Towards optimal anticipatory action: Maximizing the effectiveness of agricultural early warning systems with operations research

Abstract

This paper explores how optimization can enhance anticipatory action by improving resource allocation in response to agricultural risks such as droughts and floods. Anticipatory action relies on early warning systems, which monitor, forecast, and communicate risks to trigger preemptive measures like cash transfers and resource distribution. However, translating forecasts into effective actions often relies on predefined thresholds that may not account for varying needs or constraints. Optimization methods, which use mathematical models and data-driven techniques, offer a structured approach to make these responses more targeted and equitable. To illustrate this, we first outline the agricultural risks posed by climate crises and the role of early warning systems and anticipatory action in mitigating them. We then introduce concepts from operations research and demonstrate how these methods can enhance anticipatory action, using examples such as distributing drought-tolerant seeds and tailoring cash transfers. Finally, we propose research directions to explore how optimization can be best applied to improve the outcomes of anticipatory action.