MULTIVARIATE ASSESSMENT OF YIELD AND ITS COMPONENTS IN OKRA [Abelmoschus esculentus (L.) Moench] GENOTYPES

Abstract

Okra [Abelmoschus esculentus (L.) Moench] is a valuable vegetable in many regions of the world, especially in the tropics and sub-tropics. It is not only a nutrient-rich vegetable but also an important medicinal herb (Yuan et al., 2014). Although India is the largest producer of okra with 6.35 million tonnes production (72.9% of total world production) from 0.53 million hectare area (Anonymous, 2015), its productivity potential is low. A major constraint in okra productivity is the low genetic potential of the present okra varieties. Improvement of okra requires a broad spectrum of genetic variability from which useful characters can be selected for developing broad-based populations to be used in hybridization programme (Lester et al., 1990; Hammond and Charrier, 1983). Genetic distance estimates form the basis for selecting parental combinations. Genetic distance estimates for population can be estimated by different methods. One of the approaches is to apply multivariate analysis as it has extensive use in summarizing and describing the inherent variation among crop genotypes. Multivariate statistical tools include Mahalanobis D2 analysis, cluster analysis, principal component analysis (PCA) and discriminate analysis (Oyelola, 2004). Mahalanobis D2 multivariate analysis (Mahalanobis, 1936) for obtaining quantitative estimates of genetic divergence and Tocher’s method for grouping of different genotypes of a particular crop are more valuable tools in choosing suitable parents for heterosis breeding. PCA can be used to uncover similarities between variables and classify the genotypes. With this backdrop, an attempt was made in the present study to assess the nature and magnitude of genetic divergence of some okra genotypes consisting of advanced breeding elite lines developed at Indian Institute of Horticultural Research (IIHR), Bangalore using various multivariate analysis tools.