Based on the data recorded on the genotypes in the present investigation, the results of the analysis of variance showed that all of the characters’ differed significantly, demonstrating the prevalence of ample genetic diversity among the genotypes (Table 1).
Character association study
Table 1: Analysis of variance for yield and seed quality traits in groundnut.
The genotypic and phenotypic correlations were estimated to determine direct and indirect effects of yield and yield contributing characters and presented in Table 2 and Fig 1.
Table 2: Correlation coefficients studied for yield and seed quality characters.
Fig 1: Correlation coefficients for yield and seed quality characters in groundnut.
Pod yield per plant showed positive and significant correlation with primary branch number (0.591), hundred pod weight (0.654), hundred kernel weight (0.694) whereas positive correlation with secondary branch number (0.11) and quality parameters viz
., protein content (0.402), sucrose content (0.122), total free aminoacids (0.052), iron (0.315) and zinc content (0.143) which suggests that increase or improvement in these characters lead to improvement in pod yield/ plant (Table 2, Fig 1). Similar kind of significant positive correlation of pod yield/plant with hundred pod weight and protein content was observed by Kumar et al., (2019), Bhargavi et al., (2016)
and Shoba et al. (2012)
. Among the quality traits, as protein content showed a negative correlation with oil content (-0.396).
Path coefficient analysis
The results of path coefficient analysis of yield and yield contributing characters are presented in Table 3.
Table 3: Direct (diagonal bold) and Indirect effects of component characters on character of interest.
The study of the interactions and relative contributions of many traits to crop development is greatly aided by genetic association. Estimates of correlation coefficients did not reflect the direct and indirect impacts of various features on the yield; they only showed the relationship between yield and yield components. This is so because the attributes that are associated do not exist alone; rather, they are connected to other elements. Dewey and Lu (1959)
path coefficient analysis suggests useful assessments of the direct and indirect causes of association and illustrates the relative value of each element contributing to the final yield. The cause-and-effect link between yield as a whole and yield component qualities was looked at using path coefficient analysis in order to obtain the developmental relations.
Plant height had positive direct effect on pod yield per plant (0.246) while the correlation of plant height with pod yield was positive and significant (0.314). The correlation between plant height and pod yield was positive and significant mainly due to positive indirect effect contribution through hundred kernel weight (0.513), shelling percent (0.713), protein content (0.096), seed micronutrient content i.e
., Zinc content (0.078). The positive direct effect of plant height on pod yield had been reported by Jain et al., (2016), Raut et al. (2010)
and John et al., (2019).
Hundred pod weight exhibited a positive direct effect on pod yield per plant (5.36) while the correlation with pod yield per plant was also positive and significant (0.74). Shelling percentage exhibited a positive direct effect on pod yield per plant (4.22) while the correlation with pod yield per plant was also positive and significant (0.819). Similar findings are seen with Korat et al., (2010), Zaman et al., (2011), Shoba et al. (2012)
and Reddy et al. (2017a and 2017b)
. Hundred kernel weight had direct negative phenotypic effect (-1.825) on pod yield per plant. whereas the correlation was negative significant (-0.282). Hundred kernel weight exerted negative direct effect (-1.825) on pod yield per plant as observed earlier by Patel and Shelke (1992)
Oil content had direct positive effect (0.193) on pod yield per plant. Its correlation with pod yield per plant was negative and significant (-0.618). The correlation between oil content and pod yield per plant was negative and significant mainly due to negative indirect effect contribution through plant height (-0.022), number of secondary branches per plant (-0.689), hundred kernel weight (-0.824) and shelling percentage (-2.289). Protein content had direct positive effect on pod yield per plant (0.505) while its correlation with pod yield was positive significant (0.683). The correlation between protein content and pod yield per plant was positive is mainly due to positive indirect effect influence through plant height (0.047), hundred kernel weight (0.043), shelling percent (3.586), sucrose content (0.036) and seed micronutrient Fe (0.039) and Zn content (0.118). Total free aminoacids (-0.216), Total soluble sugars (-0.045) and iron content (-0.202) exerted negative direct effect on pod yield per plant. The lower residual effect (0.0076) indicated that sufficient contribution in pod yield per plant has been explained by the independent variables included in the analysis.
Path coefficient analysis revealed that Hundred pod weight (5.36) exerted the highest positive direct effect on pod yield per plant followed by shelling percentage (4.22), primary branches per plant, hundred pod weight, oil content and protein content. The negative direct effect was showed on pod yield by hundred kernel weight, sucrose content, total soluble sugars and iron content.
Principal component analysis (PCA)
The PCA based on correlation matrix on the mean values of the groundnut genotypes was performed which provided a reduced a dimension model that could indicate measured differences among the genotypes in the population. The results revealed the importance of first five Principal Components (PCs) in discriminating the groundnut population. Since first five PCs selected as it explains 73.24% of variation and had Eigen values greater than 1. The eigen values and associated cumulative percentage of variation explained by eigen vectors have been presented in Table 4 and Table 5 which shows the scree plot graph (Fig 2) for variation explained by various principal components.
Table 4: Eigen values and proportion of variation for different principal components.
Table 5: Eigen vectors for different Principal components.
Fig 2: Scree plot for variation explained by principal components.
The first principal component gave high positive weight (0.459) to Hundred pod weight and Hundred kernel weight (0.414), similarly second, third, fourth and fifth Principal components gave high positive weights to Shelling percentage (0.458), sucrose content (0.51), secondary branches per plant (0.369) and Iron content (0.443) respectively. From the eigen loadings, the first principal component is strongly correlated with primary branch number, pod yield/plant, hundred pod weight and kernel weight. Out of these, PC1 was most strongly correlated with hundred pod weight and hundred kernel weight.
An attempt has been made to observe the variation explained by seven quantitative and six qualitative characters along one and two principal component vectors i.e
., Biplot (Fig 3 and Fig 4).
Fig 3: Genotype by trait Biplot showing distribution of genotypes across first two PCs.
Fig 4: PCA Biplot showing variation among traits.
From Biplot, 14 characters were grouped into five groups. Primary branches per plant, Zinc content, Protein content, Pod yield per plant were grouped in same cluster; hundred kernel weight and hundred pod weight as single group and Secondary branches per plant, total free aminoacids, total soluble sugars and plant height as one group. Those genotypes nearer to each trait can be said as best suited for those traits respectively. The genotypes TAG-24 and Abhaya are best suited for shelling percentage. Genotype Rohini was highly suitable for oil content and Dheeraj for total soluble sugars. Genotype Nithya Haritha was highly suitable for protein content and contributed more to this trait. There is high correlation between hundred kernel weight and hundred pod weight and also between total free Aminoacids and plant height.