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Asymptotic Confidence Regions for Biadditive Models: Interpreting Genotype‐Environment Interactions
Author(s) -
Denis JeanBaptiste,
Gower John C.
Publication year - 1996
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2986069
Subject(s) - genotype , confidence interval , statistics , econometrics , mathematics , biology , genetics , gene
SUMMARY 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 recommendation and genotype selection.