Assessing the genetic diversity of twenty one Colombo limon L. genotypes through multivariate and covariance matrix analysis

Abstract

Article Details: Received: 2020-10-02 | Accepted: 2020-11-18 | Available online: 2021-06-30 https://doi.org/10.15414/afz.2021.24.02.110-116

Genetic diversity of selected 21 colombo lemon (Colombo limon L.) genotypes were evaluated through multivariate analysis and also through covariance matrix to identify the promising parents for an auspicious crossing program for development of location specific high yielding genotypes. To view the endurance of significant heterogeneity, the genetic constitution of selected 21 genotypes were classified into four clusters (I, II, III & IV). Among them, the assemblage cluster II dominated the maximum number of genetic constitution, whereas assemblage cluster I possessed minimum number of genetic constitution. Surrounded by the genetic constitution, the first principal axis largely affords 47.1% of the variation. Inter genotypic gap was observed the highest (13.82) between the genetic constitution in both ‘CL002’ and ‘CL017’ genotypes, followed by genotypes ‘CL002’ and ‘CL019’. The maximum intracluster distance was noticed (23.33) for cluster IV, whereas cluster III showed the lowest intracluster distance (10.62). In between assemblage clusters I and IV showed the highest interassemblage distance (165.64), while the lowest distance (44.84) was estimated between II and III assemblages. Considering the cluster mean bulky fruit remarked in cluster IV (256.00 g). Besides these the highest fruit yield plant-1 was also recorded in cluster IV (20.50 kg). In respect of both quantitative parameters, assemblage IV was quite diverse compare with all other assemblages. Contemplating the immensity of genetic distance and quantitative performance the genotypes ‘CL002’, ‘CL005’, ‘CL006’ and ‘CL011’ from cluster IV obviously pertinent for productive crossing program to promote high yielding colombo lemon genotypes.

Keywords: genetic diversity, colombo lemon, genotypes, multivariate analysis, clusters

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