AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management factors. In this research, to solve this problem and develop a model with fewer input data, artificial neural network (ANN), support vector regression (SVR), and combined support vector regression with firefly algorithm (SVR-FFA) were used. For this purpose, 440 scenarios were created in 2 farms located in Iran, and the values of yield and biomass obtained by the AquaCrop model were compared with intelligent models. Also, consumable seed and irrigation were considered as inputs of intelligent models. The 99WestW2 farm in Miandoab had a seed yield of 6.588 t/ha, and the WestW10 farm in Mahabad had a seed yield of 5.05 t/ha. The results of this research showed that for both 99WestW2 and WestW10 farms, the SVR-FFA3 model was able to have the lowest amount of error so that for the amount of grain yield, the error values for the farms were 0.033 and 0.069 t/ha, respectively. The error value of biomass was obtained for farms as 0.057 and 0.066 t/ha respectively. After SVR-FFA model, SVR and ANN models also showed good performance due to proper accuracy and saving time. Finally, SVR-FFA, SVR, and ANN models were able to predict yield and biomass values in the shortest time and with the highest accuracy with only two inputs.
Keywords: Agriculture; AquaCrop; Modeling; Wheat.
© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.