Optimizing Planting Structure and Allocating Limited Irrigation Water in a Hybrid Irrigated and Rain-Fed Agricultural Region

A reasonable planting structure and limited allocation of irrigation water are essential for the sustainable development of hybrid irrigated and rain-fed agriculture, which is crucial for global food and water security. To address this issue, a stochastic multiobjective fuzzy mixed-integer programming approach is proposed to optimize crop planting areas and monthly irrigation water allocation in hybrid irrigated and rain-fed agricultural regions. The uncertainties in the proposed approach are expressed as probability distributions and fuzzy constraints. Binary variables are used to determine whether supplementary irrigation complies with the irrigation area ratio policy. This approach is applied to a case study of the crop planting area and irrigation water allocation planning in Guyuan city, Ningxia Hui Autonomous Region, Northwestern China. Four objectives are considered: maximizing economic benefits, maximizing crop nutrient production, maximizing water allocation in critical growth stages, and minimizing the Gini coefficient. The optimal results for different water levels and satisfactory degrees of decision makers for available soil resources were obtained. Compared with current management, the optimized economic benefits and water allocation in critical growth stages improved by 2.7%–35.1% and 185.3%–269.6%, respectively. However, the Gini coefficient decreased by 9.6%–11.6%. Compared with traditional multiobjective programming, the proposed approach has advantages in terms of reflecting uncertainties through discrete random and fuzzy sets, providing more reliable decision plans. Our research offers a mathematical approach for decision makers to plan agricultural water and soil resources in hybrid irrigated and rain-fed agricultural regions.