Wheat rose to 508.51 USd/Bu on September 2, 2025, up 1.25% from the previous day. Over the past month, Wheat's price has fallen 1.59%, and is down 7.88% 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 September of 2025.

This data set contains Ontario wheat 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.

Corn rose to 399.02 USd/BU on September 1, 2025, up 0.26% from the previous day. Over the past month, Corn's price has risen 3.11%, but it is still 0.49% lower 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 September of 2025.

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

Get statistical data on weekly spot market and forward contract winter wheat 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 winter wheat basis
• 10-year average for winter wheat basis
• 10-year average for 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.

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

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.

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).

Global Wheat Prices - Historical chart and current data through 2025.

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 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

Soybeans fell to 1,027.42 USd/Bu on September 2, 2025, down 0.73% from the previous day. Over the past month, Soybeans's price has risen 6.03%, and is up 3.10% 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 September of 2025.

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: SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, 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: Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, Market Average

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.

Russia Average Export Price: Wheat data was reported at 206.000 USD/Ton in Nov 2018. This records an increase from the previous number of 202.000 USD/Ton for Oct 2018. Russia Average Export Price: Wheat data is updated monthly, averaging 189.000 USD/Ton from Jan 2013 (Median) to Nov 2018, with 71 observations. The data reached an all-time high of 338.700 USD/Ton in Jan 2013 and a record low of 156.000 USD/Ton in Aug 2016. Russia Average Export Price: Wheat data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.PC001: Average Export Price.

Time series of major commodity prices and indices including iron, cooper, wheat, gold, oil. Data comes from the International Monetary Fund (IMF).All rights are reservedDataDataset contains Monthly prices for 53 commodities and 10 indexes, starting from 1980 to 2016, Last updated on march 17, 2016. The reference year for indexes are 2005 (meaning the value of indexes are 100 and all other values are relative to that year).LicenseThe IMF grants permission to visit its Sites and to download and copy information, documents, and materials from the Sites for personal, noncommercial usage only, without any right to resell or redistribute or to compile or create derivative works, subject to these Terms and Conditions of Usage and also subject to more specific restrictions that may apply to particular information within the Sites. Any rights not expressly granted herein are reserved.For more information please visit: Copyright and Usage.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

TR/CC CRB Wheat Index: A Reliable Indicator of Global Wheat Prices?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Get Daily/weekly data of Wholesale prices of Wheat. Price is monitored for essential commodities based on data collected from 75 market centres spread across the country representing North, West, East, South and North-eastern regions of the country. Price Monitoring Cell (PMC) in the Department of Consumer Affairs is responsible for monitoring prices of selected essential commodities. The Quality and variety of the item for which prices are reported may vary from centre to centre but remains the same for a given centre. Generally, prices are reported for the Fair Average Quality of the item for a given centre. Every centre has a standard quality and variety of item for which prices are reported by them.

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: the national-level season-average price received by farmers and 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.