RESISTANCE INDUCTION MANAGEMENT ALTERS SOYBEAN ARCHITECTURE, YIELD COMPONENTS, AND PRODUCTIVITY
DOI:
https://doi.org/10.66104/6rws9277Keywords:
Foliar health; Glycine max; induced resistance; multivariate analyses; reproductive efficiency.Abstract
The study evaluated different resistance induction managements in soybean, highlighting that combinations of inducers and fungicides can significantly modify plant architecture, yield components, and productivity under Cerrado conditions. The research emphasizes the economic importance of the crop, the impacts of foliar and late-season diseases, the limitations of chemical control alone, and the potential of resistance inducers to optimize plant health and morphophysiological performance. The experiment was conducted in Mineiros, Goiás, on an Oxisol under tropical Aw climate, using a randomized block design with four managements (ISR1–ISR4) applied to the cultivar Brasmax Olimpo IPRO. Treatments were performed at vegetative and reproductive stages, evaluating structural traits (branching, node number, stem height and diameter), reproductive traits (number of pods and grains per category), and yield. Analyses included descriptive statistics, PCA, LASSO, and path analysis to identify variables most influencing yield. ISR1 and ISR2 promoted higher vegetative vigor, increased three- and four-grain pods, and achieved superior yields, up to 70.09 bags ha⁻¹. ISR3 resulted in a more compact architecture and lower yield, while ISR4 showed intermediate performance. Path analysis indicated P3S, P4S, BRN, MNN, PLH, and TPD as positive contributors to yield. It is concluded that managements balancing vegetative vigor and a high proportion of three- and four-grain pods are more efficient in maximizing soybean productivity.
Downloads
References
Ceresini, P. C., Silva, T. C., Vicentini, S. N. C., Júnior, R. P. L., Moreira, S. I., Castro-Ríos, K., de Jesus Júnior, W. C. (2024). Strategies for managing fungicide resistance in the Brazilian tropical agroecosystem: Safeguarding food safety, health, and the environmental quality. Tropical Plant Pathology, 49(1), 36-70. DOI: https://doi.org/10.1007/s40858-023-00632-2
Chicowski, A. S., Bredow, M., Utiyama, A. S., Marcelino-Guimarães, F. C., Whitham, S. A. (2023). Soybean–Phakopsora pachyrhizi interactions: advances and challenges in understanding soybean rust. Tropical Plant Pathology, 48(1), 75–90.
Cruz, C. D., Nascimento, M., Regazzi, A. J., Carneiro, P. C. S. (2020). Multivariate approaches in selecting superior soybean genotypes based on agronomic and physiological traits. Euphytica, 216(5), 1–16.
Fontes, B. A., Silva, L. C., Picanço, B. M., Guimarães, F. M., Zambolim, L., Rodrigues, F. A. (2024). Resistance in soybean against infection by Phakopsora pachyrhizi: current knowledge and future perspectives. Plants, 13(5), 1–25. DOI: https://doi.org/10.3390/plants13223161
Gabardo, G. C., Santos, I. dos, Costa, A. T., & Vargas, L. (2021). Alternative products to control late season diseases in soybean. Ciência Rural, 51(3), e20200432.
Khan, J., Akhtar, J., Rafique, M., & Saleem, N. (2025). Comparative evaluation of hybrid and individual models for soybean disease severity prediction using machine learning approaches. Scientific Reports, 15(1), 1125–1140. DOI: https://doi.org/10.1038/s41598-025-99427-5
Klosowski, A. C., May De Mio, L. L., Mezzomo, R. F., Ward, N. A., Godoy, C. V. (2021). Sensitivity of Phakopsora pachyrhizi to fungicides and implications for disease management in soybean. Plant Disease, 105(3), 698–706.
Li, C., Wang, Y., Zhao, L., & Liu, B. (2024). Molecular and genetic basis of plant architecture in soybean. Frontiers in Plant Science, 15, 1–18. DOI: https://doi.org/10.3389/fpls.2024.1477616
Obua, T., Egesa, S. A., & Osiru, D. (2024). Unravelling yield and yield-related traits in soybean using GGE biplot and path analysis. Agronomy, 14(4), 915. DOI: https://doi.org/10.3390/agronomy14122826
Paraginski, R. T., Luz, A. R., Thiel, A. M., Castagnaro, R., Sponchiado, J. C., Pivetta, L. A. (2024). Correlation between productive components and grain yield of soybean cultivars sown in the northwest region of Rio Grande do Sul. Revista Ceres, 71(2), 153–162. DOI: https://doi.org/10.1590/0034-737x2024710017
Picanço, M. C., Silva, L. C., Azevedo, L. A. S., Moraes, M. C. B., Faria, M. V., Rodrigues, F. A. (2022). Potentiation of soybean resistance against Phakopsora pachyrhizi infection using phosphite combined with free amino acids. Plant Pathology, 71(7), 1355–1367. DOI: https://doi.org/10.1111/ppa.13582
Rigon, J. P. G., Capuani, S., Rosa, T. C., Lenz, G., Zanon, A. J. (2020). Effects of plant density on soybean agronomic traits and grain yield. Pesquisa Agropecuária Brasileira, 55, e01693.
Siqueira Filho, J. A., Silva, D. C. G., Juliatti, F. C., Juliatti, B. C., Rios, J. A. (2025). Fungicide mixtures to control Asian soybean rust. Revista de Ciências Agrárias, 48(1), 88–97.
Yang, Y., Liu, Z., Zhao, H., Li, Z., Li, W., Yang, C., Wang, Z. (2023). Induced defense response in soybean to Sclerotinia sclerotiorum by the antibiotic wuyiencin. Plant Disease, 107(5), 1323–1332. DOI: https://doi.org/10.1094/PDIS-03-22-0582-RE
Zuffo, A. M., Bruzi, A. T., Zambiazzi, E. V., Soares, I. O., Silva, K. B., Rezende, P. M. (2020). Correlations and path analysis in agronomic traits of soybeans under defoliation. Bioscience Journal, 36(2), 515–523. DOI: https://doi.org/10.14393/BJ-v36n5a2020-48220
Zuffo, A. M., Bruzi, A. T., Zambiazzi, E. V., Soares, I. O., Silva, K. B., Rezende, P. M. (2025). Correlations and path analysis of soybean cultivars sown in two seasons. Contribuciones a las Ciencias Sociales, 16(1), 54–68. DOI: https://doi.org/10.55905/revconv.18n.2-182
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Julia Dias Costa, Ivan Ricardo Carvalho, Diego Oliveira Ribeiro, Glicélia Pereira Silva, Alexandre Igor de Azevedo Pereira, Carmen Rosa da Silva Curvelo, Rodrigo Vieira da Silva, Deborah Amorim Martins, Luiz Leonardo Ferreira
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
Authors retain copyright and grant the journal the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution License, which permits the sharing of the work with proper acknowledgment of authorship and initial publication in this journal;
Authors are authorized to enter into separate, additional agreements for the non-exclusive distribution of the version of the work published in this journal (e.g., posting in an institutional repository or publishing it as a book chapter), provided that authorship and initial publication in this journal are properly acknowledged, and that the work is adapted to the template of the respective repository;
Authors are permitted and encouraged to post and distribute their work online (e.g., in institutional repositories or on their personal websites) at any point before or during the editorial process, as this may lead to productive exchanges and increase the impact and citation of the published work (see The Effect of Open Access);
Authors are responsible for correctly providing their personal information, including name, keywords, abstracts, and other relevant data, thereby defining how they wish to be cited. The journal’s editorial board is not responsible for any errors or inconsistencies in these records.
PRIVACY POLICY
The names and email addresses provided to this journal will be used exclusively for the purposes of this publication and will not be made available for any other purpose or to third parties.
Note: All content of the work is the sole responsibility of the author and the advisor.
