Multivariate analysis of soybean genotypes

Abstract

The experiments were conducted using randomized complete block design with three replications at the research field of Agriculture Botany Division, Khumaltar, Lalitpur, Nepal in 2016 and 2017 to evaluate sixteen soybean genotypes using multivariate analysis. The results showed the significant (p <0.05) differences among genotypes for plant height, days to maturity plant and hundred seeds weight and grain yield. Cluster analysis based on these traits, sixteen soybean genotypes were divided the genotypes into four clusters. The soybean genotypes grouped into cluster 1 showed the highest value for days to maturity. The genotypes belonged to cluster 2 had the highest values for grain yield and plant height. The principle components analysis showed that PC1 and PC2 having eigen values the highest than unity explained 76.6% of total variability among soybean genotypes attributable to plant height, days to maturity, number of pods/plant, 100 seed weight and grain yield. The genotypes showing wide diversity in cluster and principle component analysis can be used as parents in hybridization programs to maximize the use of genetic diversity and expression of heterosis and develop high yielding soybean varieties.

Keywords

• clustering
• grain yield
• principal component analysis and soybean