SukhaRakshak AI – transforming drought preparedness in India
India’s increasing drought frequency and intensity demand a shift from reactive relief measures to anticipatory, risk-informed management. SukhaRakshak AI offers a transformative solution by delivering predictive, hyper-local drought intelligence that enables farmers, extension workers, and policyma...
| Main Authors: | , , , , , |
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| Format: | Brief |
| Language: | Inglés |
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International Water Management Institute
2025
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| Online Access: | https://hdl.handle.net/10568/180380 |
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| author | Amarnath, Giriraj Schmitter, Petra Sikka, Alok Rao, K. V. Sudhishri, S. Singh, V. K. |
| author_browse | Amarnath, Giriraj Rao, K. V. Schmitter, Petra Sikka, Alok Singh, V. K. Sudhishri, S. |
| author_facet | Amarnath, Giriraj Schmitter, Petra Sikka, Alok Rao, K. V. Sudhishri, S. Singh, V. K. |
| author_sort | Amarnath, Giriraj |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | India’s increasing drought frequency and intensity demand a shift from reactive relief measures to anticipatory, risk-informed management. SukhaRakshak AI offers a transformative solution by delivering predictive, hyper-local drought intelligence that enables farmers, extension workers, and policymakers to act before drought impacts escalate. By integrating climate, hydrological, agronomic, and socio-economic data, the platform supports early warning, preparedness, and targeted response at village to district scales.
SukhaRakshak AI is designed to complement and strengthen existing national and state frameworks, including PMFBY, PMKSY (including WDC–PMKSY 2.0), the National Mission for Sustainable Agriculture, NDMA drought guidelines, NRAA watershed guidelines, and state contingency plans. Its predictive insights improve the timing, targeting, and effectiveness of interventions such as crop advisories, water management, insurance triggers, and drought declarations.
A core strength of the platform is its focus on last-mile delivery. Localized advisories disseminated through SMS, IVR, WhatsApp, and community radio in 22+ Indian languages—leveraging AI4Bharat—ensure accessibility for smallholders, women farmers, and marginalized communities.
Beyond agriculture, SukhaRakshak AI benefits drought managers, insurers, agri-businesses, and researchers by enabling proactive resource allocation and resilience planning. Institutionalizing AI-driven drought services within official protocols, financing, and extension systems is critical to scaling impact nationally and building long-term drought preparedness, food security, and livelihood resilience. |
| format | Brief |
| id | CGSpace180380 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Water Management Institute |
| publisherStr | International Water Management Institute |
| record_format | dspace |
| spelling | CGSpace1803802026-01-23T02:06:45Z SukhaRakshak AI – transforming drought preparedness in India Amarnath, Giriraj Schmitter, Petra Sikka, Alok Rao, K. V. Sudhishri, S. Singh, V. K. artificial intelligence drought risk reduction policies India’s increasing drought frequency and intensity demand a shift from reactive relief measures to anticipatory, risk-informed management. SukhaRakshak AI offers a transformative solution by delivering predictive, hyper-local drought intelligence that enables farmers, extension workers, and policymakers to act before drought impacts escalate. By integrating climate, hydrological, agronomic, and socio-economic data, the platform supports early warning, preparedness, and targeted response at village to district scales. SukhaRakshak AI is designed to complement and strengthen existing national and state frameworks, including PMFBY, PMKSY (including WDC–PMKSY 2.0), the National Mission for Sustainable Agriculture, NDMA drought guidelines, NRAA watershed guidelines, and state contingency plans. Its predictive insights improve the timing, targeting, and effectiveness of interventions such as crop advisories, water management, insurance triggers, and drought declarations. A core strength of the platform is its focus on last-mile delivery. Localized advisories disseminated through SMS, IVR, WhatsApp, and community radio in 22+ Indian languages—leveraging AI4Bharat—ensure accessibility for smallholders, women farmers, and marginalized communities. Beyond agriculture, SukhaRakshak AI benefits drought managers, insurers, agri-businesses, and researchers by enabling proactive resource allocation and resilience planning. Institutionalizing AI-driven drought services within official protocols, financing, and extension systems is critical to scaling impact nationally and building long-term drought preparedness, food security, and livelihood resilience. 2025-12-30 2026-01-22T09:17:14Z 2026-01-22T09:17:14Z Brief https://hdl.handle.net/10568/180380 en Open Access application/pdf International Water Management Institute Amarnath, G.; Schmitter, P.; Sikka, A.; Rao, K. V.; Sudhishri, S.; Singh, V. K. 2025. SukhaRakshak AI – transforming drought preparedness in India. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Climate Action Program; CGIAR Sustainable Farming Program; CGIAR Accelerator for Digital Transformation. 16p. |
| spellingShingle | artificial intelligence drought risk reduction policies Amarnath, Giriraj Schmitter, Petra Sikka, Alok Rao, K. V. Sudhishri, S. Singh, V. K. SukhaRakshak AI – transforming drought preparedness in India |
| title | SukhaRakshak AI – transforming drought preparedness in India |
| title_full | SukhaRakshak AI – transforming drought preparedness in India |
| title_fullStr | SukhaRakshak AI – transforming drought preparedness in India |
| title_full_unstemmed | SukhaRakshak AI – transforming drought preparedness in India |
| title_short | SukhaRakshak AI – transforming drought preparedness in India |
| title_sort | sukharakshak ai transforming drought preparedness in india |
| topic | artificial intelligence drought risk reduction policies |
| url | https://hdl.handle.net/10568/180380 |
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