Work

Multi-objective optimized allocation of arid saline farmlands and irrigation water resources for sustainable agriculture

Year: 2025

Type: article

Abstract: Agricultural production in arid regions faces challenges from water scarcity, soil salinization, and land and water resources allocation. To address these issues, a multi-objective framework for the allocation of saline farmlands and irrigation water resources was formulated, incorporating rarely-considered root-zone salinity dynamics. Taking the arid oasis Manas River Basin in Xinjiang, China as an example, a verified remote sensing inversion method was applied to estimate the spatial distribution of 0–80 cm root-zone soil salt content ( SSC ) for different crops. This was then used to train XGBoost models to predict root-zone SSC , total actual evapotranspiration, and annual maximum NDVI under various land and water allocation scenarios. Based on regional goals of reducing irrigation from 1.58 × 10 9 to 1.20 × 10 9 m 3 , NSGA-II was employed to optimize land and water allocation with three objectives including maximum total cotton yield ( TCY ), minimum SSC , and maximum irrigation water productivity ( WP I ). The XGBoost models achieved high accuracy ( R 2 > 0.90). Optimization showed root-zone SSC could fall by 0.06 g kg −1 , with minimal WP I improvement , and TCY drops by over 2 × 10 8 kg, representing ∼28 % reduction from the 2022 baseline of 7.08 × 10⁸ kg, due to reduced planting area. Referring to SSC inversion data, crop area was adjusted in GIS by removing higher-salinity pixels (ranked in descending order), with an average area deviation of less than 0.8 % from optimization results. By integrating high-resolution SSC estimation, XGBoost and NSGA-II algorithm, this study offers a practical framework for precise saline farmland management and efficient water use in arid regions. • Root-zone salinity control is integrated into multi-objective optimization. • XGBoost enables precise prediction of root-zone salinity from land and water use. • Land-water allocation is regulated by combining XGBoost and NSGA-II. • Multi-objective optimization allows adjustment of crop planting structure.

Source: Agricultural Water Management

DOI: 10.1016/j.agwat.2025.109929