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.

Get statistical data on weekly spot market and forward contract corn prices in Ontario. Data includes: * old and new crop Chicago Board of Trade (CBOT) prices * old and new crop weekly unadjusted basis * old and new crop weekly adjusted basis * old crop weekly cash price * new crop cash price * cash price spread * CBOT price spread * Canadian dollar value * 5-year average for corn basis * 10-year average for corn basis * 10-year average cash price Statistical data are compiled to serve as a source of agriculture and food statistics for the province of Ontario. Data are prepared primarily by Statistics and Economics staff of the Ministry of Agriculture, Food and Rural Affairs, in co-operation with the Agriculture Division of Statistics Canada and various government departments and farm marketing boards.

Corn rose to 399.78 USd/BU on July 23, 2025, up 0.13% from the previous day. Over the past month, Corn's price has fallen 3.96%, and is down 4.36% 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.

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).

A "spread" can have multiple meanings, but it generally implies a difference between two comparable measures. These can be differences across space, across time, or across anything with a similar attribute. For example, in the stock market, there is a spread between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept.

In this dataset, spread refers to differences in prices between two locations, an origin (e.g., Illinois, Iowa, etc.) and a destination (e.g., Louisiana Gulf, Pacific Northwest, etc.). Mathematically, it is the destination price minus the origin price.

Price spreads are closely linked to transportation. They tend to reflect the costs of moving goods from one point to another, all else constant. Fluctuations in spreads can change the flow of goods (where it may be more profitable to ship to a different location), as well as indicate changes in transportation availability (e.g., disruptions). For more information on how price spreads are linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6).

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain basis (https://agtransport.usda.gov/d/v85y-3hep). 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, and there needs to be both an origin and a destination to have a price spread).

The origin and destination prices come from the grain prices dataset.

Get statistical data on weekly spot market and forward contract corn prices in Ontario. Data includes: * old and new crop Chicago Board of Trade (CBOT) prices * old and new crop weekly unadjusted basis * old and new crop weekly adjusted basis * old crop weekly cash price * new crop cash price * cash price spread * CBOT price spread * Canadian dollar value * 5-year average for corn basis * 10-year average for corn basis * 10-year average cash price Statistical data are compiled to serve as a source of agriculture and food statistics for the province of Ontario. Data are prepared primarily by Statistics and Economics staff of the Ministry of Agriculture, Food and Rural Affairs, in co-operation with the Agriculture Division of Statistics Canada and various government departments and farm marketing boards.

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.

The dataset contains daily price ranges calculated from the daily high and low prices for Chicago Wheat, Corn, and Oats futures contracts, starting in 1877. The data is manually extracted from the ``Annual Reports of the Trade and Commerce of Chicago'' (today, the Chicago Board of Trade, CBOT, which is part of the CME group).

The price range is calculated as Ranget = ln(Ht) - ln(Lt), where Ht and Lt are the highest and lowest price observed on trading day t.

Description of the dataset:

Date: The trading day, format dd-mm-yyyy

Range_W_F1: Price range Wheat futures, First expiration (nearby contract)

Range_W_F2: Price range Wheat futures, Second expiration

Range_C_F1: Price range Corn futures, First expiration (nearby contract)

Range_C_F2: Price range Corn futures, Second expiration

Range_O_F1: Price range Oats futures, First expiration (nearby contract)

Range_O_F2: Price range Oats futures, Second expiration

This data set contains Ontario Wheat Prices 2010-present collected by the Ridgetown College. 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. Data are available on a monthly basis. For historical price data going back to 1991, please contact the DRC at drchelp@uoguelph.ca

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.

The dataset consists of 4 EXCEL files of 590 data entries. The soybean meal and corn prices in the wholesale markets include the average prices of soybean meal and corn markets nationwide from 2019 to 2022, measured on a weekly, monthly, and quarterly basis. Each entry is expressed in yuan per kilogram, with a total of 239 items for each time scale. The dataset involves processed monthly and quarterly data, with the weekly data retained in their raw form, sourced directly from the Animal Husbandry and Veterinary Bureau of the Ministry of Agriculture. The soybean meal and corn prices in the retail markets include the average prices of soybean meal and corn markets nationwide and 29 provinces from 2019 to 2022, measured on a monthly and quarterly basis. Each entry is expressed in yuan per kilogram, with a total of 56 items for each time scale. The dataset involves processed quarterly data, with the monthly data retained in their raw form, sourced directly from the CHINA Animal Veterinary Information Net of the National Animal Husbandry General Station.

Soybeans rose to 1,007.26 USd/Bu on July 24, 2025, up 0.15% from the previous day. Over the past month, Soybeans's price has fallen 1.75%, and is down 9.48% 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.

The monthly price of wheat (hard red winter) in the United States reached an all time high in May 2022, at over *** U.S. dollars per metric ton. The unprecedented price increase began in mid-2020, due to the impact of the Covid-19 pandemic, and was later exacerbated by the Russo-Ukrainian War in March 2022. Before the war, Russia and Ukraine were among the world's five largest wheat exporters, and around one third of all international wheat imports came from these two countries. The increase of 96 dollars per ton between February and March 2022 was the single largest price hike in U.S. history, and was only the second time that prices had exceeded 400 dollars - the first time this happened was due to the financial crisis of 2008. In the five years before the Covid-19 pandemic, the price of wheat generally fluctuated between 150 and 230 dollars per ton.

Farm product prices, crops and livestock, by province (in dollars per metric tonne unless otherwise noted). Data are available on a monthly basis.

The project connects farmers with suppliers, improve agriculture extension services and inputs, and stimulate market growth to present new opportunities for millions of households to improve their standard of living and quality of life. This data asset contains agricultural prices for select markets on a monthly basis from January 2000 to December 2016 in Kebbi State. Data consists of 14 files with monthly prices for maize, millet, sorghum and rice in select markets in Kebbi State.

China Wholesale Price: Corn data was reported at 2.260 RMB/kg in 23 Apr 2025. This records an increase from the previous number of 2.240 RMB/kg for 16 Apr 2025. China Wholesale Price: Corn data is updated daily, averaging 2.360 RMB/kg from Jan 2009 (Median) to 23 Apr 2025, with 811 observations. The data reached an all-time high of 3.000 RMB/kg in 07 Dec 2022 and a record low of 1.500 RMB/kg in 18 Feb 2009. China Wholesale Price: Corn data remains active status in CEIC and is reported by National Development and Reform Commission. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RID: Livestock Breeding Condition.

Interactive chart of historical daily wheat prices back to 1975. The price shown is in U.S. Dollars per bushel.

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.

The goal of this study is to examine the significance of grain prices for economic and social history of the early modern period and its role for the evaluation of socio-economic phenomena. Grain prices in Cologne serve as an example because at least within Germany, it has the most frequently reported data and for a time period of 267 years, weekly data is available. The study is not about the specific economic history of Cologne; based on the Cologne series edited by Ebeling/Irsigler 1976/77 the general significance of grain prices is discussed. Grain prices are also put into a broader framework to acknowledge their meaning. The study contains grain prices from at the earliest 1500 to at the latest 1800 as nominal prices as well as converted in grams of silver. Due to these standards the classification of the territory needs to be limited on the German territory. The previous evaluation of grain price series from Cologne are summarized and critically discussed. The discussion is focused on data source base, how this base was chosen and the relevant institutional features of grain provision in Cologne. The question, in how far grain prices fulfill the required condition to serve as indicators for economic development, can be reconstructed not only looking at studies on development of prices. Especially the importance of grain in alimentation of the early modern period, the consumption of grain and the quantitative restriction of grain demand are essential parts of the analysis. Until long into the 19th century grain prices were influenced by strong political regulations. Therefore it will be analyzed as well which economic policy measures concretely influenced grain prices and in how far these issues are covered in the theoretical economic literature. The transition of the grain market in the middle of the 18th century marks the beginning of the scientific investigation on prices and their history. The analysis of the statistical material and of the underlying sources until the present age can only be undertaken for some basic works, due to the richness of material. Subdivision of the study (list of tables): - Indices of nominal rye prices in Germany (1500 - 1800)- Rye prices in Germany in g silver/ hectoliters (1500 - 1800)

Abstract copyright UK Data Service and data collection copyright owner.

The period of the study was characterised by major improvements in financial institutions and transport. The objectives of the study were to see how pricing behaviour changed as a result of this. Since the prices are weekly and by county, it is possible to see how seasonality and regional patterns changed over time. Quantifying the effects of these changes should enable us to see their relative importance in promoting economic growth.

The general aim of our research has been to increase our understanding of market integration and its effect on economic performance - particularly in the context of financial markets and commodity markets. Our specific objectives were (i) to create a major dataset of eighteenth and nineteenth century grain prices which can be used within our research project and which could also potentially be useful for other researchers; (ii) to estimate an econometric model to quantify the changes in financial markets, especially the spread of country banks (iii) to estimate an econometric model to measure the effects of transport networks; (iv) to use our results from to estimate the social welfare gains from market integration and to relate the improvements in market integration to the major changes in agricultural practice.
Main Topics:

The data consist of weekly prices on a county basis for England (40 counties plus London) and Wales (initially only data for North and South Wales, but for 12 counties after 1790) for domestically produced wheat, barley, oats and beans.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.