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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.

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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.

Item Type: Journal Article
Copyright Holders: 1996 Royal Statisical Society
ISSN: 1467-9876
Keywords: asymptotic variances and covariances; biadditive models; biplot; confidence regions; genotype-by-environment interactions; Tukey non-additivity; two-way tables
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
Item ID: 24129
Depositing User: Sarah Frain
Date Deposited: 11 May 2011 09:55
Last Modified: 15 Jan 2016 15:04
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