Optimization of agricultural crop pattern based on water footprint methodology

Area of Science:

• Agricultural Water Management
• Hydrology
• Environmental Science

Background:

• Agriculture is the largest global freshwater consumer, necessitating water-saving strategies.
• Existing crop optimization studies often prioritize yield over water conservation and lack practical application.
• Water footprint assessments are crucial for understanding agricultural water demand.

Purpose of the Study:

• To evaluate the water-saving potential in agriculture via crop pattern optimization using water footprint (WF) assessments.
• To integrate high-resolution spatial WF data with a multi-objective optimization framework.
• To provide practical recommendations for crop pattern design in the Ceyhan River Basin, Türkiye.

Main Methods:

• Utilized the Soil and Water Assessment Tool (SWAT) for high-resolution spatial WF data.
• Developed a multi-objective optimization framework balancing blue, green, and grey water components.
• Simulated six scenarios with varying WF reduction priorities.

Main Results:

• Scenario S1 achieved a 23.4% total WF reduction (614 million m³).
• Scenario S6 attained a 47.9% blue WF reduction (464 million m³).
• An intermediate scenario (S5) offered balanced WF reduction and practical crop distribution.

Conclusions:

• Crop pattern optimization is a viable strategy for significant agricultural water savings.
• Incentive mechanisms are recommended to encourage farmer adoption of optimized patterns.
• The study provides a decision-support tool for sustainable water management and food/water security.