Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators

Abstract

Copyright © Cambridge University Press 2018. Prior to applying the cropping system model-CERES-Rice model to deep water rice (DWR), it is important to estimate the rice genetic coefficients (GC). The goal of the current study was to compare two methods for estimating GC using a GC calculator (GENCALC) and generalized likelihood uncertainty estimation (GLUE) for three flooded rice (FDR) varieties. Data from a field experiment on the effect of planting date and variety on FDR production was conducted in 2009 on a DWR area in Bang Taen His Majesty's Private Development Project, Prachin Buri, Thailand. The experimental design was split-plot with four main plots (planting dates) and three sub-plots (FDR varieties) with four replications. The simulated values for anthesis date, maturity date and grain weight using GENCALC produced normalized root mean square errors (RMSEn) of 3.97, 3.69 and 3.68, while using GLUE produced RMSEn of 3.67, 2.50 and 3.68, respectively. The simulated grain number and grain yield under GENCALC GC were not significantly different from the observed values but were higher than simulated values for GLUE GC. Simulated values of above-ground biomass for both GENCALC (11 727 kg/ha) and GLUE GC (11 544 kg/ha) were overestimated compared to observed values (8512 kg/ha). In addition, good agreements of leaf N values were found with D-index values of 0.94 and 0.96 using GENACALC and GLUE GC simulations, respectively. Therefore, the GENCALC and GLUE GC estimators of DSSAT can both be used for estimating GC of FDR in the DWR area in Thailand and similar agro-ecosystems in Southeast Asia.