A comparison of multiple calibration and ensembling methods for estimating genetic coefficients of CERES-Rice to simulate phenology and yields

Abstract

Estimating genetic coefficients of a new rice cultivar is important when a crop model is used to test its performance. Here, we estimated genetic coefficients of two rice genotypes in the Philippines, namely, inbred PSB Rc82 and hybrid Mestizo 20, for CERES-Rice using parameter estimation methods including, GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimation) and NMCGA (Noisy Monte Carlo Genetic Algorithm). Ensembling of genetic coefficients were also employed. Model calibrations were done during the 2012 wet season using observed anthesis and maturity dates and yields as calibration data. Validation was done during the dry season of that year. Calibration results suggest that genetic coefficients estimated by different methods vary and are not consistent in predicting phenology and yield accurately. One method is better in predicting phenology, while another, for yield. However, arithmetic averaging of genetic coefficients, and weighted averaging based on parameter estimation methods’ performances worked well. In this study, arithmetic averaging of model parameters during calibration produced the best predictions of phenology and yield, in both rice genotypes, and this performance persisted during validation.