LAUSR

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters (GSPs). As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat genotypes that do not have phenotypic records in the reference population. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP simulated more heritable GSPs compared to the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number and grain protein content, but showed comparable performance to the CGM-WGP for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population) through the prediction of biophysical relations between the GSPs and environmental inputs. CGM-WGP also showed superior performance when predicting unrelated individuals that clustered separately from the reference population in a PCA plot. Genome-wide association mapping linked the GSPs to previously reported loci for agronomic and physiological traits. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGPs using high-throughput phenotypic measures that are cheaper and less laborious to collect.