Introduction

Agriculture in semi-arid regions faces critical challenges concerning the sustainability of water and soil resources, intensified by climate change, hydrological stresses, and ecosystem degradation. Dezful County, located in Khuzestan province in southwestern Iran, exemplifies such conditions with a semi-arid to arid climate, very hot summers, and annual rainfall ranging from 300 to 400 mm. Although the county benefits from the Dez River and extensive irrigation networks, the current cropping pattern—dominated by irrigated wheat (45.85% of cultivated area) and forage maize (16.04%)—places immense pressure on surface and groundwater resources. While this pattern aligns with national food security policies, it overlooks the hidden environmental costs, including energy consumption, carbon emissions, and ecological resource depletion. This study aims to analyze and optimize agricultural resource consumption in Dezful County by employing an integrated water–carbon–emergy nexus approach. Unlike conventional sectoral management, this framework recognizes the complex interdependencies within food production systems. The research addresses the structural predominance of water-intensive crops and provides a quantitative decision-support tool for sustainable cropping pattern redesign.

Materials and Methods

The study was conducted in two main phases. In the first phase, the water footprint (WF), carbon footprint (CF), and input emergy were quantified for major crops under the existing cropping pattern (109,268 ha). The water footprint was disaggregated into blue (surface/groundwater), green (effective rainfall), gray (nitrogen pollution), and white (irrigation water losses) components using standard FAO-based equations (CROPWAT model, USDA rainfall method). Carbon footprint was calculated following ISO 14040/14044 LCA guidelines from cradle to farm gate, including all inputs (fertilizers, pesticides, seeds, irrigation water, fuel, electricity) with emission factors from Iranian national reports and global databases. Emergy analysis quantified all renewable and non‑renewable inputs (sun, wind, rain, soil erosion, fossil fuels, machinery, electricity, seeds, labor, fertilizers, and organic matter) after converting them into solar emjoules using specific transformities.



In the second phase, a multi‑objective mathematical programming (MOP) model was developed. The decision variables included cultivated area, net benefit, nexus index, water footprint, carbon footprint, and emergy per crop. Five objective functions were defined: maximizing net profit, maximizing the water‑carbon‑emergy nexus index, and minimizing water footprint, carbon footprint, and input emergy. Constraints included land availability (monthly and total), minimum and maximum crop areas, water balance (monthly supply limits), and floor constraints ensuring that net profit, nexus index, and environmental indicators do not worsen below current levels. The model was solved using fuzzy nonlinear programming with weights derived from expert opinions (AHP method) in GAMS software. Additionally, coupling degree (C) and coupling coordination degree (D) indices were computed to quantify interactions among emergy, carbon, and water flows.



Results and Discussion

The results confirm that the current cropping pattern imposes substantial ecological pressure. The water footprint analysis revealed that rice (3.99% of area) derived about 52% from blue water and 45% from white water (losses), indicating extreme dependence on abstracted water. In contrast, wheat and barley showed higher green water shares (approximately 50% and 56%, respectively), signaling better adaptation to effective rainfall. The carbon footprint was highest for tomato (12.4%), onion (12%), and sugar beet (10.7%), mainly due to high nitrogen fertilizer use, irrigation energy, and mechanization. Emergy input was dominated by alfalfa (18.5%), grain maize (9.8%), and forage maize (8.2%), reflecting heavy reliance on purchased non‑renewable inputs such as chemical fertilizers, fossil fuels, and pumping energy.

When comparing single‑objective scenarios, maximizing net profit alone increased profit by 4.7% and slightly reduced water footprint and emergy but left carbon footprint unchanged. Minimizing water footprint drastically reduced rice area (by ~94%) and curbed other water‑intensive crops. Minimizing carbon or emergy led to reallocation away from high‑input crops like sugar beet. However, single‑objective approaches caused extreme, often unrealistic, shifts. The multi‑objective optimization achieved a superior balance: water footprint decreased by 16.8%, carbon footprint by 17.3%, and input emergy by 9.2%, while farmers' net profit remained unchanged. Total irrigation water consumption declined by 21%. The composite nexus sustainability index (coupling coordination degree) improved from 0.76 under the current pattern to 0.97 under the optimal pattern—a 27% relative enhancement. This near‑ideal value indicates high resilience, effective synergy among water, carbon, and emergy components, and reduced vulnerability to climatic and resource shocks.

Conclusion

The 27% improvement in the nexus index confirms that transitioning from the current cropping pattern to a multi‑objective optimized plan significantly enhances both ecological sustainability and resource allocation efficiency. The MOP framework successfully reconciled conflicting economic and environmental objectives without penalizing farmers' income. For Dezful and other semi‑arid regions, adopting a strategic cropping plan based on the water‑carbon‑emergy nexus is an effective solution to simultaneously safeguard food security and ecological integrity. Policy implications include urgent revision of water allocation policies, promotion of low‑pressure irrigation technologies, optimization of chemical inputs (especially nitrogen fertilizers), and realignment of economic incentives to favor crops with lower environmental footprints. The proposed model is generalizable beyond Dezful and can serve as a decision‑support tool for sustainable agricultural planning in resource‑stressed regions worldwide.