Modelling perennial saffron growth and yield under different irrigation water salinities, irrigation water levels and planting methods

Decision-making on efficient field management for crop production requires modeling crop growth and yield, especially in relation to varying climate issues. In this regard, the SYEM model was developed and modified for saffron. The data used in this research were obtained from Dastranj and Sepaskhah (2019). In the current study, the ability of the modified SYEM model to simulate soil water and salt transport, saffron growth and its yield components under different irrigation water salinities [0.45 (well water, S 1 ), 1.0 (S 2 ), 2.0 (S 3 ), 3.0 (S 4 ) dS m −1 ], irrigation water levels [100 % (I 1 ). 75 % (I 2 ) and 50 % (I 3 ) of saffron water requirement (W R )] and planting methods [basin (P 1 ) and in-furrow (P 2 ) planting] was investigated. According to the NRMSE and d indices, the modified SYEM model simulated ET, E, and T with good accuracy (NRMSE ranging from 10 % to 20 %). Also, the values of NRMSE and d (index of agreement) for predicting dry matter, saffron yield, and corm yield varied between 10 % and 20 % and 0.85 and 0.95, which indicated a good ability of the model in simulating these parameters in both calibration and validation steps. The model's ability to simulate LAI during the growing season was good in the calibration step; however, it was fairly acceptable in the validation step. Also, the model was not able to predict the day on which the maximum LAI occurred. Soil water salinity and soil water content were also simulated by modified SYEM with an acceptable accuracy (NRMSE from 20 % to 30 %) in both calibration and validation steps. The model overestimated the soil water salinity, especially in the in-furrow planting. Finally, it is concluded that the modified SYEM model is a simple and user-friendly tool for predicting saffron yield, thereby facilitating better field management. • The SYEM model is a user-friendly tool for facilitating better saffron field management. • This model simulated ET, E and T with good accuracy (NRMSE 10–20 %). • Dry matter, yield and corm were simulated with good accuracy (NRMSE 10–20 %). • LAI simulation was good in calibration and acceptable in validation. • Soil water content and salinity were simulated with acceptable accuracy in evaluation.