Assessment of Groundnut (Arachis hypogaea L.) Cultivars on Yield and Yield Contributing Traits using Principal Component Analysis

Abstract

Background: The breeder’s ability to identify and exploit genetic diversity at the genotypic level is essential for the success of any breeding effort. It requires evaluating various treatments for a variety of traits in order to choose parents with the greatest genetic diversity. The purpose of this research is to characterize the genetic variation among 48 groundnut accessions with 6 checks. Methods: Experiment was conducted to study groundnut genotypes for 15 varied traits such as phenological, yield attributing and biochemical ones. Data analysis for principal component analysis (PCA) is done using R software. Result: Principal component analysis (PCA) was carried out to investigate the principal components of variance usingtraits of study. Total variance among characters are classified into 15 principal components. Out of which first five components have eigen value greater than 1 contributing to 65.9% total variance. PC 1 alone accounted for highest variance of 16.3%, followed by PC 2 with 12.6%. PC1 explained a large amount of variation in important traits as number of mature kernels per plant, total number of kernels per plant, pod yield per plant and kernel yield per plant. PC 2 have positive influence on days to 50% flowering, days to maturity, number of branches per plant, biological yield per plant, number of mature kernels per plant, total number of kernels per plant, sound mature kernel percentage, oil content and harvest index.