Genetic Diversity in Bt-Cotton (Gossypium hirsutum L) Genotypes for Yield and Fibre Quality Traits using Multivariate Analysis

Abstract

Aim: The primary objective of the present investigation was to evaluate the genetic diversity among twenty-four Gossypium hirsutum genotypes for yield and fibre quality traits using multivariate statistical approaches. This evaluation aims to identify genetically divergent parental lines suitable for breeding programs focused on yield improvement and fibre quality enhancement. Design: A field experiment was conducted in Kharif 2022–23 using a randomized block design in three number of replications with the spacing of 67.5 x 30 cm. Methodology: Twenty-four cotton genotypes were analyzed using multivariate techniques specifically, cluster analysis (based on Ward’s method) and principal component analysis (PCA using Kaiser’s criterion) to quantify genetic diversity and trait contribution to variability. Eight yield and fibre quality attributes were recorded. Data analysis was executed using R software (version R4.2.1). Results: Cluster analysis grouped the genotypes into three distinct clusters, with Cluster 2 having the highest number of genotypes (12), followed by Cluster 1 (11) and Cluster 3 comprising a single genotype (HAU Bt-5). PCA revealed that the first two principal components accounted for 71.08% of the total phenotypic variance. PC1, contributing 57.70%, was predominantly influenced by boll number, seed cotton yield, ginning out turn and micronaire. The trait biplot and clustering patterns underscored significant diversity among genotypes, highlighting potential combinations for hybridization. Conclusion: Significant genetic variability was observed among genotypes. Traits such as seed cotton yield, boll number, boll weight and ginning out turn were key contributors to diversity. The multivariate approach effectively identified superior parental lines for use in cotton breeding programs.