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Cluster and Principal Component Analysis among Bread Wheat (Triticum Aestivum L) Genotypes in Mid Rift Valley of Oromia, Ethiopia

Urgaya Balcha, Firew Mekbib, Dagnachew Lule

Cluster and principal component analysis techniques are suitable in identification plant traits separately and it helps breeders to genetic improvement of traits in bread wheat genotypes. This research was conducted at Adami Tulu, mid rift valley of Oromia, Ethiopia, with the objectives of studying the extent of clustering of bread wheat genotypes and identifying the important traits in distinguishing the genotypes. A total of 36 breads wheat genotypes were evaluated in 6 × 6 simple lattice design during 2017-2018 cropping season. Analysis of variance showed the existence of highly significant (P ≤ 0.01) variation among genotypes for most of the studied traits. Cluster analysis revealed that the 36 breads wheat genotypes were grouped into 4 clusters. The Principal Component Analysis (PCA) showed that the first 7 principal components with Eigen values greater than one combined explained about 82.82% of the total variation. The study showed the presence of possibility of improving yield and other desirable characters through selection. However, this study was conducted for one growing season and therefore further testing in different locations for more than one cropping season is necessary.