Principal component analysis
The goal of principal component analysis is to find a limited number of linear combinations that account for the majority of the variation in the data being used. ANOVA for seed yield and its components in sunflower lines is depicted in Table 1 indicating significant difference obtained in all traits studied. Principal components analysis was used in this study to classify the most important characters and view them in more visual dimensions using linear combinations of variables that account for the majority of the variance in the original set of variables. Out of nine components, four components had more than one eigen value (Table 2). The first principal component explained 33.40 per cent of total variation, while the second, third, fourth, fifth, sixth, seventh, eighth and ninth principal components explained 3.30, 14.10, 11.19, 6.30, 4.90, 3.60, 1.90 and 0.60 per cent, respectively (Table 2 and Fig 1).
The principal factor study
(Kaiser 1958) did not yield a simple image of the characters’ interactions. As some factors had very high variable loading and others had low (Table 3). The data in the table says that PF-1 was loaded on plant height and SPAD readings @ 45 days, similarly PF-8 loaded on SPAD @ 60 days and test weight respectively. The highest loading recorded was PF-9 and PF-8.
Principal component analysis was also used in oat by
Vaisi et al., (2013) and
Krishna et al., (2014), who proposed transferring several associated variables into a few separate principal components, which explained much of the heterogeneity in the original collection.
Hemavathy (2020) performed a sweet corn principal component research study and found similar results. In the Fig 2 wider angle indicates maximum diverse which indicates negative correlation and lesser or acute angle indicating more correlation. Results in figure view also depicted at the end.
PCA will allow for the depiction of individual differences as well as the identification of possible groups. The original variables are linearly transformed into a new set of uncorrelated variables known as principle components to achieve the reduction.
Maruthi Sankar et al., (1999) have assessed the variability of eight plant traits for growth of sunflower and reduced the dimensionality to two principal components, which extracted about 80% of variance in the original data.
Genotypic and phenotypic correlation
Correlations are useful for identifying the main factors that determine final grain production, they only give a partial picture of the relative importance of direct and indirect impact on the individual elements. Yield is a complicated trait that is influenced by the number of other characteristics that can have both positive and negative effects on it. As a result, understanding the mechanism of interaction, consequences and cause of relationship will aid in the selection of breeding methods for increasing yield in sunflower. The magnitude of phenotypic correlation is lower than genotypic correlation, but they have a similar trend in direction, according to correlation analysis. Low phenotypic association indicates that the environment has an effect on the expression of these traits. Certain traits had significant positive correlation and certain had significant positive correlation. Yield per plant and plant height had significant positive correlation and rest had significant negative correlation. Similar results were obtained by
Arunkumar et al., 2014. This indicates that selection of traits like plant height and yield per plant can be used as major criteria for improving the yield (Table 4 and 5).
The lines which are having high heritability and greater amount genotypic coefficient of variation need to be selected and also the lines having high plant height and high seed yield per plant leads to increase in total yield per hectare basis. Thus there might be certain traits depicting negative correlation and certain positive correlation, but breeders need to concentrate on the traits which are having significant positive correlation in order to improve crop yield.
Diversity analysis
Genetic variety is required for any crop improvement program since it aids in the generation of superior recombinants by allowing for the selection of parents with greater variability for various traits. Many crops has lost its diversity in recent decades as local types have been replaced with high-yielding variants. Genetic divergence study assesses the genetic diversity of a population.
D2 statistics is a method of calculating genetic divergence in germplasm collections using a numerical technique. The parents for the hybridization program should be chosen based on the magnitude of genetic distance, the contribution of different characters to total divergence and the magnitude of cluster means for distinct characters with the most heterosis. The D2 study of 45 sunflower lines resulted in nine clusters, with cluster I and II having the most (20) and 14 lines and cluster VI to IX had one each. Cluster III and IV had the greatest intra-cluster distance. Clusters II, III and IV had stronger cluster means for most of the characters, hence lines 17, 11, 12, 45 and 43 can be used in crossing for crop improvement (Table 6,7). Similar results were obtained by Poonia and Phogat, 2017 while working in oat.
