Optimal agricultural water reallocation via multi-objective masterpiece optimisation for enhanced economic returns

Abstract

Water scarcity and inefficient allocation practices increasingly threaten agricultural productivity, particularly in arid and semi-arid regions where irrigation accounts for over 70% of freshwater withdrawals. Existing allocation frameworks often fail to balance competing objectives, maximising farm income, improving water-use efficiency, ensuring equitable distribution, and promoting long-term sustainability, due to the complexity and nonlinearity inherent in agricultural water systems. This study addresses the research gap by developing a multi-objective optimisation framework based on the masterpiece optimisation algorithm (MOA), a nature-inspired metaheuristic that mimics the creative process of artists generating masterpieces through three distinct phases: learning from existing works, seeking inspiration from masterpieces, and improving upon previous creations. The MOA’s unique tri-phase mechanism enables robust exploration and exploitation of the solution space to identify Pareto-optimal water allocation strategies across heterogeneous agricultural consumers with diverse cropping patterns, irrigation technologies, and economic constraints. The algorithm simultaneously optimises multiple conflicting objectives including net farm income, volumetric water productivity, distributional equity, and environmental sustainability indicators. Expected outcomes include a 15–25% increase in net agricultural income, a 20–30% reduction in water consumption per unit output, improved equity metrics across farm sizes, and enhanced resilience to drought scenarios. The model generates decision-support tools that transparently present trade-offs among economic, environmental, and social objectives. Findings offer direct policy relevance for irrigation districts, river basin authorities, and regional planning agencies seeking data-driven, adaptable solutions for sustainable water governance in agriculture.