Legume Research

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Legume Research, volume 39 issue 4 (august 2016) : 523-527

Multivariate analysis in some genotypes of mungbean [Vigna radiata (L.) Wilczek] on the basis of agronomic traits of two consecutive growing cycles

Moushree Sarkar*and Sabyasachi Kundagrami
1<p>Department of Genetics and Plant Breeding, Institute of Agricultural Science, University of Calcutta,&nbsp;51/2, Hazra Road, Kolkata-700 019, West Bengal, India.</p>
Cite article:- Kundagrami Sabyasachi Sarkar*and Moushree (2016). Multivariate analysis in some genotypes of mungbean [Vigna radiata (L.) Wilczek] on the basis of agronomic traits of two consecutive growing cycles . Legume Research. 39(4): 523-527. doi: 10.18805/lr.v0iOF.11037.

An experiment to evaluate eleven agro morphological traits in twenty three genotypes of mungbean [Vigna radiata (L.) Wilczek] in the field experiment was conducted in two consecutive years 2013 and 2014. Data were recorded on quantitative as well as visually assayed agro morphological traits including days to 50 per cent flowering, days to 1st picking of pods, days to final picking, plant height (cm), number of secondary branches per plant, number of pods per plant, pod length (cm), number of seeds per pod, 100 seed weight (g), seed yield/plant (g) and harvest index (%). All the traits were analysed using multivariate statistical analysis. Most of the important evaluated traits showed a wide range of variation. Cluster analysis using UPGMA method grouped the genotypes into five clusters. The component of mungbean genotypes among different clusters was varied from two to nine genotypes. The maximum number of genotypes i.e., 9 is found in cluster III followed by cluster IV comprising of 6 genotypes. Cluster V showed the maximum mean value for plant height, branch/plant, pods/plant, seed/pod, seed yield/plant and lowest values for days to 50% maturity, 1st picking and days to maturity. Principal component analysis revealed that the first five main PCAs amounted 71.11% of the total variation among genotypes. PC1 accounts for maximum variability in the data with respect to succeeding components.

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