aceMapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment
In India, regional yield gaps continue to widen despite increased agricultural productivity, owing to socioeconomic inequality and climate variability. This study develops a novel Yield Gap Vulnerability (YGV) framework to measure agricultural fragility at the district-level by integrating agricultural, hydrological, meteorological, and socioeconomic indicators to observed yield gaps for major cereals (rice and wheat) and nutri-crops (maize and millet). An integrated Machine Learning (ML) approach is used that c…
The integrated ML-based Yield Gap Vulnerability framework provides a data-driven decision-support tool that can support sustainable agricultural management, spatial planning and risk reduction by identifying vulnerability hotspots for region-specific measures in India.
Despite increased modal yields for rice and wheat, regional yield gaps continue to widen and the share of high-vulnerability districts has risen, particularly in resource-stressed regions such as the Indo-Gangetic Plains, driven by socioeconomic inequality and climate variability.
Evidence
- Peer-reviewedJournal of Environmental Management2026-08-29
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Truvace Impact Record TRV-2026-0939, v1: “Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment.” Truvace, 2026-08-31. /record/TRV-2026-0939 (accessed at citation time). sha256 c55eda1789d3804e…
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