Corn fell to 462 USd/BU on March 27, 2026, down 1.07% from the previous day. Over the past month, Corn's price has risen 6.64%, and is up 1.93% 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 March of 2026.

Corn Prices - Historical chart and current data through 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.

In 2025, the average price of one bushel of corn was around **** U.S. dollars. That year, the United States was the largest producer of corn in the world.

View monthly updates and historical trends for US Corn Farm Price Received. from United States. Source: US Department of Agriculture. Track economic data …

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.

Wheat rose to 605.75 USd/Bu on March 27, 2026, up 0.12% from the previous day. Over the past month, Wheat's price has risen 5.44%, and is up 14.67% 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 March of 2026.

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.

Corn Futures: TR/CC CRB Corn Index Projects Moderate Price Fluctuations

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

China Wholesale Price: Corn data was reported at 2.380 RMB/kg in 18 Mar 2026. This records an increase from the previous number of 2.360 RMB/kg for 11 Mar 2026. China Wholesale Price: Corn data is updated daily, averaging 1.800 RMB/kg from Jan 2009 (Median) to 18 Mar 2026, with 855 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.

Soybeans fell to 1,159.50 USd/Bu on March 27, 2026, down 1.21% from the previous day. Over the past month, Soybeans's price has risen 0.83%, and is up 13.34% 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 March of 2026.

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.

Cornindex: The Future of Corn Pricing?

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

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.

In Q4 2025, North America, the Maize Price Index rose by 3.47% quarter-over-quarter, reflecting Pacific-coast basis strength. Check detailed insights for Europe, MEA, South America and APAC.

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.

Corn Price Volatility Expected, Impacting TR/CC CRB Corn Index

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

The China Corn Market size was valued at USD 6.17 Million in 2023 and is projected to reach USD 6.52 Million by 2032, exhibiting a CAGR of 3.10 % during the forecast periods. Recent developments include: June 2022: The Chinese National Crop Variety Approval Committee released two sets of standards to clear the cultivation of genetically modified (GM) crops in China. For the commercial production of GM maize in China, the government has two steps in these regulations: a "safety certificate" and a "variety approval" before crops can be commercially cultivated in the provinces., July 2021: Chinese farmers sharply increased corn planting to cash in on demand-fuelled record prices, a trend that cooled the country's rampant import appetite. This expansion, mainly at the expense of soybeans and other crops, including sorghum and edible beans, boosted China's maize output in 2021-22 by at least 6 percent., June 2021: China started sustainable production of maize that could 'boost yields and cut greenhouse gas emissions and fertilizer use' in the country by 2035.. Notable trends are: Increasing Demand for Corn as Animal-based Protein Sources.

Graph and download economic data for Global price of Wheat (PWHEAMTUSDM) from Jan 2003 to Jan 2026 about wheat, World, and price.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT .

Abstract:

The author`s analysis explains to what extent the Central European agriculture and food industry has managed to satisfy the demand of the population in the centuries since the Middle Ages. For this purpose, the author collects and analyses prices, wages, rents, agricultural products, and population movements, as well as the costs of living of broad levels of the population. The price data at hand (prices of wheat and rye in Germany, Europe and America) provide a substantial basis for his analysis.

On the basis of the long-term fluctuation of corn prices in England, France, Northern Italy, Germany and Austria, three waves of development can be identified:

1. An upswing in the 13th and partly also at the beginning of the 14th century is followed by a downswing in the late Middle Ages.
2. Another upswing in the 16th century was interrupted in the 17th century;
3. a third upswing in the 18th century dissolved in the 19th century into shorter and partly opposed movements that merge again only in the late 19th and 20th century.

What do these waves mean?

There are two approaches which could explain these developments: 1. Such price fluctuations are the consequence of a fluctuating supply of money in the Central European economy. 2. The rise in prices is caused by the growing demand of a rapidly growing population. On the one hand, the author verifies the ´laws of development´ by MALTHUS and RICARDO on the basis of the historical facts. On the other hand, the historical series of developments are interpreted by way of an appropriate scheme of terms and relations regarding their meaning.

Topics:

Tables in the ZA-Online-Database HISTAT: - prices of rye in Germany (1341-1940) - prices of wheat and rye in Europe and America (1991-1830) - prices of wheat and rye in Central Europe (1201-1960)

View monthly updates and historical trends for US Sorghum Price. Source: International Monetary Fund. Track economic data with YCharts analytics.

All white, long-grain, uncooked rice regardless of package type or size. Includes organic and non-organic."

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