Remote Sensing Reveals Adoption of Sustainable Rice Farming Practices Across Punjab, India

• URL: http://arxiv.org/abs/2507.08605v1
• Date: Fri, 11 Jul 2025 14:00:43 GMT
• Title: Remote Sensing Reveals Adoption of Sustainable Rice Farming Practices Across Punjab, India
• Authors: Ando Shah, Rajveer Singh, Akram Zaytar, Girmaw Abebe Tadesse, Caleb Robinson, Negar Tafti, Stephen A. Wood, Rahul Dodhia, Juan M. Lavista Ferres,
• Abstract summary: Rice cultivation consumes 24-30% of global freshwater, creating water management challenges in major rice-producing regions.<n>We developed a novel remote sensing framework to monitor sustainable water management practices at scale in Punjab, India.<n>This study provides policymakers with a powerful tool to track sustainable water management adoption, target interventions, and measure program impacts at scale.
• Score: 3.1462853484338305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rice cultivation consumes 24-30% of global freshwater, creating critical water management challenges in major rice-producing regions. Sustainable irrigation practices like direct seeded rice (DSR) and alternate wetting and drying (AWD) can reduce water use by 20-40% while maintaining yields, helping secure long-term agricultural productivity as water scarcity intensifies - a key component of the Zero Hunger Sustainable Development Goal. However, limited data on adoption rates of these practices prevents evidence-based policymaking and targeted resource allocation. We developed a novel remote sensing framework to monitor sustainable water management practices at scale in Punjab, India - a region facing severe groundwater depletion of 41.6 cm/year. To collect essential ground truth data, we partnered with the Nature Conservancy's Promoting Regenerative and No-burn Agriculture (PRANA) program, which trained approximately 1,400 farmers on water-saving techniques while documenting their field-level practices. Using this data, we created a classification system with Sentinel-1 satellite imagery that separates water management along sowing and irrigation dimensions. Our approach achieved a 78% F1-score in distinguishing DSR from traditional puddled transplanted rice without requiring prior knowledge of planting dates. We demonstrated scalability by mapping DSR adoption across approximately 3 million agricultural plots in Punjab, with district-level predictions showing strong correlation (Pearson=0.77, RBO= 0.77) with government records. This study provides policymakers with a powerful tool to track sustainable water management adoption, target interventions, and measure program impacts at scale.

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