View monthly updates and historical trends for Agriculture Price Index. Source: World Bank. Track economic data with YCharts analytics.

CROP PRICE PREDICTION

INTRODUCTION

Crop yield prediction plays a vital role in modern agriculture, especially with the increasing adoption of data-driven approaches. Comprehensive datasets, including information about soil composition, environmental factors, historical yield data, and crop management practices, are crucial for accurate yield predictions. Analyzing and understanding these datasets can provide valuable insights for optimizing agricultural practices and ensuring sustainable crop production.

WHY IS YIELD PREDICTION IMPORTANT?

1. Optimized Production: Accurate yield predictions help farmers optimize their production plans by adjusting planting schedules, selecting suitable crop varieties, and implementing appropriate management practices.

2. Resource Efficiency: Data-driven yield predictions enable efficient use of resources such as water, fertilizers, and pesticides, leading to cost savings and reduced environmental impact.

3. Risk Mitigation: Predicting yields allows farmers to anticipate and mitigate risks such as crop failures due to adverse weather conditions, pest infestations, or diseases.

4. Market Planning: Yield predictions assist farmers in planning for market fluctuations by forecasting supply and demand dynamics, thereby optimizing marketing strategies and maximizing profits.

5. Sustainability: By promoting optimal resource use and reducing waste, accurate yield predictions contribute to sustainable agriculture practices and environmental conservation.

CONCLUSION

Accurate crop yield prediction is essential for modern agriculture's success, enabling farmers to make informed decisions, optimize resource use, mitigate risks, and enhance sustainability. Leveraging comprehensive datasets and advanced analytical tools empowers farmers and agricultural experts to improve production efficiency, resilience, and profitability, ultimately ensuring food security and environmental stewardship in the agricultural sector.

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 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 Index of Farm Prices of Crops for United States (M04059USM323NNBR) from Jan 1910 to Dec 1966 about crop, agriculture, price index, indexes, price, and USA.

Farm product price index (FPPI) for Canada and the provinces (index, 2007=100). Data are available on a monthly basis.

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

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Breaker Stock and Checks and Undergrades (WPU017108) from Dec 1991 to Aug 2025 about checkable, stocks, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

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.

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.

Graph and download economic data for Index of Prices Received by Farmers, All Groups for United States (M0460AUSM319NNBR) from Jan 1910 to Jan 1967 about agriculture, price index, indexes, price, and USA.

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.

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.

Permutable AI’s Wheat Intelligence tracks global wheat markets with millisecond precision, providing data and insights that reflect both supply-side fundamentals and sentiment-driven price movements. Our platform captures key market signals including: Supply and yield factors – monitoring crop conditions, planting, and harvest updates across major producing regions. Trade and demand dynamics – tracking import/export quotas, government procurement, and global consumption trends. Weather and climate impacts – identifying droughts, floods, storms, and seasonal variations affecting wheat production. Story signal analysis – detecting early headlines (e.g., wheat_first_headline_detection), monitoring volume build-up around market-moving stories, and flagging direction changes in sentiment (e.g., food security risks, geopolitical tensions, fertiliser costs). With the ability to process millions of global news stories in real time, Permutable AI enables traders, analysts, and policymakers to anticipate shifts in wheat supply, pricing, and sentiment before they impact the market.

The agricultural price index represents prices received by farmers for all US agricultural products (both livestock and crops) with base year 2011. Data and forecast are sourced from the US Department of Agriculture (USDA). The value-weighted for either livestock or crops is based on the value of total production.