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District-wise yearly area, yield and production. This file group has two files: 1. Area, production and yield and 2. High yielding varieties The area, production yield file includes data on 20 major crops that include cereals, pulses, oilseeds, cotton, sugarcane, total fruits and vegetables. Yield is calculated based on area and production.

The data are for the annual area and production under the crops. The percent area under each crop is calculated by dividing crop area by Gross Cropped Area (GCA variable generated using a defined methodology).

For more details see definition and standards and in the data documentation manual the section on ‘data clarification and anomalies’.For season wise crop area and production data refer to season wise area and production of crops under additional data); for breakup of fruits and vegetables data by type also see files on area and vegetables under additional data.

The second file is on High Yielding Varieties (HYV / hybrids) has data on area under HYVs for 5 major cereal crops. The data on HYVs has a number of gaps in recent years implying that the area is completely under HYVs and hence no longer reported / some states do not publish this data.

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The Agriculture Industry in India Report is Segmented by Commodity Type (Cereals and Grains, Pulses and Oilseed, and More). The Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Export Analysis (Value and Volume), Import Analysis (Value and Volume), and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

India Agricultural Production: Major Crops: Achievements: Pulses data was reported at 27.504 Ton mn in 2023. This records an increase from the previous number of 27.302 Ton mn for 2022. India Agricultural Production: Major Crops: Achievements: Pulses data is updated yearly, averaging 12.840 Ton mn from Mar 1956 (Median) to 2023, with 68 observations. The data reached an all-time high of 27.504 Ton mn in 2023 and a record low of 8.350 Ton mn in 1967. India Agricultural Production: Major Crops: Achievements: Pulses data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIB002: Agricultural Production: Targets & Achievement of Major Crops.

Almost *** million were employed in India's agriculture sector in financial year 2024. However, the number of employees was the highest in fiscal year 2022 at over *** million. The industry saw an overall decline in the number of employees.

Actual value and historical data chart for India Employment In Agriculture Percent Of Total Employment

This Dataset consists of Fiscal Year and Crop-wise Area, Production and Yield statistics for All India.

This dataset encompasses extensive information on crop production in India, spanning multiple years and offering insights into agricultural trends and patterns. The dataset consists of over 246,000 records, capturing a wide array of variables related to crop production, and is intended to facilitate advanced analyses such as predictive modeling and the extraction of key insights for stakeholders in the agri-food sector.

Temporal Coverage: - The dataset covers multiple years, providing a longitudinal view of crop production trends in India. This temporal dimension is crucial for analyzing changes over time and understanding long-term patterns.

Geographical Scope: - Data is collected across various states and regions of India, reflecting the diverse agricultural landscape of the country. Regional variations in crop production can be analyzed to identify local factors affecting yields.

Crop Types: - The dataset includes information on various crop types grown across different regions. This classification allows for detailed analysis of specific crops, their production levels, and their sensitivity to various factors.

Production Metrics: - Metrics related to crop production such as yield (e.g., tons per hectare), total production volume, and harvested area are included. These metrics are essential for evaluating productivity and efficiency.

Data Quality and Completeness: - The dataset is likely to include a mix of structured and unstructured data. Data quality may vary, and preprocessing steps such as cleaning and normalization may be necessary to ensure accurate analyses.

Applications and Objectives:

Predictive Modeling: - The primary goal of analyzing this dataset is to develop predictive models for crop production. By leveraging historical data, machine learning algorithms can forecast future production levels and identify potential risks.

Insight Extraction: - The dataset provides an opportunity to uncover key indicators and metrics that significantly influence crop production. Insights can help stakeholders make informed decisions regarding crop management, resource allocation, and policy formulation.

Trend Analysis: - Longitudinal analysis of the data can reveal trends and patterns in crop production, helping to understand how factors such as technological advancements, policy changes, and environmental conditions affect agriculture.

Stakeholder Collaboration: - The dataset supports the development of collaboration platforms that connect various stakeholders in the agri-food sector. By integrating data from multiple sources, stakeholders can collaborate more effectively to address challenges and optimize production.

Key Features: 1. State_Name: Represents the name of the state in India where the crop data was recorded. 2. District_Name: Specifies the district within the state where the crop data was collected. 3. Crop_Year: Indicates the year in which the crop was harvested. 4. Season: Denotes the agricultural season (e.g., Kharif, Rabi) during which the crop was grown. 5. Crop: Identifies the type of crop that was cultivated. 6. Area: Represents the total land area used for cultivating the crop. 7. Production: Indicates the total quantity of the crop produced from the specified area.

India Precision Agriculture Market was valued at USD 102.31 Million in 2024 and is anticipated to reach USD 145.49 million with a CAGR of 6.12% through the forecast period.

Data and insights on the Agriculture sector in India - production and yield per hectare, minimum support price (MSP), and comparison with global peers.

Rajasthan is a state of India that has been incorporating trending agricultural methods due to the scarcity of the water as a resource and thus becomes a unique hotspot for data scientists and machine learning engineers to use their predictive analysis methods in the domain of agriculture for future.

This dataset provides comprehensive agricultural data for Rajasthan, India from 2018 to 2019. It contains four of his CSV files: Crop_Production_Data.csv, Water_Usage_Data.csv, Soil_Analysis_Data.csv, and Crop_Price_Data.csv. This dataset will be a valuable resource for researchers and data scientists working in the agricultural sector, enabling them to analyze and model various aspects of agricultural practices and market dynamics in Rajasthan.

1. Crop_Production_Data.csv: This file contains crop production information such as district, crop type, season, area (hectares), yield (quintals) and production (tonnes). Researchers and analysts can use this data to study crop productivity, analyze seasonal variations, and identify patterns of crop production in different districts of Rajasthan.
2. water_usage_data.csv: The Water_usage_data.csv file provides insight into water usage in agriculture. This includes details such as district, crop type, irrigation method used, water consumption (liters/ha), and water availability (liters/ha). This information can be used to analyze water management practices, assess the efficiency of different irrigation methods, and study the impact of water availability on crop production.
3. Soil analysis_data.csv: This file contains the soil quality, including area, soil type, pH, percentage of organic matter, nitrogen content (kg/ha), phosphorus content (kg/ha), and potassium content (kg/ha). Contains analytical data. Researchers and scientists can use this data to study soil properties, assess soil fertility, and understand the nutrient content of different types of soils in Rajasthan.
4. Crop_price_data.csv: The Crop_price_data.csv file contains data about crop prices for different markets (mandi) in different counties. This includes information such as district, crop type, market, date and price (INR/100 weight approx). This dataset is useful for studying crop price trends, analyzing market trends and developing crop price forecasting models in Rajasthan.

Researchers and data scientists can use this dataset to perform a variety of analyzes and studies, including:

• Crop productivity analysis and forecasting.
• Research on water management and irrigation optimization.
• Soil fertility assessment and soil health analysis.
• Market analysis and crop price forecasting.
• Evaluation of agricultural policies and interventions.
• Assessment of the impact of climate change on agriculture in Rajasthan.

India Agricultural Production: Major Crops: Achievements: Wheat data was reported at 112.743 Ton mn in 2023. This records an increase from the previous number of 107.742 Ton mn for 2022. India Agricultural Production: Major Crops: Achievements: Wheat data is updated yearly, averaging 51.980 Ton mn from Mar 1956 (Median) to 2023, with 68 observations. The data reached an all-time high of 112.743 Ton mn in 2023 and a record low of 7.990 Ton mn in 1958. India Agricultural Production: Major Crops: Achievements: Wheat data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIB002: Agricultural Production: Targets & Achievement of Major Crops.

GDP from Agriculture in India decreased to 5683.74 INR Billion in the second quarter of 2025 from 6773.89 INR Billion in the first quarter of 2025. This dataset provides - India Gdp From Agriculture- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Sustainable Agriculture in India

The aim of this Dataset is to help Indian Agriculture eco-system with right data. As there is a lack of correct data to get insights into Indian Agriculture, I this this is the best time to collect and create a data set that can help the Indian Agriculture.

More Data, More Insights.

Content

• Agri Commodity Prices and Trading info
• Weather (historical) Data that effects Agriculture
• Land Usage Statistics Per Crop
• Acreage for Each Crop
• Crop Yield Info
• Agri Inputs Data
• Crop Pest and Disease info
• Retail and Wholesale prices for all Agri Commodities
• all factors and sources that affect Agriculture and Agri Commodity prices

Acknowledgements

Inspiration

Help Agriculture. Help Farmers. Help the Agriculture eco-system

Help Needed

We need your help in identifying all the Datasets and if you are willing to share your proprietary data on Kaggle to help everyone.

The India Agricultural Biologicals Market Report is Segmented by Function (Crop Nutrition and Crop Protection) and Crop Type (Cash Crops, Horticultural Crops, and Row Crops). The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

Agricultural land (sq. km) in India was reported at 1785710 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. India - Agricultural land (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Agricultural land in India amounted to over *** million hectares in financial year 2023. This was a decrease in agricultural land when compared to the previous year. That year, Haryana and Punjab were the leading Indian states with land available for agricultural purposes.  Cultivation and farming Of all agricultural land in India, *** million hectares has been cultivated. This makes India one of the largest agricultural economies globally. Major land types include alluvial soil in the Indo-Gangetic plains, black soil in Deccan, and red and laterite soils in the southern and eastern regions – all supporting the cultivation of rice, wheat, pulses, cotton, and oilseeds across varying climatic zones. Digitalization in farming Of recent policy developments from the central government towards agriculture has been the promotion of natural farming and digital land records. Initiatives like PM-KISAN provide income support to farmers. Additionally, states including Madhya Pradesh and Uttar Pradesh are digitizing land ownership data to reduce disputes. While controversial, several other states have eased land leasing laws to attract private investment and improve land utilization.

The Indian agriculture market size was approximately USD 457.26 Billion in 2024. The market is projected to grow at a CAGR of 4.90% between 2025 and 2034, reaching a value of around USD 737.77 Billion by 2034.

🌾 Indian Agriculture Crop Production and Yield (District-level, 1998–2021)

📖 Description

This dataset provides district-level crop production, cultivated area, and yield data for India covering the years 1998 to 2020-21. It includes details of major food grains, pulses, oilseeds, and commercial crops across all districts of India.

The dataset is curated from:

Kaggle – India Agriculture Crop Production Open Government Data Platform, India (data.gov.in)

This makes it one of the most detailed publicly available datasets for Indian agriculture, enabling district-level mapping and visualization of crop performance.

📑 Dataset Content

The dataset contains 575,879 rows × 8 columns with district-level agricultural data. State_Name → Name of the State/UT (e.g., West Bengal, Maharashtra, Tamil Nadu) District_Name → Name of the District (e.g., Purulia, Akola, Madurai) Crop_Year → Year of crop production (1997 – 2020-21) Season → Agricultural season (Kharif, Rabi, Summer, Whole Year, Autumn, Winter) Crop → Name of the crop grown (e.g., Rice, Wheat, Sugarcane, Banana, Cotton) Area (Hectares) → Total cultivated area under the crop (in hectares) Production (Quintals) → Total crop output (in Quintals) Yield (Quintals/Hectare) → Calculated crop productivity = Production / Area

Agricultural Procurement: Wheat: All India data was reported at 26,202.000 Ton th in 2024. This records an increase from the previous number of 18,792.000 Ton th for 2023. Agricultural Procurement: Wheat: All India data is updated yearly, averaging 22,601.500 Ton th from Mar 1997 (Median) to 2024, with 28 observations. The data reached an all-time high of 43,344.000 Ton th in 2022 and a record low of 8,162.000 Ton th in 1997. Agricultural Procurement: Wheat: All India data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RII012: Agricultural Procurement: Wheat: by Major States.

Chhattisgarh has the highest allocation of the state budget to agriculture in India for the fiscal year 2026. Telangana followed, with agriculture receiving approximately ** percent of the state's total budget. On average, states are estimated to spend *** percent of their budget on agriculture.