Simulation of saffron growth using AquaCrop model with high-resolution measured data

Saffron is one of the most expensive and worth-to-cultivate crops in countries struggling with water scarcity due to its low water requirement and compatibility with arid climates. This study was conducted to calibrate and validate the AquaCrop model for saffron at the research farm of the Ferdowsi University of Mashhad (FUM), Iran, in two growing seasons of 2021-2022 (two-year-old saffron) and 2022-2023 (three-year-old saffron). The soil water content, canopy cover, and biomass values were measured continuously during both growing seasons. The field measurements of the first and second growing seasons were used to calibrate and validate the AquaCrop model, respectively. Furthermore, using the Particle Swarm Optimization (PSO) algorithm, a new module for the inverse solution of the AquaCrop model has been presented. The module was successfully employed to fine-tune sensitive soil parameters of the field. The results showed that the AquaCrop model could accurately simulate soil water content, biomass, and canopy cover in both growing seasons. The root mean square error (RMSE) of simulations for soil water content, biomass, and canopy cover comparing to in-situ measured data was 8.3 mm, 0.3 ton ha(-1) and 3.4% for the first year and 5.5 mm, 0.4 ton ha(-1) and 2.7% for the second year, respectively. In these simulations, the saffron daughter corm production was considered as the crop yield, because the stigma yield is affected by the climatic conditions of the past year while corms are produced at the end of the prevailing season. To investigate the effect of cold and heat on saffron yield, the hottest and coldest growing seasons in the past 30 years were determined in 6 regions besides Mashhad, and the biomass in these seasons was simulated using the model. It was concluded that the cold growing season had a more significant effect than the hot season in reducing the saffron corm yield.