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Analysis of a three-way interaction including multi-attributes

By: Contributor(s): Material type: ArticleArticleLanguage: English Publication details: Victoria (Australia) : CSIRO Publishing, 2006.ISSN:
  • 0004-9409
Subject(s): Online resources: In: Australian Journal of Agricultural Research v. 57, no. 11, p. 1185-1193634477Summary: The additive main effect and multiplicative interaction (AMMI) has been widely used for studying and interpreting genotype×environment interaction (GEI) in agricultural experiments using multi-environment trials (METs). When METs are performed across several years the interaction is referred to as a 3-mode (3-way) data array, inwhich the modes are genotypes, environments, and years. The 3-way array can be applied to other conditions or factors artificially created by the researcher, such as different sowing dates or plant densities, etc. Three-way interaction data can be studied using the AMMI analysis. The objective of this study is to apply the 3-mode AMMI to 2 datasets. Dataset 1 comprises genotype (25)×location (4)×sowing time (4) interaction; 8 traits were measured. The structure of dataset 2 is genotype (20)×irrigation regimes (4)×year (3) on grain yield. Results of the 3-way AMMI on dataset 1 show that several important 3-way interactions were not detected when condensing location (4)×sowing time (4) into environments (16). An alternative 3-way array, genotype×attribute×locations for the early sowing date in Year 1, is considered. Results of the 3-way AMMI on dataset 2 show that different patterns of response of genotypes can be found at different irrigation methods and years.
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The additive main effect and multiplicative interaction (AMMI) has been widely used for studying and interpreting genotype×environment interaction (GEI) in agricultural experiments using multi-environment trials (METs). When METs are performed across several years the interaction is referred to as a 3-mode (3-way) data array, inwhich the modes are genotypes, environments, and years. The 3-way array can be applied to other conditions or factors artificially created by the researcher, such as different sowing dates or plant densities, etc. Three-way interaction data can be studied using the AMMI analysis. The objective of this study is to apply the 3-mode AMMI to 2 datasets. Dataset 1 comprises genotype (25)×location (4)×sowing time (4) interaction; 8 traits were measured. The structure of dataset 2 is genotype (20)×irrigation regimes (4)×year (3) on grain yield. Results of the 3-way AMMI on dataset 1 show that several important 3-way interactions were not detected when condensing location (4)×sowing time (4) into environments (16). An alternative 3-way array, genotype×attribute×locations for the early sowing date in Year 1, is considered. Results of the 3-way AMMI on dataset 2 show that different patterns of response of genotypes can be found at different irrigation methods and years.

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