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Crop Production and Nutrient Analysis India
Data Science and Analytics
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About
Unlock detailed insights into Indian agriculture with this robust dataset designed for crop production analysis and yield prediction. The collection aggregates vital agricultural parameters, including soil composition (Nitrogen, Phosphorus, Potassium, and pH levels), climatic conditions (temperature and rainfall), and crop-specific metrics across various Indian states. It captures the interplay between environmental factors and agricultural output, making it an essential resource for training machine learning models, performing exploratory data analysis, and developing simulation tools for the agrarian sector. The data facilitates the examination of crop performance across different seasons (Kharif and Rabi) and helps in determining optimal conditions for specific crops like rice and maize.
Columns
- farm: An identifier or index for the agricultural record.
- State_Name: The Indian state where the farming takes place (e.g., Uttar Pradesh, Madhya Pradesh).
- Crop_Type: The seasonal classification of the crop, such as Kharif or Rabi.
- Crop: The specific variety of crop being cultivated (e.g., Rice, Maize).
- N: The ratio or content of Nitrogen in the soil (Range: 10 to 180).
- P: The ratio or content of Phosphorus in the soil (Range: 10 to 125).
- K: The ratio or content of Potassium in the soil (Range: 10 to 200).
- pH: The acidity or alkalinity level of the soil (Range: 3.82 to 7.00).
- rainfall: The amount of rainfall received in millimetres (Range: ~3.27 to 3322).
- temperature: The ambient temperature in degrees Celsius (Range: ~1.18 to 35.3).
- Area_in_hectares: The total land area used for cultivation (up to ~726k hectares).
- Production_in_tons: The total crop production volume in tons.
- Yield_ton_per_hec: The calculated yield per hectare of land.
Distribution
This dataset is provided in CSV format and contains approximately 99,800 records (rows) and 13 columns. The file size is roughly 9.89 MB. The data is structured to ensure high validity, with key columns such as State_Name, Crop_Type, and Crop showing 100% valid entries without mismatched or missing values.
Usage
- Crop Yield Prediction: Train regression models to forecast agricultural output based on soil and weather parameters.
- Fertilizer Optimisation: Analyse the N, P, K, and pH levels to recommend optimal soil treatments.
- Regional Agriculture Analysis: Compare crop performance across different Indian states and seasons.
- Climatic Impact Studies: Assess the correlation between rainfall, temperature fluctuations, and agricultural productivity.
- Simulation and Modelling: Create digital twins of farming environments for academic or commercial simulations.
Coverage
- Geographic Scope: The data covers multiple states in India, with significant representation from Uttar Pradesh and Madhya Pradesh.
- Demographic/Sector Scope: Focuses on the agricultural sector, specifically crop production and farm management.
- Temporal/Seasonal Scope: Includes data across major cropping seasons, specifically Kharif and Rabi.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: For building predictive models and performing exploratory data analysis.
- Agricultural Researchers: For studying soil health and crop suitability in different climatic zones.
- EdTech and Students: For learning classification and regression techniques using real-world agricultural data.
- Agri-Tech Startups: For developing decision support systems for farmers.
Dataset Name Suggestions
- Indian Crop Production and Soil Attributes
- India Agriculture Yield and Climate Dataset
- Crop Production and Nutrient Analysis India
- Indian Agrarian Soil and Yield Metrics
Attributes
Original Data Source: Crop Production and Nutrient Analysis India
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