Geoinformation in support of agricultural management: a case study applied to sugarcane producers in Costa Rica

Bryan Alemán-Montes

https://orcid.org/0000-0003-4349-2255

Costa Rica

Universidad de Costa Rica image/svg+xml

Centro de Investigaciones Agronómicas

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Accepted:

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Published: 2026-01-08

DOI: https://doi.org/10.4995/raet.2026.25205
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degree thesis

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This research was not funded

Abstract:

Tesis doctoral

Autor: Bryan Alemán-Montes
Directores: Dr. Pere Serra Ruiz y Dra. Alaitz Zabala Torres
Lugar: Universitat Autònoma de Barcelona
Fecha: 19/09/2025
Calificación: Sobresaliente Cum Laude
Disponible: http://hdl.handle.net/10803/695313
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References:

Alemán-Montes, B., Serra, P., Zabala, A. 2023a. Modelos para la estimación del rendimiento de la caña de azúcar en Costa Rica con datos de campo e índices de vegetación. Revista de Teledetección, 61, 1–13. https://doi.org/10.4995/raet.2023.18705

Alemán-Montes, B., Zabala, A., Henríquez, C., Serra, P. 2023b. Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat Time-Series Data and Their Spatial Validation at Different Scales in Costa Rica. Remote Sensing, 15, 5476. https://doi.org/10.3390/rs15235476

Alemán-Montes, B., Zabala, A., Serra, P. 2025a. Classification of Sugarcane Varieties With Harmonized Sentinel-2 and Landsat-8/9 Data Using Parametric and Non-Parametric Methods. GeoFocus, 35, 25–46. https://doi.org/10.21138/GF.906

Alemán-Montes, B., Serra, P., Zabala, A., Masó, J., Pons, X. 2025b. A near real-time spatial decision support system for improving sugarcane monitoring through a satellite mapping web browser. Smart Agricultural Technology, 12, 101084. https://doi.org/10.1016/j.atech.2025.101084

Babu, A.S., Adeyeye, S.A.O. 2024. Extraction of sugar from sugar beets and cane sugar. In Extraction Processes in the Food Industry (pp. 177–196). Woodhead Publishing. https://doi.org/10.1016/B978-0-12-819516-1.00007-7

Cook, S.E., Bramley, R.G. V. 1998. Precision agriculture - Opportunities, benefits and pitfalls of site-specific crop management in Australia. Australian Journal of Experimental Agriculture, 38, 753–763. https://doi.org/10.1071/EA97156

Galvão, L.S., Formaggio, A.R., Tisot, D.A. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94, 523–534. https://doi.org/10.1016/j.rse.2004.11.012

LAICA, (Liga Agrícola Industrial de la Caña de Azúcar). 2024. Informe de Progreso Zafra 2023-2024. https://laica.cr/wp-content/uploads/2025/03/reporte-de-sostenibilidad-2023-2024-p.pdf

Lanucara, S., Praticò, S., Pioggia, G., Di Fazio, S., Modica, G. 2024. Web-based spatial decision support system for precision agriculture: A tool for delineating dynamic management unit zones (MUZs). Smart Agricultural Technology, 8. https://doi.org/10.1016/j.atech.2024.100444

Masó, J., Zabala, A., & Pons, X. 2020. Protected areas from space map browser with fast visualization and analytical operations on the fly. Characterizing statistical uncertainties and balancing them with visual perception. ISPRS International Journal of Geo-Information, 9, 1–27. https://doi.org/10.3390/ijgi9050300

McCown, R.L., Carberry, P.S., Hochman, Z., Dalgliesh, N.P., Foale, M.A. 2009. Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action research. Crop and Pasture Science, 60, 1017–1030. https://doi.org/10.1071/CP08455

Som-Ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., Immitzer, M. 2021. Remote sensing applications in sugarcane cultivation: A review. Remote Sensing, 13, 1–46. https://doi.org/https://doi.org/10.3390/rs13204040

Zhao, Y., Yu, L. X., Ai, J., Zhang, Z.F., Deng, J., Zhang, Y. Bin. 2023. Climate Variations in the Low-Latitude Plateau Contribute to Different Sugarcane (Saccharum spp.) Yields and Sugar Contents in China. Plants, 12, 2712. https://doi.org/10.3390/plants12142712

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