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Results and Biological Interpretation of Shifted Multiplicative Model Clustering of Durum Wheat Cultivars and Test Site
Author(s) -
Abdalla Osman S.,
Crossa José,
Cornelius Paul L.
Publication year - 1997
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1997.0011183x003700010014x
Subject(s) - biology , cultivar , germplasm , selection (genetic algorithm) , cluster analysis , plant breeding , agronomy , microbiology and biotechnology , statistics , mathematics , artificial intelligence , computer science
The examination of crossover interactions (COI) (genotypic rank change) and identification of subsets of homogeneous groups of sites and cultivars without COI are important in the interpretation of cultivar trials in breeding and agronomy. The shifted multiplicative model (SHMM) clustering method was applied to a CIMMYT durum wheat ( Triticum turgidum L. var. durum ) yield trial consisting of 24 cuitivars grown at 40 international sites during the 1990‐1991 season. The objectives were to identify subsets of sites and cultivars with negligible genotypic rank change and attempt to give a biological interpretation for the resulting clusters. The SHMM with one multiplicative term (SHMM 1 ) provided an adequate fit for seven final groups of cultivars and eight final groups of sites with small numbers of COI. Grouping of cultivars greatly reflected similarity in genetic backgrounds and, consequently, similarity of response to test environments. Differential responses within the same genetic background were attributed to differences in simply inherited traits such as plant height and disease resistance. The observed grouping of sites was generally associated with latitude, while environmental conditions that influenced crop phenology and cropping cycle delineated the final groupings. The results suggest that groups formed based on SHMM clustering methods have valid biological basis. Routine use of SHMM clustering methods could increase selection efficiency through the identification and selection of superior cultivars within clusters having negligible COI. Similarly, groups of test sites that represent similar selection environments could be identified and that would facilitate identification of key test sites as well as decision making concerning exchange of germplasm and information.