An integrated data assimilation, crop modeling, and multi-objective optimization framework for improving cotton irrigation water use efficiency

Global water scarcity necessitates efficient irrigation management to ensure sustainable agriculture. This study developed an integrated framework to optimize agricultural water use in data-scarce regions, combining multi-source data acquisition, AquaCrop model calibration, and multi-objective optimization. Specifically, a Markov chain Monte Carlo (MCMC) method was used to calibrate AquaCrop parameters by assimilating observed cotton canopy cover and yield data, achieving high simulation accuracy during calibration (with average R² values of 0.91 for canopy cover and 0.97 for yield). Following calibration, global sensitivity analysis of the model parameters was conducted using the Sobol method, revealing that canopy decline coefficient (CDC), canopy growth coefficient (CGC), and days from sowing to emergence (Emergence) are the most influential parameters affecting canopy cover and yield. Subsequently, Non-dominated Sorting Genetic Algorithm III (NSGA-III) optimized soil moisture thresholds (SMTs) for four distinct cotton growth stages to simultaneously maximize yield and minimize irrigation water consumption. Results showed that maintaining original irrigation amounts could increase cotton yields by 32.09–131.11 %, while maintaining original yields could reduce water consumption by 26.39–77.78 % through optimized irrigation scheduling. This data-driven framework offers a robust strategy for enhancing water use efficiency and guiding sustainable agricultural water management in water-stressed regions.