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

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.

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

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.

Daily market prices of agricultural commodities across India from 2001-2025. Contains 75+ million records covering 374 unique commodities and 1,504 varieties from various mandis (wholesale markets). Commodity Like: Vegetables, Fruits, Grains, Spices, etc.

Cleaned, deduplicated, and sorted by date and commodity for analysis.

Column Schema

Data sourced from the Government of India's Open Data Platform.

License: Government Open Data License - India (GODL-India) https://www.data.gov.in/Godl

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.

Graph and download economic data for Export Price Index (End Use): Agricultural Commodities (IQAG) from Mar 1985 to Aug 2025 about end use, agriculture, exports, commodities, price index, indexes, price, and USA.

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.

This dataset provides the current daily prices of various commodities sourced from multiple markets (mandis) across different regions. It includes detailed information on the market names, commodity types, and their respective prices, offering a snapshot of real-time agricultural and other commodity market trends. The data is valuable for farmers, traders, and analysts to monitor price fluctuations, compare regional price variations, and make informed decisions. It offers insights into supply and demand dynamics, and market conditions, and helps in understanding the economic factors affecting commodity pricing. This dataset supports decision-making, price forecasting, and market research.

The World Bank’s Commodity Price historical data and forecasts are published quarterly, in January, April, July and October. The price forecasts go up to 2030. Topics: Agriculture & Rural Development

Tradefeeds Commodity Prices API enables you to get commodity prices data of the major commodity asset types like energy commodities, metals, industrial commodities, agricultural commodities and livestock commodities. Each commodity asset type has a different historical coverage. A commodity within its commodity asset type has not the same historical coverage as another commodity from the group. For example, within the group of energy commodities, crude oil has a historical coverage of 34 years while coal of only 13 years. Tradefeeds offers commodity prices data either via JSON REST API, or via downloadable databases in CSV or Excel format.

If you are interested to learn more, check out the company website: https://tradefeeds.com/commodities-prices-api/

Argentina Commodities Prices Index: USD: Agriculture data was reported at 163.412 Dec2001=100 in Oct 2018. This records an increase from the previous number of 159.657 Dec2001=100 for Sep 2018. Argentina Commodities Prices Index: USD: Agriculture data is updated monthly, averaging 163.687 Dec2001=100 from Jan 1997 (Median) to Oct 2018, with 262 observations. The data reached an all-time high of 314.574 Dec2001=100 in Aug 2012 and a record low of 89.400 Dec2001=100 in Jul 1999. Argentina Commodities Prices Index: USD: Agriculture data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.I032: Commodities Prices Index.

Agmarknet India Commodity Prices (October 2024 – August 2025)

🧩 Overview

This dataset contains daily agricultural commodity price data scraped from the Agmarknet (Government of India) portal for the period October 2024 to August 2025.
It provides granular market-level data across multiple states, including information on commodity, variety, grade, and minimum, maximum, and modal prices (in Rs./Quintal).

With over 1.1 million records, this dataset offers valuable insights into agricultural price fluctuations and regional market dynamics in India.

📅 Time Period

October 2024 – August 2025

📊 Dataset Columns Description

🌾 Data Source

Data has been scraped from the official Agmarknet portal maintained by the Directorate of Marketing & Inspection (DMI) under the Ministry of Agriculture and Farmers Welfare, Government of India.
👉 https://agmarknet.gov.in/

📈 Possible Use Cases

• Agricultural price forecasting using machine learning
• Commodity-wise seasonal and regional trend analysis
• Correlation between price volatility and climatic or policy factors
• Decision support for farmers and agri-businesses
• Market intelligence and demand-supply analysis

🧮 Dataset Statistics

• Records: 1,118,899
• Features: 11
• Coverage: 8 Indian states and 1160 markets
• Frequency: Daily

⚖️ License

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
You are free to use, share, and adapt this dataset for any purpose, provided that you give appropriate credit and share derivatives under the same license.

📢 Citation

If you use this dataset in your research, please cite it as:

Anish (2025). Agmarknet India Commodity Prices (October 2024 – August 2025).
Retrieved from https://agmarknet.gov.in/

🧠 Acknowledgments

Special thanks to the Ministry of Agriculture & Farmers Welfare, Government of India, for maintaining open access to Agmarknet data, which enables valuable research and innovation in agricultural analytics.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Tomatoes (WPU01130217) from Jan 1947 to Sep 2025 about agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

This study develops an integrated forecasting framework that combines Machine Learning (Random Forest), Deep Learning (LSTM), and Econometric (VECM) approaches to analyse the dynamic behaviour of Indian pepper and turmeric prices. The models incorporate major macroeconomic determinants, including GDP, Consumer Price Index (CPI), exchange rate, gold price, interest rate, trade volume, and foreign institutional investments (FII), to capture both non-linear and long-term relationships. Model performance was evaluated using RMSE, MAE, and MAPE metrics, alongside SHAP-based feature explainability analysis.

USDA Economic Research Service (ERS) compares prices paid by consumers for food with prices received by farmers for corresponding commodities. This data set reports these comparisons for a variety of foods sold through retail food stores such as supermarkets and super centers. Comparisons are made for individual foods and groupings of individual foods-market baskets-that represent what a typical U.S. household buys at retail in a year. The retail costs of these baskets are compared with the money received by farmers for a corresponding basket of agricultural commodities.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: Web page with links to Excel files For complete information, please visit https://data.gov.

As an essential part of daily life, the drastic fluctuations in agricultural commodity prices significantly impact producers’ motivation and consumers’ quality of life, further exacerbating market uncertainty and unsustainability. The ability to scientifically and effectively predict agricultural commodity prices is of great significance for the rational deployment of market mechanisms, the timely adjustment of supply chains, and the promotion of food policy adjustments. This paper proposes a sustainable hybrid model SV-PSO-BiLSTM which integrates Seasonal-Trend decomposition procedure based on Loess (STL), Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. This innovative approach first performs seasonal decomposition of the original data using the STL method, then applies the VMD method for double decomposition of the residual components, reconstructs the data based on sample entropy, and finally predicts agricultural commodity market prices using the BiLSTM network model optimized by the PSO algorithm. This paper investigates the market price dynamics of four agricultural commodities (chili, garlic, ginger, and pork) and one agricultural financial derivative (soybean futures). The experimental results indicate that the proposed SV-PSO-BiLSTM hybrid model achieves average values of 0.2241 for root mean square error (RMSE), 0.1665 for mean absolute error (MAE), 0.0207 for mean absolute percentage error (MAPE), and 0.9851 for the coefficient of determination (R2). These results surpass those of other comparative models, demonstrating stronger generalization, reliability, and stability. The research findings can provide effective guidance for the reasonable regulation of agricultural commodity market prices and further promote the healthy and sustainable development of the agricultural commodity industry.

Permutable AI’s Agricultural Commodities Intelligence provides comprehensive real-time insights into global agricultural markets. Covering grains, softs, livestock, and other key commodities, our system analyses critical fundamentals including supply and demand dynamics, crop yields, weather and climate impacts, and trade flows. Beyond fundamentals, our platform applies advanced story signal analysis to detect and track impactful market narratives. These include: New Story Breakouts – identifying the first emergence of market-moving events (e.g., drought warnings or trade policy changes). Story Volume Build Up – monitoring increasing coverage of agricultural developments such as export restrictions or yield forecasts. Story Direction Shifts – flagging changes in sentiment around ongoing narratives (e.g., moving from optimism to concern about harvests). Persistent Stories – capturing narratives that continue to shape market conditions, such as climate change impacts or supply chain disruptions. By integrating structured news analysis with macro fundamentals, Permutable AI equips traders, analysts, and corporates with actionable intelligence to anticipate risks and opportunities across the agricultural sector.

Procurement Support Prices of Agricultural Commodities, The Punjab 2012-2021