Wheat rose to 505.46 USd/Bu on October 21, 2025, up 0.14% from the previous day. Over the past month, Wheat's price has fallen 1.04%, and is down 12.25% 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.

Corn rose to 423.84 USd/BU on October 21, 2025, up 0.14% from the previous day. Over the past month, Corn's price has risen 0.50%, and is up 1.76% 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 October of 2025.

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.

Graph and download economic data for Global price of Wheat (PWHEAMTUSDM) from Jan 1990 to Jun 2025 about wheat, World, and 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.

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 Range_t = ln(H_t) - ln(L_t), where H_t and L_t 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

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: North Eastern, Rift Valley, Coast, Eastern, Nairobi, , Central, Nyanza, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Market Average

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 December 31, 2024.

Soybeans rose to 1,036.34 USd/Bu on October 21, 2025, up 0.44% from the previous day. Over the past month, Soybeans's price has risen 2.51%, and is up 4.50% 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 October of 2025.

Historical Manitoba market crop prices, based on weekly surveys of grain buyers' pricing This table contains weekly and monthly prices for different crops, based on weekly surveys of grain buyers' pricing, as well as other publicly available market sources. The table contains prices for the current and nine previous years. Each weekly dataset contains prices on the sales that occurred from Friday to Thursday. The monthly price represents a simple average of the corresponding weekly prices. For more information on major crops in Manitoba please visit ARD Livestock Markets and Statistics. This table is used in the Manitoba Crop Prices Historical dashboard. Fields included [Alias (Field name): Field description] Year (Year): Year from the selection of last 10 years including current year Period (Period): Period of time to be presented on charts from the selection of Monthly and Weekly PeriodNo (PeriodNo): Serial number of period (1-12 for monthly presentation, 1-52 for weekly presentation). For weekly presentation, each week contains prices on sales that occurred from Friday to Thursday (e.g., Week 1 of 2021 represents sales between Friday, Jan. 1, 2021 and Thursday, Jan. 7, 2021, with the corresponding report published by the department on Friday, Jan. 8, 2021). For monthly presentation, each month contains simple average prices of weeks, which had more than two days of corresponding month in the period from Monday to Friday (e.g., if the first day of the month is Monday, Tuesday or Wednesday, monthly average includes this week; but if the first day of the month is Thursday or Friday, the weekly prices are included in monthly average prices of a previous month). Price (Price): Crop price for the corresponding period of time, in C$ per tonne Crop Category (Crop): Category of grains and oilseeds from the selection of: Wheat, Northern Hard Red; Wheat, Western Red Spring; Wheat, Red Winter; Wheat, Special Purpose (Low Vomi); Barley, #1CW; Corn, #2; Oats, #2CW; Flaxseed, #1CW; Canola, #1CR; Canola Meal, 34%, Altona; Soybeans; Soymeal, 46%, Wpg; Peas, #2 Yellow.

This table contains weekly and monthly average prices for different Manitoba crops. Prices are based on weekly surveys of grain buyers' pricing as well as other publicly available market sources. The table contains prices for current and previous years, as well as five-year average weekly and monthly prices. Each weekly dataset contains prices on the sales that occurred from Friday to Thursday. The monthly price represents a simple average of the corresponding weekly prices. For more information on major crops in Manitoba please visit ARD Livestock Markets and Statistics website. This table is used in the Manitoba Crop Prices and Manitoba Crop Prices Current year dashboards. Fields included [Alias (Field name): Field description]

Period (Period): Period of time to be presented on charts from the selection of Monthly and Weekly. PeriodNo (PeriodNo): Serial number of period (1-12 for monthly presentation, 1-52 for weekly presentation) – For weekly presentation, each week contains prices on sales that occurred from Friday to Thursday (e.g., Week 1 of 2021 represents sales between Friday, Jan. 1, 2021 and Thursday, Jan. 7, 2021, with the corresponding report published by the department on Friday, Jan. 8, 2021). For monthly presentation, each month contain a simple average price of weeks, which had more than two days of corresponding month in the period from Monday to Frida (e.g., i.e. if the first day of month is Monday, Tuesday or Wednesday, monthly average includes this week. But if the first day of month is Thursday or Friday, the weekly prices are included in monthly average prices of a pervious month). Crop Category (Crop): Category of grains and oilseeds from the selection of:

Wheat, Northern Hard Red; Wheat, Western Red Spring; Wheat, Red Winter; Wheat, Special Purpose (Low Vomi); Barley, #1CW; Corn, #2; Oats, #2CW; Flaxseed, #1CW; Canola, #1CR; Canola Meal, 34%, Altona; Soybeans; Soymeal, 46%, Wpg; Peas, #2 Yellow; Wheat, Western Red Spring;

Previous Year Price (Previous): Crop price in corresponding period of previous year, in C$ per tonne. Current Year Price (Current): Crop price in corresponding period of current year, in C$ per tonne. 5-Year Average Price (Average5): Crop price in corresponding period averaged over last five years (excluding current year), in C$ per tonne

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: West, East, South, Market Average

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Market Average

Cereals Price Index in World decreased to 105 Index Points in September from 105.60 Index Points in August of 2025. This dataset includes a chart with historical data for World Cereals Price Index.

Corn Prices - Historical chart and current data through 2025.

Coffee fell to 405.39 USd/Lbs on October 21, 2025, down 0.16% from the previous day. Over the past month, Coffee's price has risen 10.36%, and is up 62.99% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on October of 2025.