Bioscience Methods 2026, Vol.17, No.5, 360-372 http://bioscipublisher.com/index.php/bm 370 Future research should move beyond descriptive composition analysis toward developmentally resolved, mechanism-based design of soybean seed quality. Reviews of soybean functional genomics emphasize that hundreds of QTLs have already been identified, but relatively few genes have been functionally validated, and progress now depends on integrating genomics, transcriptomics, proteomics, and transformation technologies more effectively. A parallel design-oriented synthesis argues that resolving the protein-oil trade-off will require seed development-focused mutant analysis, multi-omics integration, and isotope-based metabolic flux studies that can capture rebalancing among protein, oil, and sucrose during filling. Sustainable soybean production will also require linking seed quality improvement with climate resilience and resource efficiency. Soybean already contributes to sustainable agriculture through biological nitrogen fixation, but future breeding must address the fact that abiotic stress disrupts seed filling, shifts the balance among protein, oil, and fatty acids, and reduces protein yield per hectare even when concentration rises under drought or heat. The most promising roadmap combines climate-resilient breeding, optimized source-sink balance and nitrogen fixation, wider use of wild and diverse germplasm, and predictive approaches that incorporate nonlinear environmental effects on seed composition across regions and maturity groups. Acknowledgments I extend my sincere gratitude to the anonymous reviewers for their valuable and insightful comments, which have greatly strengthened this paper. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aulia R., Kim Y., Amanah H.Z., Andi A.M.A., Kim H., Kim H., Lee W.H., Kim K.H., Baek J.H., and Cho B.K., 2022, Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging, Infrared Physics and Technology, 127: 104365. https://doi.org/10.1016/j.infrared.2022.104365 Ayanlade T.T., Van der Laan L., Liu Q., Gangopadhyay T., Shook J., Singh A., Ganapathysubramanian B., Sarkar S., and Singh A.K., 2026, Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction, Frontiers in Artificial Intelligence, 9: 1750108. https://doi.org/10.3389/frai.2026.1750108 Baek J.H., Lee E., Kim N., Kim S.L., Choi I., Ji H., Chung Y.S., Choi M.S., Moon J.K., and Kim K.H., 2020, High throughput phenotyping for various traits on soybean seeds using image analysis, Sensors, 20(1): 248. https://doi.org/10.3390/s20010248 Bu M., Zhang Y., Xu W., Li Y., Yu H., Zhang Y., Yang S., Bhat J.A., and Feng X., 2026, Genome-wide detection of superior haplotypes for seed oil and protein content in Northeast China soybean (Glycine max L.) germplasm, Frontiers in Plant Science, 17: 1767299. https://doi.org/10.3389/fpls.2026.1767299 Carciochi W.D., Grassini P., Naeve S., Specht J.E., Mamo M., Seymour R., Nygren A., Mueller N., Sivits S., Proctor C., Rees J., Whitney T., and La Menza N.C., 2023, Irrigation increases on-farm soybean yields in water-limited environments without a trade-off in seed protein concentration, Field Crops Research, 304: 109163. https://doi.org/10.1016/j.fcr.2023.109163 Chiozza M.V., Shook J.M., Van der Laan L., Singh A.K., and Miguez F.E., 2025, Comprehensive assessment of soybean seed composition from field trials spanning 22 US states and 24 years: Predictive insights, Crop Science, 65(4): e70142. https://doi.org/10.1002/csc2.70142 Cui Y., Wang Z., Li M., Li X., Wang S., Liu C., Xin D., Qi Z., Chen Q., Yang M., and Zhao Y., 2026, Comparative metabolomics analysis of seed composition accumulation in soybean (Glycine max L.) differing in protein and oil content, Plant, Cell and Environment, 49(7): 3925-3941. https://doi.org/10.1111/pce.15448 Di Mauro G., Schwalbert R., Alvarez Prado S., Saks M.G., Ramirez H., Costanzi J., and Parra G., 2023, Exploring practical nutrition options for maximizing seed yield and protein concentration in soybean, European Journal of Agronomy, 146: 126794. https://doi.org/10.1016/j.eja.2023.126794 Duan Z., Li Q., Wang H., He X., and Zhang M., 2023, Genetic regulatory networks of soybean seed size, oil and protein contents, Frontiers in Plant Science, 14: 1160418. https://doi.org/10.3389/fpls.2023.1160418 Duc N.T., Ramlal A., Rajendran A., Raju D., Lal S.K., Kumar S., Sahoo R.N., and Chinnusamy V., 2023, Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean, Frontiers in Plant Science, 14: 1206357. https://doi.org/10.3389/fpls.2023.1206357
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