Artificial intelligence for agricultural drought risk reduction: A governance framework for climate-resilient water management

This paper examines how artificial intelligence (AI) can support agricultural drought risk reduction through climate-resilient water management. Using California as the principal case, with comparative analysis of Australia, Mediterranean Europe, Israel, India, and lower-resource agricultural systems, the paper presents a conceptual and applied review rather than an original field trial or econometric analysis. It synthesizes evidence from disaster risk reduction (DRR), resilience theory, irrigation science, agricultural economics, digital agriculture, machine learning, and water governance. The study develops an “Adaptive AI-Enabled Drought Risk Reduction Framework” that positions AI as socio-technical infrastructure for anticipatory drought governance rather than as an agricultural automation tool alone.

The review finds that AI contributes most when it improves the timing, transparency, accountability, and adaptability of decisions before drought losses become irreversible. Benefits include crop-stress detection, water accounting, irrigation triage, producer documentation and basin-level allocation. However, these benefits depend on reliable data, institutional trust, affordability, extension support, transparent algorithms and safeguards against model failure, climate non-stationarity and digital exclusion. The paper concludes that AI should be evaluated by whether it reduces vulnerability, protects producer livelihoods, improves the legitimacy of scarce-water decisions and strengthens climate-resilient water governance.