Assessment of genetic variability for yield and its component traits in soybean [Glycine max (L.) Merrill

Introduction

Soybean (Glycine max L. Merrill) is regarded as one of the most valuable crops because of its high protein and oil content. Belonging to the family Leguminosae, it is a self-pollinated species with a chromosome number of 2n = 40. Globally, soybean has become a crucial crop to meet the rising demand for food and livestock feed. Its versatility makes it important not only as a direct food source but also as a major ingredient in animal nutrition and as a raw material for various industrial uses, which has expanded its cultivation well beyond its center of origin. Owing to its exceptional nutritional profile, soybean is often termed a "wonder crop" and a cost-effective source of high-quality protein. The protein is complete, supplying all nine essential amino acids, which has earned soybean the name "miracle bean" in nutritional terms (Quayam et al., 1985) [24] . Historical records suggest its domestication dates back to around the 11th century B.C. in the Yellow River Basin of China (Hymowitz and Newell, 1981) [12] . Soybean seeds contain about 40-42% high-quality protein and 18-22% oil, of which nearly 85% are unsaturated fatty acids, and they are naturally cholesterol-free while also rich in minerals. Due to these attributes, soybean has been aptly described as "the meat that grows on plants" (Arshad et al., 2006) [3] . Today, it stands as the world's most widely produced oilseed crop, with its protein being particularly well balanced for essential amino acids and minerals.

Materials and Methods

The present investigation was conducted the experimental farm of "All India Coordinated Research Project on Soybean'' Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. (M.S.) during Kharif-2024 in Randomized Block Design with two replications. The field was divided into two homogenous replication blocks. Fifty genotypes were planted randomly in two replications. Observation on ten characters were recoded on randomly selected five plants from each genotype and average value was used for statistical analysis. The data is subjected to different statistical analysis viz., analysis of variance, magnitude of genetic variability was performed following standard procedures.

Results and Discussion

Analysis of Variance

Table 1 displays the analysis of variance for ten characters. Significant variations were found in the genotypes of every character under study, according to the investigation. This showed that all of the characters in the genotypes under investigation had enough variability, thus selection could be used to enhance the features. Ramteke et al. (2010) [25] , Masreshaw et al. (2022) [17] , and Patel et al. (2025) [22] have also reported similar findings.

Genetic variability Parameters

The estimates of mean, range, genotypic coefficient of variability, phenotypic coefficient of variability, heritability, genetic advance and genetic advance as percent of mean for ten characters of soybean are shown in Table 2.

Coefficient of variation

Seed yield per plant recorded the highest genotypic coefficient of variation (GCV, 48.13) and phenotypic coefficient of variation (PCV, 48.72), followed by number of pods per plant (32.69 and 33.10), number of branches per plant (22.76 The coefficients of variation were estimated following Burton (1952) [7] and categorized as low (<10%), moderate (10-20%) and high (>20%) as suggested by Sivasubramanian and Menon (1973) [28] . Based on this classification, high variability (both GCV and PCV) was observed for seed yield per plant, number of pods per plant, number of branches per plant, and 100-seed weight. Moderate variability was recorded for days to 50% flowering, plant height, number of nodes, and number of pod clusters, whereas days to maturity and number of seeds per pod exhibited low variability.

The present findings are in line with earlier reports. Patel et al. (2025) [22] and Saicharan et al. (2022) [26] also documented maximum GCV and PCV for seed yield per plant. Similar results for high variability in number of branches per plant were reported by Gobezie et al. (2024) [11] , Chandrawat et al. (2017) [8] , and Vasanth et al. (2023) [30] . Mitiku et al. (2025) [18] observed high GCV and PCV for 100-seed weight, while Bairagi et al. (2023) [4] and Parindiyal et al. (2023) [21] reported similar findings for number of pods per plant. Bhangare et al. (2024) [6] indicated moderate variability for plant height, number of nodes, pod clusters, and days to 50% flowering. Low estimates of GCV and PCV for days to maturity and number of seeds per pod were also reported by Khumukcham et al. (2022) [15] and Sidramappa et al. (2019) [27] . Across all traits, PCV values were marginally higher than GCV, suggesting some influence of environmental factors. However, the narrow difference between the two indicates that environmental effects were relatively minor, and the observed variability is largely genetic in nature.

Heritability

Heritability estimates among the studied traits varied from 22.91% to 98.03%. The values were computed following the method of Burton (1952) [7] and categorized as low (<30%), moderate (30-60%), and high (>60%). High heritability was recorded for most traits, with the maximum observed for days to 50% flowering (98.03%), followed by days to maturity (97.79%), seed yield per plant (97.59%), number of pods per plant (97.54%), plant height (97.11%), 100-seed weight (96.34%), number of nodes per plant (94.39%), number of branches per plant (74.72%), and number of pod clusters per plant (69.26%). The only trait showing low heritability was number of seeds per pod (22.91%). Overall, high heritability values for most characters suggest that these traits are largely governed by additive gene effects and can be effectively improved through selection. The present findings are consistent with earlier reports: days to 50% flowering (Pushpa Reni and Koteshwara Rao, 2013; Dutta et al., 2021; Sidramappa et al., 2019) [23,10,27] ; days to maturity (Khumukcham et al., 2022) [15] ; plant height (Masreshaw et al., 2022; Patel et al., 2025) [17,22] ; number of nodes per plant (Joshi et al., 2018) [13] ; number of branches per plant (Akram et al., 2016) [1] ; number of pod clusters per plant (Bairagi et al., 2023; Vasanth et al., 2023) [4,30] ; number of pods per plant (Bhangare et al., 2024) [6] ; 100-seed weight (Masreshaw et al., 2022) [17] ; and seed yield per plant (Gobezie et al., 2024) [11] .

Genetic Advance

Genetic advance was estimated following the method of Johnson et al. (1955) [14] and categorized as low (<10%), moderate (10-20%), and high (>20%). In the present study, genetic advance ranged from 0.08 to 38.80. The highest values were obtained for number of pods per plant (38.80) and plant height (33.23), indicating strong potential for improvement through selection. In contrast, low genetic advance was observed for days to 50% flowering (9.47), days to maturity (8.88), seed yield per plant (6.77), number of nodes per plant (5.69), number of pod clusters per plant (4.13), 100-seed weight (3.90), number of branches per plant (1.45), and number of seeds per plant (0.08). These results align with earlier findings. Days to 50% flowering showed low genetic advance as reported by Khumukcham et al. (2022) [15] and Bhangare et al. (2024) [6] . Days to maturity also exhibited low estimates, consistent with Ramteke et al. (2010) [25] . High genetic advance for plant height was similarly observed by Amogne (2022) [2] . For number of branches per plant, low estimates were reported by Neelima et al. (2018) [20] and Nayak et al. (2024) [19] , while number of seeds per plant also showed low values, as supported by Thakur and Vedna Kumari (2024) [29] and Yechalew (2019) [31] . High genetic advance for number of pods per plant was reported by Khumukcham et al. (2022) [15] and Akram et al. (2016) [1] . In contrast, 100-seed weight and seed yield per plant showed low genetic advance, which is in agreement with Sidramappa et al. (2019) [27] .

Genetic advance as percent of mean

Genetic advance expressed as a percentage of mean was computed according to Johnson et al. (1955) [14] and classified as low (<10%), moderate (10-20%), and high (>20%). In the present study, high genetic advance as percent of mean was recorded for seed yield per plant (97.96%), followed by number of pods per plant (66.51%), 100-seed weight (43.39%), number of branches per plant (40.53%), plant height (39.83%), number of nodes per plant (30.54%), number of pod clusters per plant (24.12%), and days to 50% flowering (21.01%). On the other hand, low genetic advance as percent of mean was observed for number of seeds per plant (3.66%) and days to maturity (8.68%). These findings are supported by earlier reports. High estimates of genetic advance as percent of mean for days to 50% flowering were reported by Sidramappa et al. (2019) [27] , while days to maturity showed low values as noted by Ramteke et al. (2010) [25] . Plant height exhibited high values in the studies of Masreshaw et al. (2022) [17] and Patel et al. (2025) [22] . Similarly, high estimates were documented for number of nodes per plant (Parindyal et al., 2023 [21] ), number of branches per plant (Nayak et al., 2024 [19] ), and number of pod clusters per plant (Vasanth et al., 2023; Joshi et al., 2018) [30,13] . Low genetic advance as percent of mean for number of seeds per pod was reported by Thakur and Vedna Kumari (2024) [29] and Yechalew (2019) [31] . For number of pods per plant, high values were also noted by Belay et al. (2022) [5] and Koraddi et al. (2019) [16] . Likewise, seed yield per plant showed high genetic advance as percent of mean, in agreement with Dubey et al. (2015) [9] .

Conclusion

The analysis of variance demonstrated significant differences among all soybean genotypes for the traits under study, confirming the existence of considerable genetic variability and indicating ample scope for selection and genetic improvement. High estimates of both genotypic and phenotypic coefficients of variation were obtained for important yield-related traits such as seed yield per plant, number of pods per plant, number of branches per plant, and 100-seed weight, reflecting a wide range of variability and strong prospects for effective selection. The relatively small gap between GCV and PCV across traits suggested a limited influence of environmental factors, implying that most of these characters are predominantly governed by genetic control. Specifically, high GCV and PCV values were recorded for seed yield per plant, number of pods per plant, number of branches per plant, and 100-seed weight. Moderate values were observed for plant height, number of nodes per plant, number of pod clusters per plant, and days to 50% flowering. In contrast, low GCV and PCV values were noted for days to maturity and number of seeds per pod. For all traits, genotypic variance was lower than phenotypic variance. High heritability estimates in combination with high genetic advance as a percentage of mean were found for seed yield per plant, number of pods per plant, 100-seed weight, number of branches per plant, plant height, number of nodes per plant, number of pod clusters per plant, and days to 50% flowering. This pattern indicates that these traits are largely governed by additive gene action, and selection based on them would be effective.

In summary, the investigation highlighted that seed yield per plant, number of pods per plant, plant height, and 100-seed weight are highly heritable and responsive to selection, making them key targets in breeding programs for improving soybean productivity. These findings are consistent with earlier reports, thereby strengthening the evidence for significant genetic potential among the studied soybean genotypes.

References

1. Akram S, Hussain BMN, Bari MAA, Burritt DJ, Hussain MA. Genetic variability and association analysis of soybean (Glycine max (L.) Merrill) for yield and yield attributing traits. Plant Gene and Trait. 2016;7(13):1-11.
2. Amogne A. Genetic variability, heritability and genetic advance in soybean [Glycine max (L.) Merrill] in Northwestern Ethiopia. Adv Crop Sci Tech. 2022;10:532.
3. Arshad M, Ali N, Ghafoor A. Character correlation and path coefficient in soybean (Glycine max (L.) Merrill). Pak J Bot. 2006;38(1):121-130.
4. Bairagi V, Mishra S, Sen R, Dixit S, Tyagi DB. Assessment of genetic variability in soybean (Glycine max L., Merrill). Biol Forum Int J. 2023;15(5):258-263.
5. Belay T, Tesfaye A, Alamerew S. Genetic variability, genetic advance, and heritability of black and brown seeded soybean [Glycine max (L.) Merrill] lines in Jimma and Bonga Southwest Ethiopia. Int J Agric Res Innov Technol. 2022;12(2):14-17.
6. Bhangare VR, Deshmukh MP, Kale SD, Kamble MS, Patil PB. Genetic variability, correlation, and path coefficient analysis for yield and yield contributing characters in soybean [Glycine max (L.) Merrill]. Int J Adv Biochem Res. 2024;8(10):249-253.
7. Burton GW. Quantitative inheritance in sesame. Proc 6th Int Grassland Congr. 1952;277-283.
8. Merrill). Electron J Plant Breed. 2015;6(1):318-325.
9. Dutta P, Goswami PK, Borah M. Assessment of genetic variability, heritability and genetic advance in soybean genotypes. Electron J Plant Breed. 2021;12(4):1461-1465.
10. Gobezie C, Atnaf M, Abate A. Assessment of genetic variability in soybean (Glycine max (L.) Merrill) genotypes at Gondar, Ethiopia. Ethiop J Agric Sci. 2024;34(4):72-99.
11. Hymowitz T, Newell CA. Taxonomy of the genus Glycine, domestication and uses of soybeans. Econ Bot. 1981;35(3):272-288.
12. Joshi D, Pendra P, Singh K, Adhikari S. Study of genetic parameters in soybean germplasm based on yield and yield contributing traits. Int J Curr Microbiol Appl Sci. 2018;1(7):700-709.
13. Johnson HW, Robinsan KF, Comstock RF. Genotypic and phenotypic correlations in soybean and their implications in selection. Agron J. 1955;47:477-485.
14. Khumukcham JD, Tandekar K, Nag S, Shrivastava R. Genetic variability analysis in soybean genotypes based on yield and yield contributing traits. Pharma Innov J. 2022;11(7):3233-3236.
15. Koraddi S, Basavaraja GT. Genetic variability studies for yield and yield component traits of soybean (Glycine max L.). Int J Curr Microbiol Appl Sci. 2019;8(2):1269-1274.
16. Masreshaw Y, Yechalew S, Abush T, Mesfin H. Genetic variability and association of traits in soybean (Glycine max (L.)) genotypes in Ethiopia. Ethiop J Crop Sci. 2022;9(2):49-74.
17. Mitiku A, Gudina G, Yasin A, Abdu M. Comprehensive assessment of genetic variability, association analysis, and elucidation of direct and indirect effects of yield and yield contributing traits from diverse exogenous soybean (Glycine max L.) genotypes in Jimma District, Southwest Ethiopia. Adv Agric. 2025;2865503:1-13.
18. Nayak S, Meena BL, Sharma SC, Sachdeva K, Himanshi. Genetic variability, heritability and genetic advance in soybean [Glycine max (L.) Merrill]. Int J Adv Biochem Res. 2024;8(11):743-745.
19. Parindiyal K, Bhardwaj R, Negi AS, Parindiyal K, Rawat A, Gulzar S, Prakash S. Genetic variability parameters study in soybean [Glycine max (L.) Merrill] genotype. Int J Agric Sci. 2023;14(1):25-28.
20. Patel J, Pandey AP, Gaur RS, Verma N. Genotypic variability, correlation, and yield component analysis in soybean (Glycine max L.). Plant Cell Biotechnol Mol Biol. 2025;26(1-2):87-102.
21. Pushpa RY, Koteswara RY. Genetic variability in soybean (Glycine max (L.) Merrill). Int J Plant Anim Environ Sci. 2013;3(4):35-38.
22. Quayum A, Rao MS, Kerketta V. Soybean-a miracle oilseed crop-its prospects and constraints in Bihar plateau. Ref 7. 1985;219-232.
23. Saicharan G, Dhuppe MV, Rathod ST, Sargar PR, Rao MV. Studies on genetic variability, heritability, and genetic advance for yield and yield contributing traits in soybean [Glycine max (L.) Merrill]. Pharma Innov J. 2022;11(12):3131-3133.
24. Sidramappa C, Konda CR, Shobharani M. Study of genetic variability for different traits in soybean. Int J Curr Microbiol Appl Sci. 2019;8(12):2991-2994.
25. Sivasubramanian S, Menon M. Heterosis and inbreeding depression in rice. Madras Agric J. 1973;60(7):1139-1141.
26. Thakur R, Kumari V. Assessment of genetic variability among soybean genotypes (Glycine max (L.) Merrill). Himachal J Agric Res. 2024;50(2):174-181.
27. Vasanth V, Anandhi K, Geetha S, Senthilraja G, Meenakshi P. Assessment of genetic variability and association studies for yield related traits in soybean. Electron J Plant Breed. 2023;14(3):1026-1034.
28. Yechalew S. Estimation of variability, correlation and path analysis in soybean (Glycine max (L.) Merrill) genotypes at Jimma, South Western Ethiopia. J Nat Sci Res. 2019;9(2):18.