Extending Finlay–Wilkinson regression with environmental covariates

Abstract

Finlay–Wilkinson regression is a popular method for analysing genotype–environment interaction in series of plant breeding and variety trials. It involves a regression on the environmental mean, indexing the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This paper reviews the development of such methods. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Two new methods are proposed for obtaining such synthetic covariates, which may be integrated into genotype‐specific regression models, that is, criss‐cross regression and a factor‐analytic approach. The main advantage of such explicit modelling is that predictions can be made also for new environments where trials have not been conducted. A published dataset is employed to illustrate the proposed methods.

SoilType: An R package to interplay soil characterization in plant science

“…However, the potential is much greater. In combination with other sources of environmental data, such EnvRtype, it is possible very dense and rich environmental relationship kernels and includes them in prediction models, such as genomic selection (Costa‐Neto et al., 2020b; Costa‐Neto et al., 2021b), GGE‐biplot (Yue et al., 2022), Finlay–Wilkinson reaction norms (Piepho, 2022), and GWAS for reaction norm regression components (Waters et al., 2022). In turn, the second function, get_soil_plot_leve(), can extrapolate any sensor measurement at the plot level, not necessarily soil sample, such as weather features.…”