Corn rose to 411.44 USd/BU on October 14, 2025, up 0.17% from the previous day. Over the past month, Corn's price has fallen 2.79%, but it is still 2.54% 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 October of 2025.

Soybeans fell to 1,003.90 USd/Bu on October 14, 2025, down 0.38% from the previous day. Over the past month, Soybeans's price has fallen 3.73%, but it is still 1.30% higher than a year ago, 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 October of 2025.

Learn about current grain prices per bushel for corn, wheat, soybeans, and oats, and how they are impacted by weather conditions, export demand, and government policies affecting production and trade.

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.

This dataset contains Ontario soybean grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data for January 2000 to May 2010, and September 2010.

Learn about the current trends in grain prices per bushel, including the factors affecting corn, wheat, soybean, and sorghum prices in September 2021. Stay informed on the fluctuations in the grain market to make informed investment decisions and agricultural policies.

This data set contains Ontario feed grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data from January 1, 2024 to March 31, 2024. Data for April 1, 2024 to December 31, 2024 will be added as it becomes available.

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.

Studies comparing profitability of tillage systems often examine narrow historic windows or exclude annual price fluctuations. This study uses a continuous corn (Zea mays L.) (CC; 1970–1990) and corn–soybean Glycine max (L.) Merr. Tillage × Fertilizer study in somewhat poorly drained soils in southern Illinois to reconstruct partial annual budgets with historical prices for crops, fertilizers, lime, herbicides, fuel, labor, and machinery. Combinations of tillage (moldboard plow [MP], chisel tillage [ChT], alternate tillage [AT], and no-till [NT]) and fertilizer (Control, N-only, N+NPK starter, NPK+NPK starter, and NPK broadcast) treatments were evaluated. The CC profits were highest in NPK-applied treatments followed by N-only and Control. The MP treatments were similar to ChT and more profitable than NT, while AT fell between. In CS, NPK-applied treatments were similar regardless of tillage. Combined costs for herbicide, machinery, labor, and diesel were higher in MP ...

Today's grain market report shows a mix of trends in the prices of corn, soybeans, wheat, rice, and oats. Corn and soybean prices increased due to strong export demand and concerns about dry weather conditions, while wheat prices declined slightly. However, analysts warn that the upward trend may not be sustainable in the long run. Overall, the grain market seems to be driven by events in the global agricultural sector, such as reports of crop yields and demand.

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).

Graph and download economic data for Global price of Soybeans (PSOYBUSDQ) from Q1 1990 to Q2 2025 about beans, World, and price.

This data set contains Ontario soybean grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data from January 1, 2024 to October 31, 2024. Data for November 1, 2024 to December 31, 2024 will be added as it becomes available.

Wheat rose to 500.25 USd/Bu on October 14, 2025, up 0.70% from the previous day. Over the past month, Wheat's price has fallen 4.71%, and is down 13.68% 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 October of 2025.

Commodity Prices: Corn, soybeans, WTI crude oil and Henry Hub natural gas, Chicago Mercantile Exchange Group .

The data shows grain prices at select inland origin points and export destination ports and the price spread between them. More specifically, this dataset compares interior prices of corn in Illinois and Nebraska with the Gulf; Iowa and Gulf soybean prices; Kansas and Gulf hard red winter wheat; and North Dakota and Portland hard red spring wheat.

"Hot dry spells and extreme rain in the United States Midwest increased corn and soybean prices"Authors:Magdalena Cornejo, Universidad Torcuato Di Tella and CONICETNicolás Merener*, Universidad Torcuato Di Tella.Ezequiel Merovich, Universidad Torcuato Di Tella*Corresponding author: nmerener@utdt.edu

The Implied Impact on Price dataset provides a cross-commodity view of how market narratives and sentiment correlate with price movements across agriculture, energy, and currencies. The data expresses implied directional impacts (positive or negative) derived from sentiment analysis and market drivers, helping traders understand how different commodities and assets may respond to external shocks. Key features in this sample include: Agriculture sensitivity: Corn shows strong positive implied impact (+0.80), while cotton and coffee exhibit pronounced negative sensitivity (-1.00). Livestock volatility: Live cattle and lean hogs display mixed impacts across markets, highlighting their sensitivity to both supply shocks and currency moves. Soft commodities: Sugar and soybeans reveal sharp negative relationships with certain drivers, balanced by pockets of positive sentiment. Cross-asset relationships: The dataset reveals how agriculture commodities correlate not only within their sector but also with energy and FX markets. For systematic and quantitative traders, this dataset offers a structured framework for: Identifying leading indicators across sectors. Testing cross-asset correlations between agriculture, energy, and currencies. Building factor models that incorporate sentiment-driven relationships alongside traditional price data. By quantifying implied impacts, this dataset helps trading desks refine models, stress test portfolios, and uncover new sources of alpha.

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.

Stay updated on the latest commodity grain prices and market conditions, including corn, wheat, soybeans, and rice. Learn about the factors affecting daily fluctuations and make informed decisions about buying and selling commodity grain futures.