Principal component analysis for groundnut yield and seed attributes (Arachis hypogaea L.)
Lokendra Singh Rajput, RS Sikarwar, Sakshi Vilas Ingle, MK Tripathi, PK Prajapati and RK Yadav
An intricate quantitative characteristic yield is highly dependent on its surroundings. Direct selection for grain production is a less effective way to boost groundnut production. The goal of the current study was to examine genotype for variability parameters in different lines. Phenotypic data was collected on thirteen quantitative characters for 56 genotypes under study carried out in randomised block design (RBD). Analysis has been done using GRAPES software program. Significant variations were found between the genotypes for every attribute in the analysis of variance, suggesting that there is a lot of genetic diversity among the genotypes. Plant height, the number of branches per plant, 100 kernel weight, kernel yield per plant are the most important characters which could be used as selection criteria for effective improvement of pod yield. Using GRAPES software, Thirteen Principal components are extracted based on mean values of which the first five PCs showed 86.24% variation with eigen values more than 1. Biplot constructed by Principal component analysis revealed Hundred pod weight and hundred kernel weight as important traits for study.
