Modelling G×E with historical weather information improves genomic prediction in new environments.

Motivation: Interaction between the genotype and the environment (G x E) has a strong impact on the yield of major crop plants. Although influential, taking G x E explicitly into account in plant breeding has remained difficult. Recently GxE has been predicted from environmental and genomic covariates, but existing works have not shown that generalization to new environments and years without access to in-season data is possible and practical applicability remains unclear. Using data from a Barley breeding programme in Finland, we construct an in silico experiment to study the viability of GxE prediction under practical constraints. Results: We show that the response to the environment of a new generation of untested Barley cultivars can be predicted in new locations and years using genomic data, machine learning and historical weather observations for the new locations. Our results highlight the need for models of G x E: non-linear effects clearly dominate linear ones, and the interaction between the soil type and daily rain is identified as the main driver for G x E for Barley in Finland. Our study implies that genomic selection can be used to capture the yield potential in G x E effects for future growth seasons, providing a possible means to achieve yield improvements, needed for feeding the growing population.