Asymptotic confidence regions for biadditive models: interpreting genotype-environment interactions

Denis, Jean-Baptise and Gower, John C. (1996). Asymptotic confidence regions for biadditive models: interpreting genotype-environment interactions. Journal of the Royal Statistical Society: Series C (Applied Statistics), 45(4) pp. 479–493.

DOI: https://doi.org/10.2307/2986069

URL: http://www.jstor.org/stable/2986069

Abstract

An understanding of how genotypes of an agricultural crop interact with the environment in which they are grown is important for assessing plant production. A breeding trial for 21 genotypes of rye-grass grown at seven locations is used to illustrate the interpretation of genotype-environment interactions. Statisticians have proposed many ways of modelling these interactions, but a subclass of bilinear models, that we term biadditive, fits especially well. We emphasize assessing and interpreting the interaction parameters of biadditive models by constructing confidence regions in biplot representations. When a biadditive model is valid, this new development underpins better informed decisions on variety recom- mendation and genotype selection.

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