Incorporating a dynamic gene-based process module into a crop simulation model

Abstract

Abstract Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as ‘genetic coefficients’. These GSPs are estimated using phenotypic observations and may not represent ‘true’ genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study that included 187 recombinant inbred lines (RILs) from a bi-parental bean family was conducted in 2011 and 2012 to measure the effects of quantitative trait loci (QTLs), environment (E) and QTL × E interactions across five sites. A dynamic mixed linear model was modified in this study to create a dynamic module that was then integrated into the Cropping System Model (CSM)-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic make-up of each genotype that is simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and G × E effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.