~ 860 ~ Journal of Pharmacognosy and Phytochemistry 2018; 7(6): 860-863 E-ISSN: 2278-4136 P-ISSN: 2349-8234 JPP 2018; 7(6): 860-863 Received: 06-09-2018 Accepted: 07-10-2018 VA Mohanlal Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University, Annamalai Nagar, Tamil Nadu, India. K Saravanan Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University, Annamalai Nagar, Tamil Nadu, India. T Sabesan Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University, Annamalai Nagar, Tamil Nadu, India. Correspondence VA Mohanlal Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University, Annamalai Nagar, Tamil Nadu, India. Multivariate analysis in blackgram ( Vigna mungo (L) hepper) genotypes VA Mohanlal, K Saravanan and T Sabesan Abstract A study was carried out to determine the relationship and genetic diversity among twenty-one blackgram genotypes using Mahalanobis’s D 2 and principal component analysis for various quantitative traits over three seasons. Analysis of variance revealed significant differences among the blackgram genotypes. Twenty-one genotypes were grouped into six clusters. The maximum intra cluster distance was observed in cluster II. The maximum inter cluster distance was found between cluster III and V (D 2 = 3402.97). The clusters II and V showed high mean values for seed yield per plant. In principal component analysis, first component had contribution from the traits viz., number of pods per plant, pod length, number of seeds per pod, seed yield per plant which accounted 35.44% to the total variability. The remaining variability of 20.06%, 12.39% and 11.23% was accounted by second, third and fourth principal components by various traits viz., number of branches, pod weight and 100 seed weight. The cumulative variance of 79.12% of total variation among eleven characters was explained by the first four axes. Keywords: Blackgram, Mahalanobis’s D 2 , principal component analysis Introduction Blackgram (Vigna mungo (L.) Hepper) is one of the important pulse crops in India. Blackgram is a rich source of protein (20.80 to 30.50 percent) and also a good source of Phosphoric acid and calcium. It is contributing 12 percent of the total pulse production in India. In spite of its importance, the productivity of this crop is relatively low. The major constrains in achieving higher productivity are lack of exploitable genetic variability, absence of suitable ideotype, poor harvest index, susceptibility to biotic and abiotic stresses, non-availability of quality seeds of improved varieties and narrow genetic base occur due to repeated usage of few parents with high degree of relatedness in crossing programmes (Hadimani et al., 2016) [1] . An assessment of the genetic diversity of pulses is an important first step in a research programme to improve crop yield. In order to improve yield, new blackgram varieties must be developed. Genetic distance estimates for grouping can be estimated by different methods. Multivariate statistical tools include Principal Component Analysis (PCA), Cluster analysis and Discriminate analysis (Oyelola, 2004) [2] . Principal component analysis (PCA) can be used to uncover similarities between variable and classify the cases (genotypes), while cluster analysis on the other hand is concerned with classifying previously unclassified materials (Leonard and Peter, 2009). Mahalanobis D 2 statistic is a powerful tool in quantifying the degree of divergence at phenotypic level. PCA is very helpful for identification of plant characters that categorize the distinctiveness among promising genotypes (Chakravorty et al., 2013) [4] . In view of these, twenty-one blackgram genotypes were evaluated by different multivariate analysis to identify genetically diverse genotypes and to identify traits that contribute to variability in the population. Materials and Methods For this experiment, twenty-one blackgram genotypes were grown in Plant Breeding Farm, Department of Genetics & Plant Breeding, Annamalai University in three seasons (Jan-2017, Jul-2017 & Jan-2018). The experiments were conducted in Randomized Block Design with two replications. Standard agronomical practices were followed to raise the crop. The mean value of two replications over seasons were used for statistical analysis. The observations were recorded for eleven quantitative characters viz., days to first flowering, plant height, number of branches, number of clusters per plant, number of pods per plant, pod length, pod weight, number of seeds per pod, seed size, 100 seed weight and seed yield per plant. The data was subjected to statistical analysis using Mahalanobis D 2 statistic and Principal Component Analysis (PCA).