Predicting cotton yield and optimizing irrigation scheduling from coupled soil water–salinity dynamics

Water scarcity and soil salinization are major factors impacting sustainable agricultural production in arid and semi-arid regions. This study developed a numerical model to estimate crop yield by coupling soil water flow and salt transport simulations with an integrated plant water deficit index (PWDI, which accounts for the relative distributions of water, salt, and roots, as well as previous stress hysteresis), as well as a daily water deficit sensitivity index derived from a sigmoid cumulative function of the normalized heat unit index. A five-year field experiment for cotton under mulched drip irrigation in Xinjiang, China, served model calibration and testing. The model reproduced soil water and salinity profiles and final cotton yield across diverse irrigation regimes, with a mean normalized root mean squared error (NRMSE) ≤ 18.3%, and coefficient of determination ( R 2 ) ≥ 0.80. Combined with a genetic–simplex optimization algorithm, a simulation–optimization framework was built to jointly optimize PWDI thresholds and water quotas for triggering timely irrigation. Based on the representative 2018 meteorological data, the relationships between indices such as maximum cotton yield, net economic benefit, irrigation water use efficiency, and salt leaching efficiency and three factors influencing initial soil moisture, salinity, and total irrigation application were established. The inverse-distance weighting method generated optimal combinations of PWDI threshold and irrigation quota matching local field conditions. Further validation over another 20 years revealed that relative yield and net benefit fluctuated between −8.3% and + 2.0% relative to the representative year, indicating its strong adaptability to interannual climate changes. The optimized scheme provides a practical and transferable basis for high-yield, economical irrigation management of cotton in arid saline regions.