Analysis of genetic variability and the pattern of genetic divergence among soybean [Glycine max (L.) Merrill] genotypes

Introduction

Soybean [Glycine max (L.) Merrill] commonly known as 'wonder crop' is known to be one of the richest sources of both oil and protein and, therefore, sometimes identified as 'gold from soil'. One of the first domesticated crops, rice was grown in central China approximately 7000 B.C. It belongs to the Fabaceae family and is a self-pollinated crop with chromosome no. 2n = 40. It is considered a 'miracle crop' due to its versatility, (its oil as edible oil is being used extensively in the world and soybean meal known to be an important source of protein and metabolizable energy for human diets and animal feeds). As one of the major oilseed crops worldwide, soybean is top in world market (Chung and Singh, 2008) [15] . Soybean is not only an oil crop but is also endowed with significant medicinal and nutritional value. They contribute several vitamins and minerals as well as a diverse range of bioactive plant compounds such as isoflavonoids, phytic acid and saponins. The genetic diversity and variability that are available today determine the extent to which most crops can be genetically improved. Genetic variability and diversity are critically important for a successful breeding program. Study of genetic diversity helps assessing amount of diversity existing in the genotypes and also provides a means to identify parents for the strengthening as well as broadening of genetic base and heterosis through hybridization. Sufficient understanding of genetic diversity will enable the plant breeders to evolve superior cultivars with desired traits. Multivariate analysis is one of the most useful technique for studying genetic variability and pattern of diversity in germplasm collections. Related to this r the two most preferred singletons for exploring the pattern of genetic diversity among genotypes based on morphometric characters are: cluster analysis and principle component analysis (PCA). Another use of genetic diversity study among different germplasm collections is to enable the accurate classification of accessions and reveal subgroups in core collection. This also shows the contribution of individual characters to the total divergence among them. Furthermore, discrimination of crop genotypes on the basis of genetic diversity can be useful for broadening the germplasm base in crop improvement programmes.

Materials and Methods

Experimental material and plan

The experiment was conducted at the Instructional cum Research (ICR) farm of Assam Agricultural University, Jorhat, Assam during Kharif 2022-23. The study site was situated at 26°45' N latitude and 94° 12' east, with an altitude of 91.00 m above MSL. The experimental material comprised forty soybean genotypes including 2 checks (described in Table 1), were procured from ICAR-Indian Institute of Soybean Research, Indore, India. The experiment was conducted in randomised block design with three replications. Each genotype was planted into tows of 2 lengths 4m spaced at a distance of 45×10 cm. The recommended agronomic practices were adopted to obtain good crop growth.

Methodology

The observations were recorded on 40 soybean genotypes (Table 1) which were received from IISR, Indore and for each variety index plant i.e.5 plants per row of selected variety was taken randomly. Readings were taken for 11 quantitative characters such as days to 50% flowering (in days), plant height (cm), no. of primary branches/plant, no. of pods/plant, no. of seeds/pod, harvest index (%), oil content (%), protein content (%), days to maturity and yield/plant (g). Observations from 40 genotypes of soybean were analyzed for variance and estimation of different variability parameters viz., genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), heritability in broad sense (h² bs) and per cent genetic advance as mean (PGAM). To understand the pattern of genetic diversity among 40 soybean genotypes, cluster analysis using Mahalanobis D² method (1936) and principal component analysis were employed based on 11 quantitative characters in the present investigation. Wilk's criterion derived by Rao (1952) was used to test the significance of difference in the mean values for all characters.

Results and Discussion

The analysis of variance (Table 2) revealed significantly high magnitude of mean sum squares for genotypes with respect to all quantitative characters under investigation including days to fifty per cent flowering, no. primarily branches/plant, no.of pods/plant, no of seeds/pod, Plant Height (cm), Hundred Seed Weight (g), Harvest Index (%), days to maturity protein contents % oil content (%) and seed yield plant. The PCV were higher as compared to the GCV for all 11 quantitative characters (Table 3) suggesting environmental effects on expression of these characters. Similar remarks were documented by Guleria et al. (2019) [23] . The traits seed yield per plant and 100-seed weight recorded high GCV and PCV (>20%) indicating the predominance of wider variability for these two characters. Malek et al.

(2014) who found consistent results for 100 seed weight with Hossain et al. (2004) [25] for seed yield per plant and 100 seed weight and by Saicharan et al. (2022) [46] for seed yield per plant. A moderate to narrow range of GCV and PCV (10-20%) were noticed for number of primary branches/plant, number of seeds/pod, number of pods/plant, days to 50% flowering, harvest index and oil content suggesting that variability present in these traits can be improved by use of breeding methods for effective selection. Similar finding was also reported by Reni and Rao (2013) [45] on number of seed per pod and days to 50% flowering; Saicharan et al., (2022) [46] for number of branches per plant. Estimates of Heritability in % Relative in all the 40 soybean genotypes for Eleven quantitative traits ranged from 73.12% to 97.60%. High heritability alongwith high genetic advance as per cent of mean (>20%) was observed for characters days to 50% flowering, number of primary branches/plant, number of pods/plant, number of seed/pod 100 seed weight, harvest index oil content & seed yield/plant. This revealed the importance of additive gene action and direct selection for genotypic expression may be beneficial for their future exploitation. Hossain et al. (2004) [25] also recorded similar observation for high heritability along with high genetic advance as % of mean. for 100 seed weight, pods per plant and seed yield per plant, Khumukcham et al. (2022) [32] for number of pods per plant, 100 seed weight, harvest index and seed yield per ha. Multivariate analysis was used to analyse the pattern of genetic divergence among soybean genotypes. Based on 11 quantitative traits, the 40 soybean genotypes were divided into four clusters (Table 4), utilizing Tocher's method of cluster analysis. This indicates that the genotypes of soybean under investigation reveal considerable divergence with respect to various morphological characters. Among the four clusters thus formed, cluster I harboured largest genotype ( 25 [54] for partitioning 50 genotypes of soybean into five clusters. The inter and intra cluster distances (Table 5), revealed that the most dissimilar clusters were cluster II and IV with a value of 2572.391 between these two clusters also reflected the diverse nature of genotypes among these two clusters. Table 6 presenting percentage contribution of all the 11 characters towards genetic divergence The microdiasim figure (table7) which depicts percentage accumulated variance for each character to genetic diversity revealed that hundred seed weight contributed maximum toward divergence followed by per plant seed yield and protein content. Characters number of primary branches per plant, oil content, number of pods per plant and days to maturity contributed almost equally in divergence. Therefore, it is better to intermate the genotypes under investigation by choosing extreme parents for those characters which are contributing significantly towards total divergence. This may facilitate a range of satisfactory levels of genetic variation towards yield improvement in soybean. Significant contribution of seed yield per plant towards genetic divergence was reported by Shinde et al. (2013) [49] and 100 seed weight as reported by Alpna et al. (2015) [2] and 100 seed weight and seed yield per plant by Mounika et al. (2022) [40] . The PCA result (Table 7) reflected the genetic diversity of soybean germplasm accessions. Scores and loadings analysis On the basis of scree plot out of 11 components (11 characters) two principal elements (PC 1 and PC 2) were observed above eigen value one, contributing about 74.93% of total variability. Accordingly, these two principal components were chosen for further interpretation. The PC 1 which contributed maximum to the variability (59.86%) had strong associations with 100 seed weight, seed yield per plant, oil content, number of pods per plant, number of primary branches per pant and harvest index on the basis of highly loaded factors. Semi curve trend observed after second PC, showed by scree plot (Fig 1) and less fluctuation within the each PC revealed that more maximum variation was loaded on PC1 so the selection of lines for characters under PC 1 may be useful for future prospects. Ghiday et al. (2015) [20] found for number of pods per plant and seed yield per plant, El-Hashash (2016) [18] for pods/plant, seed yield/plant and harvest index and Verma et al., (2021) [55] for number of pods/plant, number of primary branches/plant and also for number of seeds/pod.

Conclusion

In the present study highly significant genetic variability was recorded among 40 genotypes of soybean on eleven quantitative traits. ANOVA detected genetic distinctions for breeding purposes. Yield potential (seed yield per plant and 100-seed weight) and number of primary branches per plant, seeds per pod, pods per plant, days to 50% flowering harvest index and oil content showed high (GCV & PCV >20%), moderate (10-20%) variability. The characters viz, days to 50% flowering, primary branches per plant, seed yield per plant and 100-seed weight exhibited high heritability along with high genetic advance indicating preponderance of additive gene action, thus direct selection will be effective.

Based on cluster analysis, the genotypes were classified into four clusters and Cluster I included 25 genotypes. Between Cluster II and IV, the maximal divergence was observed.

The traits that most contributed to divergence were 100-seed weight, seed yield and seed protein. PC analysis showed that PC1 and PC2 were able to explain 74.93% of the variance with significant association with yield traits. For the purpose of breeding better soybean cultivars, hybrids between different genotypes separated from Clusters II and IV should be developed.

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