Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena

ilustraciones, fotografías, mapas

Autores:
Rincón Rodríguez, Cristian Andrés
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86023
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86023
https://repositorio.unal.edu.co/
Palabra clave:
630 - Agricultura y tecnologías relacionadas
Zonas de cultivo - Caribe (Región) - Colombia
Banano - Industria - Caribe (Región) - Colombia
Cultivos - Efectos de la sal - Caribe (Región) - Colombia
Degradación del suelo - Caribe (Región) - Colombia
Espectroscopia de Infrarrojos
Suelos afectados por sales
Espectroscopia de Infrarrojo Cercano
Cultivo de banano
Variabilidad espacial
Salts Affected Soils
Near Infrared Spectroscopy
Banana plantations
Spatial variability
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_fd2afb897fbcbb8ad901a65fc2c6e5bd
oai_identifier_str oai:repositorio.unal.edu.co:unal/86023
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
dc.title.translated.eng.fl_str_mv Spatial variability of salt-affected soil in Zona Bananera, Magdalena
title Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
spellingShingle Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
630 - Agricultura y tecnologías relacionadas
Zonas de cultivo - Caribe (Región) - Colombia
Banano - Industria - Caribe (Región) - Colombia
Cultivos - Efectos de la sal - Caribe (Región) - Colombia
Degradación del suelo - Caribe (Región) - Colombia
Espectroscopia de Infrarrojos
Suelos afectados por sales
Espectroscopia de Infrarrojo Cercano
Cultivo de banano
Variabilidad espacial
Salts Affected Soils
Near Infrared Spectroscopy
Banana plantations
Spatial variability
title_short Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
title_full Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
title_fullStr Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
title_full_unstemmed Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
title_sort Variabilidad espacial de suelos afectados por sales en Zona Bananera, Magdalena
dc.creator.fl_str_mv Rincón Rodríguez, Cristian Andrés
dc.contributor.advisor.none.fl_str_mv Loaiza Úsuga, Juan Carlos
Rubiano Sanabria, Yolanda
dc.contributor.author.none.fl_str_mv Rincón Rodríguez, Cristian Andrés
dc.contributor.orcid.spa.fl_str_mv Rincón Rodríguez, Cristian Andrés [0009-0007-0628-9908]
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visua lizador/generarCurriculoCv.do? cod_rh=0001700554
dc.contributor.researchgate.spa.fl_str_mv https://www.researchgate.net/profile/Cristian-Rincon-15
dc.subject.ddc.spa.fl_str_mv 630 - Agricultura y tecnologías relacionadas
topic 630 - Agricultura y tecnologías relacionadas
Zonas de cultivo - Caribe (Región) - Colombia
Banano - Industria - Caribe (Región) - Colombia
Cultivos - Efectos de la sal - Caribe (Región) - Colombia
Degradación del suelo - Caribe (Región) - Colombia
Espectroscopia de Infrarrojos
Suelos afectados por sales
Espectroscopia de Infrarrojo Cercano
Cultivo de banano
Variabilidad espacial
Salts Affected Soils
Near Infrared Spectroscopy
Banana plantations
Spatial variability
dc.subject.lemb.none.fl_str_mv Zonas de cultivo - Caribe (Región) - Colombia
Banano - Industria - Caribe (Región) - Colombia
Cultivos - Efectos de la sal - Caribe (Región) - Colombia
Degradación del suelo - Caribe (Región) - Colombia
Espectroscopia de Infrarrojos
dc.subject.proposal.spa.fl_str_mv Suelos afectados por sales
Espectroscopia de Infrarrojo Cercano
Cultivo de banano
Variabilidad espacial
dc.subject.proposal.eng.fl_str_mv Salts Affected Soils
Near Infrared Spectroscopy
Banana plantations
Spatial variability
description ilustraciones, fotografías, mapas
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-05-06T14:56:52Z
dc.date.available.none.fl_str_mv 2024-05-06T14:56:52Z
dc.date.issued.none.fl_str_mv 2024-04
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86023
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/86023
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.indexed.spa.fl_str_mv LaReferencia
dc.relation.references.spa.fl_str_mv Abdennour, M. A., Douaoui, A., Bradai, A., Bennacer, A., & Pulido Fernández, M. (2019). Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish Journal of Soil Science, 9. https://doi.org/10.3232/SJSS.2019.V9.N2.04
Agudelo, L. (2011). La industria bananera y el inicio de los conflictos sociales del siglo XX. Credencial historia, Rev. Credencial, no. 258.
Allbed, A., Kumar, L., & Aldakheel, Y. Y. (2014). Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 230–231, 1–8. https://doi.org/10.1016/j.geoderma.2014.03.025
Barreto Filho, F. L., Guerra, H. O. C., & Gheyi, H. R. (2003). Conductividad hidráulica en un suelo aluvial en respuesta al porcentaje de sodio intercambiable. Revista Brasileira de Engenharia Agrícola e Ambiental, 7(2), 403–407. https://doi.org/10.1590/S1415-43662003000200037
Barreto, A. C., Ferreira Neto, M., Oliveira, R. P. de, Moreira, L. C. J., Medeiros, J. F. de, & Sá, F. V. da S. (2023). Comparative analysis of spectral indexes for soil salinity mapping in irrigated areas in a semi-arid region, Brazil. Journal of Arid Environments, 209, 104888. https://doi.org/10.1016/j.jaridenv.2022.104888
Bastidas-obando, E. (2010). Caracterización espectral y mineralógica de los suelos del valle del río Cauca por espectroscopía visible e infrarroja (400 - 2.500 nm). Agronomía Colombiana, 28(2), 291–301.
Bernal, G. (2016). Caracterización geomorfológica de la llanura deltaica del rio magdalena con énfasis en el sistema lagunar de la ciénaga grande de santa marta, Colombia. Bulletin of Marine and Coastal Research, 25. https://doi.org/10.25268/bimc.invemar.1996.25.0.369
Bernal, G., & Betancur, J. (2016). Sedimentología de lagunas costeras: ciénaga grande de santa marta y ciénaga de pajarales. Bulletin of Marine and Coastal Research, 25. https://doi.org/10.25268/bimc.invemar.1996.25.0.370
Bilgili, A. V., Cullu, M. A., van Es, H., Aydemir, A., & Aydemir, S. (2011). The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey. Arid Land Research and Management, 25(1), 19–37. https://doi.org/10.1080/15324982.2010.528153
Bressler, E., Carter, D. L., & McNeal, B. L. (1982). Saline and sodic soils: principles, dynamics, modeling. Springer-Verlag.
Buol, S., Southard, R., & Graham, R. (2011). Soil Genesis and Classification Sixth Edition (J. W. & Sons (Ed.)).
Camacho-Tamayo, J. H., Forero-Cabrera, N. M., Ramírez-López, L., & Rubiano, Y. (2017). Near-infrared spectroscopic assessment of soil texture in an oxisol of the eastern plains of Colombia. Colombia Forestal, 20(1), 5–18. https://doi.org/10.14483/udistrital.jour.colomb.for.2017.1.a01
Cambou, A., Barthès, B. G., Moulin, P., Chauvin, L., Faye, E. H., Masse, D., Chevallier, T., & Chapuis-Lardy, L. (2022). Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal). Catena, 212, 106075. https://doi.org/10.1016/j.catena.2022.106075
Case, I. E., & Macdonald, W. D. (1973). Regional Gravity Anomalies and Crustal Structure in Northern Colombia. Geological Society of America Bulletin, 84(9), 2905. https://doi.org/10.1130/0016-7606(1973)84<2905:RGAACS>2.0.CO;2
da Silva, R. B., Rosa, J. S., Packer, A. P., Bento, C. B., & de Melo Silva, F. A. (2022). A soil quality physical–chemical approach 30 years after land-use change from forest to banana plantation. Environmental Monitoring and Assessment, 194(7), 482. https://doi.org/10.1007/s10661-022-10167-9
Damodaran, T., Mishra, V. K., Sharma, D. K., Jha, S. K., Verma, C. L., Rai, R. B., Kannan, R., Nayak, A. K., Dhama, K., Of, M., For, S. S., Banana, S., & In, P. (2013). Management of sub-soil sodicity for sustainable banana production in sodic soil-an approach. Int. J. Curr. Res, 5(7), 1930–1934.
De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18. https://doi.org/10.1016/S0169-7439(99)00047-7
Delgadillo-Duran, D. A., Vargas-García, C. A., Varón-Ramírez, V. M., Calderón, F., Montenegro, A. C., & Reyes-Herrera, P. H. (2022). Vis–NIR spectroscopy and machine learning methods to diagnose chemical properties in Colombian sugarcane soils. Geoderma Regional, 31, e00588. https://doi.org/10.1016/j.geodrs.2022.e00588
El-Fadel, M., Deeb, T., Alameddine, I., Zurayk, R., & Chaaban, J. (2018). Impact of groundwater salinity on agricultural productivity with climate change implications. International Journal of Sustainable Development and Planning, 13(03), 445–456. https://doi.org/10.2495/SDP-V13-N3-445-456
Eriksson, L. E. Johansson, N. Kettaneh-Wold, J. Trygg, C. W. y S. W. (2006). Multi- and megavariate data analysis : Part I: Basic principles and applications (U. A. Umeå (Ed.)).
ESA, (2012). Sentinel-2 MSI User Guide, European Space Agency (ESA), ttps://sentinels.copernicus.eu/web/sentinel/ user-guides/sentinel-2-msi.
Espinal, L.S. (1991) Apuntes ecológicos, Departamento de Ciencias de la Tierra, Universidad Nacional de Colombia, Sede Medellín, Medellín: Editorial Lealon,
Fanning, D.S; Fanning, M. C. B. (1989). Soil morphology & genesis classification. John Wiley and Sons Inc.
Fernández-Buces, N., Siebe, C., Cram, S., & Palacio, J. L. (2006). Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments, 65(4), 644–667. https://doi.org/10.1016/j.jaridenv.2005.08.005
Ferreira, R. de A., Teixeira, G., & Peternelli, L. A. (2022). Kennard-Stone method outperforms the Random Sampling in the selection of calibration samples in SNPs and NIR data. Ciência Rural, 52(5). https://doi.org/10.1590/0103-8478cr20201072
Gómez, J., Montes, N.E. (2020). Mapa Geológico de Colombia 2020. Escala 1:1 000 000. Servicio Geológico Colombiano (p. 2). https://www2.sgc.gov.co/MGC/Documents/MGC_2020/mgc2020.pdfHaq
Gozukara, G., Altunbas, S., Dengiz, O., & Adak, A. (2022). Assessing the effect of soil to water ratios and sampling strategies on the prediction of EC and pH using pXRF and Vis-NIR spectra. Computers and Electronics in Agriculture, 203, 107459. https://doi.org/10.1016/j.compag.2022.107459
Haq, Y. ul, Shahbaz, M., Asif, H. M. S., Al-Laith, A., & Alsabban, W. H. (2023). Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan. Sustainability, 15(17), 12943. https://doi.org/10.3390/su151712943
He, Y., Huang, M., García, A., Hernández, A., & Song, H. (2007). Prediction of soil macronutrients content using near-infrared spectroscopy. Computers and Electronics in Agriculture, 58(2), 144–153. https://doi.org/10.1016/j.compag.2007.03.011
Herczeg, A. L., Torgersen, T., Chivas, A. R., & Habermehl, M. A. (1991). Geochemistry of ground waters from the Great Artesian Basin, Australia. Journal of Hydrology, 126(3–4), 225–245. https://doi.org/10.1016/0022-1694(91)90158-E
Hou, J., & Rusuli, Y. (2023). Estimation of soil salt content in the Bosten Lake watershed, Northwest China based on a support vector machine model and optimal spectral indices. PLOS ONE, 18(2), e0273738. https://doi.org/10.1371/journal.pone.0273738
IDEAM, 2017.Mapa nacional de degradación de suelos por salinización, Bogotá: IDEAM, Grupo de Suelos y Tierras, Subdirección de Ecosistemas e Información Ambiental,
IGAC. (2009). Estudio General de Suelos y Zonificación de Tierras: Departamento de Magdalena, escala 1:100.000 (Imprenta nacional de Colombia (Ed.)).
IGAC. (2018). Sistema de Clasificación Geomorfológica Aplicado a los Levantamientos de Suelos (Imprenta N).
IUSS Working Group WRB. 2022. World Reference Base for Soil Resources. International soil classification system for naming soils and creating legends for soil maps. 4th edition. International Union of Soil Sciences (IUSS), Vienna, Austria.
Jia, P., Zhang, J., He, W., Hu, Y., Zeng, R., Zamanian, K., Jia, K., & Zhao, X. (2022). Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity. Remote Sensing, 14(11), 2602. https://doi.org/10.3390/rs14112602
Jin, X., Du, J., Liu, H., Wang, Z., & Song, K. (2016). Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model. Agricultural and Forest Meteorology, 218–219, 250–260. https://doi.org/10.1016/j.agrformet.2015.12.062
Jinfu, Z., Kelong, C., Shengkui, C., Liang, C., Yanpeng, W., Baoliang, L., & Yongsheng, W. (2011). Spatial Variability Analysis of Soil Salt and Its Component Ions in Qinghai Lake Region. Procedia Environmental Sciences, 10, 2431–2436. https://doi.org/10.1016/j.proenv.2011.09.378
Johnston, K., Ver Hoef, J. M., Krivoruchko, K., & Lucas, N. (2001). Using ArcGIS geostatistical analyst (Vol. 380). Redlands: Esri.
Laga, I., & Kleiber, W. (2017). The modified Matérn process. Stat, 6(1), 241–247. https://doi.org/10.1002/sta4.152
Lao, C., Chen, J., Zhang, Z., Chen, Y., Ma, Y., Chen, H., Gu, X., Ning, J., Jin, J., & Li, X. (2021). Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection. Computers and Electronics in Agriculture, 182, 106031. https://doi.org/10.1016/j.compag.2021.106031
Li, J., Pu, L., Han, M., Zhu, M., Zhang, R., & Xiang, Y. (2014). Soil salinization research in China: Advances and prospects. Journal of Geographical Sciences, 24(5), 943–960. https://doi.org/10.1007/s11442-014-1130-2
Li, J., Pu, L., Zhu, M., Zhang, J., Li, P., Dai, X., Xu, Y., & Liu, L. (2014). Evolution of soil properties following reclamation in coastal areas: A review. Geoderma, 226–227, 130–139. https://doi.org/10.1016/j.geoderma.2014.02.003
Li, Y., Yang, J., Yu, M., Zhao, W., Xiao, Y., Zhou, D., Zhan, C., Yu, Y., Zhang, J., Lv, Z., & Yu, J. (2019). Different effects of NaCl and Na2SO4 on the carbon mineralization of an estuarine wetland soil. Geoderma, 344, 179–183. https://doi.org/10.1016/j.geoderma.2019.02.035
Lin, H. (2011). Three Principles of Soil Change and Pedogenesis in Time and Space. Soil Science Society of America Journal, 75(6), 2049–2070. https://doi.org/10.2136/sssaj2011.0130
Londoño Ardila, J. H. (2017). Suelos afectados por sales en la zona bananera de Santa Marta. Universidad Nacional de Colombia, Sede Medellín.
LR, (2020) Sector bananero genera 50.000 empleos según el presidente de ASBAMA, in Agronegocios, Bogotá: Editorial la República,
Ma, Y., Chen, H., Zhao, G., Wang, Z., & Wang, D. (2020). Spectral Index Fusion for Salinized Soil Salinity Inversion Using Sentinel-2A and UAV Images in a Coastal Area. IEEE Access, 8, 159595–159608. https://doi.org/10.1109/ACCESS.2020.3020325
Maertens, M., De Lannoy, G. J. M., Vincent, F., Massart, S., Giménez, R., Houspanossian, J., Gasparri, I., & Vanacker, V. (2022). Spatial patterns of soil salinity in the central Argentinean Dry Chaco. Anthropocene, 37, 100322. https://doi.org/10.1016/j.ancene.2022.100322
Mahajan, G. R., Das, B., Gaikwad, B., Murgaonkar, D., Desai, A., Morajkar, S., Patel, K. P., & Kulkarni, R. M. (2021). Monitoring properties of the salt-affected soils by multivariate analysis of the visible and near-infrared hyperspectral data. Catena, 198, 105041. https://doi.org/10.1016/j.catena.2020.105041
Melendez-Pastor, I., Navarro-Pedreño, J., Koch, M., & Gómez, I. (2010). Applying imaging spectroscopy techniques to map saline soils with ASTER images. Geoderma, 158(1–2), 55–65. https://doi.org/10.1016/j.geoderma.2010.02.015
Metternicht, G., & Alfred Zinck, J. (2008). Soil Salinity and Salinization Hazard. In Remote Sensing of Soil Salinization (Issue December). https://doi.org/10.1201/9781420065039.pt1
Metternicht, G., & Zinck, A. (2009). Remote sensing of soil salinization: Impact on land management. CRC Press
Mevik, B. H., Wehrens, R., Liland, K. H., & Hiemstra, P. (2016). Rpackage: pls, Partial Least Squares and Principal Component Regression. R Package version, 2.
Miloš, B., Bensa, A., & Japundžić-Palenkić, B. (2022). Evaluation of Vis-NIR preprocessing combined with PLS regression for estimation soil organic carbon, cation exchange capacity and clay from eastern Croatia. Geoderma Regional, 30, e00558. https://doi.org/10.1016/j.geodrs.2022.e00558
Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32(9), 1378–1388. https://doi.org/10.1016/j.cageo.2005.12.009
Mohamed, E. S., Saleh, A. M., Belal, A. B., & Gad, A. (2018). Application of near-infrared reflectance for quantitative assessment of soil properties. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 1–14. https://doi.org/10.1016/j.ejrs.2017.02.001
Morales, G. M.-A. (2009). La regresión por mínimos cuadrados parciales: orígenes y evolución. Historia de la Probabilidad y la Estadística (IV). Universidad Complutense de Madrid.
Mouazen, A. M., Kuang, B., De Baerdemaeker, J., & Ramon, H. (2010). Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158(1–2), 23–31. https://doi.org/10.1016/j.geoderma.2010.03.001
Neira F., Sanchez R., Otero J., Marroquin P., Gama D., F. M. (2021). National study of soil degradation by salinization in Colombia. in: Halt soil salinization, boost soil productivity – Proceedings of the Global Symposium on Salt-affected Soils. 20 – 22 October 2021. FAO. Rome. p 84 - 85. https://doi.org/10.4060/cb9565enGlobal FAO
Ninomiya, Y. (2002). Mapping quartz, carbonate minerals, and mafic-ultramafic rocks using remotely sensed multispectral thermal infrared ASTER (X. P. Maldague & A. E. Rozlosnik (Eds.); pp. 191–202). https://doi.org/10.1117/12.459566
Olivares Campos, B. O. (2023). Evaluation of the Incidence of Banana Wilt and its Relationship with Soil Properties (pp. 95–117). https://doi.org/10.1007/978-3-031-34475-6_4
Omuto, C.T., Vargas, R.R., El Mobarak, A.M., Mohamed, N., Viatkin, K., Yigini Y. (2020). Mapping of salt-affected soils: Technical manual. Rome, FAO https://doi.org/10.4060/ca9215en
Otero, J., Gómez, C., & Sánchez, R. (2002). Zonificación de los procesos de salinización de los suelos de Colombia (Subdirección). IDEAM.
Panagopoulos, T., Jesus, J., Antunes, M. D. C., & Beltrão, J. (2006). Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. European Journal of Agronomy, 24(1), 1–10. https://doi.org/10.1016/j.eja.2005.03.001
Peng, J., Ji, W., Ma, Z., Li, S., Chen, S., Zhou, L., & Shi, Z. (2016). Predicting total dissolved salts and soluble ion concentrations in agricultural soils using portable visible near-infrared and mid-infrared spectrometers. Biosystems Engineering, 152, 94–103. https://doi.org/10.1016/j.biosystemseng.2016.04.015
Pessoa, L. G. M., Freire, M. B. G. dos S., Green, C. H. M., Miranda, M. F. A., Filho, J. C. de A., & Pessoa, W. R. L. S. (2022). Assessment of soil salinity status under different land-use conditions in the semiarid region of Northeastern Brazil. Ecological Indicators, 141, 109139. https://doi.org/10.1016/j.ecolind.2022.109139
Pla, I. (2014). Nuevas experiencias en la evaluación y diagnóstico de procesos de salinización y sodificación de suelos en américa latina. Suelos Ecuatoriales, 44, 125–137. https://doi.org/10.47864
Pla, Sentís, I. (2014). Advances in the prognosis of soil sodicity under dryland irrigated conditions. International Soil and Water Conservation Research, 2(4), 50–63. https://doi.org/10.1016/S2095-6339(15)30058-7
Pla, I. (1996). Soil salinization and land desertification. soil degradation and desertification in mediterranean environments. 105-129
Ramirez Lopez, L., Stevens, A., Orellano, C., Viscarra Rossel, R., Shen, Z., Wadoux, A., Breure, T. (2023). resemble: Regression and similarity evaluation for memory-based learning in spectral chemometrics. R package version 2.2.2, https://CRAN.R-project.org/package=resemble.
Ramírez, P. B., Calderón, F. J., Jastrow, J. D., Ping, C.-L., & Matamala, R. (2023). Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better? Geoderma Regional, 32, e00617. https://doi.org/10.1016/j.geodrs.2023.e00617
Ramirez-Lopez, L., Behrens, T., Schmidt, K., Rossel, R. A. V., Demattê, J. A. M., & Scholten, T. (2013). Distance and similarity-search metrics for use with soil vis–NIR spectra. Geoderma, 199, 43–53. https://doi.org/10.1016/j.geoderma.2012.08.035
Ravi, I., & Vaganan, M. M. (2016). Abiotic Stress Tolerance in Banana. In Abiotic Stress Physiology of Horticultural Crops (pp. 207–222). Springer India. https://doi.org/10.1007/978-81-322-2725-0_12
Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
Rincon-Rodriguez, C.A., Loaiza-Usuga, J.C., and Rubiano-Sanabria, Y., (2021).Preliminary study of Salt affected soils in the Zona Bananera, Magdalena (Colombia), Proc.Global Symp. on Salt Afected Soils, Oct. 20–22, 2021, Global Soil Partnership, FAO,.https://www.fao.org/fileadmin/user_upload/GSP/GSAS21/069.pdf.
Rincón, C. A., Loaiza-Usuga, J. C., Rubiano, Y., Castañeda, D. (2023). Use of NIRS in Soil Properties Evaluation Related to Soil Salinity and Sodicity in Colombian Caribbean Coast. Moscow University Soil Science Bulletin. 78(5), 439-450.
RStudio Team, RStudio (2020): Integrated Development for R. RStudio, Boston, MA: PBC. http://www.rstudio.com/.
Santos, C. R. C. dos, Matsunaga, A. T., Costa, L. R. R., Santos, M. L. dos, Brasil Neto, A. B., Rodrigues, R. P., Maciel, M. de N. M., & Melo, V. S. de. (2023). Spatial variability of soil fertility under agroforestry system and native forest in eastern Amazonia, Brazil. Bioscience Journal, 39, e39015. https://doi.org/10.14393/BJ-v39n0a2023-62830
Savitzky, A., & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627–1639. https://doi.org/10.1021/ac60214a047
Soil Science Division Staff. (2017) Soil survey manual, USDA. Handbook 18, Washington, D.C., C. Ditzler, K.
Soil Survey Staff. (2022a). Keys to Soil Taxonomy, 13th edition. USDA Natural Resources Conservation Service.
Soil Survey Staff. 2022. Kellogg Soil Survey Laboratory methods manual. Soil Survey Investigations Report No. 42, Version 6.0. U.S. Department of Agriculture, Natural Resources Conservation Service.
Strauss, T., & von Maltitz, M. J. (2017). Generalising Ward’s Method for Use with Manhattan Distances. PLOS ONE, 12(1), e0168288. https://doi.org/10.1371/journal.pone.0168288
Suleymanov, A., Gabbasova, I., Abakumov, E., & Kostecki, J. (2021). Soil salinity assessment from satellite data in the Trans-Ural steppe zone (Southern Ural, Russia). Soil Science Annual. https://doi.org/10.37501/soilsa/132233
Taleisnik, E., & Lavado, R. S. (Eds.). (2021). Saline and Alkaline Soils in Latin America. Springer International Publishing. https://doi.org/10.1007/978-3-030-52592-7
Tian, A., Zhao, J., Fu, C., & Xiong, H. (2022). Estimation of SO42− ion in saline soil using VIS-NIR spectroscopy under different human activity stress. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 282, 121647. https://doi.org/10.1016/j.saa.2022.121647
Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Ubugunov, L. L., & Ubugunova, V. I. (2011). Floodplain soils in the lower reaches of the Khovd River in the Great Lakes Basin of Mongolia. Eurasian Soil Science, 44(11), 1184–1192. https://doi.org/10.1134/S106422931111007X
Urrego, L. E., Correa-Metrio, A., González, C., Castaño, A. R., & Yokoyama, Y. (2013). Contrasting responses of two Caribbean mangroves to sea-level rise in the Guajira Peninsula (Colombian Caribbean). Palaeogeography, Palaeoclimatology, Palaeoecology, 370, 92–102. https://doi.org/10.1016/j.palaeo.2012.11.023
U.S. Salinity Laboratory Staff. (1954). Diagnosis and improvement of saline and alkali soils. USDA Agricultural Handbook No. 60. U.S. Government Printing Office
Velásquez, E., Lavelle, P., Barrios, E., Joffre, R., & Reversat, F. (2005). Evaluating soil quality in tropical agroecosystems of Colombia using NIRS. Soil Biology and Biochemistry, 37(5), 889–898. https://doi.org/10.1016/j.soilbio.2004.09.009
Velez, M. I., Escobar, J., Brenner, M., Rangel, O., Betancourt, A., Jaramillo, A. J., Curtis, J. H., & Moreno, J. L. (2014). Middle to late Holocene relative sea level rise, climate variability and environmental change along the Colombian Caribbean coast. The Holocene, 24(8), 898–907. https://doi.org/10.1177/0959683614534740
Voigt, C., Klipsch, S., Herwartz, D., Chong, G., & Staubwasser, M. (2020). The spatial distribution of soluble salts in the surface soil of the Atacama Desert and their relationship to hyperaridity. Global and Planetary Change, 184, 103077. https://doi.org/10.1016/j.gloplacha.2019.103077
Wackernagel, H. (1995). Ordinary Kriging. In Multivariate Geostatistics (pp. 74–81). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-03098-1_11
Wan, M., Qu, M., Hu, W., Li, W., Zhang, C., Cheng, H., & Huang, B. (2019). Estimation of soil pH using PXRF spectrometry and Vis-NIR spectroscopy for rapid environmental risk assessment of soil heavy metals. Process Safety and Environmental Protection, 132, 73–81. https://doi.org/10.1016/j.psep.2019.09.025
Wang, L., & Wang, R. (2022). Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 283, 121707. https://doi.org/10.1016/j.saa.2022.121707
Wang, Q., Li, P., & Chen, X. (2012). Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment. Geoderma, 170, 103–111. https://doi.org/10.1016/j.geoderma.2011.10.015
Wold, S., & Sjöström, M. (1977). SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy (pp. 243–282). https://doi.org/10.1021/bk-1977-0052.ch012
Yang, F., Zhang, G.-L., Sauer, D., Yang, F., Yang, R.-M., Liu, F., Song, X.-D., Zhao, Y.-G., Li, D.-C., & Yang, J.-L. (2020). The geomorphology – sediment distribution – soil formation nexus on the northeastern Qinghai-Tibetan Plateau: Implications for landscape evolution. Geomorphology, 354, 107040. https://doi.org/10.1016/j.geomorph.2020.107040
Yudina, A., & Kuzyakov, Y. (2023). Dual nature of soil structure: The unity of aggregates and pores. Geoderma, 434, 116478. https://doi.org/10.1016/j.geoderma.2023.116478
Zapata, R. (2022). Los procesos químicos del suelo. Facultad de Ciencias Universidad Nacional de Colombia.
Zeaiter, M., & Rutledge, D. (2009). Preprocessing Methods. In Comprehensive Chemometrics (pp. 121–231). Elsevier. https://doi.org/10.1016/B978-044452701-1.00074-0
Zhou, Y., Chen, S., Hu, B., Ji, W., Li, S., Hong, Y., Xu, H., Wang, N., Xue, J., Zhang, X., Xiao, Y., & Shi, Z. (2022). Global Soil Salinity Prediction by Open Soil Vis-NIR Spectral Library. Remote Sensing, 14(21), 5627. https://doi.org/10.3390/rs14215627
Zinck, J. A. (1987). Aplicación de la geomorfología al levantamiento de suelos en zonas aluviales y definición del ambiente geomorfológico con fines de descripción de suelos.
Zinck, J. A. (1988). Physiography and Soils. In International Institute for Geoinformation Science and Earth Observation (ITC) (Internatio). https://webapps.itc.utwente.nl/librarywww/papers_1989/tech/zinck_phy.pdf
Zinck, J. A., Metternicht, G., Bocco, G., & Del Valle, H. F. (Eds.). (2016). Geopedology. Springer International Publishing. https://doi.org/10.1007/978-3-319-19159-1
Zontov, Y. V., Rodionova, O. Y., Kucheryavskiy, S. V., & Pomerantsev, A. L. (2020). PLS-DA – A MATLAB GUI tool for hard and soft approaches to partial least squares discriminant analysis. Chemometrics and Intelligent Laboratory Systems, 203, 104064. https://doi.org/10.1016/j.chemolab.2020.104064
Zovko, M., Romić, D., Colombo, C., Di Iorio, E., Romić, M., Buttafuoco, G., & Castrignanò, A. (2018). A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia. Geoderma, 332, 60–72. https://doi.org/10.1016/j.geoderma.2018.07.005
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 85 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.region.none.fl_str_mv Caribe (Región) -- Colombia
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Medellín - Ciencias - Maestría en Ciencias - Geomorfología y Suelos
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Medellín
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/86023/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/86023/2/1082987155_2024.pdf
https://repositorio.unal.edu.co/bitstream/unal/86023/3/U.FT.09.006.004%20Licencia%20para%20publicaci%c3%b3n%20de%20obras%20en%20el%20Repositorio%20Institucional%20UNAL%20v4.pdf
https://repositorio.unal.edu.co/bitstream/unal/86023/4/1082987155_2024.pdf.jpg
bitstream.checksum.fl_str_mv eb34b1cf90b7e1103fc9dfd26be24b4a
f162eedf869e44b88d3aad1b4788fb7c
156f6ef83ccbfd073c9fa61bc67e57a9
c2def60c182db40462062528376d5e35
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814090204759195648
spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Loaiza Úsuga, Juan Carlos055b5a48b5571c52e5b22475b14eabadRubiano Sanabria, Yolanda019716440377435f3ee30eb40d6935daRincón Rodríguez, Cristian Andrés244ddd802b96cf1d46cfbba69456ea53Rincón Rodríguez, Cristian Andrés [0009-0007-0628-9908]https://scienti.minciencias.gov.co/cvlac/visua lizador/generarCurriculoCv.do? cod_rh=0001700554https://www.researchgate.net/profile/Cristian-Rincon-152024-05-06T14:56:52Z2024-05-06T14:56:52Z2024-04https://repositorio.unal.edu.co/handle/unal/86023Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, mapasLos suelos afectados por sales son la principal degradación en la Región Caribe Colombiana, lo cual pone en riesgo la seguridad alimentaria y la paz en nuestro país. El primer paso para poder abordar el problema es la cartografía y seguimiento de los suelos afectados por sales SAS. No obstante, los altos costos de la elaboración de mapas, debido a los análisis químicos específicos de salinidad que son requeridos y la gran área que ocupa hacen que el mapeo a nivel detallado muchas veces sea inviable. En esta investigación planteamos el uso de la Espectroscopia de Infrarrojo Cercano NIRS para la evaluación de los SAS estableciendo primero la relación suelo y paisaje en el piedemonte aluvial de la Sierra Nevada de Santa Marta, para suelos cultivados con banano en el municipio Zona Bananera. Se desarrollaron varios modelos espectrales mediante el uso de modelos de regresión y aprendizaje automático Machine Learning que permitieron evaluar la variabilidad espacial de las propiedades del suelo asociadas a la salinidad. Este trabajo se divide en dos capítulos, el primero enfocado a la relación suelo-paisaje, donde se elaboró un mapa geomorfológico y se describió un perfil de suelos por cada geoforma, para determinar así la distribución de los SAS con respecto al paisaje y las morfodinámicas que han tenido lugar sobre el suelo. Además, se usaron modelos de regresión de mínimos de cuadrados parciales PLS, PLSR y PCR para la elaboración de los modelos espectrales con el fin de predecir mediante NIRS los iones SO42-, HCO3-, CO32-, Cl-, y para los parámetros de pH y CE. Se encontraron valores de R2 significativos en su mayoría superior a 0,5, con los cuales se elaboraron los respectivos mapas y se pudo determinar la variabilidad espacial de estas propiedades y correlacionarla con los mapas obtenidos mediante los métodos convencionales. Esto evidenció una estrecha relación en geoformas asociadas a dinámicas fluvio-marinas y las sales. Por otra parte, un segundo capítulo fue enfocado al aprendizaje de máquinas con métodos supervisados y no supervisados para determinar los suelos afectados y no afectados por sales y mediante el uso de OPLS-DA, donde se obtuvo un R2 de 0,76. Además, se evaluó la variabilidad espacial de las siguientes propiedades: pH, CE, Ca2+, Mg2+, K+, Na+, RAS (Relación de Adsorción de Sodio), PSI (Porcentaje de Sodio Intercambiable) mediante el uso de la geoestadísticas, con datos obtenidos a partir de análisis de suelos de la Zona Bananera. Para esto se evaluaron distintos modelos espectrales obtenidos con herramientas de regresión como Operador de Selección y Contracción Mínima Absoluta LASSO, Componentes Principales de Regresión PCR, Regresión Parcial de Mínimos Cuadrados PLSR y Mínimos Cuadrados Parciales PLS. Obteniendo resultados aceptables para la mayoría de variables, además de mostrar la tendencia de las propiedades del suelo asociadas a las sales hacia el complejo lagunar Ciénaga Grande de Santa Marta. Este trabajo mostró el potencial de NIRS para la evaluación de SAS, como una alternativa para el mapeo de suelos en la Región Caribe Colombiana. Esto es uno de los primeros trabajos de espectroscopia en suelos afectados por sales en suelos cultivados con banano en la Zona Bananera del Magdalena, elaborando una biblioteca espectral, contribuyendo significativamente a la ciencia en el uso de sensores remotos. Además, se convierte en un aporte social pudiendo ser implantado como el punto de partida para el monitoreo de SAS en el área de estudio. (Tomado de la fuente)Salts affected soils are the main degradation in the Colombian Caribbean Region, which threats food security and peace in our country. The first step to address the problem is the mapping and monitoring of salts-affected soils (SAS). However, the high costs of map production, due to specific salinity chemical analyses required and the vast area they cover, often render detailed mapping unfeasible. In this research, we propose the use of Near Infrared Spectroscopy (NIRS) for SAS evaluation, initially establishing the soil-landscape relationship in the alluvial piedmont of the Sierra Nevada de Santa Marta, for banana-cultivated soils in the Zona Bananera municipality. Several spectral models were developed using regression models and Machine Learning algorithms, allowing the assessment of spatial variability of soil properties associated with salinity. This work is divided into two chapters: the first focusing on the soil-landscape relationship, where a geomorphological map was developed, and a soil profile was described for each landform, to determine the distribution of SAS concerning landscape and soil morphodynamics. Additionally, Partial Least Squares Regression (PLS), PLS Regression (PLSR), and Principal Component Regression (PCR) models were used to develop spectral models to predict SO42-, HCO3-, CO32-, Cl- ions, pH, and EC parameters through NIRS. Significant R2 values mostly exceeding 0.5 were found, with which the respective maps were developed, determining the spatial variability of these properties and correlating them with maps obtained through conventional methods. This revealed a close relationship between landforms associated with fluvial-marine dynamics and salts. On the other hand, a second chapter focused on machine learning with supervised and unsupervised methods to determine soils affected and unaffected by salts, and through the use of OPLS-DA, an R2 of 0.76 was obtained. Additionally, the spatial variability of the following properties was evaluated: pH, EC, Ca2+, Mg2+, K+, Na+, SAR (Sodium Adsorption Ratio), ESP (Exchangeable Sodium Percentage) using geostatistics, with data obtained from soil analysis in the Zona Bananera. For this, different spectral models obtained with regression tools such as Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and Partial Least Squares (PLS) were evaluated. Acceptable results were obtained for most variables, in addition to showing the trend of soil properties associated with salts towards the Ciénaga Grande de Santa Marta lagoon complex. This work demonstrated the potential of NIRS for SAS evaluation as an alternative for soil mapping in the Colombian Caribbean Region. This is one of the first spectroscopy research on salts affected soil in banana-cultivated soils in the Magdalena Banana Zone, compiling a spectral library, significantly contributing to science in remote sensing use. Additionally, it becomes a social contribution that could be implemented as the starting point for SAS monitoring in the study area.MaestríaMagíster en Ciencias - Geomorfología y SuelosCiencias Naturales.Sede Medellín85 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Ciencias - Maestría en Ciencias - Geomorfología y SuelosFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín630 - Agricultura y tecnologías relacionadasZonas de cultivo - Caribe (Región) - ColombiaBanano - Industria - Caribe (Región) - ColombiaCultivos - Efectos de la sal - Caribe (Región) - ColombiaDegradación del suelo - Caribe (Región) - ColombiaEspectroscopia de InfrarrojosSuelos afectados por salesEspectroscopia de Infrarrojo CercanoCultivo de bananoVariabilidad espacialSalts Affected SoilsNear Infrared SpectroscopyBanana plantationsSpatial variabilityVariabilidad espacial de suelos afectados por sales en Zona Bananera, MagdalenaSpatial variability of salt-affected soil in Zona Bananera, MagdalenaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMCaribe (Región) -- ColombiaLaReferenciaAbdennour, M. A., Douaoui, A., Bradai, A., Bennacer, A., & Pulido Fernández, M. (2019). Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria. Spanish Journal of Soil Science, 9. https://doi.org/10.3232/SJSS.2019.V9.N2.04Agudelo, L. (2011). La industria bananera y el inicio de los conflictos sociales del siglo XX. Credencial historia, Rev. Credencial, no. 258.Allbed, A., Kumar, L., & Aldakheel, Y. Y. (2014). Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 230–231, 1–8. https://doi.org/10.1016/j.geoderma.2014.03.025Barreto Filho, F. L., Guerra, H. O. C., & Gheyi, H. R. (2003). Conductividad hidráulica en un suelo aluvial en respuesta al porcentaje de sodio intercambiable. Revista Brasileira de Engenharia Agrícola e Ambiental, 7(2), 403–407. https://doi.org/10.1590/S1415-43662003000200037Barreto, A. C., Ferreira Neto, M., Oliveira, R. P. de, Moreira, L. C. J., Medeiros, J. F. de, & Sá, F. V. da S. (2023). Comparative analysis of spectral indexes for soil salinity mapping in irrigated areas in a semi-arid region, Brazil. Journal of Arid Environments, 209, 104888. https://doi.org/10.1016/j.jaridenv.2022.104888Bastidas-obando, E. (2010). Caracterización espectral y mineralógica de los suelos del valle del río Cauca por espectroscopía visible e infrarroja (400 - 2.500 nm). Agronomía Colombiana, 28(2), 291–301.Bernal, G. (2016). Caracterización geomorfológica de la llanura deltaica del rio magdalena con énfasis en el sistema lagunar de la ciénaga grande de santa marta, Colombia. Bulletin of Marine and Coastal Research, 25. https://doi.org/10.25268/bimc.invemar.1996.25.0.369Bernal, G., & Betancur, J. (2016). Sedimentología de lagunas costeras: ciénaga grande de santa marta y ciénaga de pajarales. Bulletin of Marine and Coastal Research, 25. https://doi.org/10.25268/bimc.invemar.1996.25.0.370Bilgili, A. V., Cullu, M. A., van Es, H., Aydemir, A., & Aydemir, S. (2011). The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey. Arid Land Research and Management, 25(1), 19–37. https://doi.org/10.1080/15324982.2010.528153Bressler, E., Carter, D. L., & McNeal, B. L. (1982). Saline and sodic soils: principles, dynamics, modeling. Springer-Verlag.Buol, S., Southard, R., & Graham, R. (2011). Soil Genesis and Classification Sixth Edition (J. W. & Sons (Ed.)).Camacho-Tamayo, J. H., Forero-Cabrera, N. M., Ramírez-López, L., & Rubiano, Y. (2017). Near-infrared spectroscopic assessment of soil texture in an oxisol of the eastern plains of Colombia. Colombia Forestal, 20(1), 5–18. https://doi.org/10.14483/udistrital.jour.colomb.for.2017.1.a01Cambou, A., Barthès, B. G., Moulin, P., Chauvin, L., Faye, E. H., Masse, D., Chevallier, T., & Chapuis-Lardy, L. (2022). Prediction of soil carbon and nitrogen contents using visible and near infrared diffuse reflectance spectroscopy in varying salt-affected soils in Sine Saloum (Senegal). Catena, 212, 106075. https://doi.org/10.1016/j.catena.2022.106075Case, I. E., & Macdonald, W. D. (1973). Regional Gravity Anomalies and Crustal Structure in Northern Colombia. Geological Society of America Bulletin, 84(9), 2905. https://doi.org/10.1130/0016-7606(1973)84<2905:RGAACS>2.0.CO;2da Silva, R. B., Rosa, J. S., Packer, A. P., Bento, C. B., & de Melo Silva, F. A. (2022). A soil quality physical–chemical approach 30 years after land-use change from forest to banana plantation. Environmental Monitoring and Assessment, 194(7), 482. https://doi.org/10.1007/s10661-022-10167-9Damodaran, T., Mishra, V. K., Sharma, D. K., Jha, S. K., Verma, C. L., Rai, R. B., Kannan, R., Nayak, A. K., Dhama, K., Of, M., For, S. S., Banana, S., & In, P. (2013). Management of sub-soil sodicity for sustainable banana production in sodic soil-an approach. Int. J. Curr. Res, 5(7), 1930–1934.De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18. https://doi.org/10.1016/S0169-7439(99)00047-7Delgadillo-Duran, D. A., Vargas-García, C. A., Varón-Ramírez, V. M., Calderón, F., Montenegro, A. C., & Reyes-Herrera, P. H. (2022). Vis–NIR spectroscopy and machine learning methods to diagnose chemical properties in Colombian sugarcane soils. Geoderma Regional, 31, e00588. https://doi.org/10.1016/j.geodrs.2022.e00588El-Fadel, M., Deeb, T., Alameddine, I., Zurayk, R., & Chaaban, J. (2018). Impact of groundwater salinity on agricultural productivity with climate change implications. International Journal of Sustainable Development and Planning, 13(03), 445–456. https://doi.org/10.2495/SDP-V13-N3-445-456Eriksson, L. E. Johansson, N. Kettaneh-Wold, J. Trygg, C. W. y S. W. (2006). Multi- and megavariate data analysis : Part I: Basic principles and applications (U. A. Umeå (Ed.)).ESA, (2012). Sentinel-2 MSI User Guide, European Space Agency (ESA), ttps://sentinels.copernicus.eu/web/sentinel/ user-guides/sentinel-2-msi.Espinal, L.S. (1991) Apuntes ecológicos, Departamento de Ciencias de la Tierra, Universidad Nacional de Colombia, Sede Medellín, Medellín: Editorial Lealon,Fanning, D.S; Fanning, M. C. B. (1989). Soil morphology & genesis classification. John Wiley and Sons Inc.Fernández-Buces, N., Siebe, C., Cram, S., & Palacio, J. L. (2006). Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments, 65(4), 644–667. https://doi.org/10.1016/j.jaridenv.2005.08.005Ferreira, R. de A., Teixeira, G., & Peternelli, L. A. (2022). Kennard-Stone method outperforms the Random Sampling in the selection of calibration samples in SNPs and NIR data. Ciência Rural, 52(5). https://doi.org/10.1590/0103-8478cr20201072Gómez, J., Montes, N.E. (2020). Mapa Geológico de Colombia 2020. Escala 1:1 000 000. Servicio Geológico Colombiano (p. 2). https://www2.sgc.gov.co/MGC/Documents/MGC_2020/mgc2020.pdfHaqGozukara, G., Altunbas, S., Dengiz, O., & Adak, A. (2022). Assessing the effect of soil to water ratios and sampling strategies on the prediction of EC and pH using pXRF and Vis-NIR spectra. Computers and Electronics in Agriculture, 203, 107459. https://doi.org/10.1016/j.compag.2022.107459Haq, Y. ul, Shahbaz, M., Asif, H. M. S., Al-Laith, A., & Alsabban, W. H. (2023). Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan. Sustainability, 15(17), 12943. https://doi.org/10.3390/su151712943He, Y., Huang, M., García, A., Hernández, A., & Song, H. (2007). Prediction of soil macronutrients content using near-infrared spectroscopy. Computers and Electronics in Agriculture, 58(2), 144–153. https://doi.org/10.1016/j.compag.2007.03.011Herczeg, A. L., Torgersen, T., Chivas, A. R., & Habermehl, M. A. (1991). Geochemistry of ground waters from the Great Artesian Basin, Australia. Journal of Hydrology, 126(3–4), 225–245. https://doi.org/10.1016/0022-1694(91)90158-EHou, J., & Rusuli, Y. (2023). Estimation of soil salt content in the Bosten Lake watershed, Northwest China based on a support vector machine model and optimal spectral indices. PLOS ONE, 18(2), e0273738. https://doi.org/10.1371/journal.pone.0273738IDEAM, 2017.Mapa nacional de degradación de suelos por salinización, Bogotá: IDEAM, Grupo de Suelos y Tierras, Subdirección de Ecosistemas e Información Ambiental,IGAC. (2009). Estudio General de Suelos y Zonificación de Tierras: Departamento de Magdalena, escala 1:100.000 (Imprenta nacional de Colombia (Ed.)).IGAC. (2018). Sistema de Clasificación Geomorfológica Aplicado a los Levantamientos de Suelos (Imprenta N).IUSS Working Group WRB. 2022. World Reference Base for Soil Resources. International soil classification system for naming soils and creating legends for soil maps. 4th edition. International Union of Soil Sciences (IUSS), Vienna, Austria.Jia, P., Zhang, J., He, W., Hu, Y., Zeng, R., Zamanian, K., Jia, K., & Zhao, X. (2022). Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity. Remote Sensing, 14(11), 2602. https://doi.org/10.3390/rs14112602Jin, X., Du, J., Liu, H., Wang, Z., & Song, K. (2016). Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model. Agricultural and Forest Meteorology, 218–219, 250–260. https://doi.org/10.1016/j.agrformet.2015.12.062Jinfu, Z., Kelong, C., Shengkui, C., Liang, C., Yanpeng, W., Baoliang, L., & Yongsheng, W. (2011). Spatial Variability Analysis of Soil Salt and Its Component Ions in Qinghai Lake Region. Procedia Environmental Sciences, 10, 2431–2436. https://doi.org/10.1016/j.proenv.2011.09.378Johnston, K., Ver Hoef, J. M., Krivoruchko, K., & Lucas, N. (2001). Using ArcGIS geostatistical analyst (Vol. 380). Redlands: Esri.Laga, I., & Kleiber, W. (2017). The modified Matérn process. Stat, 6(1), 241–247. https://doi.org/10.1002/sta4.152Lao, C., Chen, J., Zhang, Z., Chen, Y., Ma, Y., Chen, H., Gu, X., Ning, J., Jin, J., & Li, X. (2021). Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection. Computers and Electronics in Agriculture, 182, 106031. https://doi.org/10.1016/j.compag.2021.106031Li, J., Pu, L., Han, M., Zhu, M., Zhang, R., & Xiang, Y. (2014). Soil salinization research in China: Advances and prospects. Journal of Geographical Sciences, 24(5), 943–960. https://doi.org/10.1007/s11442-014-1130-2Li, J., Pu, L., Zhu, M., Zhang, J., Li, P., Dai, X., Xu, Y., & Liu, L. (2014). Evolution of soil properties following reclamation in coastal areas: A review. Geoderma, 226–227, 130–139. https://doi.org/10.1016/j.geoderma.2014.02.003Li, Y., Yang, J., Yu, M., Zhao, W., Xiao, Y., Zhou, D., Zhan, C., Yu, Y., Zhang, J., Lv, Z., & Yu, J. (2019). Different effects of NaCl and Na2SO4 on the carbon mineralization of an estuarine wetland soil. Geoderma, 344, 179–183. https://doi.org/10.1016/j.geoderma.2019.02.035Lin, H. (2011). Three Principles of Soil Change and Pedogenesis in Time and Space. Soil Science Society of America Journal, 75(6), 2049–2070. https://doi.org/10.2136/sssaj2011.0130Londoño Ardila, J. H. (2017). Suelos afectados por sales en la zona bananera de Santa Marta. Universidad Nacional de Colombia, Sede Medellín.LR, (2020) Sector bananero genera 50.000 empleos según el presidente de ASBAMA, in Agronegocios, Bogotá: Editorial la República,Ma, Y., Chen, H., Zhao, G., Wang, Z., & Wang, D. (2020). Spectral Index Fusion for Salinized Soil Salinity Inversion Using Sentinel-2A and UAV Images in a Coastal Area. IEEE Access, 8, 159595–159608. https://doi.org/10.1109/ACCESS.2020.3020325Maertens, M., De Lannoy, G. J. M., Vincent, F., Massart, S., Giménez, R., Houspanossian, J., Gasparri, I., & Vanacker, V. (2022). Spatial patterns of soil salinity in the central Argentinean Dry Chaco. Anthropocene, 37, 100322. https://doi.org/10.1016/j.ancene.2022.100322Mahajan, G. R., Das, B., Gaikwad, B., Murgaonkar, D., Desai, A., Morajkar, S., Patel, K. P., & Kulkarni, R. M. (2021). Monitoring properties of the salt-affected soils by multivariate analysis of the visible and near-infrared hyperspectral data. Catena, 198, 105041. https://doi.org/10.1016/j.catena.2020.105041Melendez-Pastor, I., Navarro-Pedreño, J., Koch, M., & Gómez, I. (2010). Applying imaging spectroscopy techniques to map saline soils with ASTER images. Geoderma, 158(1–2), 55–65. https://doi.org/10.1016/j.geoderma.2010.02.015Metternicht, G., & Alfred Zinck, J. (2008). Soil Salinity and Salinization Hazard. In Remote Sensing of Soil Salinization (Issue December). https://doi.org/10.1201/9781420065039.pt1Metternicht, G., & Zinck, A. (2009). Remote sensing of soil salinization: Impact on land management. CRC PressMevik, B. H., Wehrens, R., Liland, K. H., & Hiemstra, P. (2016). Rpackage: pls, Partial Least Squares and Principal Component Regression. R Package version, 2.Miloš, B., Bensa, A., & Japundžić-Palenkić, B. (2022). Evaluation of Vis-NIR preprocessing combined with PLS regression for estimation soil organic carbon, cation exchange capacity and clay from eastern Croatia. Geoderma Regional, 30, e00558. https://doi.org/10.1016/j.geodrs.2022.e00558Minasny, B., & McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32(9), 1378–1388. https://doi.org/10.1016/j.cageo.2005.12.009Mohamed, E. S., Saleh, A. M., Belal, A. B., & Gad, A. (2018). Application of near-infrared reflectance for quantitative assessment of soil properties. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 1–14. https://doi.org/10.1016/j.ejrs.2017.02.001Morales, G. M.-A. (2009). La regresión por mínimos cuadrados parciales: orígenes y evolución. Historia de la Probabilidad y la Estadística (IV). Universidad Complutense de Madrid.Mouazen, A. M., Kuang, B., De Baerdemaeker, J., & Ramon, H. (2010). Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158(1–2), 23–31. https://doi.org/10.1016/j.geoderma.2010.03.001Neira F., Sanchez R., Otero J., Marroquin P., Gama D., F. M. (2021). National study of soil degradation by salinization in Colombia. in: Halt soil salinization, boost soil productivity – Proceedings of the Global Symposium on Salt-affected Soils. 20 – 22 October 2021. FAO. Rome. p 84 - 85. https://doi.org/10.4060/cb9565enGlobal FAONinomiya, Y. (2002). Mapping quartz, carbonate minerals, and mafic-ultramafic rocks using remotely sensed multispectral thermal infrared ASTER (X. P. Maldague & A. E. Rozlosnik (Eds.); pp. 191–202). https://doi.org/10.1117/12.459566Olivares Campos, B. O. (2023). Evaluation of the Incidence of Banana Wilt and its Relationship with Soil Properties (pp. 95–117). https://doi.org/10.1007/978-3-031-34475-6_4Omuto, C.T., Vargas, R.R., El Mobarak, A.M., Mohamed, N., Viatkin, K., Yigini Y. (2020). Mapping of salt-affected soils: Technical manual. Rome, FAO https://doi.org/10.4060/ca9215enOtero, J., Gómez, C., & Sánchez, R. (2002). Zonificación de los procesos de salinización de los suelos de Colombia (Subdirección). IDEAM.Panagopoulos, T., Jesus, J., Antunes, M. D. C., & Beltrão, J. (2006). Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. European Journal of Agronomy, 24(1), 1–10. https://doi.org/10.1016/j.eja.2005.03.001Peng, J., Ji, W., Ma, Z., Li, S., Chen, S., Zhou, L., & Shi, Z. (2016). Predicting total dissolved salts and soluble ion concentrations in agricultural soils using portable visible near-infrared and mid-infrared spectrometers. Biosystems Engineering, 152, 94–103. https://doi.org/10.1016/j.biosystemseng.2016.04.015Pessoa, L. G. M., Freire, M. B. G. dos S., Green, C. H. M., Miranda, M. F. A., Filho, J. C. de A., & Pessoa, W. R. L. S. (2022). Assessment of soil salinity status under different land-use conditions in the semiarid region of Northeastern Brazil. Ecological Indicators, 141, 109139. https://doi.org/10.1016/j.ecolind.2022.109139Pla, I. (2014). Nuevas experiencias en la evaluación y diagnóstico de procesos de salinización y sodificación de suelos en américa latina. Suelos Ecuatoriales, 44, 125–137. https://doi.org/10.47864Pla, Sentís, I. (2014). Advances in the prognosis of soil sodicity under dryland irrigated conditions. International Soil and Water Conservation Research, 2(4), 50–63. https://doi.org/10.1016/S2095-6339(15)30058-7Pla, I. (1996). Soil salinization and land desertification. soil degradation and desertification in mediterranean environments. 105-129Ramirez Lopez, L., Stevens, A., Orellano, C., Viscarra Rossel, R., Shen, Z., Wadoux, A., Breure, T. (2023). resemble: Regression and similarity evaluation for memory-based learning in spectral chemometrics. R package version 2.2.2, https://CRAN.R-project.org/package=resemble.Ramírez, P. B., Calderón, F. J., Jastrow, J. D., Ping, C.-L., & Matamala, R. (2023). Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better? Geoderma Regional, 32, e00617. https://doi.org/10.1016/j.geodrs.2023.e00617Ramirez-Lopez, L., Behrens, T., Schmidt, K., Rossel, R. A. V., Demattê, J. A. M., & Scholten, T. (2013). Distance and similarity-search metrics for use with soil vis–NIR spectra. Geoderma, 199, 43–53. https://doi.org/10.1016/j.geoderma.2012.08.035Ravi, I., & Vaganan, M. M. (2016). Abiotic Stress Tolerance in Banana. In Abiotic Stress Physiology of Horticultural Crops (pp. 207–222). Springer India. https://doi.org/10.1007/978-81-322-2725-0_12Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002Rincon-Rodriguez, C.A., Loaiza-Usuga, J.C., and Rubiano-Sanabria, Y., (2021).Preliminary study of Salt affected soils in the Zona Bananera, Magdalena (Colombia), Proc.Global Symp. on Salt Afected Soils, Oct. 20–22, 2021, Global Soil Partnership, FAO,.https://www.fao.org/fileadmin/user_upload/GSP/GSAS21/069.pdf.Rincón, C. A., Loaiza-Usuga, J. C., Rubiano, Y., Castañeda, D. (2023). Use of NIRS in Soil Properties Evaluation Related to Soil Salinity and Sodicity in Colombian Caribbean Coast. Moscow University Soil Science Bulletin. 78(5), 439-450.RStudio Team, RStudio (2020): Integrated Development for R. RStudio, Boston, MA: PBC. http://www.rstudio.com/.Santos, C. R. C. dos, Matsunaga, A. T., Costa, L. R. R., Santos, M. L. dos, Brasil Neto, A. B., Rodrigues, R. P., Maciel, M. de N. M., & Melo, V. S. de. (2023). Spatial variability of soil fertility under agroforestry system and native forest in eastern Amazonia, Brazil. Bioscience Journal, 39, e39015. https://doi.org/10.14393/BJ-v39n0a2023-62830Savitzky, A., & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627–1639. https://doi.org/10.1021/ac60214a047Soil Science Division Staff. (2017) Soil survey manual, USDA. Handbook 18, Washington, D.C., C. Ditzler, K.Soil Survey Staff. (2022a). Keys to Soil Taxonomy, 13th edition. USDA Natural Resources Conservation Service.Soil Survey Staff. 2022. Kellogg Soil Survey Laboratory methods manual. Soil Survey Investigations Report No. 42, Version 6.0. U.S. Department of Agriculture, Natural Resources Conservation Service.Strauss, T., & von Maltitz, M. J. (2017). Generalising Ward’s Method for Use with Manhattan Distances. PLOS ONE, 12(1), e0168288. https://doi.org/10.1371/journal.pone.0168288Suleymanov, A., Gabbasova, I., Abakumov, E., & Kostecki, J. (2021). Soil salinity assessment from satellite data in the Trans-Ural steppe zone (Southern Ural, Russia). Soil Science Annual. https://doi.org/10.37501/soilsa/132233Taleisnik, E., & Lavado, R. S. (Eds.). (2021). Saline and Alkaline Soils in Latin America. Springer International Publishing. https://doi.org/10.1007/978-3-030-52592-7Tian, A., Zhao, J., Fu, C., & Xiong, H. (2022). Estimation of SO42− ion in saline soil using VIS-NIR spectroscopy under different human activity stress. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 282, 121647. https://doi.org/10.1016/j.saa.2022.121647Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.xUbugunov, L. L., & Ubugunova, V. I. (2011). Floodplain soils in the lower reaches of the Khovd River in the Great Lakes Basin of Mongolia. Eurasian Soil Science, 44(11), 1184–1192. https://doi.org/10.1134/S106422931111007XUrrego, L. E., Correa-Metrio, A., González, C., Castaño, A. R., & Yokoyama, Y. (2013). Contrasting responses of two Caribbean mangroves to sea-level rise in the Guajira Peninsula (Colombian Caribbean). Palaeogeography, Palaeoclimatology, Palaeoecology, 370, 92–102. https://doi.org/10.1016/j.palaeo.2012.11.023U.S. Salinity Laboratory Staff. (1954). Diagnosis and improvement of saline and alkali soils. USDA Agricultural Handbook No. 60. U.S. Government Printing OfficeVelásquez, E., Lavelle, P., Barrios, E., Joffre, R., & Reversat, F. (2005). Evaluating soil quality in tropical agroecosystems of Colombia using NIRS. Soil Biology and Biochemistry, 37(5), 889–898. https://doi.org/10.1016/j.soilbio.2004.09.009Velez, M. I., Escobar, J., Brenner, M., Rangel, O., Betancourt, A., Jaramillo, A. J., Curtis, J. H., & Moreno, J. L. (2014). Middle to late Holocene relative sea level rise, climate variability and environmental change along the Colombian Caribbean coast. The Holocene, 24(8), 898–907. https://doi.org/10.1177/0959683614534740Voigt, C., Klipsch, S., Herwartz, D., Chong, G., & Staubwasser, M. (2020). The spatial distribution of soluble salts in the surface soil of the Atacama Desert and their relationship to hyperaridity. Global and Planetary Change, 184, 103077. https://doi.org/10.1016/j.gloplacha.2019.103077Wackernagel, H. (1995). Ordinary Kriging. In Multivariate Geostatistics (pp. 74–81). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-03098-1_11Wan, M., Qu, M., Hu, W., Li, W., Zhang, C., Cheng, H., & Huang, B. (2019). Estimation of soil pH using PXRF spectrometry and Vis-NIR spectroscopy for rapid environmental risk assessment of soil heavy metals. Process Safety and Environmental Protection, 132, 73–81. https://doi.org/10.1016/j.psep.2019.09.025Wang, L., & Wang, R. (2022). Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 283, 121707. https://doi.org/10.1016/j.saa.2022.121707Wang, Q., Li, P., & Chen, X. (2012). Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment. Geoderma, 170, 103–111. https://doi.org/10.1016/j.geoderma.2011.10.015Wold, S., & Sjöström, M. (1977). SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy (pp. 243–282). https://doi.org/10.1021/bk-1977-0052.ch012Yang, F., Zhang, G.-L., Sauer, D., Yang, F., Yang, R.-M., Liu, F., Song, X.-D., Zhao, Y.-G., Li, D.-C., & Yang, J.-L. (2020). The geomorphology – sediment distribution – soil formation nexus on the northeastern Qinghai-Tibetan Plateau: Implications for landscape evolution. Geomorphology, 354, 107040. https://doi.org/10.1016/j.geomorph.2020.107040Yudina, A., & Kuzyakov, Y. (2023). Dual nature of soil structure: The unity of aggregates and pores. Geoderma, 434, 116478. https://doi.org/10.1016/j.geoderma.2023.116478Zapata, R. (2022). Los procesos químicos del suelo. Facultad de Ciencias Universidad Nacional de Colombia.Zeaiter, M., & Rutledge, D. (2009). Preprocessing Methods. In Comprehensive Chemometrics (pp. 121–231). Elsevier. https://doi.org/10.1016/B978-044452701-1.00074-0Zhou, Y., Chen, S., Hu, B., Ji, W., Li, S., Hong, Y., Xu, H., Wang, N., Xue, J., Zhang, X., Xiao, Y., & Shi, Z. (2022). Global Soil Salinity Prediction by Open Soil Vis-NIR Spectral Library. Remote Sensing, 14(21), 5627. https://doi.org/10.3390/rs14215627Zinck, J. A. (1987). Aplicación de la geomorfología al levantamiento de suelos en zonas aluviales y definición del ambiente geomorfológico con fines de descripción de suelos.Zinck, J. A. (1988). Physiography and Soils. In International Institute for Geoinformation Science and Earth Observation (ITC) (Internatio). https://webapps.itc.utwente.nl/librarywww/papers_1989/tech/zinck_phy.pdfZinck, J. A., Metternicht, G., Bocco, G., & Del Valle, H. F. (Eds.). (2016). Geopedology. Springer International Publishing. https://doi.org/10.1007/978-3-319-19159-1Zontov, Y. V., Rodionova, O. Y., Kucheryavskiy, S. V., & Pomerantsev, A. L. (2020). PLS-DA – A MATLAB GUI tool for hard and soft approaches to partial least squares discriminant analysis. Chemometrics and Intelligent Laboratory Systems, 203, 104064. https://doi.org/10.1016/j.chemolab.2020.104064Zovko, M., Romić, D., Colombo, C., Di Iorio, E., Romić, M., Buttafuoco, G., & Castrignanò, A. (2018). A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia. Geoderma, 332, 60–72. https://doi.org/10.1016/j.geoderma.2018.07.005InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86023/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1082987155_2024.pdf1082987155_2024.pdfTesis de Maestría en Ciencias - Geomorfología y Suelosapplication/pdf3083290https://repositorio.unal.edu.co/bitstream/unal/86023/2/1082987155_2024.pdff162eedf869e44b88d3aad1b4788fb7cMD52CC-LICENSEU.FT.09.006.004 Licencia para publicación de obras en el Repositorio Institucional UNAL v4.pdfU.FT.09.006.004 Licencia para publicación de obras en el Repositorio Institucional UNAL v4.pdfapplication/pdf282441https://repositorio.unal.edu.co/bitstream/unal/86023/3/U.FT.09.006.004%20Licencia%20para%20publicaci%c3%b3n%20de%20obras%20en%20el%20Repositorio%20Institucional%20UNAL%20v4.pdf156f6ef83ccbfd073c9fa61bc67e57a9MD53THUMBNAIL1082987155_2024.pdf.jpg1082987155_2024.pdf.jpgGenerated Thumbnailimage/jpeg4171https://repositorio.unal.edu.co/bitstream/unal/86023/4/1082987155_2024.pdf.jpgc2def60c182db40462062528376d5e35MD54unal/86023oai:repositorio.unal.edu.co:unal/860232024-08-24 23:12:45.048Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Publicaciones similares