Received: 18 th Sept-2013 Revised: 24 th Sept-2013 Accepted: 28 th Sept-2013 Research Article PRINCIPAL COMPONENT AND CLUSTER ANALYSES IN PIGEONPEA [Cajanus cajan (L.) MILLSP] R. Rekha 1 , L. Prasanthi 2 , M. Reddi Sekhar 1 and M. Shanti Priya 1 . 1 Department of Genetics and Plant Breeding, S.V. Agricultural College, Tirupati-517502, Andhra Pradesh, India. 2 Senior scientist, Department of Genetics and Plant Breeding, Regional Agricultural Research Station, Tirupati-517 502, Andhra Pradesh, India. ABSTRACT: Forty nine genotypes of pigeonpea representing the broad spectrum of variation were assessed for twelve characters using principal component analysis and cluster analysis. These genotypes were grouped into 8 clusters by using cluster analysis. Principal component analysis identified five principal components with eigen values more than one which contributed 80.10 per cent of the cumulative variance. The genotypes LRG-41 and SM- 97, MRG-1001, WRG 51-Y, RST-16 and ICP 7035 were selected from the above analysis appeared to be desirable for inclusion in crossing programme aimed for improvement of pigeonpea. Key words: Genetic divergence, Cluster analysis, Principal component analysis. INTRODUCTION Pigeonpea is one of the important pulse crops grown in India and consumed in diet as main source of protein. In any crop improvement programme genetic diversity is an essential pre-requisite for hybridization. Divergence studies indicated that geographical diversity is always not necessarily associated with the genetic diversity. Hence selection of parents for hybridization should be based more on genetic diversity rather than geographic diversity. Inclusion of diverse parents in hybridization helps in the isolation of superior recombinants. The divergence analysis by means of principal component analysis and hierarchical cluster analysis have been shown to be useful in selecting genetically distant parents for hybridization. The principal component technique has been applied in pigeonpea by Kalaimagal et al. (2008). Therefore, the present investigation is an attempt to study the genetic divergence in forty nine genotypes of pigeonpea based on principal component analysis and hierarchical cluster analysis. MATERIAL AND METHODS Forty nine genotypes of pigeonpea were grown in randomized block design with three replications during late kharif 2008-09 at Dry land farm of S.V.Agricultural College, Tirupati, Andhra Pradesh. Each genotype was sown in two rows of 3m length in each replication with a recommended spacing of 90×20 cm. the observations were recorded on five randomly selected plants of each genotype in each replication for the characters viz. plant height, number of primary branches per plant, number of secondary branches per plant, number of pods per plant, pod length, number of seeds per pod, 100 seed weight, protein content and phenol content. The observations on days to 50% flowering and days to maturity were recorded on per plot basis and the mean values of twelve characters were used for statistical analysis. The protein content was estimated using the procedure suggested by Lowry et al. (1951) and the phenol content was estimated using the method suggested by Sadasivam and Manickam (1962). The data were statistically analyzed to study the genetic diversity by principal component analysis as described by Jackson (1991) and hierarchical cluster analysis as described by Anderberg (1993). RESULTS AND DISCUSSION The analysis of variance revealed highly significant differences among the forty nine genotypes of pigeonpea indicating that the existence of substantial genetic variability for all the characters under study. Principal component analysis identified five principal components with eigen values more than one which contributed 80.10 per cent of cumulative variance (Table.2). The first principal component (PC1) contributed maximum towards variability (28.44) with high significant positive loading of number of secondary branches per plant (0.445) followed by number of pods per plant (0.431) and plant height (0.339). The second principal component (PC2) accounted 18.88 per cent of total variance and it reflected significant positive loading of days to maturity (0.336) followed by days to 50% flowering (0.287) and protein content (0.259). International Journal of Applied Biology and Pharmaceutical Technology Page: 424 Available online at www.ijabpt.com