Corn rose to 401.53 USd/BU on September 10, 2025, up 0.07% from the previous day. Over the past month, Corn's price has risen 4.29%, but it is still 0.80% 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.

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.

Wheat fell to 499.28 USd/Bu on September 10, 2025, down 0.29% from the previous day. Over the past month, Wheat's price has fallen 3.05%, and is down 13.81% 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.

Corn Prices - Historical chart and current data through 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 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

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 Agribusiness, in co-operation with the Agriculture Division of Statistics Canada and various government departments and farm marketing boards.

Soybeans fell to 1,007.94 USd/Bu on September 10, 2025, down 0.35% from the previous day. Over the past month, Soybeans's price has risen 1.63%, and is up 0.74% 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.

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

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.

This dataset provides values for CORN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset contains Ontario corn 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, 2025 to July 31, 2025. Data for August 1, 2025 to December 31, 2025 will be added as it becomes available.

Chicago Corn Prices - Historical chart and current data through 1951.

This data set contains Ontario corn 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.

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).

Datasets which include tables and figures after computing the cointegration analysis using the Johansen test for maize prices in Botswana and South Africa. The data showcases the relationship of maize prices between Botswana and South African maize markets. It also includes the time series analysis for maize prices according to grades between the mentioned countries.

The fluctuations in corn prices not only increase uncertainty in the market but also affect farmers’ planting decisions and income stability, while also impeding crucial investments in sustainable agricultural practices. Collectively, these factors jeopardize the long-term sustainability of the corn sector. In order to address the challenges posed by maize price volatility to the sustainability of the industry, this study proposes a multi-module wavelet transform-based fusion forecasting model: the TLDCF-TSD-BiTCEN-BiLSTM-FECAM (TLDCF-TSD-BBF) model, which is capable of accurately predicting short-term maize price volatility, thereby enhancing the sustainability of the industry. The model integrates a three-layer decomposition combined dual-filter time-series denoising method (TLDCF-TSD), a bidirectional time-convolutional enhancement network (BiTCEN), a bidirectional long- and short-term memory network (BiLSTM), and a frequency-enhanced channel attention mechanism (FECAM) to improve prediction accuracy and robustness. First, TLDCF-TSD is used to decompose the corn price time series into multiple scales, effectively separating the frequency components, extracting the signal details and trend information, and reducing the data complexity and non-stationarity. Secondly, BiTCEN designed in this paper effectively captures the short-term dependencies in the corn price data through the unique bidirectional structure and the special hybrid convolutional structure, and then accurately extracts the local features of the data, while BiLSTM mines the long-term trends and complex dependencies in the data by exploiting its bidirectional processing and long-term memory capabilities. Finally, FECAM enhances the focus on key temporal features in the frequency domain by grouping the input features along the channel dimensions and applying discrete cosine transform to generate attention vectors, improving the prediction accuracy and robustness of the model. The dataset utilized in this study was sourced from the BREC Agricultural Big Data platform, ensuring the reliability and accuracy of the corn price data for our analysis. This study utilizes price data from China’s five major corn-producing regions as a case study to demonstrate the efficacy of the proposed model in corn price forecasting. Through extensive experimentation, it has been established that the model significantly outperforms existing baseline models across various evaluation metrics. To be more specific, when dealing with different datasets, its MAE values are 0.0093, 0.0137, 0.0081, 0.0055, and 0.0101 respectively; the MSE values are 0.0002, 0.0002, 0.0001, 0.0001, and 0.0002 respectively; the MAPE values are 1.3630, 1.7456, 1.1905, 0.8456, and 1.7567 respectively; and the R2 values are 0.9891, 0.9888, 0.9943, 0.9955, and 0.9933 respectively. These data fully demonstrate the excellent performance of this model.

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.

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 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 Corn Index: A Reliable Gauge of Global Corn 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