Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta
ilustraciones, gráficas, tablas
- Autores:
-
Fernández Martínez, Felipe
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/79000
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
Reflectancia
Estimación de las existencias de carbono
Propiedades del suelo
reflectance
carbon stock assessments
soil properties
Soil organic carbon stock
Bulk density
Soil spectroscopy
NIR
Spline
Geostatistics
Stock de carbono orgánico de suelo
Densidad aparente
Espectroscopía de suelos
NIR
Spline
Geoestadística
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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network_acronym_str |
UNACIONAL2 |
network_name_str |
Universidad Nacional de Colombia |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
dc.title.translated.eng.fl_str_mv |
Model development to determine the soil organic carbon stock based on diffuse reflectance. Case: Carimagua – Meta |
title |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
spellingShingle |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales Reflectancia Estimación de las existencias de carbono Propiedades del suelo reflectance carbon stock assessments soil properties Soil organic carbon stock Bulk density Soil spectroscopy NIR Spline Geostatistics Stock de carbono orgánico de suelo Densidad aparente Espectroscopía de suelos NIR Spline Geoestadística |
title_short |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
title_full |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
title_fullStr |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
title_full_unstemmed |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
title_sort |
Desarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – Meta |
dc.creator.fl_str_mv |
Fernández Martínez, Felipe |
dc.contributor.advisor.spa.fl_str_mv |
Camacho Tamayo, Jesús Hernán Rubiano Sanabria, Yolanda |
dc.contributor.author.spa.fl_str_mv |
Fernández Martínez, Felipe |
dc.contributor.researchgroup.spa.fl_str_mv |
Ingeniería de Biosistemas |
dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales |
topic |
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales Reflectancia Estimación de las existencias de carbono Propiedades del suelo reflectance carbon stock assessments soil properties Soil organic carbon stock Bulk density Soil spectroscopy NIR Spline Geostatistics Stock de carbono orgánico de suelo Densidad aparente Espectroscopía de suelos NIR Spline Geoestadística |
dc.subject.agrovoc.spa.fl_str_mv |
Reflectancia Estimación de las existencias de carbono Propiedades del suelo |
dc.subject.agrovoc.eng.fl_str_mv |
reflectance carbon stock assessments soil properties |
dc.subject.proposal.eng.fl_str_mv |
Soil organic carbon stock Bulk density Soil spectroscopy NIR Spline Geostatistics |
dc.subject.proposal.spa.fl_str_mv |
Stock de carbono orgánico de suelo Densidad aparente Espectroscopía de suelos NIR Spline Geoestadística |
description |
ilustraciones, gráficas, tablas |
publishDate |
2020 |
dc.date.issued.spa.fl_str_mv |
2020-11-11 |
dc.date.accessioned.spa.fl_str_mv |
2021-01-29T22:30:20Z |
dc.date.available.spa.fl_str_mv |
2021-01-29T22:30:20Z |
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/79000 |
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.none.fl_str_mv |
https://repositorio.unal.edu.co/ |
url |
https://repositorio.unal.edu.co/handle/unal/79000 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.references.spa.fl_str_mv |
Al-Asadi, R. A., & Mouazen, A. M. (2014). Combining frequency domain reflectometry and visible and near infrared spectroscopy for assessment of soil bulk density. Soil and Tillage Research, 135, 60–70. https://doi.org/10.1016/j.still.2013.09.002 Alarcón Jiménez, M. F. (2013). Determinación de zonas de manejo agrícola basadas en el rendimiento de maíz y su relación con atributos edáficos en la altillanura plana [Universidad Nacional de Colombia]. https://repositorio.unal.edu.co/handle/unal/20706 Aldana-Jague, E., Heckrath, G., Macdonald, A., van Wesemael, B., & Van Oost, K. (2016). UAS-based soil carbon mapping using VIS-NIR (480-1000 nm) multi-spectral imaging: Potential and limitations. Geoderma, 275, 55–66. https://doi.org/10.1016/j.geoderma.2016.04.012 Allo, M., Todoroff, P., Jameux, M., Stern, M., Paulin, L., & Albrecht, A. (2020). Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy. Catena, 189, 104452. https://doi.org/10.1016/j.catena.2020.104452 Allory, V., Cambou, A., Moulin, P., Schwartz, C., Cannavo, P., Vidal-Beaudet, L., & Barthès, B. G. (2019). Quantification of soil organic carbon stock in urban soils using visible and near infrared reflectance spectroscopy (VNIRS) in situ or in laboratory conditions. Science of the Total Environment, 686, 764–773. https://doi.org/10.1016/j.scitotenv.2019.05.192 Araújo, S. R., Söderström, M., Eriksson, J., Isendahl, C., Stenborg, P., & Demattê, J. A. M. (2015). Determining soil properties in Amazonian Dark Earths by reflectance spectroscopy. Geoderma, 237, 308–317. https://doi.org/10.1016/j.geoderma.2014.09.014 Arzuaga, S. A., Toledo, D. M., Leiva, S. M. C., & Vázquez, S. (2016). Carbon and nitrogen stocks and stratification ratios in oxisols under forest systems. Ciencia Del Suelo, 34(1), 13–20 Atehortúa, M. R. (2016). Stock de carbono del suelo, a escala local, en ocho sistemas de uso agrícola del piedemonte llanero [Universidad Nacional de Colombia]. https://repositorio.unal.edu.co/handle/unal/56121 Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777. https://doi.org/10.1366/0003702894202201 Bellon-Maurel, V., & McBratney, A. (2011). Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - Critical review and research perspectives. Soil Biology and Biochemistry, 43(7), 1398–1410. https://doi.org/10.1016/j.soilbio.2011.02.019 Ben-Dor, E., Inbar, Y., & Chen, Y. (1997). The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process. Remote Sensing of Environment, 61(1), 1–15. https://doi.org/10.1016/S0034-4257(96)00120-4 Ben Dor, E., Ong, C., & Lau, I. C. (2015). Reflectance measurements of soils in the laboratory: Standards and protocols. Geoderma, 245–246, 112–124. https://doi.org/10.1016/j.geoderma.2015.01.002 Beretta, A. N., Silbermann, A. V., Paladino, L., Torres, D., Bassahun, D., Musselli, R., & García-Lamohte, A. (2014). Análisis de textura del suelo con hidrómetro: Modificaciones al método de Bouyoucus. Ciencia e Investigacion Agraria, 41(2), 263–271. https://doi.org/10.4067/S0718-16202014000200013 Bigham, J. M., Golden, D. C., Bowen, L. H., Buol, S. W., & Weed, S. B. (1978). Iron Oxide Mineralogy of Well‐drained Ultisols and Oxisols: I. Characterization of Iron Oxides in Soil Clays by Mössbauer Spectroscopy, X‐ray Diffractometry, and Selected Chemical Techniques. Soil Science Society of America Journal, 42(5), 816–825. https://doi.org/https://doi.org/10.2136/sssaj1978.03615995004200050033x Biondi, F., Myers, D. E., & Avery, C. C. (1994). Geostatistically modelling stem size and increment in an old-growth forest. Canadian Journal of Forest Research, 24(7), 1354–1368. Blaschek, M., Roudier, P., Poggio, M., & Hedley, C. B. (2019). Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra. Scientific Reports, 9(1), 1–10. https://doi.org/10.1038/s41598-019-49226-6 Bonett, J. P. (2013). Uso de la espectroscopía de reflectancia difusa (MIR) para la determinación de las propiedades químicas en suelos agrícolas de Colombia [Universidad Nacional de Colombia, sede Bogotá.]. https://repositorio.unal.edu.co/handle/unal/51527 Borja Martínez, K., Mercado Lázaro, J., & Combatt Caballero, E. M. (2015). Dispersantes químicos y cuantificación de fracciones texturales por los métodos Bouyoucos y pipeta. Acta Agronomica, 64(4), 308–314. https://doi.org/10.15446/acag.v64n4.45722 Brodský, L., Vašát, R., Klement, A., Zádorová, T., & Jakšík, O. (2013). Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping. Geoderma, 199, 54–63. https://doi.org/10.1016/j.geoderma.2012.11.006 Camacho-Tamayo, J. H. (2013). Uso de la reflectancia difusa -NIR en la determinación de características físicas y químicas de un Oxisol. Carimagüa -Meta [Universidad Nacional de Colombia, Sede Bogotá]. https://repositorio.unal.edu.co/handle/unal/20889 Camacho-Tamayo, J. H., Forero-Cabrera, N. M., Ramírez L., L., & Rubiano S., Y. (2017). Evaluación de textura del suelo con espectroscopía de infrarrojo cercano en un oxisol de Colombia. Colombia Forestal, 20(1), 5–18 Camacho-Tamayo, J. H., Rubiano, Y. S., & Hurtado, M. del P. (2014). Near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an Oxisol. Agronomía Colombiana, 32(1), 86–94. Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., & Konopka, A. E. (1994). Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Science Society of America Journal, 58(5), 1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x Cambou, A., Cardinael, R., Kouakoua, E., Villeneuve, M., Durand, C., & Barthès, B. G. (2016). Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field. Geoderma, 261, 151–159. https://doi.org/10.1016/j.geoderma.2015.07.007 Campos, B.-H. C. de, Amado, T. J. C., Bayer, C., Nicoloso, R. da S., & Fiorin, J. E. (2011). Carbon stock and its compartments in a subtropical oxisol under long-term tillage and crop rotation systems. Revista Brasileira de Ciência Do Solo, 35(3), 805–817. https://doi.org/10.1590/s0100-06832011000300016 Chartin, C., Stevens, A., Goidts, E., Krüger, I., Carnol, M., & Van Wesemael, B. (2016). Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia). Geoderma Regional, 9, 73–86. https://doi.org/10.1016/j.geodrs.2016.12.006 Chaudhari, P. R., Ahire, D. V, Ahire, V. D., Chkravarty, M., & Maity, S. (2013). Soil Bulk Density as related to Soil Texture, Organic Matter Content and available total Nutrients of Coimbatore Soil. International Journal of Scientific and Research Publications, 3(1), 2250–3153. https://doi.org/10.2136/sssaj2015.11.0407 Combatt-Caballero, E. M., Palencia-L, M., & Borja-M, K. (2018). Particle size distribution by Bouyoucos in slightly alkaline soils from department of Cordoba, Colombia. Acta Agronomica, 67(1), 126–132. https://doi.org/10.15446/acag.v67n1.60762 Comino, F., Aranda, V., García-Ruiz, R., Ayora-Cañada, M. J., & Domínguez-Vidal, A. (2018). Infrared spectroscopy as a tool for the assessment of soil biological quality in agricultural soils under contrasting management practices. Ecological Indicators, 87, 117–126. https://doi.org/10.1016/j.ecolind.2017.12.046 Conforti, M., Buttafuoco, G., Leone, A. P., Aucelli, P. P. C., Robustelli, G., & Scarciglia, F. (2013). Studying the relationship between water-induced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis: A case study in southern Italy. Catena, 110, 44–58. https://doi.org/10.1016/j.catena.2013.06.013 Conforti, M., Castrignanò, A., Robustelli, G., Scarciglia, F., Stelluti, M., & Buttafuoco, G. (2015). Laboratory-based Vis-NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. Catena, 124, 60–67. https://doi.org/10.1016/j.catena.2014.09.004 Cortés, A., & Malagón, D. (1984). Los levantamientos agrológicos y sus aplicaciones múltiples. Universidad de Bogotá Jorge Tadeo Lozano. Curcio, D., Ciraolo, G., Asaro, F. D., & Minacapilli, M. (2013). Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences, 19, 494–503. https://doi.org/10.1016/j.proenv.2013.06.056 Day, P. R. (1965). Particle fractionation and particle size analysis. In C. A. Black (Ed.), Method of soil analysis, Part I (pp. 565–566). Soil Science Society of America. https://doi.org/10.2134/agronmonogr9.1.c43 Demattê, José, Horák-Terra, I., Beirigo, R. M., Terra, F. da S., Marques, K. P. P., Fongaro, C. T., Silva, A. C., & Vidal-Torrado, P. (2017). Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring. Journal of Environmental Management, 197, 50–62. https://doi.org/10.1016/j.jenvman.2017.03.014 Demattê, José, Sousa, A. A., Alves, M. C., Nanni, M. R., Fiorio, P. R., & Campos, R. C. (2006). Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma, 135, 179–195. https://doi.org/10.1016/j.geoderma.2005.12.002 Demattê, Jose, Terra, F. da S., & Quartaroli, C. F. (2012). Spectral behavior of some modal soil profiles from São Paulo State, Brazil. Soil and Plant Nutrition, 71(3), 413–423. Dokuchaev, V. (1883). Russian Chernozem. In Selected works of V.V. Dokuchaev, Vol I. Israel Program for Scientific Translations, Jerusalem (translated in 1967). Dumas, J. B. A. (1831). Procédés de l’analyse organique. Annales de Chimie et de Physique, 247(47), 198–213. Escribano, P., Schmid, T., Chabrillat, S., Rodríguez C, E., & García, M. (2017). Optical Remote Sensing for Soil Mapping and Monitoring. In Soil Mapping and Process Modeling for Sustainable Land Use Management (p. 39). Elsevier. ESRI. (2018). ArcGIS 10.6 (10.6). Environmental Systems Research Institute. FAO. (2009). Guía para la descripción de suelos. In Organización de las Naciones Unidas para la Agricultura y la Alimentación. FAO. http://www.fao.org/3/a-a0541s.pdf FAO. (2011). El estado de los recursos de tierras y aguas del mundo para la alimentación y la agricultura. Roma, 50. https://doi.org/10.1017/CBO9781107415324.004 FAO. (2019). Measuring and modelling soil carbon stocks and stock changes in livestock production systems Guidelines for assessment (Version 1). FAO. http://www.fao.org/3/ca2934en/CA2934EN.pdf Gamez Ávila, C. C. (2019). Uso de modelos espectrales VIS – NIR para la determinación del contenido de carbono del suelo [Universidad Nacional de Colombia, Sede Bogotá]. https://repositorio.unal.edu.co/handle/unal/76626 Gras, J. P., Barthès, B. G., Mahaut, B., & Trupin, S. (2014). Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils. Geoderma, 214–215, 126–134. https://doi.org/10.1016/j.geoderma.2013.09.021 Hengl, T. (2009). A Practical guide to Geostatistical Mapping (2nd ed.). Office for Official Publications of the European Communities. Hobley, E., & Prater, I. (2019). Estimating soil texture from vis–NIR spectra. European Journal of Soil Science, 70(1), 83–95. https://doi.org/10.1111/ejss.12733 Huang, J., Hartemink, A. E., & Zhang, Y. (2019). Climate and land-use change effects on soil carbon stocks over 150 years in Wisconsin, USA. Remote Sensing, 11(12), 1504. https://doi.org/10.3390/rs11121504 Huertas, H. D., Rangel, J. A., & Parra, A. S. (2018). Caracterización de la fertilidad química de los suelos en sistemas productivos de la altillanura plana, Meta, Colombia. Revista Luna Azul, 46(46), 54–69. https://doi.org/10.17151/luaz.2018.46.5 IGAC. (2004). Estudio general de suelos y zonificación de tierras: Departamento de Meta. Instituto Geográfico Agustín Codazzi, Subdirección de Agrología. IPCC. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Volume 4 - Agriculture, forestry and other land use (E. Calvo Buendia, K. Tanabe, A. Kranjc, J. Baasansuren, M. Fukuda, S. Ngarize, A. Osako, Y. Pyrozhenko, P. Shermanau, & S. Federici (eds.)). The Intergovernmental Panel on Climate Change (IPCC). IUSS Working Group WRB. (2015). Base referencial mundial del recurso suelo 2014, Actualización 2015. In Base referencial mundial del recurso suelo. FAO. http://www.fao.org/3/i3794es/I3794es.pdf Jaconi, A., Don, A., & Freibauer, A. (2017). Prediction of soil organic carbon at the country scale : stratification strategies for near-infrared data. European Journal of Soil Science, 68(6), 1–11. https://doi.org/10.1111/ejss.12485 Jaimes, W., Navas, G., Salamanca, C., & Conde, A. (2003). Estudio Detallado de Suelos de la Estación Experimental de CORPOICA “Sabanas” en la Altillanura Colombiana. Corpoica. Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M. F., Bampa, F., van Wesemael, B., Harrison, R. B., Guerrini, I. A., Richter, D. de B., Rustad, L., Lorenz, K., Chabbi, A., & Miglietta, F. (2014). Current status, uncertainty and future needs in soil organic carbon monitoring. Science of the Total Environment, 468–469, 376–383. https://doi.org/10.1016/j.scitotenv.2013.08.026 Jenny, H. (1941). Factors of soil formation: a system of quantitative pedology (Vol. 68, Issue 4). Dover Publications. https://doi.org/10.1016/0016-7061(95)90014-4 Jia, X., Chen, S., Yang, Y., Zhou, L., Yu, W., & Shi, Z. (2017). Organic carbon prediction in soil cores using VNIR and MIR techniques in an alpine landscape. Scientific Reports, 7(1), 2144. https://doi.org/10.1038/s41598-017-02061-z Johnson, J. M. F., Franzluebbers, A. J., Weyers, S. L., & Reicosky, D. C. (2007). Agricultural opportunities to mitigate greenhouse gas emissions. Environmental Pollution, 150(1), 107–124. https://doi.org/10.1016/j.envpol.2007.06.030 Katuwal, S., Knadel, M., Norgaard, T., Moldrup, P., Greve, M. H., & de Jonge, L. W. (2020). Predicting the dry bulk density of soils across Denmark: Comparison of single-parameter, multi-parameter, and vis–NIR based models. Geoderma, 361(May 2019), 114080. https://doi.org/10.1016/j.geoderma.2019.114080 Kennard, R. W., & Stone, L. A. (1969). Computer Aided Design of Experiments. Technometrics, 11(1), 137–148. Kuang, B., & Mouazen, A. M. (2013). Non-biased prediction of soil organic carbon and total nitrogen with vis-NIR spectroscopy, as affected by soil moisture content and texture. Biosystems Engineering, 114(3), 249–258. https://doi.org/10.1016/j.biosystemseng.2013.01.005 Laamrani, A., Berg, A. A., Voroney, P., Feilhauer, H., Blackburn, L., March, M., Dao, P. D., He, Y., & Martin, R. C. (2019). Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario , Canada. Remote Sensing, 11(11), 1–15. Landcare Research. (2016). Agricultural Greenhouse Gases: Mapping Soil Organic. https://www.nzagrc.org.nz/fact-sheets,listing,232,reducing-new-zealands-agricultural-emissions-mapping-soil-organic-carbon-stocks.html Lee, J., Hopmans, J. W., Rolston, D. E., Baer, S. G., & Six, J. (2009). Determining soil carbon stock changes: Simple bulk density corrections fail. Agriculture, Ecosystems and Environment, 134(3–4), 251–256. https://doi.org/10.1016/j.agee.2009.07.006 Leone, A. P., Leone, G., Leone, N., Galeone, C., Grilli, E., Orefice, N., & Ancona, V. (2019). Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil. Water (Switzerland), 11(1712), 1–16. Leone, A. P., Viscarra-Rossel, R. A., Amenta, P., & Buondonno, A. (2012). Prediction of Soil Properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean Soils from Southern Italy. Current Analytical Chemistry, 8(2), 283–299. https://doi.org/10.2174/157341112800392571 Liu, S., Shen, H., Chen, S., Zhao, X., Biswas, A., Jia, X., Shi, Z., & Fang, J. (2019). Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment. Geoderma, 348, 37–44. https://doi.org/10.1016/j.geoderma.2019.04.003 Malone, B. (2016). ithir: Functions and Algorithms Specific to Pedometrics. R package version 1.0/r126. https://rdrr.io/rforge/ithir/ Malone, B. P., Styc, Q., Minasny, B., & McBratney, A. B. (2017). Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data. Geoderma, 290, 91–99. https://doi.org/10.1016/j.geoderma.2016.12.008 Marques, J. D. de O., Luizão, F. J., Teixeira, W. G., Vitel, C. M., & Marques, E. M. de A. (2016). Soil organic carbon, carbon stock and their relationships to physical attributes under forest soils in central Amazonia. Revista Arvore, 40(2), 197–208. https://doi.org/10.1590/0100-67622016000200002 Martín-López, J. M., & Quintero-Arias, G. (2017). Effect of soil use and coverage on the spectral response of an oxisol in the VIS-NIR-MIR region. Journal of Imaging, 3(1). https://doi.org/10.3390/jimaging3010010 Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246 McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. In Geoderma (Vol. 117, Issues 1–2). https://doi.org/10.1016/S0016-7061(03)00223-4 McBratney, A. B., & Viscarra Rossel, R. A. (2008). Diffuse Reflectance Spectroscopy as a Tool for Digital Soil Mapping. In A. E. Hartemink, A. McBratney, & M. L. Mendonça Santos (Eds.), Digital Soil Mapping with Limited Data (1st ed., pp. 165–172). Springer. Moreira, C. S., Brunet, D., Verneyre, L., Sá, S. M. O., Galdos, M. V., Cerri, C. C., & Bernoux, M. (2009). Near infrared spectroscopy for soil bulk density assesment. European Journal of Soil Science, 60, 785–791. https://doi.org/10.1111/j.1365-2389.2009.01170.x Murphy, B. W. (2015). Impact of soil organic matter on soil properties - A review with emphasis on Australian soils. Soil Research, 53(6), 605–635. https://doi.org/10.1071/SR14246 Nawar, S., & Mouazen, A. M. (2019). On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning. Soil and Tillage Research, 190, 120–127. https://doi.org/10.1016/j.still.2019.03.006 Nawar, Said, Buddenbaum, H., Hill, J., Kozak, J., & Mouazen, A. M. (2016). Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil and Tillage Research, 155, 510–522. https://doi.org/10.1016/j.still.2015.07.021 Nocita, M., Kooistra, L., Bachmann, M., Müller, A., Powell, M., & Weel, S. (2011). Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa. Geoderma, 167–168, 295–302. https://doi.org/10.1016/j.geoderma.2011.09.018 Nocita, M., Stevens, A., Noon, C., & Van Wesemael, B. (2013). Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 199, 37–42. https://doi.org/10.1016/j.geoderma.2012.07.020 Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B., & Montanarella, L. (2014). Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry, 68, 337–347. https://doi.org/10.1016/j.soilbio.2013.10.022 Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., Ben Dor, E., Brown, D. J., Clairotte, M., Csorba, A., Dardenne, P., Demattê, J. A. M., Genot, V., Guerrero, C., Knadel, M., Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., … Wetterlind, J. (2015). Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring (pp. 139–159). https://doi.org/10.1016/bs.agron.2015.02.002 Oliver, M. A., & Webster, R. (2015). Basics Steps in Geostatistics: The Variogram and Kriging. Springer. https://doi.org/10.1007/978-3-319-15865-5 Pinheiro, É. F. M., Ceddia, M. B., Clingensmith, C. M., Grunwald, S., & Vasques, G. M. (2017). Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon. Remote Sensing, 9(4), 1–22. https://doi.org/10.3390/rs9040293 Ponce‐Hernandez, R., Marriott, F. H. C., & Beckett, P. H. T. (1986). An improved method for reconstructing a soil profile from analyses of a small number of samples. Journal of Soil Science, 37(3), 455–467. https://doi.org/10.1111/j.1365-2389.1986.tb00377.x Poppiel, R. R., Lacerda P., M., Pereira, M., Almeida Junior, D. O., Demattê, J., Romero, D. J., Sato, M., Almeida J, L. R., & Moreira C, L. F. (2018). Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties. Revista Brasilera de Ciencia Do Solo, 42, 1–26. R Core Team. (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/ Ramirez-Lopez, L., Reina-Sánchez, A., & Camacho-Tamayo, J. H. (2008). Variabilidad espacial de atributos físicos de un Typic Haplustox de los Llanos Orientales de Colombia. Engenharia Agrícola, 28(1), 55–63. https://doi.org/10.1590/S0100-69162008000100006 Ramirez-Lopez, L. (2009). Pedologia quantitativa: espectrometria VIS-NIR-SWIR e mapeamento digital de solos [Universidade de São Paulo]. https://teses.usp.br/teses/disponiveis/11/11140/tde-23062009-140151/publico/Leonardo_Lopez.pdf Ramirez-Lopez, L., Schmidt, K., Behrens, T., van Wesemael, B., Demattê, J. A. M., & Scholten, T. (2014). Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, 226–227(1), 140–150. https://doi.org/10.1016/j.geoderma.2014.02.002 Ratner, B. (2009). The correlation coefficient: Its values range between +1/-1, or do they. Journal of Targeting, Measurement and Analysis for Marketing, 17(2), 139–142. https://doi.org/10.1057/jt.2009.5 Rippstein, G., Escobar, E., Toledo, J. M., Fisher, M., & Mesa, E. (2001). Caracterización de comunidades vegetales de la altillanura en el Centro de Investigación Agropecuaria Carimagua, en Meta, Colombia. In Georges Rippstein, G. Escobar, & F. Motta (Eds.), Agroecología y Biodiversidad de las Sabanas en los Llanos Orientales (pp. 22–45). CIAT, Centro Internacional de Agricultura Tropical. Rivera, M., Amezquita, E., Bernal, J. H., & Rao, I. M. (2013). Las Sabanas de los Llanos Orientales de Colombia: Caracterización Biofísica e Importancia para la Producción Agropecuaria. In Sistemas Agropastoriles: un Enfoque Integrado para el Manejo Sostenible de Oxisoles de los Llanos Orientales de Colombia (pp. 3–13). Centro Internacional de Agricultura Tropical (CIAT). Rodríguez-Cabrera., J., Cortiza Mora, A. W., Pereira Marín, C. A., Chacón Iznaga, A., Gattorno Muñoz, S., Rodríguez López, O., Rodríguez Urrutia, A., Jiménez Carrazana, R., & Torres Artiles, P. N. (2015). Determinación VIS/NIR del contenido de materia orgánica en suelos agrícolas Pardos mullidos medianamente lavados. (Spanish). Revista Centro Agrícola, 42(3), 5–12. http://cagricola.uclv.edu.cu/index.php/es/volumen-42-2015/numero-3-2015/47-determinacion-vis-nir-del-contenido-de-materia-organica-en-suelos-agricolas-pardos-mullidos-medianamente-lavados Roudier, P., Hedley, C. B., Lobsey, C. R., Viscarra Rossel, R. A., & Leroux, C. (2017). Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon. Geoderma, 296, 98–107. https://doi.org/10.1016/j.geoderma.2017.02.014 Sá, J. C. de M., & Lal, R. (2009). Stratification ratio of soil organic matter pools as an indicator of carbon sequestration in a tillage chronosequence on a Brazilian Oxisol. Soil and Tillage Research, 103(1), 46–56. https://doi.org/10.1016/j.still.2008.09.003 Savitzky, A., & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627–1639. Serna-Izasa, R. A., Rippstein, G., Grollier, C., & Mesa, E. (2001). Biodiversidad de la vegetación de la sabana en la altillanura plana y la serranía de los llanos orientales. In Georges Rippstein, G. Escobar, & F. Motta (Eds.), Agroecología y Biodiversidad de las Sabanas en los Llanos Orientales (pp. 46–63). CIAT, Centro Internacional de Agricultura Tropical. Shenk, J. S., & Westerhaus, M. O. (1996). Calibration the ISI way. In A. M. C. Davis & P. Williams (Eds.), Near infrared spectroscopy : the future waves (pp. 198–202). NIR Publications. Silva-Parra, A. (2018). Modelación de los stocks de carbono del suelo y las emisiones de dióxido de carbono (GEI) en sistemas productivos de la Altillanura Plana. ORINOQUIA, 22(2), 158–171. https://doi.org/10.22579/20112629.525 Soil Survey Staff. (2014). Keys to soil taxonomy. In Soil Conservation Service (Vol. 12). https://doi.org/10.1109/TIP.2005.854494 Stenberg, B. (2010). Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon. Geoderma, 158, 15–22. https://doi.org/10.1016/j.geoderma.2010.04.008 Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010). Visible and Near Infrared Spectroscopy in Soil Science. In Advances in Agronomy (1st ed., Vol. 107). Elsevier Inc. https://doi.org/10.1016/S0065-2113(10)07005-7 Stevens, A., Nocita, M., Tóth, G., Montanarella, L., & van Wesemael, B. (2013). Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy. PloS One, 8(6), e66409. https://doi.org/10.1371/journal.pone.0066409 Stevens, A., Ramirez-Lopez, L., & Hans, G. (2020). Prospectr package: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data. https://cran.r-project.org/web/packages/prospectr/index.html Stevens, A., van Wesemael, B., Bartholomeus, H., Rosillon, D., Tychon, B., & Ben-Dor, E. (2008). Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma, 144, 395–404. https://doi.org/10.1016/j.geoderma.2007.12.009 Stockmann, U., Adams, M. A., Crawford, J. W., Field, D. J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A. B., Courcelles, V. de R. de, Singh, K., Wheeler, I., Abbott, L., Angers, D. A., Baldock, J., Bird, M., Brookes, P. C., Chenu, C., Jastrow, J. D., Lal, R., … Zimmermann, M. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems and Environment, 164, 80–99. https://doi.org/10.1016/j.agee.2012.10.001 Summers, D., Lewis, M., Ostendorf, B., & Chittleborough, D. (2011). Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11, 123–131. https://doi.org/10.1016/j.ecolind.2009.05.001 Tümsavaş, Z., Tekin, Y., Ulusoy, Y., & Mouazen, A. M. (2019). Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy. Biosystems Engineering, 177, 90–100. https://doi.org/10.1016/j.biosystemseng.2018.06.008 Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., & Skjemstad, J. O. (2006a). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1–2), 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007 Viscarra Rossel, R. A., McGlynn, R. N., & McBratney, A. B. (2006b). Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma, 137(1–2), 70–82. https://doi.org/10.1016/j.geoderma.2006.07.004 Viscarra Rossel, R. A., & Webster, R. (2012). Predicting soil properties from the Australian soil visible – near infrared spectroscopic database. European Journal of Soil Science, 63, 848–860. https://doi.org/10.1111/j.1365-2389.2012.01495.x Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D. J., Demattê, J. A. M., Shepherd, K. D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B. G., Bartholomeus, H. M., Bayer, A. D., Bernoux, M., Böttcher, K., Brodský, L., Du, C. W., Chappell, A., … Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198–230. https://doi.org/10.1016/j.earscirev.2016.01.012 Wetterlind, J., Stenberg, B., & Söderström, M. (2008). The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture, 9(1–2), 57–69. https://doi.org/10.1007/s11119-007-9051-z Wilding, L. P. (1985). Soil spatial variability: Its documentation, accommodation and implication to soil surveys. In D. R. Nielsen & J. Bouma (Eds.), Soil Spatial Variability (pp. 166–194). Pudoc. Williams, P. C. (2003). Near-infrared technology ‒ Getting the best out of light: A short course in the practical implementation of near-infrared spectroscopy for the user. (2 ed). PDK Projects Inc. Wold, S., Ruhe, A., Wold, H., & Dunn III, W. L. (1984). The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing, 5(3), 735–743. https://doi.org/https://doi.org/10.1137/0905052 Xie, X. L., & Li, A. B. (2016). Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy. Biosystems Engineering, 152, 126–137. https://doi.org/10.1016/j.biosystemseng.2016.06.007 Zimmermann, M., Leifeld, J., & Fuhrer, J. (2007). Quantifying soil organic carbon fractions by infrared-spectroscopy. Soil Biology and Biochemistry, 39(1), 224–231. https://doi.org/10.1016/j.soilbio.2006.07.010 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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xiv, 105 páginas xiv, 105 páginas |
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application/pdf |
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Colombia |
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Carimagua Meta |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias Agrarias - Maestría en Ciencias Agrarias |
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Universidad Nacional de Colombia |
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Facultad de Ciencias Agrarias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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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_abf2Camacho Tamayo, Jesús Hernán751ca1d6-9adc-44c7-b8e5-1d8e73dcd6a8Rubiano Sanabria, Yolanda5e8d3b909a5cb385af8df08fbac96d6cFernández Martínez, Felipe5e4cde74314706cf2e95c6e4937ad452Ingeniería de Biosistemas2021-01-29T22:30:20Z2021-01-29T22:30:20Z2020-11-11https://repositorio.unal.edu.co/handle/unal/79000Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasEl Stock de Carbono Orgánico del Suelo (SCOS) es un aspecto determinante para evaluar la calidad en los agroecosistemas, y a su vez cumple una función fundamental en la mitigación del cambio climático. Debido a esto, es ideal monitorear el SCOS a diferentes escalas espaciales y temporales, lo cual representa una inversión de recursos difícil de satisfacer. El objetivo de esta investigación fue desarrollar un modelo con base en espectroscopía de reflectancia difusa en Infrarrojo Cercano (NIR, por sus siglas en inglés) para estimar el SCOS en un Oxisol de Colombia. Mediante una red rígida de 70 puntos en 248 ha, fueron recolectadas 313 muestras de suelo en cinco profundidades definidas de 0-10, 10-20, 20-30, 30-40 y 40-50 cm. A cada muestra se le determinó el contenido de Carbono Orgánico del Suelo (COS), Densidad Aparente (DA), fracciones texturales y porosidades por medio de metodologías convencionales de laboratorio, así como también el cálculo del SCOS. Así mismo, fueron adquiridas firmas espectrales en el rango NIR de cada muestra de suelo que, junto con los datos medidos en laboratorio, se usaron para alimentar los modelos de estimación aplicando regresión de mínimos cuadrados parciales (PLSR). Se alcanzó un modelo de alta representatividad para la estimación de SCOS (R2 = 0,93, RMSE = 2,12 tC/ha, RPD = 3,69), lo cual se corroboró con la variabilidad espacial evaluada con splines de profundidad y superficies de interpolación geoestadística. La espectroscopía de reflectancia difusa en NIR mostró ser una alternativa viable para la estimación del SCOS. (Texto tomado de la fuente).Soil Organic Carbon Stock (SOCS) is a determining factor to evaluate the quality of agroecosystems and at the same time, plays a fundamental role in mitigating climate change. This highlights the importance of monitoring SOCS at different spatial and temporal scales, which represents a high demand of resources. The aim of this research was to develop a model based on Near Infrared (NIR) diffuse reflectance spectroscopy to estimate the SOCS of a Colombian Oxisol. Using a rigid grid system of 70 points in 248 ha, 313 soil samples were collected at five defined depths of 0-10, 10-20, 20-30, 30-40 and 40-50 cm. Soil Organic Carbon (SOC), Bulk Density (BD), textural fractions and porosities were determined for each sample using conventional laboratory methodologies, as well as the SOCS calculation. Likewise, spectral signatures were acquired in the NIR range of each soil sample, and together with laboratory data, were used to build the estimation models applying Partial Least Squares Regression (PLSR). A highly representative model was obtained for the estimation of SOCS (R2 = 0.93, RMSE = 2.12 tC/ha, RPD = 3.69), which was corroborated with the spatial variability evaluated with depth splines and geostatistical interpolation surfaces. NIR diffuse reflectance spectroscopy proved to be a viable alternative for estimating SOCS.Universidad Nacional de ColombiaModelamiento del contenido de carbono de los suelos de la altillanura plana del municipio de Puerto Gaitán (Meta)Incluye anexosMaestríaMagíster en Ciencias AgrariasSuelos y aguasCiencias Agronómicasxiv, 105 páginasxiv, 105 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en Ciencias AgrariasUniversidad Nacional de ColombiaFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materialesReflectanciaEstimación de las existencias de carbonoPropiedades del sueloreflectancecarbon stock assessmentssoil propertiesSoil organic carbon stockBulk densitySoil spectroscopyNIRSplineGeostatisticsStock de carbono orgánico de sueloDensidad aparenteEspectroscopía de suelosNIRSplineGeoestadísticaDesarrollo de modelo para determinar el stock de carbono orgánico del suelo con base en reflectancia difusa. Caso: Carimagua – MetaModel development to determine the soil organic carbon stock based on diffuse reflectance. Case: Carimagua – MetaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiaCarimaguaMetaAl-Asadi, R. A., & Mouazen, A. M. (2014). Combining frequency domain reflectometry and visible and near infrared spectroscopy for assessment of soil bulk density. Soil and Tillage Research, 135, 60–70. https://doi.org/10.1016/j.still.2013.09.002Alarcón Jiménez, M. F. (2013). Determinación de zonas de manejo agrícola basadas en el rendimiento de maíz y su relación con atributos edáficos en la altillanura plana [Universidad Nacional de Colombia]. https://repositorio.unal.edu.co/handle/unal/20706Aldana-Jague, E., Heckrath, G., Macdonald, A., van Wesemael, B., & Van Oost, K. (2016). UAS-based soil carbon mapping using VIS-NIR (480-1000 nm) multi-spectral imaging: Potential and limitations. Geoderma, 275, 55–66. https://doi.org/10.1016/j.geoderma.2016.04.012Allo, M., Todoroff, P., Jameux, M., Stern, M., Paulin, L., & Albrecht, A. (2020). Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy. Catena, 189, 104452. https://doi.org/10.1016/j.catena.2020.104452Allory, V., Cambou, A., Moulin, P., Schwartz, C., Cannavo, P., Vidal-Beaudet, L., & Barthès, B. G. (2019). Quantification of soil organic carbon stock in urban soils using visible and near infrared reflectance spectroscopy (VNIRS) in situ or in laboratory conditions. Science of the Total Environment, 686, 764–773. https://doi.org/10.1016/j.scitotenv.2019.05.192Araújo, S. R., Söderström, M., Eriksson, J., Isendahl, C., Stenborg, P., & Demattê, J. A. M. (2015). Determining soil properties in Amazonian Dark Earths by reflectance spectroscopy. Geoderma, 237, 308–317. https://doi.org/10.1016/j.geoderma.2014.09.014Arzuaga, S. A., Toledo, D. M., Leiva, S. M. C., & Vázquez, S. (2016). Carbon and nitrogen stocks and stratification ratios in oxisols under forest systems. Ciencia Del Suelo, 34(1), 13–20Atehortúa, M. R. (2016). Stock de carbono del suelo, a escala local, en ocho sistemas de uso agrícola del piedemonte llanero [Universidad Nacional de Colombia]. https://repositorio.unal.edu.co/handle/unal/56121Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43(5), 772–777. https://doi.org/10.1366/0003702894202201Bellon-Maurel, V., & McBratney, A. (2011). Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils - Critical review and research perspectives. Soil Biology and Biochemistry, 43(7), 1398–1410. https://doi.org/10.1016/j.soilbio.2011.02.019Ben-Dor, E., Inbar, Y., & Chen, Y. (1997). The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process. Remote Sensing of Environment, 61(1), 1–15. https://doi.org/10.1016/S0034-4257(96)00120-4Ben Dor, E., Ong, C., & Lau, I. C. (2015). Reflectance measurements of soils in the laboratory: Standards and protocols. Geoderma, 245–246, 112–124. https://doi.org/10.1016/j.geoderma.2015.01.002Beretta, A. N., Silbermann, A. V., Paladino, L., Torres, D., Bassahun, D., Musselli, R., & García-Lamohte, A. (2014). Análisis de textura del suelo con hidrómetro: Modificaciones al método de Bouyoucus. Ciencia e Investigacion Agraria, 41(2), 263–271. https://doi.org/10.4067/S0718-16202014000200013Bigham, J. M., Golden, D. C., Bowen, L. H., Buol, S. W., & Weed, S. B. (1978). Iron Oxide Mineralogy of Well‐drained Ultisols and Oxisols: I. Characterization of Iron Oxides in Soil Clays by Mössbauer Spectroscopy, X‐ray Diffractometry, and Selected Chemical Techniques. Soil Science Society of America Journal, 42(5), 816–825. https://doi.org/https://doi.org/10.2136/sssaj1978.03615995004200050033xBiondi, F., Myers, D. E., & Avery, C. C. (1994). Geostatistically modelling stem size and increment in an old-growth forest. Canadian Journal of Forest Research, 24(7), 1354–1368.Blaschek, M., Roudier, P., Poggio, M., & Hedley, C. B. (2019). Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra. Scientific Reports, 9(1), 1–10. https://doi.org/10.1038/s41598-019-49226-6Bonett, J. P. (2013). Uso de la espectroscopía de reflectancia difusa (MIR) para la determinación de las propiedades químicas en suelos agrícolas de Colombia [Universidad Nacional de Colombia, sede Bogotá.]. https://repositorio.unal.edu.co/handle/unal/51527Borja Martínez, K., Mercado Lázaro, J., & Combatt Caballero, E. M. (2015). Dispersantes químicos y cuantificación de fracciones texturales por los métodos Bouyoucos y pipeta. Acta Agronomica, 64(4), 308–314. https://doi.org/10.15446/acag.v64n4.45722Brodský, L., Vašát, R., Klement, A., Zádorová, T., & Jakšík, O. (2013). Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping. Geoderma, 199, 54–63. https://doi.org/10.1016/j.geoderma.2012.11.006Camacho-Tamayo, J. H. (2013). Uso de la reflectancia difusa -NIR en la determinación de características físicas y químicas de un Oxisol. Carimagüa -Meta [Universidad Nacional de Colombia, Sede Bogotá]. https://repositorio.unal.edu.co/handle/unal/20889Camacho-Tamayo, J. H., Forero-Cabrera, N. M., Ramírez L., L., & Rubiano S., Y. (2017). Evaluación de textura del suelo con espectroscopía de infrarrojo cercano en un oxisol de Colombia. Colombia Forestal, 20(1), 5–18Camacho-Tamayo, J. H., Rubiano, Y. S., & Hurtado, M. del P. (2014). Near-infrared (NIR) diffuse reflectance spectroscopy for the prediction of carbon and nitrogen in an Oxisol. Agronomía Colombiana, 32(1), 86–94.Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., & Konopka, A. E. (1994). Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Science Society of America Journal, 58(5), 1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033xCambou, A., Cardinael, R., Kouakoua, E., Villeneuve, M., Durand, C., & Barthès, B. G. (2016). Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field. Geoderma, 261, 151–159. https://doi.org/10.1016/j.geoderma.2015.07.007Campos, B.-H. C. de, Amado, T. J. C., Bayer, C., Nicoloso, R. da S., & Fiorin, J. E. (2011). Carbon stock and its compartments in a subtropical oxisol under long-term tillage and crop rotation systems. Revista Brasileira de Ciência Do Solo, 35(3), 805–817. https://doi.org/10.1590/s0100-06832011000300016Chartin, C., Stevens, A., Goidts, E., Krüger, I., Carnol, M., & Van Wesemael, B. (2016). Mapping Soil Organic Carbon stocks and estimating uncertainties at the regional scale following a legacy sampling strategy (Southern Belgium, Wallonia). Geoderma Regional, 9, 73–86. https://doi.org/10.1016/j.geodrs.2016.12.006Chaudhari, P. R., Ahire, D. V, Ahire, V. D., Chkravarty, M., & Maity, S. (2013). Soil Bulk Density as related to Soil Texture, Organic Matter Content and available total Nutrients of Coimbatore Soil. International Journal of Scientific and Research Publications, 3(1), 2250–3153. https://doi.org/10.2136/sssaj2015.11.0407Combatt-Caballero, E. M., Palencia-L, M., & Borja-M, K. (2018). Particle size distribution by Bouyoucos in slightly alkaline soils from department of Cordoba, Colombia. Acta Agronomica, 67(1), 126–132. https://doi.org/10.15446/acag.v67n1.60762Comino, F., Aranda, V., García-Ruiz, R., Ayora-Cañada, M. J., & Domínguez-Vidal, A. (2018). Infrared spectroscopy as a tool for the assessment of soil biological quality in agricultural soils under contrasting management practices. Ecological Indicators, 87, 117–126. https://doi.org/10.1016/j.ecolind.2017.12.046Conforti, M., Buttafuoco, G., Leone, A. P., Aucelli, P. P. C., Robustelli, G., & Scarciglia, F. (2013). Studying the relationship between water-induced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis: A case study in southern Italy. Catena, 110, 44–58. https://doi.org/10.1016/j.catena.2013.06.013Conforti, M., Castrignanò, A., Robustelli, G., Scarciglia, F., Stelluti, M., & Buttafuoco, G. (2015). Laboratory-based Vis-NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. Catena, 124, 60–67. https://doi.org/10.1016/j.catena.2014.09.004Cortés, A., & Malagón, D. (1984). Los levantamientos agrológicos y sus aplicaciones múltiples. Universidad de Bogotá Jorge Tadeo Lozano.Curcio, D., Ciraolo, G., Asaro, F. D., & Minacapilli, M. (2013). Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy. Procedia Environmental Sciences, 19, 494–503. https://doi.org/10.1016/j.proenv.2013.06.056Day, P. R. (1965). Particle fractionation and particle size analysis. In C. A. Black (Ed.), Method of soil analysis, Part I (pp. 565–566). Soil Science Society of America. https://doi.org/10.2134/agronmonogr9.1.c43Demattê, José, Horák-Terra, I., Beirigo, R. M., Terra, F. da S., Marques, K. P. P., Fongaro, C. T., Silva, A. C., & Vidal-Torrado, P. (2017). Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring. Journal of Environmental Management, 197, 50–62. https://doi.org/10.1016/j.jenvman.2017.03.014Demattê, José, Sousa, A. A., Alves, M. C., Nanni, M. R., Fiorio, P. R., & Campos, R. C. (2006). Determining soil water status and other soil characteristics by spectral proximal sensing. Geoderma, 135, 179–195. https://doi.org/10.1016/j.geoderma.2005.12.002Demattê, Jose, Terra, F. da S., & Quartaroli, C. F. (2012). Spectral behavior of some modal soil profiles from São Paulo State, Brazil. Soil and Plant Nutrition, 71(3), 413–423.Dokuchaev, V. (1883). Russian Chernozem. In Selected works of V.V. Dokuchaev, Vol I. Israel Program for Scientific Translations, Jerusalem (translated in 1967).Dumas, J. B. A. (1831). Procédés de l’analyse organique. Annales de Chimie et de Physique, 247(47), 198–213.Escribano, P., Schmid, T., Chabrillat, S., Rodríguez C, E., & García, M. (2017). Optical Remote Sensing for Soil Mapping and Monitoring. In Soil Mapping and Process Modeling for Sustainable Land Use Management (p. 39). Elsevier.ESRI. (2018). ArcGIS 10.6 (10.6). Environmental Systems Research Institute.FAO. (2009). Guía para la descripción de suelos. In Organización de las Naciones Unidas para la Agricultura y la Alimentación. FAO. http://www.fao.org/3/a-a0541s.pdfFAO. (2011). El estado de los recursos de tierras y aguas del mundo para la alimentación y la agricultura. Roma, 50. https://doi.org/10.1017/CBO9781107415324.004FAO. (2019). Measuring and modelling soil carbon stocks and stock changes in livestock production systems Guidelines for assessment (Version 1). FAO. http://www.fao.org/3/ca2934en/CA2934EN.pdfGamez Ávila, C. C. (2019). Uso de modelos espectrales VIS – NIR para la determinación del contenido de carbono del suelo [Universidad Nacional de Colombia, Sede Bogotá]. https://repositorio.unal.edu.co/handle/unal/76626Gras, J. P., Barthès, B. G., Mahaut, B., & Trupin, S. (2014). Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils. Geoderma, 214–215, 126–134. https://doi.org/10.1016/j.geoderma.2013.09.021Hengl, T. (2009). A Practical guide to Geostatistical Mapping (2nd ed.). Office for Official Publications of the European Communities.Hobley, E., & Prater, I. (2019). Estimating soil texture from vis–NIR spectra. European Journal of Soil Science, 70(1), 83–95. https://doi.org/10.1111/ejss.12733Huang, J., Hartemink, A. E., & Zhang, Y. (2019). Climate and land-use change effects on soil carbon stocks over 150 years in Wisconsin, USA. Remote Sensing, 11(12), 1504. https://doi.org/10.3390/rs11121504Huertas, H. D., Rangel, J. A., & Parra, A. S. (2018). Caracterización de la fertilidad química de los suelos en sistemas productivos de la altillanura plana, Meta, Colombia. Revista Luna Azul, 46(46), 54–69. https://doi.org/10.17151/luaz.2018.46.5IGAC. (2004). Estudio general de suelos y zonificación de tierras: Departamento de Meta. Instituto Geográfico Agustín Codazzi, Subdirección de Agrología.IPCC. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Volume 4 - Agriculture, forestry and other land use (E. Calvo Buendia, K. Tanabe, A. Kranjc, J. Baasansuren, M. Fukuda, S. Ngarize, A. Osako, Y. Pyrozhenko, P. Shermanau, & S. Federici (eds.)). The Intergovernmental Panel on Climate Change (IPCC).IUSS Working Group WRB. (2015). Base referencial mundial del recurso suelo 2014, Actualización 2015. In Base referencial mundial del recurso suelo. FAO. http://www.fao.org/3/i3794es/I3794es.pdfJaconi, A., Don, A., & Freibauer, A. (2017). Prediction of soil organic carbon at the country scale : stratification strategies for near-infrared data. European Journal of Soil Science, 68(6), 1–11. https://doi.org/10.1111/ejss.12485Jaimes, W., Navas, G., Salamanca, C., & Conde, A. (2003). Estudio Detallado de Suelos de la Estación Experimental de CORPOICA “Sabanas” en la Altillanura Colombiana. Corpoica.Jandl, R., Rodeghiero, M., Martinez, C., Cotrufo, M. F., Bampa, F., van Wesemael, B., Harrison, R. B., Guerrini, I. A., Richter, D. de B., Rustad, L., Lorenz, K., Chabbi, A., & Miglietta, F. (2014). Current status, uncertainty and future needs in soil organic carbon monitoring. Science of the Total Environment, 468–469, 376–383. https://doi.org/10.1016/j.scitotenv.2013.08.026Jenny, H. (1941). Factors of soil formation: a system of quantitative pedology (Vol. 68, Issue 4). Dover Publications. https://doi.org/10.1016/0016-7061(95)90014-4Jia, X., Chen, S., Yang, Y., Zhou, L., Yu, W., & Shi, Z. (2017). Organic carbon prediction in soil cores using VNIR and MIR techniques in an alpine landscape. Scientific Reports, 7(1), 2144. https://doi.org/10.1038/s41598-017-02061-zJohnson, J. M. F., Franzluebbers, A. J., Weyers, S. L., & Reicosky, D. C. (2007). Agricultural opportunities to mitigate greenhouse gas emissions. Environmental Pollution, 150(1), 107–124. https://doi.org/10.1016/j.envpol.2007.06.030Katuwal, S., Knadel, M., Norgaard, T., Moldrup, P., Greve, M. H., & de Jonge, L. W. (2020). Predicting the dry bulk density of soils across Denmark: Comparison of single-parameter, multi-parameter, and vis–NIR based models. Geoderma, 361(May 2019), 114080. https://doi.org/10.1016/j.geoderma.2019.114080Kennard, R. W., & Stone, L. A. (1969). Computer Aided Design of Experiments. Technometrics, 11(1), 137–148.Kuang, B., & Mouazen, A. M. (2013). Non-biased prediction of soil organic carbon and total nitrogen with vis-NIR spectroscopy, as affected by soil moisture content and texture. Biosystems Engineering, 114(3), 249–258. https://doi.org/10.1016/j.biosystemseng.2013.01.005Laamrani, A., Berg, A. A., Voroney, P., Feilhauer, H., Blackburn, L., March, M., Dao, P. D., He, Y., & Martin, R. C. (2019). Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario , Canada. Remote Sensing, 11(11), 1–15.Landcare Research. (2016). Agricultural Greenhouse Gases: Mapping Soil Organic. https://www.nzagrc.org.nz/fact-sheets,listing,232,reducing-new-zealands-agricultural-emissions-mapping-soil-organic-carbon-stocks.htmlLee, J., Hopmans, J. W., Rolston, D. E., Baer, S. G., & Six, J. (2009). Determining soil carbon stock changes: Simple bulk density corrections fail. Agriculture, Ecosystems and Environment, 134(3–4), 251–256. https://doi.org/10.1016/j.agee.2009.07.006Leone, A. P., Leone, G., Leone, N., Galeone, C., Grilli, E., Orefice, N., & Ancona, V. (2019). Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil. Water (Switzerland), 11(1712), 1–16.Leone, A. P., Viscarra-Rossel, R. A., Amenta, P., & Buondonno, A. (2012). Prediction of Soil Properties with PLSR and vis-NIR Spectroscopy: Application to Mediterranean Soils from Southern Italy. Current Analytical Chemistry, 8(2), 283–299. https://doi.org/10.2174/157341112800392571Liu, S., Shen, H., Chen, S., Zhao, X., Biswas, A., Jia, X., Shi, Z., & Fang, J. (2019). Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment. Geoderma, 348, 37–44. https://doi.org/10.1016/j.geoderma.2019.04.003Malone, B. (2016). ithir: Functions and Algorithms Specific to Pedometrics. R package version 1.0/r126. https://rdrr.io/rforge/ithir/Malone, B. P., Styc, Q., Minasny, B., & McBratney, A. B. (2017). Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data. Geoderma, 290, 91–99. https://doi.org/10.1016/j.geoderma.2016.12.008Marques, J. D. de O., Luizão, F. J., Teixeira, W. G., Vitel, C. M., & Marques, E. M. de A. (2016). Soil organic carbon, carbon stock and their relationships to physical attributes under forest soils in central Amazonia. Revista Arvore, 40(2), 197–208. https://doi.org/10.1590/0100-67622016000200002Martín-López, J. M., & Quintero-Arias, G. (2017). Effect of soil use and coverage on the spectral response of an oxisol in the VIS-NIR-MIR region. Journal of Imaging, 3(1). https://doi.org/10.3390/jimaging3010010Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. In Geoderma (Vol. 117, Issues 1–2). https://doi.org/10.1016/S0016-7061(03)00223-4McBratney, A. B., & Viscarra Rossel, R. A. (2008). Diffuse Reflectance Spectroscopy as a Tool for Digital Soil Mapping. In A. E. Hartemink, A. McBratney, & M. L. Mendonça Santos (Eds.), Digital Soil Mapping with Limited Data (1st ed., pp. 165–172). Springer.Moreira, C. S., Brunet, D., Verneyre, L., Sá, S. M. O., Galdos, M. V., Cerri, C. C., & Bernoux, M. (2009). Near infrared spectroscopy for soil bulk density assesment. European Journal of Soil Science, 60, 785–791. https://doi.org/10.1111/j.1365-2389.2009.01170.xMurphy, B. W. (2015). Impact of soil organic matter on soil properties - A review with emphasis on Australian soils. Soil Research, 53(6), 605–635. https://doi.org/10.1071/SR14246Nawar, S., & Mouazen, A. M. (2019). On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning. Soil and Tillage Research, 190, 120–127. https://doi.org/10.1016/j.still.2019.03.006Nawar, Said, Buddenbaum, H., Hill, J., Kozak, J., & Mouazen, A. M. (2016). Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil and Tillage Research, 155, 510–522. https://doi.org/10.1016/j.still.2015.07.021Nocita, M., Kooistra, L., Bachmann, M., Müller, A., Powell, M., & Weel, S. (2011). Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa. Geoderma, 167–168, 295–302. https://doi.org/10.1016/j.geoderma.2011.09.018Nocita, M., Stevens, A., Noon, C., & Van Wesemael, B. (2013). Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy. Geoderma, 199, 37–42. https://doi.org/10.1016/j.geoderma.2012.07.020Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B., & Montanarella, L. (2014). Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biology and Biochemistry, 68, 337–347. https://doi.org/10.1016/j.soilbio.2013.10.022Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., Ben Dor, E., Brown, D. J., Clairotte, M., Csorba, A., Dardenne, P., Demattê, J. A. M., Genot, V., Guerrero, C., Knadel, M., Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., … Wetterlind, J. (2015). Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring (pp. 139–159). https://doi.org/10.1016/bs.agron.2015.02.002Oliver, M. A., & Webster, R. (2015). Basics Steps in Geostatistics: The Variogram and Kriging. Springer. https://doi.org/10.1007/978-3-319-15865-5Pinheiro, É. F. M., Ceddia, M. B., Clingensmith, C. M., Grunwald, S., & Vasques, G. M. (2017). Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon. Remote Sensing, 9(4), 1–22. https://doi.org/10.3390/rs9040293Ponce‐Hernandez, R., Marriott, F. H. C., & Beckett, P. H. T. (1986). An improved method for reconstructing a soil profile from analyses of a small number of samples. Journal of Soil Science, 37(3), 455–467. https://doi.org/10.1111/j.1365-2389.1986.tb00377.xPoppiel, R. R., Lacerda P., M., Pereira, M., Almeida Junior, D. O., Demattê, J., Romero, D. J., Sato, M., Almeida J, L. R., & Moreira C, L. F. (2018). Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties. Revista Brasilera de Ciencia Do Solo, 42, 1–26.R Core Team. (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/Ramirez-Lopez, L., Reina-Sánchez, A., & Camacho-Tamayo, J. H. (2008). Variabilidad espacial de atributos físicos de un Typic Haplustox de los Llanos Orientales de Colombia. Engenharia Agrícola, 28(1), 55–63. https://doi.org/10.1590/S0100-69162008000100006Ramirez-Lopez, L. (2009). Pedologia quantitativa: espectrometria VIS-NIR-SWIR e mapeamento digital de solos [Universidade de São Paulo]. https://teses.usp.br/teses/disponiveis/11/11140/tde-23062009-140151/publico/Leonardo_Lopez.pdfRamirez-Lopez, L., Schmidt, K., Behrens, T., van Wesemael, B., Demattê, J. A. M., & Scholten, T. (2014). Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, 226–227(1), 140–150. https://doi.org/10.1016/j.geoderma.2014.02.002Ratner, B. (2009). The correlation coefficient: Its values range between +1/-1, or do they. Journal of Targeting, Measurement and Analysis for Marketing, 17(2), 139–142. https://doi.org/10.1057/jt.2009.5Rippstein, G., Escobar, E., Toledo, J. M., Fisher, M., & Mesa, E. (2001). Caracterización de comunidades vegetales de la altillanura en el Centro de Investigación Agropecuaria Carimagua, en Meta, Colombia. In Georges Rippstein, G. Escobar, & F. Motta (Eds.), Agroecología y Biodiversidad de las Sabanas en los Llanos Orientales (pp. 22–45). CIAT, Centro Internacional de Agricultura Tropical.Rivera, M., Amezquita, E., Bernal, J. H., & Rao, I. M. (2013). Las Sabanas de los Llanos Orientales de Colombia: Caracterización Biofísica e Importancia para la Producción Agropecuaria. In Sistemas Agropastoriles: un Enfoque Integrado para el Manejo Sostenible de Oxisoles de los Llanos Orientales de Colombia (pp. 3–13). Centro Internacional de Agricultura Tropical (CIAT).Rodríguez-Cabrera., J., Cortiza Mora, A. W., Pereira Marín, C. A., Chacón Iznaga, A., Gattorno Muñoz, S., Rodríguez López, O., Rodríguez Urrutia, A., Jiménez Carrazana, R., & Torres Artiles, P. N. (2015). Determinación VIS/NIR del contenido de materia orgánica en suelos agrícolas Pardos mullidos medianamente lavados. (Spanish). Revista Centro Agrícola, 42(3), 5–12. http://cagricola.uclv.edu.cu/index.php/es/volumen-42-2015/numero-3-2015/47-determinacion-vis-nir-del-contenido-de-materia-organica-en-suelos-agricolas-pardos-mullidos-medianamente-lavadosRoudier, P., Hedley, C. B., Lobsey, C. R., Viscarra Rossel, R. A., & Leroux, C. (2017). Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon. Geoderma, 296, 98–107. https://doi.org/10.1016/j.geoderma.2017.02.014Sá, J. C. de M., & Lal, R. (2009). Stratification ratio of soil organic matter pools as an indicator of carbon sequestration in a tillage chronosequence on a Brazilian Oxisol. Soil and Tillage Research, 103(1), 46–56. https://doi.org/10.1016/j.still.2008.09.003Savitzky, A., & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), 1627–1639.Serna-Izasa, R. A., Rippstein, G., Grollier, C., & Mesa, E. (2001). Biodiversidad de la vegetación de la sabana en la altillanura plana y la serranía de los llanos orientales. In Georges Rippstein, G. Escobar, & F. Motta (Eds.), Agroecología y Biodiversidad de las Sabanas en los Llanos Orientales (pp. 46–63). CIAT, Centro Internacional de Agricultura Tropical.Shenk, J. S., & Westerhaus, M. O. (1996). Calibration the ISI way. In A. M. C. Davis & P. Williams (Eds.), Near infrared spectroscopy : the future waves (pp. 198–202). NIR Publications.Silva-Parra, A. (2018). Modelación de los stocks de carbono del suelo y las emisiones de dióxido de carbono (GEI) en sistemas productivos de la Altillanura Plana. ORINOQUIA, 22(2), 158–171. https://doi.org/10.22579/20112629.525Soil Survey Staff. (2014). Keys to soil taxonomy. In Soil Conservation Service (Vol. 12). https://doi.org/10.1109/TIP.2005.854494Stenberg, B. (2010). Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon. Geoderma, 158, 15–22. https://doi.org/10.1016/j.geoderma.2010.04.008Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010). Visible and Near Infrared Spectroscopy in Soil Science. In Advances in Agronomy (1st ed., Vol. 107). Elsevier Inc. https://doi.org/10.1016/S0065-2113(10)07005-7Stevens, A., Nocita, M., Tóth, G., Montanarella, L., & van Wesemael, B. (2013). Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy. PloS One, 8(6), e66409. https://doi.org/10.1371/journal.pone.0066409Stevens, A., Ramirez-Lopez, L., & Hans, G. (2020). Prospectr package: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data. https://cran.r-project.org/web/packages/prospectr/index.htmlStevens, A., van Wesemael, B., Bartholomeus, H., Rosillon, D., Tychon, B., & Ben-Dor, E. (2008). Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma, 144, 395–404. https://doi.org/10.1016/j.geoderma.2007.12.009Stockmann, U., Adams, M. A., Crawford, J. W., Field, D. J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A. B., Courcelles, V. de R. de, Singh, K., Wheeler, I., Abbott, L., Angers, D. A., Baldock, J., Bird, M., Brookes, P. C., Chenu, C., Jastrow, J. D., Lal, R., … Zimmermann, M. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems and Environment, 164, 80–99. https://doi.org/10.1016/j.agee.2012.10.001Summers, D., Lewis, M., Ostendorf, B., & Chittleborough, D. (2011). Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties. Ecological Indicators, 11, 123–131. https://doi.org/10.1016/j.ecolind.2009.05.001Tümsavaş, Z., Tekin, Y., Ulusoy, Y., & Mouazen, A. M. (2019). Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy. Biosystems Engineering, 177, 90–100. https://doi.org/10.1016/j.biosystemseng.2018.06.008Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., & Skjemstad, J. O. (2006a). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1–2), 59–75. https://doi.org/10.1016/j.geoderma.2005.03.007Viscarra Rossel, R. A., McGlynn, R. N., & McBratney, A. B. (2006b). Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma, 137(1–2), 70–82. https://doi.org/10.1016/j.geoderma.2006.07.004Viscarra Rossel, R. A., & Webster, R. (2012). Predicting soil properties from the Australian soil visible – near infrared spectroscopic database. European Journal of Soil Science, 63, 848–860. https://doi.org/10.1111/j.1365-2389.2012.01495.xViscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D. J., Demattê, J. A. M., Shepherd, K. D., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi, H., Barthès, B. G., Bartholomeus, H. M., Bayer, A. D., Bernoux, M., Böttcher, K., Brodský, L., Du, C. W., Chappell, A., … Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198–230. https://doi.org/10.1016/j.earscirev.2016.01.012Wetterlind, J., Stenberg, B., & Söderström, M. (2008). The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale. Precision Agriculture, 9(1–2), 57–69. https://doi.org/10.1007/s11119-007-9051-zWilding, L. P. (1985). Soil spatial variability: Its documentation, accommodation and implication to soil surveys. In D. R. Nielsen & J. Bouma (Eds.), Soil Spatial Variability (pp. 166–194). Pudoc.Williams, P. C. (2003). Near-infrared technology ‒ Getting the best out of light: A short course in the practical implementation of near-infrared spectroscopy for the user. (2 ed). PDK Projects Inc.Wold, S., Ruhe, A., Wold, H., & Dunn III, W. L. (1984). The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing, 5(3), 735–743. https://doi.org/https://doi.org/10.1137/0905052Xie, X. L., & Li, A. B. (2016). Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy. Biosystems Engineering, 152, 126–137. https://doi.org/10.1016/j.biosystemseng.2016.06.007Zimmermann, M., Leifeld, J., & Fuhrer, J. (2007). Quantifying soil organic carbon fractions by infrared-spectroscopy. 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