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Multivariate Analysis of Quantitative Traits in Field Pea (Pisum sativum var. arvense)
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Methods: The data were recorded on thirteen quantitative characters for the study of genetic diversity. The mean data of each characters were subjected to cluster analysis by using D2 Mahalanobis clustering method. The principle component analysis (PCA) for measuring genetic divergence was done by XLSTAT and R 4.0 statistical package.
Result: Eighty-four germplasms including checks were categorized into five distinct clusters, indicates the occurrence of high genetic diversity in the evaluated set of germplasm. Between cluster III and IV highest inter-cluster distance was observed, indicates the maximum diversity among genotypes of these clusters. Considerable differences were observed for cluster mean among different distinct clusters for all the thirteen characters. The hybridization programme involving genotypes from cluster III and cluster IV may be used to isolate suitable segregants. Principal component analysis grouped different traits under study into thirteen principal components (PCs) in which only five PCs with eigen value >1 accounted 70.97% of total variation present in genotypes. The traits falling to these five PCs may be given due importance in field pea improvement programmes.
As per the vision of IIPR, Kanpur, the population of India is continuously increasing and it will likely to reach 1.68 billion by 2030. The demand of pulses for year 2030 is forecast to reach 32 million tonnes with an anticipated yearly needed growth rate of 4.2%. In total output of pulses, a quantum hike is required to increase the availability per capita and to meet the challenges of rising population. Therefore, high yielding variety of field pea with good qualities of seed are needed. For this purpose, selection of genetically diverse parental genotype to be used in hybridization programme is based on the assumption that “Crosses involving divergent parents offer greater possibility of obtaining desirable segregants in the segregating generations”. Several researchers addressed the need of a diverse parent to obtain superior genotypes in the segregating generations (Kumar and Kumar, 2016; Singh et al., 2017 and Prasad et al., 2018). Therefore, efforts should be made to increase the wider use of existing diversity from germplasm collection.
MATERIALS AND METHODS
RESULT AND DISCUSSION
Dendrogram of eighty-four field pea genotypes was constructed by Ward clustering method (Ward, 1963) to calculate the appropriate genotypic variability existing among all studies clusters (Fig 1).
The diversity was also endorsed by the appreciable amount of difference between cluster means for different characters (Table 2). Cluster V exhibited the highest mean value for branches plant-1, nodes plant-1, effective nodes plant-1, effective pods plant-1, biological yield plant-1 and seed yield plant-1 as well as least mean values for days to 50% flowering and days to maturity. The highest mean value for days to maturity, plant height and pod length found in cluster IV and for days to 50% flowering and seeds pod-1 in cluster II. Cluster III had highest mean value for 100-seed weight and harvest index as well as least mean values for plant height, branches plant-1, nodes plant-1, effective pods plant-1, biological yield plant-1 and seed yield plant-1. None of the characters had highest mean value in cluster I. These findings revealed that different clusters were superior for different characters.
Principle component analysis (PCA)
PCA is a statistical method of multivariate analysis which reduce the set of large number of variables to set of small number of linearly uncorrelated variables, which can explain the most of variation present in the original variables (Anderson, 1972 and Morrison, 1982). The outcome of PCA revealed that only the first five principle components (PCs) displayed more than 1.00 eigen value and demonstrated a maximum variability of around 70.97% among field pea germplasm with respect to yield component traits (Table 3 and Fig 2). The traits falling to these five PCs may be given due importance in field pea improvement programmes.
The PC1 had the highest variability (25.49%) followed by PC2 (17.34%), PC3 (11.63%), PC4 (8.78%) and PC5 (7.72%). The present study was also supported by the previous work done by Hanci and Cebeci (2018). Eigen values helps to decide that how many variables to retain. The sum of the eigen values is generally equal to the number of variables (Sharma, 1998).
First principle component (PC1) showed variation of 25.49% in which seed yield plant-1, effective pods plant-1, biological yield plant-1, seeds pod-1, effective nodes plant-1, harvest index, branches plant-1, plant height, pod length, nodes plant-1 and 100-seed weight were major positive contributors, while days to 50% flowering and days to maturity had negative weights. Similar pattern using PC analysis in field pea for such traits was reported by Parihar et al., (2014). The characters contributed to the variation in the PC1, forms a larger percentage in the variation among all genotypes. The traits found positive in PC2 were 100-seed weight, harvest index, pod length, seeds pod-1, days to maturity, seed yield plant-1, days to 50% flowering and effective nodes plant-1. While, plant height, branches plant-1, nodes plant-1, biological yield plant-1 and effective pods plant-1 had the highest negative weights for PC2. Another additional variation of 11.63% and 8.78% had sown by the third, fourth respectively. These findings were consistent with the analysis by Hanci and Cebeci (2018), who reported variation of 9.93% and 8.23% for third and fourth components, respectively. The PC3 was described only by days to 50% flowering, days to maturity, plant height, branches plant-1, nodes plant-1, effective pods plant-1, seeds pod-1, pod length and biological yield plant-1 with positive factor loadings, meanwhile the remaining traits in that PC obtained negative loadings. PC4 was compiled with plant height, nodes plant-1, effective nodes plant-1, seeds pod-1 and pod length with their positive loading and negative loading factors were discovered for remaining traits in this PC. The PC5 was explained by variance due to days to 50% flowering, days to maturity, plant height, effective nodes plant-1, effective pods plant-1, biological yield plant-1, harvest index and seed yield plant-1 having positive factors loading, while branches plant-1, nodes plant-1, seeds pod-1, pod length and 100-seed weight occur negative loadings. These conclusions are in correspondence with that of Habtamu and Million (2013), Parihar et al., (2014) and Umar et al., (2014). The value of positive and negative loading reveals positive and negative association patterns between the PCs and the variables. Therefore, the characters listed above, which load strongly positive or negative, have contributed most to diversity and have been the ones that have most categorized the clusters.
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