Predicting sugar beet leaf area index: evaluating performance of double sigmoid functions under different irrigation and plant density scenarios.

The leaf area index (LAI) dynamics in sugar beet follow a double sigmoidal curve, modeled as the subtraction of two sigmoid functions. In this study, we examined the accuracy of 15 different sigmoid functions in describing the sugar beet LAI variation based on growing degree days (GDD) and days after planting (DAP) in different irrigation treatments and crop densities under direct and transplant cultivation. The results showed that the Logistic-Richards (LR) and Hill-Hill functions (HH) effectively modeled the measured LAI data over the GDD and DAP-based growing period for both direct sowing and transplant cultivation. The LR (NRMSE = 0.04, d = 0.99, MRE = -0.006) and HH (NRMSE = 0.05, d = 0.99, MRE = -0.01) achieved the best performance for direct sowing calibration based on GDD. In contrast, the Von Bertalanffy, Weibull, and Hill functions were not suitable for describing sugar beet LAI dynamics. Adjusting function coefficients to account for environmental factors such as seasonal applied water, rainfall, and planting density generally led to decreased predictive accuracy, under direct and transplant cultivation. Therefore, LR functions can be valuable for modelling sugar beet LAI variation under various irrigation treatments and crop densities throughout the growing season.