Extending Finlay–Wilkinson regression with environmental covariates - Génétique Diversité et Ecophysiologie des Céréales Access content directly
Journal Articles Plant Breeding Year : 2023

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.
Fichier principal
Vignette du fichier
2023 - Piepho_Plant Breeding - .pdf (458.99 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
licence : CC BY - Attribution

Dates and versions

hal-04235715 , version 1 (10-10-2023)

Licence

Attribution

Identifiers

Cite

Hans‐peter Piepho, Justin Blancon. Extending Finlay–Wilkinson regression with environmental covariates. Plant Breeding, 2023, 142 (5), pp.621-631. ⟨10.1111/pbr.13130⟩. ⟨hal-04235715⟩
26 View
15 Download

Altmetric

Share

Gmail Facebook X LinkedIn More