Unlocking the Genetic Potential of Soybean (Glycine max L.) Accessions for Enhanced Yield and Crude Protein

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AgroEnvironmental Sustainability
Santosh Bhandari , Krishna Hari Dhakal , Madhav Prasad Pandey , Sagar Lamsal , Roshan Ghimire

Abstract

To elucidate improved germplasm for grain yield and seed protein content, ten soybean accessions were evaluated in a replicated randomized complete block design. This study reveals substantial phenotypic variation, with protein content spanning 34.48% to 42.10%. PK-7394 recorded the highest protein level (42.10%) but showed reduced grain yield, whereas Hardee produced a maximum yield of 3.89 t ha⁻¹. TGX-1990-114FN combined high protein content (40.98%) with competitive yield (3.85 t ha⁻¹), thereby corroborating its potential as a prime genetic resource. Correlational analyses revealed positive associations between grain yield and nodule count (r = 0.55) as well as seed diameter (r = 0.16). Protein content exhibited a high Shannon-Wiener diversity index (1.09), highlighting variability across accessions. By integrating high yield and enhanced protein content, TGX-1990-114FN emerged as an optimal genetic resource for breeding programs focused on developing high-yielding and nutritionally enriched soybean varieties. Such findings offer valuable insights into advancing agricultural productivity and addressing global food security challenges.

Keywords

diversity index grain yield seed protein soybean

References

  1. Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459. [Google Scholar]
  2. Anand, K. J., Shrivastava, M. K., Amrate, P. K., Patel, T., & Singh, Y. (2024). Morphological Characterization-based Optimal Trait Selection for Improving Yield and Stability of Soybean (Glycine max L. Merrill). International Journal of Bio-Resource & Stress Management, 15(10), 1-10. [Google Scholar]
  3. Assefa, Y., Bajjalieh, N., Archontoulis, S., Casteel, S., Davidson, D., Kovács, P., Naeve, S., & Ciampitti, I. A. (2018). Spatial Characterization of Soybean Yield and Quality (Amino Acids, Oil, and Protein) for United States. Scientific Reports, 8(1). https://doi.org/10.1038/S41598-018-32895-0 [Google Scholar]
  4. Basnet, B. (2024a). Deciphering genetic variability and phenotype expression, assessing drought stress tolerance and multi-trait stability index of (Vigna radiata) genotypes in Chitwan, Nepal. Cogent Food & Agriculture, 10(1), 2417843. https://doi.org/10.1080/23311932.2024.2417843 [Google Scholar]
  5. Basnet, B. (2024b). Deciphering genetic variability and phenotype expression, assessing drought stress tolerance and multi-trait stability index of (Vigna radiata) genotypes in Chitwan, Nepal. Cogent Food and Agriculture, 10(1), 2417843. https://doi.org/10.1080/23311932.2024.2417843 [Google Scholar]
  6. Basnet, B., Upreti, U., & Thapaliya, K. P. (2024a). Genotypic variations in postfertility traits and yield components of mung bean (Vigna radiata (L.) R. Wilczek) germplasms in Chitwan, Nepal. Heliyon, 10(20), e39226. https://doi.org/10.1016/j.heliyon.2024.e39226 [Google Scholar]
  7. Chakelie, G., Atnaf, M., Abate, A. (2024). Assessment of Genetic Variability in Soybean (Glycine max (L.) Merrill) Genotypes at Gondar, Ethiopia. Ethiopian Journal of Agricultural Sciences, 34(4), 72-99. [Google Scholar]
  8. Chiemeke, F. K., Olasanmi, B., Agre, P. A., Mushoriwa, H., Chigeza, G., & Abebe, A. T. (2024). Genetic Diversity and Population Structure Analysis of Soybean [Glycine max (L.) Merrill] Genotypes Using Agro-Morphological Traits and SNP Markers. Genes, 15(11), 1373. https://doi.org/10.3390/GENES15111373/S1 [Google Scholar]
  9. Ciampitti, I. A., de Borja Reis, A. F., Córdova, S. C., Castellano, M. J., Archontoulis, S. V., Correndo, A. A., Antunes De Almeida, L. F., & Moro Rosso, L. H. (2021). Revisiting Biological Nitrogen Fixation Dynamics in Soybeans. Frontiers in Plant Science, 12, 727021. https://doi.org/10.3389/FPLS.2021.727021 [Google Scholar]
  10. DB, G., R., D., S, S., A., S., & Kumar, S. (2014). Grain Legumes in Nepal: Present Scenario and Future Prospects. World Journal of Agricultural Research, 2(5), 216–222. https://doi.org/10.12691/WJAR-2-5-3 [Google Scholar]
  11. Fattah, A., Negara, A., Supriadi, K., Hannan, M. F. I., Ardjanhar, A., Beding, P. A., Najamuddin, E., Pustika, A. B., Susilawati, S., Nonci, N., Latifah, E., Arifin, Z., Istiqomah, N., Udiarto, B. K., & Dewayani, W. (2024). Characteristics of several soybean varieties (Glycine max L.) and weed management systems in an effort to increase productivity in low land rice. Frontiers in Sustainable Food Systems, 8, 1418759. https://doi.org/10.3389/FSUFS.2024.1418759 [Google Scholar]
  12. Gharti, D. B., Darai, R., Subedi, S., Sarker, A., & Kumar, S. (2014). Grain legumes in Nepal: Present scenario and future prospects. World Journal of Agricultural Research, 2(5), 216-222. [Google Scholar]
  13. Ghimire, N. H., Dhakal, K. H., Pandey, M. P., Joshi, B. K., Khanal, B., Nh, G., Dhakal, K. H., Pandey, M. P., Joshi, B. K., & Khanal, B. (2024). Assessment of Agronomic Traits and Molecular Diversity Using SSR Markers in Soyabean, (Glycine Max (L.) Merr. Accessions in Nepal. Agronomy Journal of Nepal, 8(1), 119–134. https://doi.org/10.3126/AJN.V8I1.70793 [Google Scholar]
  14. Ghimire, N. H., Dhakal, K. H., Pandey, M. P., Joshi, B. K., Khanal, B. (2024). Assessment of Agronomic Traits and Molecular Diversity Using SSR Markers in Soyabean, (Glycine Max (L.) Merr. Accessions in Nepal. Agronomy Journal of Nepal, 8, 119–134. https://doi.org/10.3126/ajn.v8i1.70793 [Google Scholar]
  15. Gomez, K. A., & Gomez, A. A. (2012). Statistical procedures for agricultural research. John wiley & sons. [Google Scholar]
  16. Grain Legumes in Nepal: Present Scenario and Future Prospects. (2023). Retrieved April 4, 2025, from https://pubs.sciepub.com/wjar/2/5/3/ [Google Scholar]
  17. Guo, B., Sun, L., Jiang, S., Ren, H., Sun, R., Wei, Z., Hong, H., Luan, X., Wang, J., Wang, X., Xu, D., Li, W., Guo, C., & Qiu, L. J. (2022a). Soybean genetic resources contributing to sustainable protein production. TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 135(11), 4095. https://doi.org/10.1007/S00122-022-04222-9 [Google Scholar]
  18. Kholmurodova, G., Tangirova, G., Rakhmankulov, M., Yuldasheva, R. (2023). Analysis of protein and oil content in seeds of soybean collection varieties. In E3S Web of Conferences (Vol. 377, p. 03016). EDP Sciences. [Google Scholar]
  19. Kong, W., Wei, M., Khan, N., Liang, J., Han, D., & Zhang, H. (2024). Assessing sustainable future of import-independent domestic soybean production in China: policy implications and projections for 2030. Frontiers in Sustainable Food Systems, 8, 1387609. https://doi.org/10.3389/FSUFS.2024.1387609 [Google Scholar]
  20. Lynch, H., Johnston, C., & Wharton, C. (2018). Plant-Based Diets: Considerations for Environmental Impact, Protein Quality, and Exercise Performance. Nutrients, 10(12), 1841. https://doi.org/10.3390/NU10121841 [Google Scholar]
  21. Manandhar, D. (2021). Review Article on Status of Soybean Production in Nepal. Reviews in Food and Agriculture, 2(2), 43–45. https://doi.org/10.26480/rfna.02.2021.43.45 [Google Scholar]
  22. Nair, R. M., Yan, M. rong, Vemula, A. K., Rathore, A., van Zonneveld, M., & Schafleitner, R. (2023). Development of core collections in soybean on the basis of seed size. Legume Science, 5(1), 1–6. https://doi.org/10.1002/leg3.158 [Google Scholar]
  23. Sharma, S., Kaur, M., Goyal, R., & Gill, B. S. (2011). Physical characteristics and nutritional composition of some new soybean (Glycine max (L.) Merrill) genotypes. Journal of Food Science and Technology, 51(3), 551. https://doi.org/10.1007/S13197-011-0517-7 [Google Scholar]
  24. Singh, A., Singh, A., & Mahama, A. A. (2023). Chapter 6: Breeding Methods. Iowa State University Digital Press. [Google Scholar]
  25. Soybean crop rotation: A “3-in-1” solution for sustainable agricultural production. (2025). Retrieved May 28, 2025, from https://vietnamagriculture.nongnghiep.vn/soybean-crop-rotation-a-3-in-1-solution-for-sustainable-agricultural-production-d753563.html [Google Scholar]
  26. Tamagno, S., Sadras, V. O., Haegele, J. W., Armstrong, P. R., & Ciampitti, I. A. (2018). Interplay between nitrogen fertilizer and biological nitrogen fixation in soybean: implications on seed yield and biomass allocation. Scientific Reports, 8(1), 17502. https://doi.org/10.1038/s41598-018-35672-1 [Google Scholar]
  27. USDA Economic Research Service Corn & Soybean Projections to 2030 | Ohio Ag Manager. (n.d.). Retrieved April 4, 2025, from https://u.osu.edu/ohioagmanager/2021/11/11/usda-economic-research-service-corn-soybean-projections-to-2030/ [Google Scholar]
  28. Xiao, X., Zou, P. R., Hu, F., Zhu, W., & Wei, Z. J. (2023). Updates on Plant-Based Protein Products as an Alternative to Animal Protein: Technology, Properties, and Their Health Benefits. Molecules, 28(10), 4016. https://doi.org/10.3390/MOLECULES28104016 [Google Scholar]
  29. Zuffo, A. M., Alcântara Neto, F. de, Zoz, T., Ratke, R. F., Aguilera, J. G., & Teodoro, P. E. (2020). Correlations and path analysis in agronomic traits of soybeans under defoliation. Bioscience Journal, 36(5), 1629–1637. https://doi.org/10.14393/BJ-v36n5a2020-48220 [Google Scholar]

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