Maize is among the most important crops in Iran. Enhancing maize irrigation management is critical for alleviating the pressure on limited freshwater resources in Khorasan Razavi province. The main objective of this study was to develop a novel irrigation scheduling optimization framework (AquaCrop plug-in-PSO) for maize, based on in-season weather forecasts integrated with the AquaCrop plug-in model and the particle swarm optimization (PSO) algorithm. During the growing season, weather forecasts combined with AquaCrop plug-inPSO algorithm create optimal irrigation plans for different crop growth stages, maximizing crop water productivity (WPC). A two-year (2021–2022) maize field irrigation experiment was conducted in Mashhad, Iran, to collect all necessary data for calibration (2021 data) and validation (2022 data) of the AquaCrop model. Three irrigation cases were then simulated (i.e., full irrigation and deficit irrigation: 70 % and 90 % of full irrigation) to evaluate the performance of the AquaCrop plug-in-PSO approach against the typical irrigation management of local farmers, as well as ET-based and soil moisture-based irrigation scheduling methods. Additionally, AquaCrop plug-in-PSO was used to evaluate the algorithm’s performance with historical weather data. The simulation results showed that the AquaCrop Plug-in-PSO, when used with weather forecast data (AquaCrop plug-in-PSO dynamic approach), outperformed all other irrigation scheduling methods for both deficit and full irrigation cases, achieving the highest WPC, ranging from 2.21 to 3.12 kg m− 3. Our simulation results demonstrate that the AquaCrop plug-in-PSO dynamic approach can be effectively used for efficient autonomous full and maize deficit irrigation scheduling in arid and semiarid regions.

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