Optimizing the Maize Irrigation Strategy and Yield Prediction under Future Climate Scenarios in the Yellow River Delta

The contradiction between water demand and supply in the Yellow River Delta restricts corn yield region. It is of great significance to formulate reasonable irrigation strategies alleviate regional use improve yield. Based on typical hydrological years (wet year, normal dry year), this study used coupling model AquaCrop, multi-objective genetic algorithm (NSGA-III), TOPSIS-Entropy established using Python language solve problem, with objectives achieving minimum (IW), maximum (Y), production rate (IWP), efficiency (WUE). was then make decisions Pareto fronts, seeking best decision under multiple objectives. results show following: (1) AquaCrop-OSPy accurately simulated maize growth process experimental area. R2 values for canopy coverage (CC) 2019, 2020, 2021 were 0.87, 0.90, 0.92, respectively, aboveground biomass (BIO) 0.97, 0.96, 0.96. (2) Compared other treatments, rainfall test area can meet period wet years, net significantly reduce IW increase Y, IWP, WUE years. (3) Using LARS-WG (a widely employed stochastic weather generator agricultural climate impact assessment) generate future scenarios externally resulted a higher CO2 concentration increased slightly reduced demand. (4) Optimizing important allowing makers promote sustainable utilization resources region crop yields.