Download This PaperDynamic Irrigation Management under Weather Uncertainty and Soil Heterogeneity
63 Pages Posted: 1 Jun 2020 Last revised: 29 Dec 2025
Date Written: December 17, 2025
Abstract
Problem Definition: This paper studies dynamic irrigation management under weather uncertainty and spatial soil heterogeneity of the field. Academic/practical relevance: Irrigation is a key method for increasing agricultural yield, addressing growing concerns about food insecurity in recent years. However, managing irrigation effectively is a complex task, particularly under uncertain weather conditions. With rising concerns about freshwater scarcity, increasing production costs, and the impacts of climate change, it is imperative to develop efficient and sustainable irrigation strategies. This paper proposes an (s, S)-type irrigation policy designed for farmers facing uncertain weather conditions and spatial soil heterogeneity. Methodologically, we build on and extend the existing operations management literature on the optimality of (s, S)-type policies by incorporating endogenous demand, providing a novel approach to irrigation management. Methodology: We formulate our problem as a stochastic dynamic program. Results: We demonstrate that an (s, S)-type irrigation policy is optimal for an approximate system with homogeneous soil structure. Building on this foundation, we derive performance bounds and develop effective heuristic policies for the more general case that incorporates soil heterogeneity. Our numerical results reveal significant improvements over current irrigation management practices. Managerial Implications: Our proposed policies could substantially enhance farmers' profit margins and water savings compared to commonly used irrigation practices. Moreover, we demonstrate that the benefits of these policies become increasingly significant as water scarcity intensifies. Consequently, the proposed irrigation policies present a compelling option for farmers prioritizing profitability and sustainability.
Keywords: Dynamic programming, Irrigation, Precision Agriculture, Sustainability
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