The analysis of variance showed highly significant differences among the 135 soybean genotypes for all the ten quantitative traits studied. The 135 soybean genotypes were grouped into twelve clusters by using UPGMA tree clustering in Ggt 2.0 software as given in (Table 2). The cluster IV was the largest cluster with 30 genotypes followed by clusters VIII, II, V and III with 24, 22, 13 and 10 genotypes respectively. Cluster VI and VII were the solitary clusters with genotype viz.,
MAUS 60 and JS 98-61 respectively. The genotypes within the same cluster showed less variation whereas between the clusters exhibited maximum variation. Therefore, the genotypes from different clusters could be selected for crop improvement.
Principal component analysis
Table 2: Clustering of 135 soybean germplasm accessions based on ten quantitative traits.
Principal component analysis was performed using the mean data of ten quantitative traits using GRAPES software. The quantitative traits of 135 germplasm accessions were categorized into ten different principal components based on the total variation. The Eigen value of first four principal components among ten PCs were more than one as given in (Table 3) and these four PCs contribute the cumulative percentage of variation of 79.77 per cent. The contribution of each trait to total variation is presented in (Table 4).
Table 3: Eigen value, percentage of variance and cumulative proportion of the principal component.
Reddy et al., (2021)
Table 4: Component matrix representing Eigen vectors and scores of principal components for the quantitative traits.
reported 68.61% of variance was contributed by first three principal components out of ten principal components formed with 24 french bean germplasms. Kumar et al., (2010)
observed 95% of total variation was contributed by first ten PCs out of the fourteen PCs formed with 64 groundnut breeding lines.
The first principal component showing 42.17% variation was associated mainly with number of clusters per plant, number of pods per plant and number of branches per plant. The outcomes of Ghiday and Sentayehu (2015)
and Dubey et al
. (2018) were similar with the present study for number of branches per plant and number of pods per plant. Jain et al., (2021)
reported 28.6% of total variation was contributed PC1 and is associated with number of pods per plant, days to flowering and plant height in 40 chick pea genotypes. The second principal component contributing 15.72% variation was mainly related to hundred seed weight and single plant yield. Similar outcomes were reported by Singh and Shrestha (2019)
and Dubey et al
. (2018) for hundred seed weight and single plant yield respectively. The third principal component conferring 11.24% variation was mainly connected with number of pods per plant and number of pods per cluster. Similar findings were observed by Ghiday and Sentayehu (2015)
for number of pods per plant. The fourth principal component exhibiting 10.64% of variation and it was mainly coupled with hundred seed weight and Dubey et al. (2018)
and Singh and Shrestha (2019)
had similar findings on hundred seed weight.
The scree plot given in (Fig 1) clearly depicts that PC1 had highest variation, followed by PC2, PC3 and PC4. Based on PC1 and PC2, the genotypes were scattered along the biplot as shown in (Fig 2).
Fig 1: Scree plot of principal component analysis of soybean germplam accessions between percentage of variance and principal components.
Fig 2: Genetic divergence of 135 soybean germplasm accessions in biplot with cos2 loadings.
*Circled in black color are diverse parents used for hybridization.
The cos2 loading value ranges from 0.00 to 1.00 and these values were used to indicate the divergence among genotypes. MACS 1460, EC 18736 and PK 1038 were the genotypes far apart, while JS 89-24, NRC 25, NRC 2007-G-1-13, NRC 43 and PK 7247 were the genotypes closer to the origin. The genotypes away from origin with high cos2 loading value exhibited maximum divergence whereas genotypes closer to origin with less cos2 loading value exhibited minimum divergence. The quantitative trait contribution to total variation and its interrelationship is shown in (Fig 3).
Fig 3: Variables plot with contribution of quantitative characters to the total divergence.
The traits viz.,
number of clusters per plant, number of pods per plant and single plant yield were far away and contributes maximum variation while the traits viz.,
number of seeds per pod, number of pods per cluster and hundred seed weight were closer to the origin and showed minimum contribution to total variation. Similar finding was reported by Vijayakumar et al. (2022)