Optimal Irrigation Scheduling for Multi-Cropping Systems: A Chance-Constrained Multi-Objective Robust Programming Under Hybrid Uncertainty

Abstract

Drought and water scarcity are threatening the food security in irrigation districts around the world, urging water-saving and highly efficient irrigation scheduling. However, the increasing uncertainty jointly caused by changing environment and human activities makes it much more difficult and unreliable. In this study, a chance-constrained multi-objective robust programming framework was proposed to optimize irrigation scheduling of multi-cropping systems, while dealing with hybrid uncertainty and multiple objectives simultaneously. It integrates deficit irrigation theory, soil water movement and reservoir regulation and was applied in a seasonal drought region of Southwest China with multi-cropping systems. The results show that: (1) the optimal irrigation schemes can substantially mitigate the seasonal drought and reveal the influence of uncertainty from parameters on the objectives; (2) compared with the current practice, the optimized irrigation scheduling can help save water for wet-season crops and reflect the response of crops to various water demand and supply scenarios; (3) the risk preferences, goal preferences, the expected objectives interval, and the preferences for robust penalty of decision makers were investigated to show their interactive influence on the results. Although there are still limitations, the developed model can help improve drought resistance, save water and realize sustainable development of agriculture.