Principal Component Analysis and Cluster Analysis of 39 Spring Soybean Germplasm Resources

7 major agronomic traits of 39 soybean germplasm resources were analyzed with the method of principal component analysis(PCA), and two principal component factors were obtained. The first principal component(MF₁) was positively correlated with plant height, bottom pod height and the number of main stem segments. The second principal component(MF₂) was positively correlated with the number of grains per plant and the grain weight per plant, and negatively correlated with the number of 100-grain weight and effective branches. The cluster analysis were studied through the integrated principal component values and the agronomic trait normalization data. 39 germplasms were divided into 3 major categories and 2 sub-categories through the former method, and divided into 3 major categories and 4 sub-categories through the latter method. By comparison, the analysis results of the two clustering methods were generally consistent.