Genetic diversity, variability and character association in soybean [Glycine max L. (Merrill)] germplasm
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Date
2020-11
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
The present investigation was carried out at N. E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India, during kharif, 2018 and 2019 with 185 genotypes (180 entries and 5 checks) of soybean for seven qualitative and twelve quantitative characters to assess genetic divergence, genetic variability, inter-character correlation and their direct and indirect effects on seed yield. Results showed that qualitative characters also played an important role in crop diversity as the characters like flower colour and hilum colour contributed maximum towards genetic diversity. For 12 quantitative character analysis of variance showed significant differences among the genotypes for all the characters under study, which shows that immense amount of genetic variability were present among the experimental material. Based on Hierarchical cluster analysis, the 185 genotypes were grouped into 4, 5 and 3 clusters during 2018, 2019 and pooled analysis, respectively. The maximum numbers of genotypes (157) were grouped in Cluster III (2018), (146) genotype in cluster V (2019) and (159) genotypes in cluster III (pooled) and minimum (1) genotype in Clusters I and III during 2018, 2019 and (2) genotype in cluster IV during pooled analysis, respectively. The maximum divergence was observed between Clusters I and IV (10.64) followed by cluster I and II (9.91), II and IV (8.95) and I and III (8.50) during 2018, clusters I and V (11.97) followed by I and IV (11.30), I and III (9.68) and II and V (9.49) during 2019 and clusters I and III (9.23) followed by I and II (8.73) and II and III (8.67) during pooled analysis. Based on intercluster distance, cluster mean values and per se performance, the potential parental combinations that could be considered for enhancing the overall yield levels in soybean were PS 1133×PS 1379, PS1133×UPSM 1099, PS 1133×PS 24, PS 1133×JS 335, PS 1133×PS 1427, PS 1133×PS 1428, PS 1133×PS 1225, PS 1379×UPSM 1099, PS 1379×PS 24, PS 1379×JS 335, PS 1379×PS 1427, PS 1379×PS 1428, PS 1379×PS 1225, UPSM 1099×PS 24, UPSM 1099×JS 335, UPSM 1099×PS 1427, UPSM 1099×PS 1428, UPSM 1099×PS 1225, PS 24×JS 335, PS 24×PS 1427, PS 24×PS 1428, PS 24×PS 1225, JS 335×PS 1427, JS 335×PS 1428, JS 335×PS 1225, PS 1427×PS 1428, PS 1427×PS 1225 and PS 1428×PS 1225 and should be promising to recover superior recombinants for yield and yield contributing traits. In general, the magnitudes of PCV were higher than those of GCV and ECV for all the characters with a close correspondence between the values of PCV and GCV for majority of the characters under study. This indicated a minimum role of environment in their expression and hence, selection based on phenotype could be rewarding for such characters. Highest values of PCV, GCV and Genetic advance as % of mean was observed for seed yield per plant, seed yield efficiency and harvest index. High estimates of heritability and genetic advance were reported for plant height, number of pods per plant, dry matter weight per plant, harvest index, seed yield efficiency, and seed yield per plant which indicated a role of additive gene effects in their expression. Correlation studies showed that seed yield exhibited significantly positive correlations with number of primary branches, number of pods per plant, dry matter weight, harvest index and seed yield efficiency during 2018, 2019 and pooled analysis. Path analysis indicated that, dry matter weight per plant, harvest index and seed yield efficiency exhibited positive direct effect in influencing seed yield. On the basis of genetic diversity analysis, the genotypes PS-1133, PS-1379,UPSM-1099, PS-24, JS-335, PS-1427, PS-1428 and PS-1225, were identified as superior donors for yield and yield contributing traits, whereas the studies on genetic variability, correlation and path coefficient analysis showed that the number of primary branches, number of pods per plant, number of seed per pod, dry matter weight, harvest index and seed yield efficiency are some of the important yield contributing characters and selection for these characters during crop improvement would be more rewarding for yield improvement in soybean.