LAS PRÁCTICAS DE MANEJO QUE INDUCEN RESISTENCIA ALTERAN LA ARQUITECTURA, LOS COMPONENTES DEL RENDIMIENTO Y LA PRODUCTIVIDAD DE LA SOJA
DOI:
https://doi.org/10.66104/6rws9277Palabras clave:
Análisis multivariado; eficiencia reproductiva; Glycine max; resistencia inducida; salud foliar.Resumen
Este estudio evaluó diferentes estrategias de manejo de inducción de resistencia en soya, destacando que las combinaciones de inductores y fungicidas pueden modificar significativamente la arquitectura de la planta, los componentes del rendimiento y la productividad en condiciones de Cerrado. La investigación enfatiza la importancia económica del cultivo, los impactos de las enfermedades foliares y de fin de ciclo, las limitaciones del control químico aislado y el potencial de los inductores de resistencia para optimizar la salud de la planta y el desempeño morfofisiológico. El experimento se realizó en Mineiros (GO), en un Oxisol bajo un clima tropical Aw, utilizando un diseño de bloques aleatorizados con cuatro estrategias de manejo (ISR1–ISR4) aplicadas al cultivar Brasmax Olimpo IPRO. Los tratamientos se llevaron a cabo en etapas vegetativas y reproductivas, evaluando variables estructurales (ramificación, número de nudos, altura y diámetro del tallo), variables reproductivas (número de vainas y granos por clase) y productividad. Los análisis incluyeron estadística descriptiva, PCA, LASSO y análisis de ruta para identificar las variables más influyentes en el rendimiento. ISR1 e ISR2 promovieron un mayor vigor vegetativo, un aumento en la proporción de vainas con tres y cuatro granos, y mayores rendimientos, alcanzando hasta 70,09 sc ha⁻¹. ISR3 resultó en una arquitectura más compacta y un menor rendimiento, mientras que ISR4 mostró un desempeño intermedio. El análisis de ruta reveló que P3S, P4S, BRN, MNN, PLH y TPD contribuyen positivamente al rendimiento. Se concluye que las prácticas de manejo que equilibran el vigor vegetativo y una alta proporción de vainas con tres y cuatro granos son más eficientes para maximizar la productividad de la soja.
Descargas
Referencias
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
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 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

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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.
