Optimizing irrigation scheduling for winter wheat using the AquaCrop model in Xinjiang, China

Area of Science:

• Agricultural Science
• Water Resource Management
• Crop Modeling

Background:

• Sustainable agricultural water management is crucial for food security, especially in water-scarce arid regions.
• Optimizing irrigation schedules is fundamental for efficient water resource utilization and crop production.

Purpose of the Study:

• To calibrate and validate the AquaCrop model for winter wheat.
• To determine optimal irrigation schedules under varying rainfall conditions (wet, normal, dry).
• To propose a multi-objective optimization approach for irrigation scheduling.

Main Methods:

• The AquaCrop model was calibrated and validated using field experimental data from 2022 to 2024.
• A multi-objective optimization approach coupled the AquaCrop model with the entropy weight method.
• Simulations were conducted for wet, normal, and dry rainfall years.

Main Results:

• The AquaCrop model accurately simulated winter wheat growth and yield.
• Maximizing yield required 15-18 irrigations (30 mm/event) at 50% readily available water (RAW).
• Maximizing water use efficiency (WUE) required 3-5 irrigations (80 mm/event) at 80-90% RAW.
• Multi-objective optimization suggested 3-4 irrigations (80 mm/event) at 90-110% RAW for balanced yield and WUE.

Conclusions:

• The study provides optimized irrigation strategies for winter wheat in arid environments.
• Findings support informed decision-making for water resource management and food security.
• The integrated modeling approach offers a robust framework for irrigation optimization.