Corn rose to 400.28 USd/BU on July 16, 2025, up 0.07% from the previous day. Over the past month, Corn's price has fallen 7.93%, and is down 2.07% 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 July of 2025.

Interactive chart of historical daily corn prices back to 1959. The price shown is in U.S. Dollars per bushel.

In Q1 2025, the Maize market experienced notable fluctuations, with prices rising sharply in January and February before declining in March. January saw a significant increase due to adverse weather in South America, particularly during the planting stages, which reduced crop yields. Coupled with the USDA’s downward revision of U.S. production estimates, this created supply concerns, driving up both domestic and international prices. The weaker U.S. dollar also boosted Maize exports, particularly to China and Mexico, further supporting the upward price trend.

In 2022, 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.

Explore the factors influencing fluctuation in global corn prices, including weather conditions, supply and demand dynamics, geopolitical events, biofuel policies, and economic changes, which have recently caused an upward trend.

China Settlement Price: Dalian Commodity Exchange: Corn: 1st Month data was reported at 2,322.000 RMB/Ton in Apr 2025. This records an increase from the previous number of 2,162.000 RMB/Ton for Mar 2025. China Settlement Price: Dalian Commodity Exchange: Corn: 1st Month data is updated monthly, averaging 2,005.000 RMB/Ton from Sep 2004 (Median) to Apr 2025, with 248 observations. The data reached an all-time high of 2,914.000 RMB/Ton in May 2022 and a record low of 1,131.000 RMB/Ton in Nov 2004. China Settlement Price: Dalian Commodity Exchange: Corn: 1st Month data remains active status in CEIC and is reported by Dalian Commodity Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Dalian Commodity Exchange: Commodity Futures: Settlement Price.

This statistic shows the price of edible corn oil in the United States from 2004/05 to 2023/24. As of 2017/2018, corn oil prices stood at around 30.35 cents per pound in the United States. By 2022/23, the prices of corn oil increased to 61.62 cents per pound.

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. In general, consumption recorded a relatively flat trend pattern. As a result, consumption attained the peak level of $11.9B. From 2023 to 2024, the growth of the market remained at a lower figure.

Graph and download economic data for Global price of Corn (PMAIZMTUSDM) from Jan 1990 to May 2025 about corn, World, and price.

This paper examines the relationship between corn prices and hog prices in the United States using monthly time-series data in a two-stage least squares regression. Ethanol production and various types of genetically modified corn seed research and development are used as instrumental variables for corn prices to account for endogeneity in the model, by removing changes in corn and hog prices that occur due to the reverse-causal relationship between the two commodities. Ethanol production was determined to be the strongest instruments for corn prices. The results indicate that increases in the price of corn increase the price of hog by a smaller, yet still significant magnitude.

In recent years, the US corn farming industry has experienced volatile revenue largely driven by changes in corn prices, production levels and crop yields. Early in the current period, up through 2022, corn prices saw a significant increase, which resulted in considerable revenue growth for farmers. This increase was fueled by high demand for biofuels and animal feeds and limited global supplies. However, as production ramps up both domestically and internationally, the industry has begun to feel downward pressure on corn prices. Record yields have led to oversupply, driving prices downward and increasing market competition. While sectors like livestock agriculture and industrial production have supported demand, the surplus has outpaced consumption and kept prices lower, causing challenges for farmers trying to maintain profit as fertilizer and seed prices stay high. Industry revenue has grown at a CAGR of 1.9% to reach an estimated $66.9 billion after a decrease of 5.6% in 2025. On the international front, the US corn export market has demonstrated resilience despite reduced demand from China, driven by geopolitical tensions and China's shift toward agricultural self-sufficiency. Retaliatory tariffs have further strained this trade relationship, while increased corn production from South America has intensified global competition, impacting US market share. However, the Russia-Ukraine conflict has disrupted Ukrainian corn supplies, leading many countries to turn to the US as an alternative source. This shift has bolstered exports to Asian and Latin American markets, mitigating the decline from China. Reduced domestic demand for products like high fructose corn syrup due to health concerns has made international trade increasingly vital for balancing the US corn market. Corn prices will remain volatile in the coming years, influenced by fluctuating crude oil prices that impact ethanol production and growing international competition. Policy changes, such as ethanol mandates and evolving trade agreements, will reshape international demand, offering growth opportunities abroad if US farmers can adapt to new biofuel and market needs. Climate change is a significant concern, threatening yields due to heat stress and shifting precipitation and insect population patterns. To protect productivity, farmers will have to invest more in fertilizers, pesticides, irrigation technologies and drought-tolerant crops. Industry revenue is forecast to grow at a CAGR of 0.7% to reach $69.1 billion in 2030.

This statistic shows the price increase in food commodities between mid-June and mid-July, 2012. The price of corn increased by 33 percent in this period.

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.

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.

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.

United States PPI: Farm Products: Grain: Others: Corn data was reported at 133.800 1982=100 in Oct 2018. This records an increase from the previous number of 133.600 1982=100 for Sep 2018. United States PPI: Farm Products: Grain: Others: Corn data is updated monthly, averaging 106.550 1982=100 from Jan 1971 (Median) to Oct 2018, with 574 observations. The data reached an all-time high of 312.300 1982=100 in Aug 2012 and a record low of 43.200 1982=100 in Nov 1971. United States PPI: Farm Products: Grain: Others: Corn data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I017: Producer Price Index: By Commodities.

In Q1 2025, U.S. corn starch prices experienced significant volatility, driven by a combination of supply constraints, demand fluctuations, and logistical challenges. In January, prices maintained an upward trend due to robust demand from sectors like pharmaceuticals and food processing, along with supply disruptions caused by rising freight costs and port congestion, particularly at the Port of Los Angeles. These logistical issues, along with inventory shortages, heightened price pressures.

The developing food crisis in wake of the Russian invasion of Ukraine and the COVID-19 pandemic affects countries around the globe differently. For many households, the rising prices of wheat and corn have a significant impact on their real income. It is estimated that Armenians will see their real household income shrink by *** percent followed by Georgians and Kyrgyzstanis. The global average amounts to a decline of **** percent.

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. China's corn market is a major global player, characterized by extensive production, government support, and a focus on diverse applications such as food, feed, and biofuels. Advanced farming technologies and various corn types drive growth. The market impacts rural economies, food security, and trade, offering advantages like enhanced food security and rural development. 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.

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.

Is the TR/CC CRB Corn Index an Accurate Reflection of Corn Market Performance?

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