An integrated modeling approach for sugar beet cultivation: achieving economic and environmental compatibility in water, food, and energy systems

The increasing challenges of water scarcity, climate change, and energy transition require a redefinition of agricultural systems. In Iran, current land allocation strategies for sugar beet fail to capture its full economic and environmental value, particularly that of post-harvest residues. This study aimed to develop and apply an integrated national-scale optimization model to maximize the economic performance of Iran’s sugar beet value chain by optimizing provincial cultivation area under a national water-use constraint while incorporating biomethane and biofertilizer production from post-harvest residues. The model integrates province-level data on yield, irrigation demand, and processing capacity, together with cultivation costs and residue valorization through anaerobic digestion into biomethane and biofertilizer. Environmental performance was characterized using proxy indicators, including the potential for CO₂ emissions reduction through biomethane substitution and reduced dependence on synthetic fertilizers, rather than a full life cycle assessment. A differential evolution algorithm is used to maximize total system profitability under a national water-use limitation implemented through a penalty-based constraint handling approach. Results show that reallocating cultivation areas based on water productivity and processing potential increases national net profit by 23.0% without increasing total water consumption. High-efficiency provinces, such as Khuzestan and Golestan, are prioritized, while less efficient regions are downscaled. By incorporating biomethane and biofertilizer production into the economic optimization framework, the model captures the additional value up to 22.13% potential revenue of post-harvest residues while simultaneously improving resource circularity and reducing dependence on synthetic fertilizers. This systems-based approach provides a scalable framework for sustainable and economically efficient agricultural planning.