Building Climate Resilience in Agrifood Systems Through AI- Powered Agromet Advisory Services

Kumar, S and Rao, K P C and Kumar, G K (2025) Building Climate Resilience in Agrifood Systems Through AI- Powered Agromet Advisory Services. In: Lecture Series on Agrometeorology, South Asian Forum for Agricultural Meteorology (SAFOAM), 06 Sep 2025.

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Abstract

South Asia’s agrifood systems are highly vulnerable to climate variability, with frequent droughts, floods, and heatwaves threatening rainfed farming and rural livelihoods. Traditional district-level advisories lack the precision and timeliness required for effective risk management at the farm scale. To address this, ICRISAT and its partners have developed AI-powered platforms—the Intelligent Systems Advisory Tool (iSAT) and Next-Gen Climate Services Dashboard (NGCS)—which integrate historical climate data, real-time forecasts, and local agronomic information to generate automated, context-specific advisories. These systems employ machine learning and decision-tree logic to translate complex weather data into actionable insights, enabling timely recommendations on sowing, irrigation, and crop protection. Field pilots across India have demonstrated significant improvements in yield and decision-making, with 72% of farmers rating the advisories as highly useful. The platforms are now being scaled under India’s Monsoon Mission-III and adapted for Africa through CGIAR’s Climate Action and Digital Transformation Accelerator initiatives, positioning AI-driven advisory systems as global public goods for climate-resilient agriculture.

Item Type: Conference or Workshop Item (Lecture)
Divisions: Global Research Program - Enabling Systems Transformation
CRP: UNSPECIFIED
Uncontrolled Keywords: artificial intelligence, climate services, weather forecasting, risk management, Southern Asia, Southern Europe, Southern Africa, South America
Subjects: Others > Livelihoods
Others > Climate Change
Depositing User: Mr Nagaraju T
Date Deposited: 10 Nov 2025 03:59
Last Modified: 10 Nov 2025 03:59
URI: http://oar.icrisat.org/id/eprint/13386
Acknowledgement: UNSPECIFIED
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