1966 to 2022 data provides a reconstructed monthly panel of irrigation for India's Rabi season crops. It translates district-level harvested crop area into consistent monthly irrigation metrics for wheat, barley, rapeseed, and sorghum. Created by Klara Kuemmerle and hosted by Harvard Dataverse, the dataset captures irrigation variation through changes in crop composition and harvested area.
Use Cases
- Model district-level monthly irrigation water volume (`water_m3`) from crop area (`seasonal_area_ha`) and assumed irrigation depth (`irr_mm_assumed`).
- Analyze temporal trends in district-wide irrigation intensity (`irr_mm_over_district`) by `year` and `month` for specific `crop` types.
- Compare irrigation water use across different `district` regions for the same `crop` and `year` to identify spatial disparities.
- Forecast future irrigation demand by training on historical `seasonal_area_ha` and `irr_mm_assumed` relationships.
Strengths
- Covers a 57-year time range from 1966 to 2022.
- Provides data at the granular district-year-month-crop observation level.
- Includes multiple calculated irrigation metrics (`water_m3`, `irr_mm_over_district`) for direct analysis.
Limitations
- Unknown total row count and sample size for statistical reliability.
- Limited to four Rabi crops (wheat, barley, rapeseed, sorghum), excluding other major crops.
- Irrigation depth (`irr_mm_assumed`) is held fixed based on assumptions, not measured values.
Provenance
- Source
- Harvard Dataverse.
- Collection Method
- Reconstruction translating district-level harvested crop area into a monthly irrigation panel.
- Time Range
- 1966–2022.
- Freshness
- Last updated March 2026.
- Geography
- India, at the district level.