Corn fell to 424.95 USd/BU on October 24, 2025, down 0.71% from the previous day. Over the past month, Corn's price has fallen 0.19%, but it is still 2.34% higher 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 October of 2025.

Get the latest insights on price movement and trend analysis of Corn Oil in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

In 2024, the U.S. wet corn market decreased by -6.9% to $10.5B, falling for the second year in a row after four years of growth. Overall, consumption saw a pronounced downturn. Over the period under review, the market attained the peak level at $14.1B in 2013; however, from 2014 to 2024, consumption stood at a somewhat lower figure.

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:

1. the national-level season-average price received by farmers and
2. 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 record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

The Corn Market size was valued at USD 305.32 billion in 2023 and is projected to reach USD 391.09 billion by 2032, exhibiting a CAGR of 3.6 % during the forecasts period. Corn which is also referred to as maize is a cereal grain which is being widely grown in the world today. It was initially cultivated in Mesoamerica and has since spread to many other regions across different continents where it is a major source of food. Corn is widely consumed as staple food and also an essential part of animal’s meal and produce biofuel. Other than corn, its derivatives can also be used for wide range of applications in food & beverages, pharmaceuticals, animal feed and other industrial applications. Growing demand of convenience and processed food due to busy lifestyle is expected to boost corn sweeteners and starch market as well as overall corn market. Recent developments include: In August 2023, Bayer has introduced biotech seeds ‘Deklab DK95R’ in Indonesia with the aim of boosting the country's corn production. , In April 2023, Origin Agritech Ltd., a Chinese agriculture technology company, announced its majority-owned joint venture agreement with Shihezi City in Xinjiang Province. The deal was made to allocate 13,300 hectares of farmland for cultivating Nutritionally Enhanced Corn (NEC). , In March 2023, Corteva Agriscience revealed its intentions to launch Vorceed Enlist corn products commercially. Vorceed Enlist corn incorporates three above-ground insect protection modes of action and three below-ground insect protection modes of action, including RNAi technology. .

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Corn on 2025-10-30. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Why did the Maize (Corn) Price Change in July 2025? The Q2 2025 Maize Spot Price in North America displayed a generally downward trajectory, with an average Quarter over Quarter price fluctuation of approximately -3.52%, reflecting a mix of price declines and modest recovery by July 2025.

This dataset provides a comprehensive and up-to-date collection of futures related to corn, oat, and other grains. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular grain at a predetermined price on a future date.

Use Cases: 1. Crop Yield Predictions: Use machine learning models to correlate grain futures prices with historical data, predicting potential harvest yields. 2. Impact Analysis of Weather Events: Implement deep learning techniques to understand the relationship between grain price movements and significant weather patterns. 3. Grain Price Forecasting: Develop time-series forecasting models to predict future grain prices, assisting traders and stakeholders in decision-making.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/agriculture-arable-barley-bread-265242/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Maximum price reached during the trading session. 4. Low: Minimum traded price during the day. 5. Close: Market's closing price. 6. Volume: Number of contracts traded during the session. 7. Ticker: Unique market quotation symbol for the grain future. 8. Commodity: Specifies the type of grain the future contract represents (e.g., corn, oat).

The global corn market reached a volume of 1183.43 MMT in 2024. The market is projected to grow at a CAGR of 1.10% between 2025 and 2034, to reach a volume of around 1320.24 MMT by 2034.

The global preserved sweet corn market reached $X in 2022, growing by X% against the previous year. The market value increased at an average annual rate of X% from 2012 to 2022; the trend pattern remained relatively stable, with somewhat noticeable fluctuations being recorded throughout the analyzed period. The most prominent rate of growth was recorded in 2019 with an increase of X% against the previous year. Over the period under review, the global market reached the peak level in 2022 and is expected to retain growth in years to come.

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

Get the latest insights on price movement and trend analysis of Corn Processing in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

The global corn planting market is experiencing robust growth, driven by increasing demand for corn-based products across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the rising global population necessitates increased food production, with corn serving as a staple crop and crucial feedstock for livestock. Secondly, the burgeoning biofuel industry significantly contributes to corn demand, as it's a primary raw material for ethanol production, a renewable energy source gaining traction worldwide. Furthermore, the increasing use of corn in various food processing industries, including the manufacturing of sweeteners, starches, and corn oils, further drives market growth. Technological advancements in seed development, such as hybrid and transgenic varieties, contribute to higher yields and enhanced crop resilience, boosting market expansion. However, factors such as climate change, unpredictable weather patterns, and fluctuating corn prices pose challenges to market stability. While the market faces some constraints, the overall trend points towards continued expansion. The segmentation of the market into online and offline sales channels, along with the different corn varieties, reflects the diverse nature of the industry and provides opportunities for tailored marketing and product development. The competitive landscape is characterized by a mix of large multinational corporations and regional players. Major players such as ADM, Bunge Limited, and Louis Dreyfus Company dominate the global market, leveraging their extensive supply chains and processing capabilities. However, the market also features several significant regional players, particularly in China, demonstrating the localized nature of corn production and distribution. The regional distribution of the market is significantly influenced by factors such as arable land availability, climatic conditions, and government policies supporting agricultural development. North America, particularly the United States, remains a significant producer and consumer of corn, while regions like Asia-Pacific, particularly China and India, are exhibiting rapid growth due to expanding populations and increasing food demands. Future growth will likely be influenced by the adoption of sustainable agricultural practices, advancements in genetic engineering, and global food security initiatives. The forecast period of 2025-2033 suggests a period of continued growth, though potential market disruptions caused by geopolitical factors and environmental changes must be considered.

In 2024, the global maize market decreased by -14% to $340.8B for the first time since 2019, thus ending a four-year rising trend. The market value increased at an average annual rate of +1.6% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded in certain years. Over the period under review, the global market attained the peak level at $396.5B in 2023, and then contracted in the following year.

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

Analysis of falling corn futures, strong export sales to Mexico, and a forecasted production decline in Brazil's 2025/26 corn crop impacting market prices.

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.

Get the latest insights on price movement and trend analysis of Corn Starch in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

Wheat fell to 511.59 USd/Bu on October 24, 2025, down 0.28% from the previous day. Over the past month, Wheat's price has fallen 2.92%, and is down 10.09% 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.

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