Corn rose to 392.28 USd/BU on July 31, 2025, up 0.14% from the previous day. Over the past month, Corn's price has fallen 6.60%, and is down 1.56% 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.

Wheat fell to 539.78 USd/Bu on July 24, 2025, down 0.13% from the previous day. Over the past month, Wheat's price has risen 2.18%, and is up 0.38% 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 July of 2025.

Soybeans fell to 977.62 USd/Bu on July 29, 2025, down 1.13% from the previous day. Over the past month, Soybeans's price has fallen 4.55%, and is down 4.63% 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.

This dataset provides a comprehensive and up-to-date collection of futures related to corn, oat, and other grains. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular grain at a predetermined price on a future date.

Use Cases: 1. Crop Yield Predictions: Use machine learning models to correlate grain futures prices with historical data, predicting potential harvest yields. 2. Impact Analysis of Weather Events: Implement deep learning techniques to understand the relationship between grain price movements and significant weather patterns. 3. Grain Price Forecasting: Develop time-series forecasting models to predict future grain prices, assisting traders and stakeholders in decision-making.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/agriculture-arable-barley-bread-265242/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Maximum price reached during the trading session. 4. Low: Minimum traded price during the day. 5. Close: Market's closing price. 6. Volume: Number of contracts traded during the session. 7. Ticker: Unique market quotation symbol for the grain future. 8. Commodity: Specifies the type of grain the future contract represents (e.g., corn, oat).

NASS Data Visualization provides a dynamic web query interface supporting searches by Commodity (e.g. Cotton, Corn, Farms & Land, Grapefruit, Hogs, Oranges, Soybeans, Wheat), Statistic type (automatically refreshed based upon choice of Commodity - e.g. Inventory, Head, Acres Planted, Acres Harvested, Production, Yield) to generate chart, table, and map visualizations by year (2001-2016), as well as a link to download the resulting data in CSV format compatible for updating databases and spreadsheets. Resources in this dataset:Resource Title: NASS Data Visualization web site. File Name: Web Page, url: https://nass.usda.gov/Data_Visualization/index.php Query interface with visualization of results as charts, tables, and maps.

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

What is it?

The “Regional self-reliance model of the New England food system” explores future scenarios of regional food self-reliance. In this model, self-reliance is defined as the ratio of production to consumption and can be expressed for individual commodities, food groups, or the overall diet. The model allows a user to define assumptions about diet composition and target self-reliance for different groups of foods. The model estimates the regional self-reliance across seven food groups (grains, vegetables, fruits, dairy, protein-rich foods, fats and oils, and sweeteners) and for the overall diet. In addition, the model calculates land requirements for producing the target amounts of food from New England agriculture. These estimates are presented beside data on current land use to place the results in context.

Why was it generated?

The model was generated as part of the New England Feeding New England (NEFNE) project. The central question of NEFNE was, "What would it take for 30% of the food consumed in New England to be regionally produced by 2030?" The model addresses the agricultural production capacity of the region, while accounting for the contribution of capture fisheries and aquaculture to food production. The purpose of the model is to estimate the production capacity of the region’s land resources to evaluate the land requirements of increasing regional self-reliance in food.

How was it generated?

A team of researchers collaborated to construct the model. The model builds on prior work on regional self-reliance, the human carrying capacity of agricultural resources, and analysis of livestock feed requirements. As described below, the model estimates the land requirements of supplying a given level of self-reliance, accounting for food needs, food losses and waste, livestock feed requirements, crop yields, and land availability.

Starting from the food consumption end of the food system, the model takes input data on food intake (in servings person^-1 day^-1) by food group (e.g., grains) and distributes consumption across primary food commodities from that food group (e.g., corn meal, wheat flour) in the Loss-Adjusted Food Supply. Intake for each primary food commodity is then converted into the equivalent quantity of agricultural commodity (in pounds year^-1) needed to supply the region with a sufficient amount of that commodity to meet the target level of self-reliance, at a given projected population size. This conversion accounts for the serving size of the commodity (in grams), losses at different stages of the food system, and processing conversions. For animal products, a further step is taken to convert the quantity of food consumed into equivalent quantities of crop biomass required to feed the requisite livestock. Land requirements for each food are determined by dividing the agricultural commodity (for plant foods) or crop biomass requirements (for animal products) by regional average yields for the appropriate crop(s).

Input data were collected from an array of secondary data sources, including, the Loss-Adjusted Food Supply, the Census of Agriculture, the New England Agricultural Bulletin, Major Land Uses, the Atlantic Coastal Cooperative Statistics Program Data Warehouse, and the NOAA Fisheries Landings data portal. Additional sources used to develop the model are cited in the workbook and reference information is provided in each worksheet. The unique contribution of the model is to organize the data in a form that permits exploration of alternative scenarios of diet, target self-reliance, and land availability for the New England region.

A variety of factors shape farmers' views as they face the rising effects of climate change and consider a range of adaptation strategies to build the resilience of their farming systems. We examine a set of related questions to explore farmers' perspectives on risks and potential shifts to their operations: (1) Relative to other environmental factors, how salient of a challenge is climate change and climate-related impacts to farmers? (2) Do farmers intend to adapt to climate impacts generally?, and (3) What factors shape their use of a specific and underexplored adaptive response—farm product diversification? The data come from a survey of 179 operators within a 30-county region of Indiana, Michigan, and Ohio. The region spans various rural-urban gradients. Respondents generally represent smaller operations [median of 80 acres (32 hectares)]. Because our selection methods aimed to over-sample from food-producing farms, 60% of respondents produced some type of food or value-added product, and 40% produced only commodity feedstocks and biofuels. Although the group as a whole indicated only “somewhat” of a concern about changing weather patterns, and half did not anticipate adapting their farming practices to climate change, farmers' responses to a write-in question denoted regional climate effects as challenges to their farms. Analysis of subgroups among the respondents, according to their views of climate change, adaptation, and further diversifying their agricultural products, distinguished farmers' family considerations, and gender. Methods to elicit subgroups included correlation, regression, cluster analysis, and an examination of the many respondents (29%) who indicated uncertainty about adapting practices. Women, who participated in 29% of responses, indicated more concern with changing weather patterns and more openness to adapting farming practices compared to men. Farmers with the most family relationships to consider, and those with the greatest aspirations to employ descendants, were the most receptive to adapting their farming practices. This was the case even when respondents' concern over climate change was low. Results point to the importance of family relationships as a factor in farmers' openness to implementing adaptive and potentially mitigative actions.

Ethanol fell to 1.74 USD/Gal on July 29, 2025, down 1.28% from the previous day. Over the past month, Ethanol's price has risen 3.42%, but it is still 1.28% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Ethanol - values, historical data, forecasts and news - updated on July of 2025.

The Mississippi River (north of St. Louis, MO) and its tributaries (e.g., the Arkansas River, Illinois River, Ohio River, etc.) make use of a series of locks and dams to bring traffic up and down the waterways. Grain generally flows south from the relatively production-rich areas of the Midwest to export ports in Louisiana and feed markets in the southeast.

This dataset provides weekly information on the amount (in tons), location, and commodity of barged grain transiting the following three major points: (1) the last lock on the Mississippi, Mississippi Locks 27 (called "Miss Locks 27" in the dataset), which captures downbound traffic from the Upper Mississippi and Missouri Rivers; (2) the last lock on the Ohio River, Olmsted Locks and Dam (called "Ohio Olmstead" in the dataset), which captures any downbound traffic on the Ohio and Tennessee Rivers; and (3) the last lock on the Arkansas River, Arkansas River Lock and Dam 1 (called "Ark Lock 1" in the dataset).

Ohio Olmsted locks replaced Ohio Locks 52 beginning in November 2018.

Commodities include "corn," "soybeans," "wheat," and "other" (oats, barley, sorghum, and rye).

Combined, these three locks give a sense of barge grain traffic (by commodity) on the Mississippi--since grain shipments heading south from the Upper Mississippi River, Illinois River, Ohio River, and Arkansas River are captured. Note, however, that this data does not include all grain barge movements on the Mississippi Rover System, as some grain originates on the Mississippi below the locking portion (south of St. Louis, MO). Grain traffic originating below Lock 27 on the Mississippi is about 10 to 30 percent of total downbound grain shipments, which varies year to year.

A similar dataset, "Upbound and Downbound Loaded and Empty Barge Movements (Count)," contains information on the count of grain barges moving down the locking system (https://agtransport.usda.gov/d/w6ip-grsn) versus this dataset that shows tonnages.

Data is collected weekly from the U.S. Army Corps of Engineers' Lock Performance Monitoring System.