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Crop Research
Year : 2023, Volume : 58, Issue : 3and4
First page : ( 178) Last page : ( 183)
Print ISSN : 0970-4884. Online ISSN : 2454-1761.
Article DOI : 10.31830/2454-1761.2023.CR-907

Assessment of genetic diversity for morphological and physiological traits in Clusterbean [Cyamopsis tetragonoloba (L.) Taub.] using principal component analysis

Priyadarshini V. M.*, Madhanakumari P.

Department of Horticulture, Faculty of Agriculture, Annamalai University, Chidambaram-608 002 (Tamil Nadu), India

*(e-mail: priya03vm@gmail.com)

Online published on 2 August, 2023.

Abstract

Clusterbean is one of the most underutilized, nutrient-rich vegetable crops with Indian origin. An attempt was made to examine the diversity of this important crop using a multivariate statistical model - Principal Component Analysis - to identify highyielding accessions. The present investigation was conducted during January to April, 2022 at Poothurai village of Villupuram district of Tamil Nadu to identify the genetically diverse outliers among the vast population of clusterbean accessions. In this study, 102 genotypes of clusterbean were evaluated collected from various regions. Fourteen quantitative and three qualitative characters were taken that showed considerable variations and divergence helping to identify promising genotypes for further exploitation. Principal component analysis showed 17 principal components of which six had eigen values greater than 1 contributing to 67.67% of variation. The first principal component had a maximum of 19.98% of total variation. Divergence in 102 germplasm was recorded for total pod yield per plant, number of pods per plant, number of pods per cluster, days to maturity, seed yield per plant and days to 50% flowering in variables supporting loadings of first principal component. They were considered as traits for selecting the genotype. In accordance with this, the promising genotypes showing high diversity for agronomic and yield traits were isolated, which can be used for clusterbean breeding.

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Keywords

Clusterbean, Diversity, Multivariate analysis, Principal component analysis.

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