Respuesta Espectral y Fenológica del Banano (Musa AAA, Simmonds) a Bioinsumos Orgánicos Basados en Trichoderma spp. y Bacillus spp. en la Región Litoral Central del Ecuador: Una Evaluación Mediante VANT e Indice NDVI
DOI:
https://doi.org/10.28940/terra.latinam..v44i.2494Palabras clave:
agricultura de precisión, bioestimulación vegetal, índices espectrales, percepción remota, rizobacteriasResumen
El banano (Musa, AAA, Simmonds), es un cultivo de importancia global, pero su producción sostenible enfrenta desafíos físicos, biológicos y sociales. Este estudio evaluó el impacto de dos bioinsumos orgánicos —un consorcio fúngico formulado in situ y un producto bacteriano comercial— en la respuesta agronómica y espectral del banano mediante tecnología VANT y espectral en la provincia de Los Ríos, Ecuador. Se implementó un diseño completamente al azar con tres tratamientos: T1 (consorcio fúngico in situ de Trichoderma spp. y Paecilomyces lilacinus), T2 (consorcio bacteriano comercial de Bacillus spp.) y T3 (control). Se utilizó un dron DJI Mavic 3M equipado con sensores multiespectrales para calcular el Índice de Vegetación de Diferencia Normalizada (NDVI) a las 6 y 28 semanas, junto con mediciones fenológicas a las 28 semanas. Ambos bioinsumos mejoraron significativamente los valores de NDVI en comparación con el control (p < 0.05). T1 mostró el mejor desempeño con un NDVI final de 0.82 (22.2% de mejora sobre el control), seguido por T2 con 0.78 (16.8% de mejora). Se encontraron correlaciones positivas fuertes y significativas (p < 0.01) entre el NDVI y todas las variables agronómicas: altura (r = 0.8167), diámetro del pseudotallo (r = 0.8572) y número de hojas (r = 0.8330). El análisis de Kruskal-Wallis confirmó diferencias significativas entre todos los tratamientos, destacando la superioridad del consorcio in situ (T1), desarrollado con muestras locales y mejor adaptado a las condiciones regionales. Las fuertes correlaciones validan al NDVI como un indicador no destructivo y preciso del crecimiento estructural del banano. Este estudio demuestra que los bioinsumos orgánicos, particularmente los consorcios multifuncionales formulados localmente, pueden mejorar significativamente el estado fisiológico y agronómico del cultivo, mientras que la teledetección basada en VANT ofrece una herramienta eficiente para la evaluación rápida y la toma de decisiones en programas de agricultura de precisión para el banano.
Descargas
Referencias
Aeberli, A., Johansen, K., Robson, A., Lamb, D. W., & Phinn, S. (2021). Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sensing 2021, Vol. 13, Page 2123, 13(11), 2123. https://doi.org/10.3390/RS13112123
Aeberli, A. ;, Phinn, S. ;, Johansen, K. ;, Robson, A. ;, Lamb, D. W., Aeberli, A., Phinn, S., Johansen, K., Robson, A., & Lamb, D. W. (2023). Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery. Remote Sensing 2023, Vol. 15, Page 679, 15(3), 679. https://doi.org/10.3390/RS15030679
Alfiky, A., & Weisskopf, L. (2021). Deciphering Trichoderma–Plant–Pathogen Interactions for Better Development of Biocontrol Applications. Journal of Fungi 2021, Vol. 7, Page 61, 7(1), 61. https://doi.org/10.3390/JOF7010061
Beaumelle, L., Tison, L., Eisenhauer, N., Hines, J., Malladi, S., Pelosi, C., Thouvenot, L., & Phillips, H. R. P. (2023). Pesticide effects on soil fauna communities—A meta-analysis. Journal of Applied Ecology, 60(7), 1239–1253. https://doi.org/10.1111/1365-2664.14437;PAGE:STRING:ARTICLE/CHAPTER
Bertola, M., Ferrarini, A., & Visioli, G. (2021). Improvement of Soil Microbial Diversity through Sustainable Agricultural Practices and Its Evaluation by -Omics Approaches: A Perspective for the Environment, Food Quality and Human Safety. Microorganisms 2021, Vol. 9, Page 1400, 9(7), 1400. https://doi.org/10.3390/MICROORGANISMS9071400
Bhandari, M., Chang, A., Jung, J., Ibrahim, A. M. H., Rudd, J. C., Baker, S., Landivar, J., Liu, S., & Landivar, J. (2023). Unmanned aerial system-based high-throughput phenotyping for plant breeding. Plant Phenome Journal, 6(1), e20058. https://doi.org/10.1002/PPJ2.20058;WGROUP:STRING:PUBLICATION
Edelstein, C. P., Isaac, M. E., Orozco-Aguilar, L., Peguero, F., Delgado-Rodríguez, D., & Cerda, R. H. (2024). Effects of functional diversity on ecosystem services in cocoa agroforestry systems in Costa Rica. Frontiers in Sustainable Food Systems, 8, 1507555. https://doi.org/10.3389/FSUFS.2024.1507555/BIBTEX
Fahad, S., Chavan, S. B., Chichaghare, A. R., Uthappa, A. R., Kumar, M., Kakade, V., Pradhan, A., Jinger, D., Rawale, G., Yadav, D. K., Kumar, V., Farooq, T. H., Ali, B., Sawant, A. V., Saud, S., Chen, S., & Poczai, P. (2022). Agroforestry Systems for Soil Health Improvement and Maintenance. Sustainability 2022, Vol. 14, Page 14877, 14(22), 14877. https://doi.org/10.3390/SU142214877
FAO. (2024). Agricultural production statistics 2010–2023. FAO ; https://openknowledge.fao.org/handle/20.500.14283/cd3755en
Gamez, R., Cardinale, M., Montes, M., Ramirez, S., Schnell, S., & Rodriguez, F. (2019). Screening, plant growth promotion and root colonization pattern of two rhizobacteria (Pseudomonas fluorescens Ps006 and Bacillus amyloliquefaciens Bs006) on banana cv. Williams (Musa acuminata Colla). Microbiological Research, 220, 12–20. https://doi.org/10.1016/J.MICRES.2018.11.006
Gargiulo, J., Clark, C., Lyons, N., de Veyrac, G., Beale, P., & Garcia, S. (2020). Spatial and Temporal Pasture Biomass Estimation Integrating Electronic Plate Meter, Planet CubeSats and Sentinel-2 Satellite Data. Remote Sensing 2020, Vol. 12, Page 3222, 12(19), 3222. https://doi.org/10.3390/RS12193222
Greenfield, L. M., Graf, M., Rengaraj, S., Bargiela, R., Williams, G., Golyshin, P. N., Chadwick, D. R., & Jones, D. L. (2022). Field response of N2O emissions, microbial communities, soil biochemical processes and winter barley growth to the addition of conventional and biodegradable microplastics. Agriculture, Ecosystems & Environment, 336, 108023. https://doi.org/10.1016/J.AGEE.2022.108023
Gu, Q., Yang, Y., Yuan, Q., Shi, G., Wu, L., Lou, Z., Huo, R., Wu, H., Borriss, R., & Gao, X. (2017). Bacillomycin D produced by Bacillus amyloliquefaciens is involved in the antagonistic interaction with the plantpathogenic fungus Fusarium graminearum. Applied and Environmental Microbiology, 83(19), 1075–1092. https://doi.org/10.1128/AEM.01075-17;WGROUP:STRING:PUBLICATION
Han, Y., Tang, C., Zhao, Q., Fan, S., Yang, P., & Zhang, J. (2022). Butyrate Mitigates Lipopolysaccharide-Induced Intestinal Morphological Changes in Weanling Piglets by Regulating the Microbiota and Energy Metabolism, and Alleviating Inflammation and Apoptosis. Microorganisms 2022, Vol. 10, Page 2001, 10(10), 2001. https://doi.org/10.3390/MICROORGANISMS10102001
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature 2020 585:7825, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2
Manganiello, G., Nicastro, N., Caputo, M., Zaccardelli, M., Cardi, T., & Pane, C. (2021). Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables. Frontiers in Plant Science, 12, 630059. https://doi.org/10.3389/FPLS.2021.630059/FULL
Martínez-Solórzano, G. E., & Rey-Brina, J. C. (2021). Bananos (Musa AAA): Importancia, producción y comercio en tiempos de Covid-19. Agronomía Mesoamericana, 32(3), 1034–1046. https://doi.org/10.15517/am.v32i3.43610
Miao, Y., Wang, L., Peng, C., Li, H., Li, X., & Zhang, M. (2022a). Banana plant counting and morphological parameters measurement based on terrestrial laser scanning. Plant Methods, 18(1), 66. https://doi.org/10.1186/S13007-022-00894-Y
Miao, Y., Wang, L., Peng, C., Li, H., Li, X., & Zhang, M. (2022b). Banana plant counting and morphological parameters measurement based on terrestrial laser scanning. Plant Methods, 18(1), 66. https://doi.org/10.1186/S13007-022-00894-Y
Oliveira Almeida, N., Martins de Oliveira, C., José Ulhoa, C., de Carvalho Barros Côrtes, M. V., Lobo Júnior, M., & Rúbia da Rocha, M. (2022). Trichoderma harzianum and Trichoderma asperellum are potential biocontrol agents of Meloidogyne javanica in banana cv. Grande Naine. Biological Control, 175, 105054. https://doi.org/10.1016/J.BIOCONTROL.2022.105054
Omer, E., Szlatenyi, D., Csenki, S., Alrwashdeh, J., Czako, I., & Láng, V. (2024). Farming Practice Variability and Its Implications for Soil Health in Agriculture: A Review. Agriculture 2024, Vol. 14, Page 2114, 14(12), 2114. https://doi.org/10.3390/AGRICULTURE14122114
Poortinga, A., Clinton, N., Saah, D., Cutter, P., Chishtie, F., Markert, K. N., Anderson, E. R., Troy, A., Fenn, M., Tran, L. H., Bean, B., Nguyen, Q., Bhandari, B., Johnson, G., & Towashiraporn, P. (2018). An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale. Remote Sensing 2018, Vol. 10, Page 760, 10(5), 760. https://doi.org/10.3390/RS10050760
Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sensing 2020, Vol. 12, Page 3136, 12(19), 3136. https://doi.org/10.3390/RS12193136
Tian, L., Zhang, W., Zhou, G. D., Li, S., Wang, Y., Yang, B., Bai, T., Fan, H., He, P., & Zheng, S. J. (2023). A biological product of Bacillus amyloliquefaciens QST713 strain for promoting banana plant growth and modifying rhizosphere soil microbial diversity and community composition. Frontiers in Microbiology, 14, 1216018. https://doi.org/10.3389/FMICB.2023.1216018/BIBTEX
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … Vázquez-Baeza, Y. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 2020 17:3, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2
Wang, M., Lin, T., Wang, L., Lin, A., Zou, K., Xu, X., Zhou, Y., Peng, Y., Meng, Q., Qian, Y., Deng, G., Wu, Z., Chen, J., Lin, J., Zhang, M., Zhu, W., Zhang, C., Zhang, D., Goh, R. S. M., … Fu, H. (2023). Uncertainty-inspired open set learning for retinal anomaly identification. Nature Communications 2023 14:1, 14(1), 6757-. https://doi.org/10.1038/s41467-023-42444-7
Wang, Y., Liao, J., Zhu, D., Ma, W., & Danesh, Y. R. (2025). Harnessing Beneficial Microbes and Sensor Technologies for Sustainable Smart Agriculture. Sensors 2025, Vol. 25, Page 6631, 25(21), 6631. https://doi.org/10.3390/S25216631
Wei, J., Liu, G., Sun, M., Wang, H., Yang, P., Cheng, S., Huang, L., Wei, S., & Liu, D. (2025a). Comprehensive analysis of morphology, transcriptomics, and metabolomics of banana (Musa spp.) molecular mechanisms related to plant height. Frontiers in Plant Science, 16, 1509193. https://doi.org/10.3389/FPLS.2025.1509193/FULL
Wei, J., Liu, G., Sun, M., Wang, H., Yang, P., Cheng, S., Huang, L., Wei, S., & Liu, D. (2025b). Comprehensive analysis of morphology, transcriptomics, and metabolomics of banana (Musa spp.) molecular mechanisms related to plant height. Frontiers in Plant Science, 16, 1509193. https://doi.org/10.3389/FPLS.2025.1509193/FULL
Yánez-Cajo, D., Vásconez-Montúfar, G., Villamar-Torres, R. O., Godoy-Montiel, L., Jazayeri, S. M., Pérez-Porras, F., & Mesas-Carrascosa, F. (2025). Banana Yield Prediction Using Random Forest, Integrating Phenology Data, Soil Properties, Spectral Technology, and UAV Imagery in the Ecuadorian Littoral Region. Sustainability 2025, Vol. 17, Page 10098, 17(22), 10098. https://doi.org/10.3390/SU172210098
Yin, H., Li, B., Liu, Y. de, Zhang, F., Su, C. tao, & Ou-yang, A. guo. (2022). Detection of early bruises on loquat using hyperspectral imaging technology coupled with band ratio and improved Otsu method. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 283, 121775. https://doi.org/10.1016/J.SAA.2022.121775
