Unraveling the relationship between fruit yield and yield related components in snake gourd genotypes using multivariate analysis Fathima A. Fahima1,*, Pugalendhi L.1, Saraswathi T.2, Manivannan N.3, Raveendran M.4 1Department of Vegetable Science, Horticulture College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India 2Department of Medicinal and Aromatic Crops, Horticulture College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India 3Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India 4Directorate of Research, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India *Corresponding Author: A. Fahima Fathima, Department of Vegetable Science, Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India, Email: fahimarahuman@gmail.com
Online published on 6 November, 2023. Abstract Background Snake gourd is a monoecious crop that prefers cross pollination. Snake gourd has a lot of potential for genetic improvement. A large variation can be produced when genetically diverse and geographically distant lines are combined. To examine the genetic diversity and connection between essential agronomic features in snake gourd, multivariate methods such as principal component analysis and cluster analysis were used. This study will use multivariate analysis to determine the genetic diversity and link between critical agronomic aspects of snake gourd. Methods A total of sixteen genotypes and two varieties of snake gourd genotypes were subjected to boxplot, principal component analysis and cluster analysis based on eleven quantitative traits. Boxplot analysis, Principal component analysis and cluster analysis were performed using R version of 4.2.1. Result Boxplot analysis depicted the frequency distribution of eleven quantitative traits among 18 snake gourd accessions. The overall variation was split into eleven principal components, out of which five major principal components contributed for variability of snake gourd genotypes by exhibiting 90.05 per cent of variability. The squared cosine variables inferred that the traits viz., days to first male flowering, days to first female flowering and days to first harvest contributed more for variability in the first component. The ward D2 method of hierarchical clustering cluster the 16 genotypes and 2 varieties in two clusters based on cluster sum of squares. Top Keywords Boxplot, Cluster, Principal components, Squared cosine. Top |