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Asymptotic Confidence Regions for Biadditive Models: Interpreting Genotype- Environment Interactions

Jean-Baptiste Denis and John C. Gower
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 45, No. 4 (1996), pp. 479-493
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2986069
Stable URL: http://www.jstor.org/stable/2986069
Page Count: 15
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Asymptotic Confidence Regions for Biadditive Models: Interpreting Genotype- Environment Interactions
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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 recommendation and genotype selection.

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