Cluster and principal component analysis for yield and yield | 108237
An International Journal

Agricultural and Biological Research

ISSN - 0970-1907
RNI # 24/103/2012-R1


Cluster and principal component analysis for yield and yield related traits of food barley (Hordeum vulgare L.) genotypes at Woreilu district, South Wollo, Ethiopia

Kibret Abebe*

Barley (Hordeum vulgare L.) is one of the most important traditional crops in Ethiopia. According to recent research, Ethiopia is considered a center of diversity for barley due to its high levels of genetic and phenotypic diversity. Multivariate techniques such as cluster and principal component analysis are important strategies for classifying and understand genetic relationships among different genotypes. The present experiment was undertaken on forty-nine six-row advanced breeding line food barley genotypes which were conducted in seven-by-seven simple lattice design at Woreilu farmer training center, in 2021/2022 main cropping season to estimate the extent of genetic variation, clustering of food barley genotypes and identifying the important traits in genotypes. The study found that the genotypes could be grouped into five distinct clusters, with the highest inter-cluster distance being between clusters I and IV. Principal component analysis also revealed that the first four principal components explained 80.36% of the total variation. The study suggests that selecting genotypes from these two clusters for hybridization may be desirable for improving yield and other desirable characteristics. However, the study was conducted for only one growing season and further testing in different locations for more than one cropping season is necessary.

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