A Review on the Optimization of Irrigation Schedules for Farmlands Based on a Simulation–Optimization Model

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules.