Phenotypic Prediction Augmented through Crop Model-Whole Genome Prediction.

The presence of Genotype-by-Environment-by-Management (G×E×M) interactions for yield presents a significant challenge for the development of prediction technologies for both product development by breeding and agronomic management of the product within agricultural systems. Recent integration of crop growth models (CGMs) and whole genome prediction (WGP) algorithms created opportunities to improve prediction of cropping systems response to genetic, management and environmental change. Advances in phenomics promise to relax the phenotyping bottleneck that currently limits generating the biological knowledge required to separate one genotype from another, and to improve and train CGMs as part of prediction frameworks. An application of the CGM-WGP is demonstrated for the gene ARGOS effect on drought tolerance in maize.