ASSESSMENT OF PHENOTYPIC DIVERSITY OF BARLEY GENOTYPES THROUGH CLUSTER AND PRINCIPAL COMPONENT ANALYSES
M. J. Y. Shtaya1* and J. M. Abdallah2
1 Department of Plant Production and Protection, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, P.O. Box 7, Nablus, Palestine.
2 Department of Animal Production, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, P.O. Box 7, Nablus, Palestine.
* Corresponding author E-mail: email@example.com
Determination of genetic diversity is useful for plant breeding and hence production of more efficient plant varieties under different conditions. Accordingly, a collection of 74 accessions of landraces and cultivated varieties of barley from different countries, mainly from the Fertile Crescent were selected, grown and analyzed for phenotypic diversity. The field experiment was conducted at the experimental farm of the Faculty of Agriculture, An-Najah National University, Tulkarm (Khadouri), Palestine in a randomized complete block design with three replications. Initially, an analysis of variance (ANOVA) was conducted to test for significant differences among barley accessions in measured traits. A two-step cluster analysis was performed using the eleven measured traits to determine the optimal number of clusters based on Shwarz’s Bayesian Criterion (BIC) then, a dendrogram was constructed using the Hierarchical Cluster analysis with Ward’s clustering method based on Squared Euclidean Distances. ANOVA revealed highly significant differences among barley accessions in all studied traits. Based on Principal Component Analysis (PCA), the first four extracted components explained 76.1% of the total variation in the 11 studied traits. The clustering analyses revealed two main clusters each can be further divided into two sub-clusters. The first cluster included 41 accessions and the second cluster included 33 accessions. Such variation among studied accessions can be utilized in designing new breeding programs and crossing nurseries for barley improvement.
Key words: Cluster analysis, Hordeum vulgare, PCA, Selection, Barley