Wang et al. (2026) Optimal Irrigation Scheduling for Multi-Cropping Systems: A Chance-Constrained Multi-Objective Robust Programming Under Hybrid Uncertainty

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Identification

• Journal: Agronomy
• Year: 2026
• Date: 2026-09-11
• Authors: Puru Wang, Shanshan Guo, Fan Zhang, Baohe Zhang
• DOI: 10.3390/agronomy16181786

Research Groups

• Institute of Water Resources Science, Southwest University, China
• College of Hydraulic Engineering, Yangtze River Water Resources Committee, China

Short Summary

This study proposes a chance-constrained multi-objective robust programming framework to optimize irrigation scheduling for multi-cropping systems under hybrid uncertainty. The framework integrates deficit irrigation theory and was applied in a seasonal drought region of Southwest China.

Objective

• Develop an optimized irrigation scheduling approach that mitigates drought impacts on multi-cropping systems while considering hybrid uncertainty and multiple objectives.

Study Configuration

• Spatial Scale: A seasonal drought region in Southwest China.
• Temporal Scale: Seasonal (annual) scale, with a focus on optimizing irrigation schedules for wet-season crops.

Methodology and Data

• Models used: Chance-constrained multi-objective robust programming framework integrating deficit irrigation theory, soil water movement, and reservoir regulation models.
• Data sources: Historical climate data, crop yield data, and irrigation system performance data from the study region.

Main Results

• The optimal irrigation schemes can substantially mitigate seasonal drought impacts on multi-cropping systems.
• The developed model reveals the influence of uncertainty from parameters on objectives and helps save water for wet-season crops compared to current practice.
• Interactive effects of risk preferences, goal preferences, expected objectives interval, and robust penalty preference on results were investigated.

Contributions

This study contributes to improving drought resistance, saving water, and realizing sustainable development of agriculture by developing an optimized irrigation scheduling approach that considers hybrid uncertainty and multiple objectives.

Funding

• National Key Research and Development Program of China (2016YFC0401402)
• Science and Technology Project of Yangtze River Water Resources Committee (2020-01-02)

Citation

@article{Wang2026Optimal,
  author = {Wang, Puru and Guo, Shanshan and Zhang, Fan and Zhang, Baohe},
  title = {Optimal Irrigation Scheduling for Multi-Cropping Systems: A Chance-Constrained Multi-Objective Robust Programming Under Hybrid Uncertainty},
  journal = {Agronomy},
  year = {2026},
  doi = {10.3390/agronomy16181786},
  url = {https://doi.org/10.3390/agronomy16181786}
}