Corn rose to 433.53 USd/BU on December 2, 2025, up 0.01% from the previous day. Over the past month, Corn's price has fallen 0.17%, but it is still 2.43% 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 December of 2025.

Wheat fell to 529.25 USd/Bu on December 1, 2025, down 0.33% from the previous day. Over the past month, Wheat's price has fallen 2.62%, and is down 1.53% 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 December of 2025.

This dataset provides a comprehensive record of daily mandi (agricultural market) prices for five of India's most important food crops — Onion, Tomato, Potato, Wheat, and Rice — across all Indian states and nearly every district. The data spans a complete two-year period, from June 6, 2023 to June 6, 2025, making it a rich resource for time-series analysis, price forecasting, and agriculture-based policy insights.

India’s agricultural markets are dynamic and influenced by a variety of factors such as seasonal variations, supply chain disruptions, regional demand, government interventions (like MSP), weather patterns, and global commodity trends. This dataset helps capture that dynamic by offering cleaned, structured, and standardized price data directly sourced from the official Agmarknet portal — a government-run platform under the Ministry of Agriculture & Farmers Welfare, Government of India. This dataset download from Agmarknet Webside and modify then
DATASET:https://www.agmarknet.gov.in/
Each record in the dataset reflects:

🌾 Dataset Highlights
✅ Time Span:

Daily data for 2 years: from 2023-06-06 to 2025-06-06

✅ Top 5 Indian Crops Covered:

🧅 Onion

🍅 Tomato

🥔 Potato

🌾 Wheat

🍚 Rice

✅ Geographic Coverage:

All 28 states and 8 union territories

Almost all districts with their corresponding mandi (market) names

✅ Price Details (Rs./Quintal):

Min_price: Minimum price for the day

Modal_price: Most commonly traded price

Max_price: Maximum price for the day

💡 Possible Use Cases
Crop price trend analysis

Forecasting and time series modeling

Agri-market insights for farmers & traders

Machine learning and data science training

📁 Column Descriptions
Column : Description
sl_no : Sequential row ID
STATE : Name of the Indian state
District Name : District name
Market Name :Mandi/market location
Commodity : Crop name (onion, wheat, etc.)
Variety : Variety/type of crop
Grade : Grading (e.g., FAQ)
Min_price : Minimum price in Rs./Quintal
Modal_price : Modal price in Rs./Quintal
Max_price : Maximum price in Rs./Quintal
Price Date : Date of price entry (YYYY-MM-DD)

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Corn (WPS012202) from Jan 1975 to Sep 2025 about corn, vegetables, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

Learn about the various factors that influence grain commodity prices, including supply and demand, weather patterns, transportation costs, and government policies. Gain insight into how traders and analysts make predictions about price movements and why understanding these factors is crucial for farmers, traders, and consumers.

United States Agricultural Price Index: Received by Farmers: Food Commodities data was reported at 88.800 2011=100 in Oct 2018. This records a decrease from the previous number of 90.600 2011=100 for Sep 2018. United States Agricultural Price Index: Received by Farmers: Food Commodities data is updated monthly, averaging 101.000 2011=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 126.000 2011=100 in Apr 2014 and a record low of 81.000 2011=100 in Feb 2010. United States Agricultural Price Index: Received by Farmers: Food Commodities data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

Agricultural commodities prices have had a small and uncertain effect on changes in food prices at least since 2008.

This dataset contains historical crop price data for Pakistan, by city. The data is collected from the Agriculture Marketing Information System (AMIS) Pakistan. AMIS is a government agency that collects and disseminates market information for agricultural commodities in Pakistan. The dataset includes the following information:

City: The city where the crop was sold Crop: The name of the crop Date: The date the crop was sold Price: The price of the crop per unit.

This dataset is a valuable resource for farmers, traders, and policymakers. It can be used to track crop prices over time and identify trends. This information can be used to make informed decisions about crop production and marketing.

The dataset is also useful for research purposes. It can be used to study the impact of factors such as weather, pests, and government policies on crop prices. This information can be used to develop strategies to improve crop production and marketing.

Description: This dataset provides daily price records for three key agricultural commodities: coffee, wheat, and corn, spanning five decades from 1973 to 2023. The dataset is a valuable resource for researchers, analysts, and enthusiasts interested in understanding the historical price trends of these essential commodities in the global market.

Columns: - Date: The date of the price record in yyyy-mm-dd format. - Coffee (USD): Daily prices of coffee in US dollars. - Wheat (USD): Daily prices of wheat in US dollars. - Corn (USD): Daily prices of corn in US dollars.

Data Source: The dataset is compiled from reliable sources and represents a comprehensive record of daily commodity prices, making it an ideal tool for studying the dynamics of these agricultural markets over the past fifty years.

Use Cases: - Analyze long-term price trends and patterns for coffee, wheat, and corn. - Create predictive models for commodity price forecasting. - Investigate the impact of various economic and environmental factors on commodity prices. - Explore correlations between commodity prices and global events.

Acknowledgments: We would like to express our gratitude to the data sources that have contributed to the compilation of this dataset, making it freely available for research and analysis.

Note: Please cite this dataset appropriately if you use it in your research or analysis.

Start exploring the world of agricultural commodity prices by downloading this dataset today!

Agricultural Commodity Prices and Rainfall Data for India

Description: This dataset contains agricultural commodity price data for various districts of India, combined with rainfall information. It includes the modal, minimum, and maximum prices for commodities across different grades, along with the recorded annual rainfall for each district. The data is useful for analyzing trends in commodity pricing, understanding the impact of rainfall on agricultural outputs, and making informed decisions in agribusiness.

Columns:

• State: The state in India where the data was collected.
• District: The district within the state.
• Commodity Name: The name of the agricultural commodity.
• Year: The year when the data was recorded.
• Grade Standard Of Commodity: The quality grade of the commodity (e.g., Large, Medium, Small).-
• Modal Price For The Commodity (UOM: INR): The most common price for the commodity in Indian Rupees (INR).
• Minimum: The minimum price recorded for the commodity in INR.
• Maximum: The maximum price recorded for the commodity in INR.
• Annual_Rainfall: The total annual rainfall in millimeters (mm) for the district in that year.

Learn about live grain commodity prices and how they impact the cost of production for farmers and the price consumers pay for food products. Track these prices on exchanges like CME, ICE, and MGEX to monitor broader trends in the agricultural industry.

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

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.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Wheat (WPU0121) from Jan 1947 to Sep 2025 about wheat, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

Learn about the various factors that influence commodity prices for wheat, including weather conditions, government policies, economic trends, and international trade patterns. Understand how these factors impact supply and demand dynamics and why it's crucial for farmers, traders, and policymakers to closely monitor wheat prices to make informed decisions.

Overview

The data refers to Daily prices of various commodities in India like Tomato, Potato, Brinjal, Wheat etc. It has the wholesale maximum price, minimum price and modal price on daily basis. the prices in the dataset refer to the wholesale prices of various commodities per quintal (100 kg) in Indian rupees. The wholesale price is the price at which goods are sold in large quantities to retailers or distributors.

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Features of the dataset include:

• State: The state in India where the market is located.
• District: The district in India where the market is located.
• Market: The name of the market.
• Commodity: The name of the commodity.
• Variety: The variety of the commodity.
• Grade: The grade or quality of the commodity.
• Min Price: (INR) The minimum wholesale price of the commodity on a given day, per quintal (100 kg).
• Max Price: (INR) The maximum wholesale price of the commodity on a given day, per quintal (100 kg).
• Modal Price: (INR) The most common or representative wholesale price of the commodity on a given day, per quintal (100 kg).

1 INR = 0.012 USD (as on 17 August, 2023)

Use Cases

Market analysis: You can use this dataset to analyze trends and patterns in the wholesale prices of various commodities across different markets in India. This can help you understand factors that affect prices, such as supply and demand, seasonality, and market conditions. Commodity recommendation: Develop recommender systems that suggest the best markets or commodities for farmers or traders to sell or buy based on their location, preferences, and market conditions.

Licensed under the Government Open Data License - India (GODL) https://data.gov.in/government-open-data-license-india

Feel free to download the data and use it in your work. I will wait for interesting notebooks from your side. Thank you

Graph and download economic data for Producer Price Index by Commodity: Farm Products (WPU01) from Jan 1913 to Sep 2025 about agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

This dataset 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 for January 2000 to May 2010, and September 2010.