ICRISAT District Level Database

ICRISAT District Level Database

Comprehensive district-level agricultural database covering 571 districts with 1,030 variables and 11M+ data points on crops, irrigation, infrastructure, and GDP from 1966-2020.

Build a multi-factor agricultural productivity model for Maharashtra that correlates irrigation, rainfall, and input usage with crop yields across decades.
Homepage

Quick Start

# Download from http://data.icrisat.org/dld/
import pandas as pd
df = pd.read_excel('ICRISAT_DLD.xlsx', sheet_name='Crop')
mh = df[df['State_Name'] == 'Maharashtra']
print(f"Maharashtra records: {len(mh)}")
print(f"Years: {mh['Year'].min()}-{mh['Year'].max()}")
print(f"Districts: {mh['District_Name'].nunique()}")
Modality
tabular
Size
74 datasets; 11M+ data points; 571 districts; 1,030 variables
License
Format
Excel
Language
en
Update Frequency
annual
Organization
ICRISAT (International Crops Research Institute for the Semi-Arid Tropics)

Schema

FieldTypeDescription
district_codestringUnique district identifier
district_namestringDistrict name
state_namestringState name (filter for Maharashtra)
yearintData year (1966-2020)
variable_namestringVariable name (crop area, yield, rainfall, irrigation, GDP, etc.)
valuefloatObserved value for the variable

Build With This

Create a district development index for Maharashtra by combining agricultural, infrastructure, and economic variables into a composite score over time
Develop a climate adaptation recommender that identifies which Maharashtra districts should shift crop patterns based on 50-year rainfall and yield trends
Build a rural-urban migration predictor using agricultural productivity decline and infrastructure growth patterns at district level

AI Use Cases

Multi-factor crop analysisIrrigation impact studiesRural development indicatorsAgricultural trend forecasting
Last verified: 2026-03-07