Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP)

Abstract

A successful strategy for prediction of crop yield that accounts for the effects of genotype and environment will open up many opportunities for enhancing the productivity of agricultural systems. Crop growth models (CGMs) have a history of application for crop management decision support. Recently whole genome prediction (WGP) methodologies have been developed and applied in breeding to enable prediction of crop traits for new genotypes and thus increase the size of plant breeding programs without the need to expand expensive field testing. The presence of Genotype-by-Environment-by-Management (GxExM) interactions for yield presents a significant challenge for the development of prediction technologies for both product development by breeding and product placement within agricultural production systems. The integration of a CGM into the algorithm for whole genome prediction WGP, referred to as CGM-WGP, has opened up the potential for prediction of GxExM interactions for breeding and product placement applications. Here a combination of simulation and empirical studies are used to explain how the CGM-WGP methodology works and to demonstrate successful reduction to practice for applications to maize breeding and product placement recommendation in the US corn belt.