilseed feedstock farm-gate model inventories, results, and uncertainty analyses for the Northern Great Plains discussed in Moeller et. al 2017. Analysis was conducted using IPCC GHG standardized emissions. Methodology is detailed in the associated publication (doi: 10.1007/s41247-017-0030-3). The supplementary information contains the names of the ecoinvent inventories; oilseed yield, seeding rates, and fertilization rates per USDA crop management zone (CMZ); climate change, freshwater eutrophication, and marine eutrophication percent contributions ReCiPe results per CMZ; Monte Carlo uncertainty results per CMZ; and farm-gate energy balance analysis results per CMZ.Resource Software Recommended: Microsoft Word 2016,url: https://products.office.com/en-us/word

An experimental study was conducted to examine the combined effects of adding a dietary protease, reducing the levels of soybean meal (SBM) and introducing corn gluten meal (CGM) in the ration of a group of broilers reared on a commercial Greek farm. Five hundred forty chicks were divided into three dietary treatments with six replicates of thirty birds each. The first group (Control) was fed a conventional diet based on corn and soybean meal, containing 21% w/w crude protein (CP). The second group (Soy-Prot) was supplied a corn and SBM-based diet containing a lower level of CP (20% w/w) and 200 mg of the protease RONOZYME® Proact per kg of feed. The third group (Gluten-Prot) was fed a diet without soybean-related constituents which was based on corn and CGM and with CP and protease contents identical to those of the diet of the Soy-Prot group. Body weight, feed intake, feed conversion ratio (FCR), intestinal microbiota populations and morphology, meat quality and cost were evaluated. Furthermore, a partial life cycle assessment (LCA) was performed in order to assess the potential environmental performance of the systems defined by these three dietary treatments and identify their environmental hot-spots. The growth performance of the broilers supplied the Soy-Prot diet was similar to the broilers supplied the Control diet. However, the broilers which were fed the Gluten-Prot diet at the end of the trial showed a tendency (P≤0.010) for lower weight gain and feed intake compared to those of the Control diet. When compared to the Control group, lower counts of C. perfringens (P≤0.05) were detected in the ileum and cecum parts, and lower counts of F. necrophorum (P≤0.001) were detected in the cecum part of the birds from the Gluten-Prot group. The evaluation of intestinal morphometry showed that the villus height and crypt depth values were not significantly different (P>0.05) among the experimental groups for the duodenum, jejunum and ileum parts. No significant differences (P>0.05) were observed in the quality of the breast and thigh meat and in the feed cost per kg body weight gain for the total duration of the growth period between the Control and Gluten-Prot broiler groups. The LCA suggested that the ammonia and nitrous oxide emissions due to litter handling constitute the farm level hot-spots for the Acidification and Eutrophication Potentials of the Control and Soy-Prot systems and the Global Warming Potential of the Gluten-Prot system, respectively. The Latin American soybean production and domestic corn production and lignite mining are important off-farm polluting processes for the studied life cycles. The Soy-Prot and Gluten-Prot systems both performed better than the Control system in nine of Environmental Impact Category Indicators assessed, with the respective differences being generally larger for the Gluten-Prot system. The environmental impact estimates are regarded as initial, indicative figures due to their inherent uncertainty. Overall, the results could be considered as positive indications in the effort to sustainably replace the conventional, soybean-dependent control diet in the specific broiler production system.

The grain price margins between buyers and sellers (i.e., basis spread) is influenced by the infrastructure used to transport crops from collection points to ports, which can be disrupted by weather extremes like floods and severe storms. Such disruptions are expected to become more frequent, potentially increasing food insecurity and impacting farm incomes. On average, the U.S. accounts for one-third of global corn and soybean production from 2012/13 to 2020/21, so the infrastructure to move crops from the main growing region to the nation’s ports is critical to global crop and food markets. Despite the critical nature of these issues, there is limited research specifically examining the effects of weather extremes on the U.S. crop transportation network. This study investigates how weather extremes disrupt crop transportation networks, and, in turn, how those disruptions affect the basis spread of corn and soybeans. It uses basis spread data from nearly 5,000 U.S. midwestern corn and soybean elevators spanning from 2012 to 2020, along with natural disaster declarations to represent weather extremes affecting crop transportation. Using a three-step process, it calculates least cost transportation routes to a port, adjusts for weather disruptions, and integrates disaster, transportation cost, and control variables into a fixed effects, panel data model that explains variation in basis spread. Results show natural disasters, particularly flash floods and winter storms, negatively affect basis spread. The cost effects of natural disasters disrupting crop transportation routes further decrease basis spread. Strengthening crop transportation infrastructure to withstand flooding and winter storms could reduce disruptions in this network. These findings underscore the value of Federal and State policies that prioritize investments in resilient transportation infrastructure, particularly in regions prone to flash floods and winter storms. Strengthening this infrastructure could not only reduce the economic costs of weather disruptions but also affect farm income and food security.

This dataset presents spatiotemporal dynamics of phosphorus (P) fertilizer management (application rate, timing, and method) at a 4km × 4 km resolution in agricultural land of the contiguous U.S. from 1850 to 2022. By harmonizing multiple data sources, we reconstructed the county-level crop-specific P fertilizer use history. We then spatialized and resampled P fertilizer use data to 4 km × 4 km gridded maps based on historical U.S. cropland distribution and crop type database developed by Ye et al. (2024).

This dataset contains (1) P fertilizer total consumption and mean application rate at the national level (Tabular); (2) P fertilizer consumption of 11 crops at the state level (Tabular); (3) P fertilizer consumption of permanent pasture (Tabular); (4) P fertilizer consumption of non-farm at the state level (Tabular); (5) P fertilizer application rate of 11 crop types at the state level (Tabular); (6) P fertilizer application rate of 11 crop types at the county level (Tabular); (7) P fertilizer application timing ratio at the state level (Tabular); (8) P fertilizer application method ratio at the state level (Tabular); (9) Gridded maps of P fertilizer application rate based on state-level data; (10) and (11) Gridded maps of P fertilizer application rate based on county-level data; (12)-(20) Gridded maps of P fertilizer application rate for each crop.

A detailed description of the data development processes, key findings, and uncertainties can be found in Cao, P., Yi, B., Bilotto, F., Gonzalez Fischer, C., Herrero, M., Lu, C.: Crop-specific Management History of Phosphorus fertilizer input (CMH-P) in the croplands of United States: Reconciliation of top-down and bottom-up data sources, is under review for the journal Earth System Science Data (ESSD). https://essd.copernicus.org/preprints/essd-2024-67/#discussion.

This work is supported by the Iowa Nutrient Research Center, the ISU College of Liberal Arts and Sciences Dean's Faculty Fellowship, and NSF CAREER grant (1945036).

This is the AmeriFlux version of the carbon flux data for the site US-Ro3 Rosemount- G19. Site Description - This tower is located in a farm field farmed in accordance with the cominant farming practice in the region: a corn/soybean rotation with chisel plow tillage in the fall following corn harvest and in the spring following soybeans.

In 2019, the aviation sector in the United States emitted 255 million metric tons of carbon dioxide (CO2) emissions, i.e., about five percent of the total domestic CO2 emissions from the energy sector. The sustainable aviation fuel (SAF) derived from carinata (Brassica carinata) could reduce CO2 emissions of the aviation sector in the United States. Therefore, it is important to estimate changes in farm economics with and without carinata for ascertaining its production feasibility. In this context, the current study first assesses a combination of 12 popular rotations of corn, cotton, peanut, and soybean with winter crops of winter wheat and carinata in South Georgia over 4 years. Then, the net present values (NPVs) of 292 feasible cropping systems over 4 years are calculated. Finally, this study develops a risk model for ascertaining the probability distributions of NPVs for selected cropping systems subject to uncertainties related to prices and yields of summer and winter crops. Carinata in the corn-corn-soybean rotation has the highest NPV ($2,996/ha). The least risky rotation is cotton-cotton-peanut, with a 58.9% probability of a positive NPV. Carinata can decrease the risk level of crop rotations by 8.1%, only if a contract price of $440.9/t is offered. Therefore, a risk averse, risk neutral, or risk acceptant farmer can potentially include carinata in the rotation. Overall, carinata would increase the profitability of farm operations and decrease risk in the SE United States, and therefore, a high likelihood exists, that farmers would adopt it for meeting the growing demand for SAF in the United States.

Major contributing processes (≥10% contribution) and their potential contribution to the total environmental impact category indicator (EICI) values for the three systems studied.

Hot-spots for the EICIs and each system studied.

Influence of diets on feeding cost of broiler chickens.