Corn rose to 433.53 USd/BU on December 2, 2025, up 0.01% from the previous day. Over the past month, Corn's price has fallen 0.17%, but it is still 2.43% higher than a year ago, 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 December of 2025.

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.

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.

Headlines of Corn and Soybean

Headlines extracted from http://soybeansandcorn.com/

Monthly Average - Chicago Board of Trade (CBOT)

https://www.macrotrends.net/2531/soybean-prices-historical-chart-data https://www.macrotrends.net/2532/corn-prices-historical-chart-data

Content

The data source of time-series were obtained from the World Agricultural Supply and Demand Estimates (WAOB) from the United States Department of Agriculture (USDA) website.

The period ranges from January 2014 to December 2020. The attributes represent several time-series features, such as planted area, harvested area, yield, beginning stocks, imports, supply, demand (World), and other estimates from countries with the most significant corn and soybean production

The CBOT is a designated contract maker of the CME Group for the future exchange where agricultural commodity contracts are traded, and the prices charged at CBOT are a benchmark in worldwide prices.

We use the textual data extracted from the website Soybean & Corn Advisor. Since 2009, the website has provided daily news and information on soybean and corn production related to the South American growth cycles, climate, infrastructure, land use, ethanol, and alternative fuel production.

Files: - All_Headlines: All headlines from 2014 to 2020 on the website (http://soybeansandcorn.com). - All_News_Corn_Soybean: All headlines and News from 2014 to 2020 on the website (http://soybeansandcorn.com). - Headlines_Corn/Soybean: headlines that have the word corn/soybean. Label2: 0 is neutral or downtrend; 1 is uptrend. Label3: -1 is downtrend ; 0 neutral; 1 uptrend. A variation of 1% of the monthly average was used for the attribution of labels. - USDA Corn/soybean: reports WASDE; - prices_historical_corn/soybean: historical value from CBOT. Source: Macrotrends.

Acknowledgements

The WASDE data is extracted from (public domain) monthly reports produced by the U. S. Department of Agriculture. The Corn and Soybean Advisor website for offering daily textual content. The macrotends by offering historical value

Inspiration

This data set was designed for forecasting tasks that consider textual data.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Corn (WPU012202) from Jan 1971 to Aug 2025 about corn, vegetables, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

This data package shows the long-run projections for the US agricultural sector to 2025 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value by the United States Department of Agriculture (USDA).

Prices are a fundamental component of exchange and have long been important to the functioning of agricultural markets. Grain prices are closely related to grain transportation, where the supply and demand for grain simultaneously determines both the price of grain, as well as the demand for grain transportation.

This data has corn, soybean, and wheat prices 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.

The data come from three sources: USDA-AMS Market News price reports, GeoGrain, and U.S. Wheat Associates. Links are included below. GeoGrain offers granular data for purchase. The GeoGrain data here is an average of those granular prices for a given state (and the "Southeast" region, which combines Arkansas, Mississippi, and Alabama).

This is one of three companion datasets. The other two are grain basis (https://agtransport.usda.gov/d/v85y-3hep) 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).

Wheat fell to 529.25 USd/Bu on December 1, 2025, down 0.33% from the previous day. Over the past month, Wheat's price has fallen 2.62%, and is down 1.53% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on December of 2025.

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.

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.

This table derives from the grain price spreads data. It includes the latest week of data, as well as averages and standard deviations of price spreads over the past year for each commodity, origin, and destination combination. An indicator is calculated as a ratio, where the numerator is the difference between this week's price spread and the associated average price spread, and the denominator is the associated standard deviation.

Soybeans rose to 1,130.79 USd/Bu on December 2, 2025, up 0.25% from the previous day. Over the past month, Soybeans's price has risen 0.99%, and is up 14.02% 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 December of 2025.

North America Feed Enzymes Market Size was valued at USD 551 Million in 2024 and is projected to reach USD 893 Million by 2032, growing at a CAGR of 6% from 2026 to 2032.Key Market Drivers:Increasing Meat Consumption and Production: The steady increase in meat consumption across North America is driving demand for feed enzymes. According to the USDA Economic Research Service, the average American consumed about 225 pounds of meat per capita in 2023, up 2.7% from the previous year. In 2023, commercial red meat production in the United States will reach 55.3 billion pounds, while broiler production will be 46.2 billion pounds. This significant production volume requires optimized feed efficiency, which enzymes provide.Feed Cost Volatility and Price Pressures: Farmers are increasingly using feed enzymes to reduce the impact of fluctuating grain prices. According to the USDA National Agricultural Statistics Service, corn prices will average $4.85 per bushel in 2023, with seasonal fluctuations of up to 18%. The USDA Economic Research Service estimates that feed accounts for 60-70% of total livestock production costs. Feed enzymes, which typically cost $0.50-2.00 per ton but improve feed conversion ratios by 3-5%, provide significant economic benefits during periods of commodity price volatility.

This work was conducted by the Diverse Rotations Improve Valuable Ecosystem Services (DRIVES) project, based in the USDA-ARS Sustainable Agricultural Systems Lab in Beltsville, MD. The DRIVES team compiled a database of 20-plus long-term cropping systems experiments in North America in order to conduct cross-site research. This repository contains all scripts from our first research paper from the DRIVES database: "Rotational complexity increases cropping system output under poorer growing conditions," published in One Earth (in press). This analysis uses crop yield and experimental design data from the DRIVES database and public data sources for crop prices and inflation. This repository includes limited datasets derived from public sources or lacking connection to site IDs. We do not have permission to share the full primary dataset, but can provide data upon request with permission from site contacts.The scripts show all data setup, analysis, and visualization steps used to investigate how crop rotation diversity (defined by rotation length and the number of species) impacts productivity of whole rotations and component crops under varying growing conditions. We used Bayesian multilevel modeling fit to data from 20 long-term cropping systems datasets in North America (434 site-years, 36,000 observations). Rotation- and crop-level productivity were quantified as dollar output, using price coefficients derived from National Agriculture Statistics Service (NASS) price data (included in repository). Growing condtions were quantified using an Environmental Index calculated from site-year average output. Bayesian multilevel models were implemented using the 'brms' R package, which is a wrapper for Stan. Descriptions of all files are included in README.pdf.

Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":

Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)

Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)

Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report

Bioenergy Cropping Systems Study for Resilient Economic Agricultural Practices in Mandan, North Dakota Rigorous economic analyses are crucial for the successful launch of lignocellulosic bioenergy facilities in 2014 and beyond. Our objectives are to (1) introduce readers to a query tool developed to use data downloaded from the Agricultural Research Service (ARS) REAPnet for constructing enterprise budgets and (2) demonstrate the use of the query tool with REAPnet data from two field research sites (Ames, IA, and Mandan, ND) for evaluating short-term economic performance of various biofuel feedstock production strategies. Our results for both sites showed that short-term (<3 years) impacts on grain profitability were lower at lower average annual crop residue removal rates. However, it will be important to monitor longer term changes to see if grain profitability declines over time and if biomass harvest degrades soil resources. Analyses for Iowa showed short-term breakeven field-edge biomass prices of $26–$42 Mg−1 among the most efficient strategies, while results for North Dakota showed breakeven prices of $54–$73 Mg−1. We suggest that development of the data query tool is important because it helps illustrate several different soil and crop management strategies that could be used to provide sustainable feedstock supplies. 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/704e95b7-54ea-46af-8373-9f510da4e30c

This data shows the weekly cost indices of transporting grain by each mode: truck, rail, barge, and ocean-going vessels. The base of each index (set to 100) is each mode’s average cost in the year 2017. For truck, the base rate is $2.65 per gallon of diesel. For rail, the base rate is $4,833.14 per railcar. (The rail rate is the near-month secondary rail market value and monthly tariff rate with fuel surcharge for select shuttle train routes, per car.) For barge, the base rate is 327 and is based on Illinois River barge rates. The ocean indices are based on the rate, per metric ton, to Japan. For the Gulf-to-Japan ocean route, the base rate is $39.33/metric ton. For the Pacific Northwest-to-Japan ocean route, the base rate is $21.05/metric ton.

The global Agricultural Loan market is poised for substantial growth, projected to reach a market size of approximately $580 million by 2025, with an estimated Compound Annual Growth Rate (CAGR) of 12% for the forecast period of 2025-2033. This robust expansion is primarily fueled by increasing global food demand driven by a growing population, necessitating significant investment in agricultural productivity and modernization. Key drivers include government initiatives promoting agricultural development and financial inclusion for farmers, technological advancements in agriculture such as precision farming and automation requiring capital investment, and the inherent need for financing to acquire land, equipment, and manage livestock. The market's segmentation reveals that Crop Farming applications will dominate, followed by Livestock Loan types, reflecting the core needs of the agricultural sector. Furthermore, the growing emphasis on sustainable and climate-resilient farming practices will necessitate loans for adopting innovative technologies and infrastructure, presenting a significant growth avenue. However, the market faces certain restraints. Fluctuations in commodity prices, adverse weather conditions, and the inherent risks associated with agriculture can impact loan repayment capabilities, leading to cautious lending practices. Regulatory hurdles and access to credit for smallholder farmers in developing regions also present challenges. Despite these obstacles, the overarching trend towards agricultural modernization, increasing farm mechanization, and the continuous need for working capital for crop cycles and animal husbandry are expected to propel the market forward. Emerging economies in the Asia Pacific and South America regions are anticipated to witness particularly strong growth due to ongoing agricultural development and increasing investments in food security. Companies operating in this space are focusing on digital lending platforms and tailored financial products to better serve the diverse needs of the agricultural sector. This report offers a deep dive into the agricultural loan market, analyzing its concentration, characteristics, trends, and future outlook. The study encompasses the historical period from 2019-2024, with a base year of 2025 and a forecast period extending to 2033. This analysis provides actionable insights for stakeholders seeking to understand the dynamics of this vital sector, which is projected to experience significant growth and evolution over the next decade. The report details the landscape of financial institutions involved, the diverse needs of agricultural enterprises, and the burgeoning technological advancements shaping the industry.

SNAP (Soil Nutrient Assessment Program), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that provides an estimate of plant-available nutrients that the soil naturally provides. Soil test fertilizer recommendations have long been predicated upon response curves generated from fertility trials across the country. These response curves have been compared to relative yield which provide probability ranges for a response to varying fertilizer inputs. Category responses include very low, low, adequate, high or very high inversely related to probability of a response to various inputs of nitrogen, phosphate, and potassium (N, P, and K). New soil test methods, increases in computing power and access to the internet have enabled development of an interactive tool that is based on plant available NPK from both the inorganic fraction and organic pool of the soil. The new methods provide an estimate of plant available nutrients that the soil naturally provides, which has largely been ignored for decades. Since we have access to large datasets we can calculate the amounts of NPK required growing crops in lbs NPK per bu of the desired crop. For example, it requires 100 lbs of N, 50 lbs P2O5, 50 lbs K2O to grow 100 bu corn. These are the base numbers from which we subtract the soil test data after converting from the analytical ppm to Lbs P2O5 or lbs K2O. This is a straight subtraction. It also eliminates the need for "calibration data" since the soil tests reflect the soils inherent fertility. Using the example above, of 100, 50, 50 of N, P, and K required and soil test results of 25, 35, 45 then the fertilizer needed would be 75 N, 15 P2O5 and 5 K2O. This is a simple approach that doesn't get lost in relative yield-crop response curves that have been used for decades from differing geographical areas. This tool will include current fertilizer prices, soil test inputs, and crop based county averages for the last 15 years that will predict the chances of making the yield goal the user inputs compared to historical yield data for their county and calculate the fertilizer cost with and without soil testing compared to user input yield goal and county average. This tool will allow the user via the internet to produce a more straightforward approach to realistically planning next year's fertilizer inputs and associated cost. It will also show the benefits of soil testing for increased fertilizer efficiency and reduced environmental impact. Resources in this dataset:Resource Title: Website Pointer to SNAP - Soil Nutrient Assessment Program. File Name: Web Page, url: https://snap.brc.tamus.edu/Home/Index The web dashboard interface for estimating local yield based on field location (state/county), crop (, area, and yield goal; and soil NPK test results (lb/acre), Results returned illustrate local yield, fertilizer cost/acre, fertilizer needed (lb/acre), and overall chance of success (%).

Monthly barge rates for downbound freight originate from 12 port locations along the Columbia-Snake River System to Portland OR or Vancouver WA. The 12 locations are as follows: (1) Lewiston, ID; Clarkston WA; and Wilma, WA, on the Snake River (2) Central Ferry, WA, and Almota, WA, on the Snake River (3) Lyons Ferry, WA, on the Snake River (4) Windust, WA, and Lower Monumental, WA, on the Snake River (5) Sheffler, WA, on the Snake River (6) Burbank, WA, Kennewick, WA, and Pasco, WA, on the Columbia River (7) Port Kelly, WA, and Wallula, WA, on the Columbia River (8) Umatilla, OR, on the Columbia River (9) Boardman, OR, and Hogue Warner, OR, on the Columbia River (10) Arlington, OR, and Roosevelt, WA, on the Columbia River (11) Biggs, OR, on the Columbia River (12) The Dalles, OR, on the Columbia River

A base rate is set for the year for each port, which runs from August 1 to July 31, in early summer. The port-to-port rates do change each month based on a fuel surcharge. To calculate the fuel surcharge, the spot price of No. 2 Low-Sulfur Diesel for Portland, OR, is recorded for every business day of the month and then averaged at the end of the month. Based on that average, the fuel surcharge rate is increased by a certain fraction of a percent over the base rate for each port location and is applied for the second following month. For example, the fuel surcharge for September would be based on July’s average spot price, and shippers would know the barge freight rate for September at the beginning of August.