Sustainable Water Footprint Management in Agriculture: A Review of Linear Programming-Based Models and Future Directions

The sustainable management of water resources in agriculture is a global imperative as climate change, population growth, and competing demands increasingly strain freshwater systems. This review systematically analyzes 58 peer-reviewed studies that utilize linear programming (LP) and its advanced variants to optimize agricultural water use, with a specific emphasis on improving water footprint (WF) efficiency. Applications are categorized into three core domains: crop allocation and land use, irrigation scheduling, and economic optimization. The findings reveal that while LP continues to dominate, alternative models—such as mixed-integer programming (MILP), weighted and fuzzy goal programming (WGP, FGP), and fractional programming—are gaining traction for their ability to address real-world complexities and multi-objective decision environments. However, critical gaps remain, particularly in the integration of WF indicators, climate variability, and socio-economic dynamics. This review not only maps the existing optimization landscape but also proposes a forward-looking research agenda. Key directions include the development of hybrid models, the explicit incorporation of WF metrics into objective functions, and the integration of decision-support systems for policy and farm-level planning. WF-aware optimization is thus positioned not merely as a technical instrument, but as a transformative tool for advancing agricultural sustainability—enhancing resilience, equity, and ecological stewardship in water-scarce regions and beyond.