Bin et al. (2026) Water-saving and economic benefits of a soil moisture threshold-based irrigation strategy for cotton in Xinjiang under climate change

Identification

• Journal: European Journal of Agronomy
• Year: 2026
• Date: 2026-05-09
• Authors: Chen Bin, Linjia Yao, Yadong Liu, Mohamed Amine Benaly, Changqing Yan, Genghong Wu, Ronghao Guan, Yi Li, Dongyan Zhang, Jie Bai, Qiuxiang Tang, Jianqiang He, Hao Feng, Qiang Yu, Gang Zhao
• DOI: 10.1016/j.eja.2026.128152

Research Groups

• State Key Laboratory of Soil and Water Conservation and Desertification Control, Northwest A&F University
• College of Natural Resources and Environment, Northwest A&F University
• Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops
• International Water Research Institute, Mohammed VI Polytechnic University (UM6P)
• College of Intelligent Equipment, Shandong University of Science and Technology
• Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University
• Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission
• College of Mechanical and Electronic Engineering, Northwest A&F University
• State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences
• College of Agronomy, Xinjiang Agricultural University

Short Summary

The study develops a soil moisture threshold-based irrigation strategy (SMTIS) for cotton in Xinjiang, China, using the AquaCrop model and a nonlinear optimization framework. The results demonstrate that SMTIS significantly reduces irrigation water use and increases water productivity and economic benefits under both historical and future climate change scenarios.

Objective

• To optimize stage-specific soil moisture thresholds to maximize irrigation water productivity while limiting yield loss to $\le 10\%$, and to evaluate the resulting water requirements, yield responses, and economic benefits across Xinjiang.

Study Configuration

• Spatial Scale: Regional (Xinjiang, China)
• Temporal Scale: Historical period (2000–2022) and two future periods (2031–2050 and 2061–2080)

Methodology and Data

• Models used: AquaCrop model coupled with a constraint-based nonlinear optimization framework.
• Data sources: Canopy cover, aboveground biomass, and seed cotton yield (for validation); climate data (historical and future projections) and soil properties.

Main Results

• Model Validation: High accuracy achieved for canopy cover, biomass, and yield ($R^2 = 0.80\text{--}0.95$; index of agreement $= 0.96\text{--}0.99$).
• Water Savings: SMTIS reduced seasonal irrigation amounts by an average of approximately $247\text{ mm}$ compared to conventional strategies.
• Water Productivity: Average increase of $0.83\text{ kg m}^{-3}$, with peak gains reaching $1.0\text{ kg m}^{-3}$ under high-emission scenarios in the 2070s.
• Economic Benefit: Mean economic gain of $1.27 \times 10^3\text{ CNY ha}^{-1}$ during the historical period.
• Driving Factors: Reference evapotranspiration and precipitation were the dominant factors influencing SMTIS effectiveness, with soil properties playing a secondary but growing role in future climates.

Contributions

• Provides regionally optimized, stage-specific soil moisture thresholds that account for crop physiological responses and spatial heterogeneity.
• Establishes a robust, climate-resilient irrigation framework that balances water conservation with economic viability in arid cotton-growing regions.

Funding

• Not specified in the provided text.

Citation

@article{Bin2026Watersaving,
  author = {Bin, Chen and Yao, Linjia and Liu, Yadong and Benaly, Mohamed Amine and Yan, Changqing and Wu, Genghong and Guan, Ronghao and Li, Yi and Zhang, Dongyan and Bai, Jie and Tang, Qiuxiang and He, Jianqiang and Feng, Hao and Yu, Qiang and Zhao, Gang},
  title = {Water-saving and economic benefits of a soil moisture threshold-based irrigation strategy for cotton in Xinjiang under climate change},
  journal = {European Journal of Agronomy},
  year = {2026},
  doi = {10.1016/j.eja.2026.128152},
  url = {https://doi.org/10.1016/j.eja.2026.128152}
}