Vol. 36 Núm. NE-1 (2026): XXVIII Congreso Internacional en Ciencias Agrícolas
Artículos de investigación

Relación entre índices de vegetación y biomasa forrajera en girasol en el Valle del Yaqui, México

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  • Elco Humberto García Bolívar,
  • Marisol Galicia Juárez,
  • Vielka Jeanethe Castañeda-Bustos,
  • Xochilt Militza Ochoa Espinoza,
  • Juan Gabriel Brigido Morales,
  • Helio Adán García Mendívil
Elco Humberto García Bolívar Universidad Autónoma de Baja California Mexicali MX

Biografía
Marisol Galicia Juárez Universidad Autónoma de Baja California Mexicali MX

Biografía
Vielka Jeanethe Castañeda-Bustos Universidad Autónoma de Baja California: Mexicali , MX

Biografía
Xochilt Militza Ochoa Espinoza INIFAP: Coyoacán, Mexico

Biografía
Juan Gabriel Brigido Morales Universidad Autónoma de Baja California: Mexicali , MX

Biografía
Helio Adán García Mendívil INIFAP: Coyoacán, Mexico

Biografía

Publicado 2026-09-23

Derechos de autor 2026 Elco Humberto García Bolívar, Marisol Galicia Juárez, Vielka Jeanethe Castañeda-Bustos, Xochilt Militza Ochoa Espinoza, Juan Gabriel Brigido Morales, Helio Adán García Mendívil

Creative Commons License

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

Cómo citar

García Bolívar, E. H., Galicia Juárez, M., Castañeda-Bustos, V. J., Ochoa Espinoza, X. M., Brigido Morales, J. G., & García Mendívil, H. A. (2026). Relación entre índices de vegetación y biomasa forrajera en girasol en el Valle del Yaqui, México. Acta Universitaria, 36(NE-1), 1-8. https://doi.org/10.15174/au.2026.4908

Resumen

La estimación tradicional del rendimiento en girasol forrajero (Helianthus annuus L., var. Girafor) es costosa y destructiva. Por ello, se evaluó la relación de los índices de vegetación (NDVI, NDRE) y valores SPAD con la biomasa en el Valle del Yaqui, Sonora, México, durante el ciclo otoño-invierno 2024-2025. Se emplearon sensores Greenseeker® (Trimble) y SPAD-502Plus (Minolta®), y un CMOS montado en VANT DJI Mavic 3 Enterprise®, con muestreos a los 60 y 86 días después de la siembra (DDS). El NDVI obtenido con GreenSeeker a los 60 DDS mostró la correlación más alta y significativa con el rendimiento (r = 0.465; p < 0.05), el NDRE fue significativo a los 86 DDS (r = 0.421; p < 0.05). El SPAD no se asoció significativamente con el rendimiento. Se concluye que NDVI (en etapas tempranas) y NDRE (en etapas avanzadas) son útiles para predecir biomasa forrajera en girasol en zonas áridas.

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Referencias

  1. Aires, U. R. V., Martins, V. S., Ferreira, L. B., Huang, Y., Heintzman, L., & Ouyang, Y. (2025). Impact of sampling techniques on crop type mapping using multi-temporal composites from Harmonized Landsat-Sentinel images. Computers and Electronics in Agriculture, 237, 110676. https://doi.org/10.1016/j.compag.2025.110676
  2. Amankulova, K., Farmonov, N., Mukhtorov, U., & Mucsi, L. (2023). Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology. Geocarto International, 38(1). https://doi.org/10.1080/10106049.2023.2197509
  3. Ang, Y., Che'Ya, N. N., Roslin, N. A., & Ismail, M. R. (2020). Rice chlorophyll content monitoring using vegetation indices from multispectral aerial imagery. Pertanika Journal of Science & Technology, 28(3), 779-795. https://www.researchgate.net/publication/343022828_Rice_Chlorophyll_Content_Monitoring_using_Vegetation_Indices_from_Multispectral_Aerial_Imagery
  4. Bijay-Singh, & Ali, A. M. (2020). Using hand-held chlorophyll meters and canopy reflectance sensors for fertilizer nitrogen management in cereals in small farms in developing countries. Sensors, 20(4), 1127. https://doi.org/10.3390/s20041127
  5. Boiarskii, B. (2019). Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. Journal of Mechanics of Continua and Mathematical Sciences, (4). https://doi.org/10.26782/jmcms.spl.4/2019.11.00003
  6. Centorame, L., Ilari, A., Del Gatto, A., & Pedretti, E. F. (2024). A systematic review on precision agriculture applied to sunflowers, the role of hyperspectral imaging. Computers and Electronics in Agriculture, 222, 109097. https://doi.org/10.1016/j.compag.2024.109097
  7. Chiradza, T. O., Mutengwa, C. S., & Chiuta, N. E. (2025). Response of sunflower genotypes to salinity stress under laboratory conditions. Stresses, 5(3), 50. https://doi.org/10.3390/stresses5030050
  8. Costa, L., Nunes, L., & Ampatzidis, Y. (2020). A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture, 172. https://doi.org/10.1016/j.compag.2020.105334
  9. De Freitas, G., Azevedo, D., Gonasalvez, E. M., Soares, G., Garafalo, L. H., Feitosa, A. C., & Calado, J. A. (2016). Growth and physiological responses of sunflowers grown under levels of water replacement and potassium fertilization. African Journal of Agricultural Research, 11(14), 1273-1281. https://doi.org/10.5897/ajar2016.10852
  10. De Souza, R., Buchhart, C., Heil, K., Plass, J., Padilla, F. M., & Schmidhalter, U. (2021). Effect of time of day and sky conditions on different vegetation indices calculated from active and passive sensors and images taken from UAV. Remote Sensing, 13(9), 1691. https://doi.org/10.3390/rs13091691
  11. Debaeke, P., Attia, F., Champolivier, L., Dejoux, J. F., Micheneau, A., Bitar, A. A., & Trépos, R. (2023). Forecasting sunflower grain yield using remote sensing data and statistical models. European Journal of Agronomy, 142, 126677. https://doi.org/10.1016/j.eja.2022.126677
  12. Hashim, W., Eng, L. S., Alkawsi, G., Ismail, R., Alkahtani, A. A., Dzulkifly, S., Baashar, Y., & Hussain, A. (2021). A hybrid vegetation detection framework: integrating vegetation indices and convolutional neural network. Symmetry, 13(11), 2190. https://doi.org/10.3390/sym13112190
  13. Hernández, A., Jensen, K., Larson, S., Larsen, R., Rigby, C., Johnson, B., Spickermann, C., & Sinton, S. (2024). Using unmanned aerial vehicles and multispectral sensors to model forage yield for grasses of semiarid landscapes. Grasses, 3(2), 84-109. https://doi.org/10.3390/grasses3020007
  14. Hnizil, O., Baidani, A., Khlila, I., Nsarellah, N., Laamari, A., & Amamou, A. (2024). Integrating NDVI, SPAD, and Canopy temperature for strategic nitrogen and seeding rate management to enhance yield, quality, and sustainability in wheat cultivation. Plants, 13(11), 1574. https://doi.org/10.3390/plants13111574
  15. Hussain, M., Farooq, S., Hasan, W., Ul-Allah, S., Tanveer, M., Farooq, M., & Nawaz, A. (2018). Drought stress in sunflower: physiological effects and its management through breeding and agronomic alternatives. Agricultural Water Management, 201, 152-166. https://doi.org/10.1016/j.agwat.2018.01.028
  16. Justo, I. (2024). Seguimiento de los puntos críticos del cultivo de girasol para producción de semillas híbridas [Trabajo de intensificación, Universidad Nacional del Sur, Departamento de Agronomía]. Repositorio Institucional de la Universidad Nacional del Sur. https://repositoriodigital.uns.edu.ar/handle/123456789/6946
  17. Karaca, C., Peña-Fleitas, M. T., Rodríguez, A., Gallardo, M., Thompson, R. B., & Padilla, F. M. (2025). Comparison of vegetation indices measured with proximal reflectance sensing to assess leaf N content and estimate crop yield in vegetable crops. Smart Agricultural Technology, 12, 101369. https://doi.org/10.1016/j.atech.2025.101369
  18. Kharuf, G. S., Hernández, S. L., Orozco, M. R., Aday, D. O. C., & Delgado, M. I. (2018). Análisis de imágenes multiespectrales adquiridas con vehículos aéreos no tripulados. Ingeniería Electrónica, Automática y Comunicaciones, 39(2), 7991. https://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1815-5928201800020079
  19. Mba, P. C., Njoku, J. N., & Uyeh, D. D. (2025). Enhancing resilience in specialty crop production in a changing climate through smart systems adoption. Smart Agricultural Technology, 11, 100897. https://doi.org/10.1016/j.atech.2025.100897
  20. Meloni, R., Cordero, E., Capo, L., Reyneri, A., Sacco, D., & Blandino, M. (2024). Optimizing nitrogen rates for winter wheat using in-season crop N status indicators. Field Crops Research, 318, 109545. https://doi.org/10.1016/j.fcr.2024.109545
  21. Mostafa, H., El-Ansary, M., Awad, M., & Husein, N. (2021). Water stress management for sunflower under heavy soil conditions. Agricultural Engineering International: CIGR Journal, 23(2), 76-84. https://cigrjournal.org/index.php/Ejounral/article/view/6639
  22. Puttha, R., Venkatachalam, K., Hanpakdeesakul, S., Wongsa, J., Parametthanuwat, T., Srean, P., Pakeechai, K., & Charoenphun, N. (2023). Exploring the potential of sunflowers: agronomy, applications, and opportunities within bio-circular-green economy. Horticulturae, 9(10), 1079. https://doi.org/10.3390/horticulturae9101079
  23. Ren, W., Li, X., Liu, T., Chen, N., Xin, M., Qi, Q., & Liu, B. (2026). Controlled-release fertilizers increase sunflower yield by regulating soil nitrogen, photosynthesis, and root structure in arid regions. Frontiers in Plant Science, 16, 1747095. https://doi.org/10.3389/fpls.2025.1747095
  24. Sarazin, V., Duclercq, J., Guillot, X., Sangwan, B., & Sangwan, R. S. (2017). Water-stressed sunflower transcriptome analysis revealed important molecular markers involved in drought stress response and tolerance. Environmental and Experimental Botany, 142, 45-53. https://doi.org/10.1016/j.envexpbot.2017.08.005
  25. SAS Institute Inc. (2023). JMP ®, Version 17.0.0 [Software].
  26. Sharifi, A., & Felegari, S. (2023). Remotely sensed normalized difference red-edge index for rangeland biomass estimation. Aircraft Engineering and Aerospace Technology: an International Journal, 95(7), 1128-1136. https://doi.org/10.1108/aeat-07-2022-0199
  27. Uddling, J., Gelang-Alfredsson, J., Piikki, K., & Pleijel, H. (2007). Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynthesis Research, 91(1), 37-46. https://doi.org/10.1007/s11120-006-9077-5
  28. Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: a review of developments and applications. Journal of Sensors, 2017(1), 1-17. https://doi.org/10.1155/2017/1353691
  29. Yin, G., Verger, A., Descals, A., Filella, I., & Peñuelas, J. (2022). A broadband green-red vegetation index for monitoring gross primary production phenology. Journal of Remote Sensing, 9764982. https://doi.org/10.34133/2022/9764982
  30. Zhang, J., Zhang, H., Sima, M. W., Trout, T. J., Malone, R. W., & Wang, L. (2021). Simulated deficit irrigation and climate change effects on sunflower production in Eastern Colorado with CSM-CROPGRO-Sunflower in RZWQM2. Agricultural Water Management, 246, 106672. https://doi.org/10.1016/j.agwat.2020.106672

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