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

From December 2019 to May 2020, an experiment was conducted at the Oilseed Research Centre of the Bangladesh Agricultural Research Institute in Gazipur, Bangladesh. The purpose was to use a multivariate technique to study the yield and contributing factors of 80 peanut genotypes. Principal component analysis and D2 analysis were used to achieve the goal. The genotypes were grouped into six groups. Cluster III had the most genotypes (21) whereas Cluster II had the least (6). The inter-cluster distances were always bigger than the intra-cluster distances, showing that genotypes from distantly linked groups have more variation. Cluster I had the most intra-cluster distance, and Cluster IV had the shortest. Clusters I and II had the greatest inter-cluster distance, followed by Clusters V and III, while Clusters IV and V had the smallest. For eight characters, the first four primary components showed the most variation (PCs). Plant height, 100 kernel weights, and shelling (percent) have a strong positive correlation with PC1, but PC2 has a strong positive correlation with yield and shelling (percent) ( percent ). The number of days to initial blooming, days to maturity, plant height, and 100 kernel weights all have a positive connection with PC3. PC4 is linked to the number of ripe pods per plant, plant height, and yield.

Please see the link :- https://www.ikprress.org/index.php/JOGAE/article/view/7063