Chen et al. (2026) Predicting cotton yield and optimizing irrigation scheduling from coupled soil water–salinity dynamics

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-08-13
• Authors: Qi Chen, Wanzi Hu, Junshuai Hao, Qiang Zuo, Jianchu Shi, Yuchuan Fan, Xun Wu, Alon Ben‐Gal
• DOI: 10.1016/j.agwat.2026.110695

Research Groups

• Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs
• State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University
• College of Jiyang, Zhejiang A&F University
• Soil, Water and Environmental Sciences, Agricultural Research Organization – Volcani Institute

Short Summary

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) and a daily water deficit sensitivity index. The model was tested against a five-year cotton field experiment in Xinjiang, China, and a simulation-optimization framework was built to jointly optimize PWDI thresholds and irrigation quotas for triggering timely irrigation.

Objective

• Develop a numerical crop yield model by coupling soil water-salt transport simulations with an integrated PWDI and a sigmoidal cumulative function of water deficit sensitivity index.
• Test the model against a five-year cotton field experiment in Xinjiang, China.
• Build a coupled simulation-optimization framework to jointly optimize PWDI thresholds and irrigation quotas for triggering timely irrigation.

Study Configuration

• Spatial Scale: Field-scale (plots with 690 cm wide, 750 cm long) in Xinjiang, China.
• Temporal Scale: Five-year experiment from 2017 to 2020 and 2023.

Methodology and Data

• Models used:
  • Numerical model for yield prediction and irrigation optimization developed using MATLAB R2018b.
  • Coupled governing equations for soil water flow and salt transport (Richards equation and advection-dispersion equation).
  • Improved Feddes model for root water uptake.
  • Sigmoid function of the normalized heat unit index to describe the cumulative evolution of λ over growing degree days.
• Data sources:
  • Field experiment data from a five-year cotton field experiment in Xinjiang, China.
  • Meteorological data (daily maximum and minimum air temperature, relative air humidity, solar radiation, wind speed, and precipitation) from an automatic agro-meteorological station.

Main Results

• The numerical model accurately predicted 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 (R2) ≥0.80.
• The simulation-optimization framework successfully optimized PWDI thresholds and irrigation quotas for triggering timely irrigation, resulting in optimal combinations of PWDI threshold and irrigation quota matching local field conditions.

Contributions

• This study provides a practical and transferable basis for high-yield, economical irrigation management of cotton in arid saline regions.
• The developed numerical model and simulation-optimization framework can be applied to other crops and regions with similar climate and soil conditions.

Funding

• This research was funded by the Key Laboratory of Arable Land Conservation in North China, Ministry of Agriculture and Rural Affairs.
• Supported by the State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University.

Citation

@article{Chen2026Predicting,
  author = {Chen, Qi and Hu, Wanzi and Hao, Junshuai and Zuo, Qiang and Shi, Jianchu and Fan, Yuchuan and Wu, Xun and Ben‐Gal, Alon},
  title = {Predicting cotton yield and optimizing irrigation scheduling from coupled soil water–salinity dynamics},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110695},
  url = {https://doi.org/10.1016/j.agwat.2026.110695}
}