acedistrict-level agricultural vulnerability to yield gaps in India assessed by ML-based YGV framework
Source article: Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment
Abstract: 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…
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Patna rice (313050258) by Chandan Singh from India. CC BY 2.0 · https://creativecommons.org/licenses/by/2.0
Researchers developed a Yield Gap Vulnerability framework for India that integrates agricultural, hydrological, meteorological and socioeconomic indicators with observed yield gaps for rice, wheat, maize and millet at district level, using an integrated Machine Learning approach with XGBoost, Random Forest and Artificial Neural Network models.
The work matters because it provides statistical evidence that vulnerability is shaped by both climatic stressors and socioeconomic and institutional capacity, offering a scalable decision-support tool for adaptive management; uncertainty remains about how hotspot identification will translate into prioritized region-specific measures and long-term risk reduction across diverse agricultural systems.
- Study integrates agricultural, hydrological, meteorological, and socioeconomic indicators to observed yield gaps for rice, wheat, maize and millet at district level in India.
- Results show pronounced spatial heterogeneity and that modal yields of rice and wheat have increased while share of high-vulnerability districts also rose in resource-stressed regions such as the Indo-Gangetic Plains.
- Maize and millet exhibit emerging yield bimodality indicating localized productivity gains from intensified management within predominantly rainfed systems.
- Model evaluation shows XGBoost consistently outperforms Random Forest and Artificial Neural Network models in both hydro-meteorology-only baseline and all-inclusive variant.
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.
The rundown
The authors built a Yield Gap Vulnerability framework at district scale in India, combining agricultural, hydrological, meteorological and socioeconomic indicators with observed yield gaps for rice, wheat, maize and millet.
By publication date 2026-08-29, evaluation found XGBoost outperformed Random Forest and Artificial Neural Network in both baseline and all-inclusive variants, and revealed spatial heterogeneity including rising high-vulnerability share in the Indo-Gangetic Plains and bimodality for maize and millet in rainfed systems.
Sources
- Peer-reviewedJournal of Environmental Management2026-08-29
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