Corn fell to 449.28 USd/BU on April 7, 2026, down 1.04% from the previous day. Over the past month, Corn's price has risen 2.69%, but it is still 4.20% 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 April of 2026.

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.

Corn Prices - Historical chart and current data through 2026.

Graph and download economic data for Global price of Corn (PMAIZMTUSDM) from Jan 1992 to Feb 2026 about corn, World, and price.

Wheat rose to 595.75 USd/Bu on April 7, 2026, up 0.08% from the previous day. Over the past month, Wheat's price has fallen 0.38%, but it is still 10.32% higher than a year ago, 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 April of 2026.

Soybeans fell to 1,156.98 USd/Bu on April 8, 2026, down 0.11% from the previous day. Over the past month, Soybeans's price has fallen 1.99%, but it is still 14.24% higher than a year ago, 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 April of 2026.

About the Dataset

This synthetic dataset simulates crop production data in the United States, covering key agricultural crops such as Corn, Wheat, and Soy. The dataset spans a five-year period (2010–2014) and includes detailed information at both the state and county levels. It has been generated to support exploratory data analysis, machine learning model testing, forecasting, and educational projects in agriculture and related fields. Context

Agriculture plays a vital role in the U.S. economy, and understanding crop production trends is important for research, policy-making, and economic forecasting. This dataset was created to mimic real-world agricultural data without compromising any confidential or proprietary information. Although the data is synthetic, it is designed to reflect plausible scenarios and trends in crop production, making it a valuable resource for analysts, data scientists, and educators who wish to work on agricultural data projects. Content

The dataset contains 2,500 records, each representing a unique instance of crop data across various regions. It includes:

Temporal Coverage: Data from the years 2010 to 2014.
Geographical Granularity: Records include both state-level and county-level information for six U.S. states (Iowa, Illinois, Nebraska, Minnesota, Indiana, and Ohio).
Crop Types: Focus on Corn, Wheat, and Soy.
Seasonal Information: Seasonal tags (Spring, Summer, Fall) that provide additional context on planting and harvest cycles.

Variables

The dataset includes the following variables:

Year: The calendar year for the record (2010–2014).
Season: The season associated with the record (Spring, Summer, Fall).
State: The U.S. state where the crop data is recorded.
County: The county within the selected state.
Crop: The type of crop, which is one of Corn, Wheat, or Soy.
Planting_Date: The date when the crop was planted. Date ranges are adjusted based on typical planting periods for each crop.
Harvest_Date: The date when the crop was harvested, generated within realistic harvesting windows.
Yield_Per_Acre: A synthetic measure of yield per acre (e.g., bushels per acre), generated within typical ranges for each crop.
Acres_Harvested: A random count of the acres harvested (ranging between 100 and 10,000 acres).
Production_Volume: The estimated total production volume calculated as the product of yield per acre and acres harvested.

Acknowledgments

Dataset Generation: This dataset was generated using Python, leveraging libraries such as Pandas, Random, and Datetime to simulate realistic crop production scenarios.
Community and Tools: Special thanks to the open-source community for providing the tools and resources that made this project possible.
Usage: This dataset is free to use for academic, research, and non-commercial purposes. If you find it helpful in your work, please consider citing or acknowledging its source in your projects.

Content

File1: Time series of commodity futures from 2000 until present. Includes 23 different commodities. File2: Categories for each commodity.

All prices are quoted in Euros.

The different commodities included in the commodity_prices.csv file are described below:

1. Energy

a/ Natural Gas: quoted in EUR/MMBtu.
b/ Low Sulphur Gas Oil: quoted in EUR/MT.
c/ WTI Crude: quoted in EUR/barrel.
d/ Brent Crude: quoted in EUR/barrel.
e/ ULS Diesel: quoted in EUR/gallon.
f/ Gasoline: quoted in EUR/gallon.

2. Industrial Metals

a/ Copper: quoted in EUR/lb.
b/ Aluminum: quoted in EUR/MT.
c/ Zinc: quoted in EUR/MT.
d/ Nickel: quoted in EUR/MT.

3. Precious Metals

a/ Gold: quoted in EUR/t oz.
b/ Silver: quoted in EUR/t oz.

4. Grains

a/ Corn: quoted in EUR/bushel.
b/ Soybeans: quoted in EUR/bushel.
c/ Wheat: quoted in EUR/bushel.
d/ Soybean Oil: quoted in EUR/lb.
e/ Soybean Meal: quoted in EUR/T.
f/ HRW Wheat: quoted in EUR/bushel.

5. Livestock

a/ Live cattle: quoted in EUR/lb.
b/ Lean Hogs: quoted in EUR/lb.

6. Softs

a/ Sugar: quoted in EUR/lb.
b/ Coffee: quoted in EUR/lb.
c/ Cotton: quoted in EUR/lb.

Inspiration:

Collated for a personal ML project. Plan to update it weekly.

Source:

Web-scraped from multiple sources.

Metadata:Data Provider: USDAData Link: USDA Quick StatsLast Updated Time: April 2, 2024Update Frequency: YearlyAdmin Unit: CountyStart Date: 1991End Date: 2023Variables in Data Table:

Field NameDescription

Year

The numeric year of the data

Period

The specific time frame, within the frequency of description (Year)

Geo Level

Aggregation level or geographic granularity of the data

State

State Name

State ANSI

American National Standards Institute (ANSI) standard 2-digit state codes

Ag District

Agricultural Statistics Districts (ASD) are defined groupings of counties

Ag District Code

Unique Agricultural Statistics Districts (ASD) Code

County

County Name

County ANSI

ANSI standard 3-digit county codes

Commodity

The primary subject of interest (e.g., Barley, Corn, Cotton)

Data Item

A method of production or action taken on the commodity

Domain

Another characteristic of operations that produce a particular commodity

Domain CategoryCategories or partitions within a domain

Value

Published data value or suppression reason code

CV (%)

Coefficient of variation, a measure of the dispersion of data points

Description:The USDA NASS dataset provides detailed information on major field crops essential to the U.S. agricultural industry. The crops covered include Barley, Corn, Cotton, Rice, Sorghum, Soybeans, and Wheat. For each crop, the dataset includes:Area Harvested: The total area of land harvested (measured in acres).Production: The quantity of crop produced (measured in bushels).Yield: The yield per unit area of land (measured in bushels per acre).Coefficient of Variation (%CV): A statistical measure reflecting the variability or dispersion of data points relative to the mean, aiding in the assessment of data consistency.The data is reported at various geographical levels, including national, state, and county, enabling users to analyze crop production dynamics across different regions. Additionally, the dataset covers multiple years or time periods, facilitating the tracking of trends and changes in crop production over time.