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PRINCIPAL COMPONENT ANALYSIS TO TEST STABILITY OF CULTIVARS ACROSS ENVIRONMENTS:- A TUTORIAL REVIEWED ARTICLE | ||
| The Iraqi Journal of Agricultural Science | ||
| Article 1, Volume 39, Issue 1, 2008, Pages 102-115 | ||
| Authors | ||
| M.M. ELSAHOOKIE; A. F. ALMEHEMDI | ||
| Abstract | ||
| Principal component analysis is a widely-used method for dimensionality reduction of multivariate clustering data transformed into low dimensional data. It minimizes the sum of squared errors. The low dimensional structure of data can be effectively captured by PCA., transformed into new van ate s. Cultivar stability simply tested at multienvironment, GGE-Biplot depends on performance and stability means. Some cultivars could show low adaptation by PCA because there is no cultivar of a superior performance at all environments. Therefore, the PCA assists to select (based on stability and performance) a cultivar at multienvironments.PCA analysis could be used for selection depends on yield stability, forage crop biomass, cotton lint yield or certain disease resistance of different cultivars of a certain crop. Furthermore this method leads to discover indirect selection for other traits. The covariate effect pattern explained 81% of the GGE pattern in some trials, suggesting that the GE pattern for yield could be effectively explored by indirect selection for these traits and others as seed yield, early flowering and lodging resistance. The traits analyses that were affected by GE interaction using GGE-Biplot method were reviewed. The differences based on environment and cultivars variations. Performance means were estimated as cultivars performance at years or locations. It was explained how to conclude from the analysis to determine the optimal cultivar in performance and stability across several environments, to show specific or wide adaptation of genotypes .Along with this article,GGE-Biplot program was included for use. | ||
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