Selection of Interspecific Peanut Arachis hypogaea L. Genotypes for Thrips Resistance Using Multivariate Analysis

Abstract

This study leverages multivariate analysis, including principal component analysis (PCA) and cluster analysis, to select peanut genotypes with resistance to thrips and desirable agronomic traits. The focus is on progenies derived from the cross between the cultivar IAC 503 4x and an interspecific synthetic amphidiploid (A. magna x A. cardenasii) 4x. Analyzing F4 generation progenies using Federer’s augmented block scheme with intercalary checks, the study evaluates resistance to thrips based on natural infestation and damage symptoms, alongside agronomic traits indicating proximity to the cultivated variety. The multivariate techniques applied are PCA and hierarchical cluster analysis using Euclidean distance and Ward’s method, and the nonhierarchical K-means method. PCA identifies two principal components explaining 78.39% of the variance, focusing on pod and grain yield, number of pods and grains, number of thrips, and visual symptom scores. This allows for the discrimination of 24 progenies based on crucial agronomic characteristics. Cluster analysis forms nine groups, with selected progenies clustering together, indicating consistency between multivariate analysis methods. These analyses effectively select segregating progenies from initial generations of peanuts, emphasizing traits related to thrips resistance and production components. The agreement between PCA and cluster analysis results highlights the efficiency of these methods in genotype selection for improved pest resistance and agronomic performance, contributing to the sustainability and economic viability of peanut production.