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Using the shifted multiplicative model to search for "separability" in crop cultivar trials

By: Cornelius, P.L.
Contributor(s): Seyedsadr, M | Crossa, J.
Material type: materialTypeLabelArticlePublisher: Berlin (Germany) : Springer-Verlag Heidelberg, 1992ISSN: 0040-5752; 1432-2242 (Online).Subject(s): Genotype environment interaction | Models | Zea mays In: Theoretical and Applied Genetics v. 84, no. 1-2, p. 161-172Summary: The shifted multiplicative model (SHMM) is used in an exploratory step-down method for identifying subsets of environments in which genotypic effects are "separable" from environmental effects. Subsets of environments are chosen on the basis of a SHMM analysis of the entire data set. SHMM analyses of the subsets may indicate a need for further subdivision and/or suggest that a different subdivision at the previous stage should be tried. The process continues until SHMM analysis indicates that a SHMM with only one multiplicative term and its "point of concurrence" outside (left or right) of the cluster of data points adequately fits the data in all subsets. The method is first illustrated with a simple example using a small data set from the statistical literature. Then results obtained in an international maize (Zea mays L.) yield trial with 20 sites and nine cultivars is presented and discussed.
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Article CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

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The shifted multiplicative model (SHMM) is used in an exploratory step-down method for identifying subsets of environments in which genotypic effects are "separable" from environmental effects. Subsets of environments are chosen on the basis of a SHMM analysis of the entire data set. SHMM analyses of the subsets may indicate a need for further subdivision and/or suggest that a different subdivision at the previous stage should be tried. The process continues until SHMM analysis indicates that a SHMM with only one multiplicative term and its "point of concurrence" outside (left or right) of the cluster of data points adequately fits the data in all subsets. The method is first illustrated with a simple example using a small data set from the statistical literature. Then results obtained in an international maize (Zea mays L.) yield trial with 20 sites and nine cultivars is presented and discussed.

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