MULTIVARIATE ANALYSIS FOR YIELD AND ITS CONTRIBUTING TRAITS IN PEANUT (Arachis hypogaea L.)

An experiment was carried out at the Oilseed Research Centre, Bangladesh Agricultural Research Institute, Gazipur, Bangladesh from December 2019 to May 2020. The goal was to investigate the yield and contributing features of 80 peanut genotypes using a multivariate approach. The goal was met by using principal component analysis and D2 analysis. Six clusters were formed from the genotypes. Cluster III had the greatest number of genotypes (21) and Cluster II had the fewest (6). The inter-cluster distances were always greater than the intra-cluster distances, indicating that there is more variety among the genotypes of distantly related groups. Cluster I had the most intra-cluster distance, while Cluster IV had the smallest. The greatest inter-cluster distance was found between clusters I and II, then V and III, and the smallest between clusters IV and V. The first four principal components showed the greatest diversity for eight characters (PCs). PC1 has a strong positive link with yield and shelling (%), whereas PC2 has a strong positive correlation with plant height, 100 kernel weights, and shelling (%). PC3 has a positive relationship with the number of days to initial flowering, days to maturity, plant height, and 100 kernel weights. PC4 has a favorable relationship with mature pods per plant, plant height, and yield.