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Research Articles

Vol. 13 No. 2 (2026)

Multi-trait evaluation of exotic bold-seeded soybean germplasm for vegetable-type breeding under Indian conditions

DOI
https://doi.org/10.14719/pst.13530
Submitted
4 January 2026
Published
14-04-2026 — Updated on 21-04-2026
Versions

Abstract

The study assessed 109 soybean genotypes including 105 exotic bold-seeded accessions and four checks to identify promising donors for vegetable soybean breeding under Indian conditions. A wide range of variability was observed for yield and related traits with 80 accessions categorised as bold seeded (>13 g/100 seeds) following distinctness, uniformity and stability (DUS) criteria. Hierarchical cluster analysis grouped the genotypes into three clusters with Cluster III showing superior combinations of bold seed size and yield. Principal component analysis explained 73.2 % of total variation and PC2 was closely associated with productivity and seed weight. Positive associations among yield traits and test weight while negative relationships with maturity traits indicated opportunities for simultaneous improvement. Using the multi-trait genotype-ideotype distance index (MGIDI), 11 superior genotypes (G34, G105, G68, G29, G65, G44, G42, G26, G104, G50, G7) were identified combining bold seed size, earliness and high yield potential. These findings provide valuable direction for breeding bold-seeded, high-yielding vegetable soybean cultivars suited to Indian conditions.

References

  1. 1. Shrestha P, Pandey MP, Dhakal KH, Ghimire SK, Thapa SB, Kandel BP. Morphological characterization and evaluation of soybean genotypes under rainfed ecosystem of Nepal. J Agric Food Chem. 2023;11:100526. https://doi.org/10.1016/j.jafr.2023.100526
  2. 2. Tian Z, Nepomuceno AL, Song Q, Stupar RM, Liu B, Kong F, et al. Soybean2035: A decadal vision for soybean functional genomics and breeding. Molecular Plant. 2025;18(2):245–71. https://doi.org/10.1016/j.molp.2025.01.004
  3. 3. Kumari S, Dambale AS, Samantara R, Jincy M, Bains G. Introduction, history, geographical distribution, importance and uses of soybean (Glycine max L.). In: Singh KP, Singh NK, T A, editors. Soybean Production Technology. Singapore: Springer; 2025. https://doi.org/10.1007/978-981-97-8677-0_1
  4. 4. Whiting RM, Torabi S, Lukens L, et al. Genomic regions associated with important seed quality traits in food-grade soybeans. BMC Plant Biol. 2020;20:485. https://doi.org/10.1186/s12870-020-02681-0
  5. 5. Meng S, Chang S, Gillen AM, Zhang Y. Protein and quality analyses of accessions from the USDA soybean germplasm collection for tofu production. Food Chemistry. 2016;213:31–9. https://doi.org/10.1016/j.foodchem.2016.06.046
  6. 6. Stanojevic SP, Barac MB, Pesic MB, Vucelic-Radovic BV. Assessment of soy genotype and processing method on quality of soybean tofu. J Agric Food Chem. 2011;59:7368–76. https://doi.org/10.1021/jf2006672
  7. 7. Singh KH, Gupta S, Shivakumar M, Nataraj V. Soybean [Glycine max (L.) Merr.] breeding. In: Prasad Dixit G, Dikshit HK, Mishra GP, Aski MS, editors. Fundamentals of Legume Breeding. Singapore: Springer; 2025.
  8. 8. Federer WT. Augmented (or hoonuiaku) designs. Hawaiian Pl Rec. 1956;55:191–208.
  9. 9. Olivoto T, Nardino M. MGIDI: A novel multi-trait index for genotype selection in plant breeding. Bioinformatics. 2020;37(10):1383–9. https://doi.org/10.1093/bioinformatics/btaa981
  10. 10. Aravind J, Sankar MS, Dhammaprakash PW, Kaur V. augmentedRCBD: Analysis of Augmented Randomised Complete Block Designs. R package version 0.1.0. 2018. https://doi.org/10.32614/CRAN.package.augmentedRCBD
  11. 11. Olivoto T, Lúcio ADC. metan: An R package for multi-environment trial analysis. Methods in Ecology and Evolution. 2020;11:783–9. https://doi.org/10.1111/2041-210X.13384
  12. 12. Mishra R, Shrivastava MK, Amrate PK, Sharma S, Singh YMK, Tripathi MK. Phenotypic diversity and trait analysis of soybean recombinant inbred lines. Plant Cell Biotechnology and Molecular Biology. 2025;26(7-8):32–52. https://doi.org/10.56557/pcbmb/2025/v26i7-89345
  13. 13. Palange NJ, Obua T, Sserumaga JP, et al. Genetic variability of anti-nutritional factors among soybean (Glycine max) germplasm. Discov Agric. 2025;3:63. https://doi.org/10.1007/s44279-025-00199-3
  14. 14. Ullah A, Akram Z, Rasool G, et al. Agro-morphological characterization and genetic variability assessment of soybean [Glycine max (L.) Merr.] germplasm for yield and quality traits. Euphytica. 2024;220:67. https://doi.org/10.1007/s10681-024-03322-5
  15. 15. Chawan K, Ravishankar, Ramesh, Onkarappa. Assessment of genetic variability based on morphometric characteristics in soybean (Glycine max L. Merrill) germplasm. Mysore J Agric Sci. 2023;57(1):109–19.
  16. 16. Desta KT, Choi YM, Jeon Y, et al. Phenotypic diversity among 575 cultivated soybean landraces collected from different provinces in Korea: A multivariate analysis. Korean Journal of Crop Science. 2024;69(2):97–110.
  17. 17. Ezin V, Moussa MS, Bachabi F, Gbemenou UH, Ahanchede A. GGE and AMMI biplot analyses of soybean (Glycine max L. Merr) for yield and its components. CABI Agriculture and Bioscience. 2025;10:0036. https://doi.org/10.1079/ab.2025.0036
  18. 18. Maranna S, Kumawat G, Nataraj V, et al. NAM population – a novel genetic resource for soybean improvement: Development and characterization for yield and attributing traits. Plant Genetic Resources: Characterization and Utilization. 2019;17(6):545–53. https://doi.org/10.1017/S1479262119000352
  19. 19. Nidhi D, Avinashe HA, Shrivastava AN. Principal component analysis in advanced genotypes of soybean [Glycine max (L.) Merrill] over seasons. Plant Archives. 2018;18:501–6.
  20. 20. Amrate PK, Nataraj V, Shivakumar M, et al. Best linear unbiased prediction (BLUP)-based models aided in selection of high yielding charcoal rot and yellow mosaic resistant soybean genotypes. Genet Resour Crop Evol. 2025;72:5593–611. https://doi.org/10.1007/s10722-024-02289-5
  21. 21. Maranna S, Kumawat G, Nataraj V, et al. Development of improved genotypes for extra early maturity, higher yield and Mungbean Yellow Mosaic India Virus (MYMIV) resistance in soybean (Glycine max). Crop & Pasture Science. 2023;74:1165–79. https://doi.org/10.1071/CP22339

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