Cluster Analysis: A Comparison of Four Methods in Rice Bean [Vigna Umbellate (Thunb.)Ohwi & Ohashi]

Twenty five rice bean elite lines from Bhubaneswar, Ludhiana and Ranchi were evaluated in RBD for nine yield component traits and seed yield. The genotypes showed wide and highly significant variations in all ten traits. Cluster analysis of the 25 rice bean genotypes based on the ten yield and component traits was done in four different methods viz. Genetic Divergence D², Canonical analysis, Similarity coefficients and Principal component analysis. In all four methods the 25 genotypes could be classified into nine different clusters. Though the composition of clusters varied with methods, there was similarity of cluster composition of D² analysis with canonical analysis and of similarity coefficient with principal component analysis. Comparison of effectiveness of the four methods of clustering in bringing out the diversity among the clusters of genotypes was done by partitioning total sum of squares among genotypes for each character into sum of squares for between and within cluster variation. The study revealed that similarity coefficient and principal component analysis were more effective in maximizing between cluster differences than D² and canonical analysis. Moreover, similarity coefficient analysis gave a hierarchical presentation and would help in further partitioning into sub-clusters. Based on intergenotype diversity in all four methods and character complementation it can be inferred that crosses of BRB-20, BRB-6, BRB-14 and BRB-20 with BRB-9 are expected to produce more transgressive segregants in later generations.