Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Mar 2024
Data description paper |
| 11 Mar 2024
| 11 Mar 2024
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
Sergio Díaz-Guadarrama
CORRESPONDING AUTHOR
Departamento de Agronomía, Facultad de Ciencias Agrarias. Universidad Nacional de Colombia, Bogotá, Colombia
Viviana M. Varón-Ramírez
Centro de Geociencias, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, 76230, Mexico
Corporación colombiana de investigación agropecuaria AGROSAVIA, C.I. Tibaitatá, Bogotá, CO-0571, Colombia
Iván Lizarazo
Departamento de Agronomía, Facultad de Ciencias Agrarias. Universidad Nacional de Colombia, Bogotá, Colombia
Mario Guevara
CORRESPONDING AUTHOR
Centro de Geociencias, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, 76230, Mexico
Department of Environmental Sciences, University of California, Riverside, Riverside, CA 92507, USA
United States Department of Agriculture, Soil Salinity National Laboratory, Riverside, CA 92507, USA
Marcos Angelini
FAO, Vialle de Terme di Caracalla, Rome, Italy
Gustavo A. Araujo-Carrillo
Corporación colombiana de investigación agropecuaria AGROSAVIA, C.I. Tibaitatá, Bogotá, CO-0571, Colombia
Jainer Argeñal
Facultad de Ciencias, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
Daphne Armas
Departamento de Agronomía, Edif. CITEIIB, Universidad de Almería, 04120 Almería, Spain
Rafael A. Balta
Dirección General de Asuntos Ambientales Agrarios, Ministerio de Desarrollo Agrario y Riego, Lima, Peru
Adriana Bolivar
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Nelson Bustamante
Servicio Agrícola y Ganadero, Santiago de Chile, Chile
Ricardo O. Dart
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Martin Dell Acqua
Direccion General de Recursos Naturales, Ministerio de Ganadería, Agricultura y Pesca, Montevideo, Uruguay
Arnulfo Encina
Facultad de Ciencias Agrarias, Universidad Nacional de Asunción, Asunción, Paraguay
Hernán Figueredo
Sociedad Boliviana de la Ciencia del Suelo, La Paz, Bolivia
Fernando Fontes
Direccion General de Recursos Naturales, Ministerio de Ganadería, Agricultura y Pesca, Montevideo, Uruguay
Joan S. Gutiérrez-Díaz
Department of Agroecology, Faculty of Science and Technology, Aarhus University, Tjele, 8830, Denmark
Wilmer Jiménez
Ministerio de Agricultura y Ganadería, Quito, 170516, Ecuador
Raúl S. Lavado
Facultad de Agronomía e INBA (CONICET/UBA), Universidad de Buenos Aires, Buenos Aires, 1417, Argentina
Jesús F. Mansilla-Baca
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Maria de Lourdes Mendonça-Santos
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Lucas M. Moretti
Estación Experimental Agropecuaria Cerro Azul, Instituto Nacional de Tecnología Agropecuaria, Misiones, Argentina
Iván D. Muñoz
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Carolina Olivera
FAO, Vialle de Terme di Caracalla, Rome, Italy
Guillermo Olmedo
FAO, Vialle de Terme di Caracalla, Rome, Italy
Christian Omuto
FAO, Vialle de Terme di Caracalla, Rome, Italy
Sol Ortiz
Secretaría de Agricultura y Desarrollo Rural, Mexico City, Mexico
Carla Pascale
Ministerio de Agricultura, Ganadería y Pesca (MAGYP), Buenos Aires, Argentina
Marco Pfeiffer
Departamento de Ingeniería y Suelos, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile
Iván A. Ramos
Instituto de Investigación Agropecuaria de Panamá, Panama City, Panama
Danny Ríos
Departamento de Ciencias del Suelo y Ordenamiento Territorial, Universidad Nacional de Asunción, Asunción, Paraguay
Rafael Rivera
Ministerio de Medio Ambiente, Santo Domingo, Dominican Republic
Lady M. Rodriguez
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Darío M. Rodríguez
Instituto de Investigación Suelos, Centro de Investigación de Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, B1686, Argentina
Albán Rosales
Instituto Nacional de Innovación y Transferencia en Tecnología Agropecuaria, San José, Costa Rica
Kenset Rosales
Ministerio de Ambiente y Recursos Naturales, Guatemala City, Guatemala
Guillermo Schulz
Instituto de Investigación Suelos, Centro de Investigación de Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, B1686, Argentina
Víctor Sevilla
Facultad de Agronomía, Universidad Central de Venezuela, Maracay, Venezuela
Leonardo M. Tenti
Instituto de Investigación Suelos, Centro de Investigación de Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, B1686, Argentina
Ronald Vargas
FAO, Vialle de Terme di Caracalla, Rome, Italy
Gustavo M. Vasques
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Yusuf Yigini
FAO, Vialle de Terme di Caracalla, Rome, Italy
Yolanda Rubiano
Departamento de Agronomía, Facultad de Ciencias Agrarias. Universidad Nacional de Colombia, Bogotá, Colombia
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This research focuses on Mexico's soil water erosion (SWE) using rainfall data to estimate erosivity. A database of daily rainfall series was developed for three climate normals –CNs– (1968–1997, 1978–2007, 1988–2017) with over 5,000 series. We found median erosivity values of 3245, 3070, and 3327 MJ mm ha-1 h-1 yr-1 for the three CNs. The resulting publicly available datasets of rainfall series and erosivity help better understand SWE and rainfall patterns across Mexico.
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These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
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Cited articles
Amirinejad, A. A., Kamble, K., Aggarwal, P., Chakraborty, D., Pradhan, S., and Mittal, R. B.: Assessment and mapping of spatial variation of soil physical health in a farm, Geoderma, 160, 292–303, https://doi.org/10.1016/j.geoderma.2010.09.021, 2011.
Angelini, M., Rodriguez, D. M., Olmedo, G. F., and Schulz, G.: Sistema de Información de Suelos del INTA (SISINTA): presente y futuro, in: XXVI Congreso Argentino de la Ciencia del Suelo, Tucumán, Argentina, 15–18 May 2018, 5 pp., https://www.researchgate.net/publication/325607030_Sistema_de_informacion_de_suelos_del_INTA_SISINTA_Presente_y_futuro (last access: 6 March 2024), 2018.
Araujo-Carrillo, G. A., Varón-Ramírez, V. M., Jaramillo-Barrios, C. I., Estupiñan-Casallas, J. M., Silva-Arero, E. A., Gómez-Latorre, D. A., and Martínez-Maldonado, F. E.: IRAKA: The first Colombian soil information system with digital soil mapping products, Catena, 196, 104940, https://doi.org/10.1016/j.catena.2020.104940, 2021.
Armas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, Á., Durante, P., and Oyonarte, C: Digital map of the organic carbon profile in the soils of Andalusia, Spain, Ecosistemas, 26, 80–88, https://doi.org/10.7818/ecos.2017.26-3.10, 2017.
Armas, D. I., Guevara, M., Bezares, F., Vargas, R., Durante, P., Osorio, V. H., Jimenez, W. A., and Oyonarte, C.: Harmonized Soil Database of Ecuador 2021 ver 3, Environmental Data Initiative [data set], https://doi.org/10.6073/pasta/1560e803953c839e7aedef78ff7d3f6c, 2022.
Batjes, N.: World inventory of soil emission potentials – WISE 2.1, International Soil Reference and Information Centre, 65 pp., https://www.isric.org/sites/default/files/ISRIC_TechPap26.pdf (last access: 6 September 2022), 1995.
Batjes, N.: Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269, 61–68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016.
Batjes, N. H., Ribeiro, E., van Oostrum, A., Leenaars, J., Hengl, T., and Mendes de Jesus, J.: WoSIS: providing standardised soil profile data for the world, Earth Syst. Sci. Data, 9, 1–14, https://doi.org/10.5194/essd-9-1-2017, 2017.
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020.
Beaudette, D. and O'Geen, A. T.: Soil-Web: An online soil survey for California, Arizona, and Nevada, Comput. Geosci., 35, 2119–2128, https://doi.org/10.1016/j.cageo.2008.10.016, 2009.
Bini, D., Santos, C. A. dos, Carmo, K. B. do, Kishino, N., Andrade, G., Zangaro, W., and Nogueira, M. A.: Effects of land use on soil organic carbon and microbial processes associated with soil health in southern Brazil, Eur. J. Soil Biol., 55, 117–123, https://doi.org/10.1016/j.ejsobi.2012.12.010, 2013.
Bockheim, J. G., Gennadiyev, A. N., Hammer, R. D., and Tandarich, J. P.: Historical development of key concepts in pedology, Geoderma, 124, 23–36, https://doi.org/10.1016/j.geoderma.2004.03.004, 2005.
Bouma, J., Broll, G., Crane, T., Dewitte, O., Gardi, C., Schulte, R., and Towers, W.: Soil information in support of policy making and awareness raising, Curr. Opin. Env. Sust., 4, 552–558, https://doi.org/10.1016/j.cosust.2012.07.001, 2012.
Chapman, A. D.: Principles of Data Quality, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen, 61 pp., https://doi.org/10.15468/doc.jrgg-a190, 2005.
Dewitte, O., Jones, A., Spaargaren, O., Breuning-Madsen, H., Brossard, M., Dampha, A., Deckers, J., Gallali, T., Hallett, S., Jones, R., Kilasara, M., Le Roux, P., Michéli, E., Montanarella, L., Thiombiano, L., Van Ranst, E., Yemefack, M., and Zougmore, R.: Harmonisation of the soil map of africa at the continental scale, Geoderma, 211–212, 138–153, https://doi.org/10.1016/j.geoderma.2013.07.007, 2013.
Diaz-Guadarrama, S. and Guevara, M.: Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC version 1.2, Zenodo [data set], https://doi.org/10.5281/zenodo.7876731, 2023.
English, L. P.: Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits, John Wiley & Sons, Inc., New York, 518 pp., 1999.
FAO: FAO y los Objetivos de Desarrollo Sostenible, https://www.fao.org/sustainable-development-goals/es/ (last access: 6 September 2022), 2017.
FAO and IIASA: Harmonized world soil database. Food and Agriculture Organization, 43, 312, 2009.
FAO and ITPS: Global Soil Organic Carbon Map (GSOCmap) Technical Report, http://esdac.jrc.ec.europa.eu/content/global-soil-organic-carbon-estimates (last access: 7 November 2023), 2018.
Garg, P. K., Garg, R. D., Shukla, G., and Srivastava, H. S.: Digital Mapping of Soil Landscape Parameters, Springer International Publishing, https://doi.org/10.1007/978-981-15-3238-2, 2020.
Gomes, L. C., Faria, R. M., de Souza, E., Veloso, G. V., Schaefer, C. E. G. R., and Filho, E. I. F.: Modelling and mapping soil organic carbon stocks in Brazil, Geoderma, 340, 337–350, https://doi.org/10.1016/j.geoderma.2019.01.007, 2019.
Greiner, L., Keller, A., Grêt-Regamey, A., and Papritz, A.: Soil function assessment: review of methods for quantifying the contributions of soils to ecosystem services, Land Use Policy, 69, 224–237, https://doi.org/10.1016/j.landusepol.2017.06.025, 2017.
Gutierrez, J., Ordoñez, N., Bolivar, A., Bunning, S., Guevara, M., Medina, E., Olivera, C., Olmedo, G. F., Rodriguez, L., Sevilla, V., and Vargas, R.: Estimación del carbono orgánico en los suelos de ecosistema de páramo en Colombia, Ecosistemas, 29, 1–10, https://doi.org/10.7818/ECOS.1855, 2020.
Hendriks, C. M. J., Stoorvogel, J., Lutz, F., and Claessens, L.: When can legacy soil data be used, and when should new data be collected instead?, Geoderma, 348, 181–188, https://doi.org/10.1016/j.geoderma.2019.04.026, 2019.
Hengl, T. and Macmillan, R. A.: Predictive Soil Mapping with R, OpenGeoHub foundation, Wageningen, the Netherlands, 370 pp., ISBN 978-0-359-30635-0, 2019.
Hopmans, J. W., Qureshi, A. S., Kisekka, I., Munns, R., Grattan, S. R., Rengasamy, P., Ben-Gal, A., Assouline, S., Javaux, M., Minhas, P. S., Raats, P. A. C., Skaggs, T. H., Wang, G., De Jong van Lier, Q., Jiao, H., Lavado, R. S., Lazarovitch, N., Li, B., and Taleisnik, E.: Critical knowledge gaps and research priorities in global soil salinity, Adv. Agron., 169, 1–191, https://doi.org/10.1016/BS.AGRON.2021.03.001, 2021.
IUSS Working Group WRB: World Reference Base for Soil Resources 2006, first update 2007, World Soil Resources Reports No. 103, FAO, Rome, 2007.
Keskin, H., Grunwald, S., and Harris, W. G.: Digital mapping of soil carbon fractions with machine learning, Geoderma, 339, 40–58, https://doi.org/10.1016/j.geoderma.2018.12.037, 2019.
Krol, B.: Towards a Data Quality Management Framework for Digital Soil Mapping with Limited Data, in: Hartemink, A. E., Mcbratney, A. B., and Mendonça-Santos, M. de L., Digital Soil Mapping with Limited Data, 137–149, Springer International Publishing, https://doi.org/10.1007/978-1-4020-8592-5_11, 2008.
Lê, S., Josse, J., and Husson, F.: FactoMineR: An R Package for Multivariate Analysis, J. Stat. Softw., 25, 1–18, https://doi.org/10.18637/jss.v025.i01, 2008.
Leenaars, J. G. B.: Africa Soil Profiles Database, Version 1.1. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa, in: ISRIC Report 2013/03, vol. 03, https://doi.org/10.1201/b16500-13, 2013.
Mcbratney, A., Mendonça Santos, M. L., and Minasny, B.: On digital soil mapping, Geoderma, 117, 1–2, https://doi.org/10.1016/S0016-7061(03)00223-4, 2003.
Otte, P., Maring, L., De Cleen, M., and Boekhold, S.: Transition in soil policy and associated knowledge development, Curr. Opin. Env. Sust., 4, 565–572, https://doi.org/10.1016/j.cosust.2012.09.006, 2012.
Owusu, S., Yigini, Y., Olmedo, G. F., and Omuto, C.: Spatial prediction of soil organic carbon stocks in Ghana using legacy data, Geoderma, 360, 114008, https://doi.org/10.1016/j.geoderma.2019.114008, 2020.
Paterson, G., Turner, D., Wiese, L., Van Zijl, G., Clarke, C., and Van Tol, J.: Spatial soil information in South Africa: Situational analysis, limitations and challenges, S. Afr. J. Sci., 111, 28–35, https://doi.org/10.17159/sajs.2015/20140178, 2015.
Pfeiffer, M., Padarian, J., Osorio, R., Bustamante, N., Olmedo, G., Guevara, M., Aburto, F., Antilen, M., Araya, E., Arellano, E., Barret, M., Barrera, J., Boeckx, P., Briceño, M., Bunning, S., Cabrol, L., Casanova, M., Cornejo, P. C. F., Curaqueo, G., Doetterl, S., Duran, P., Escudey, M., Espinoza, A., Francke, S., Fuentes, J. P., Fuentes, M., Gajardo, G., García, R., Gallaud, A., Galleguillos, M., Gomez, A., Hidalgo, M., Ivelic-Sáez, J., Mashalaba, L., Matus, F., Mora, M., Mora, J., Muñoz, C., Norambuena, P., Olivera, C., Ovalle, C., Panichini, M., Pauchard, A., Perez-Quezada, J., Radic, S., Ramirez, J., Riveras, N., Ruiz,G., Salazar, O., Salgado, I., Seguel, O., Sepúlveda, M., Sierra, C., Tapia, Y., Toledo, B., Torrico, J. M., Valle, S., Vargas, R., Wolff, M., and Zagal, E.: CHLSOC: The Chilean Soil Organic Carbon database [data set], https://doi.org/10.17605/OSF.IO/NMYS3, 2019.
Pham, K., Kim, D., Yoon, Y., and Choi, H.: Analysis of neural network based pedotransfer function for predicting soil water characteristic curve, Geoderma, 351, 92–102, https://doi.org/10.1016/j.geoderma.2019.05.013, 2019.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021.
Rossiter, D.: Digital soil resource inventories: status and prospects, Soil Use Manage., 20, 296–301, https://doi.org/10.1111/j.1475-2743.2004.tb00372.x, 2004.
Rossiter, D.: Past, present & future of information technology in pedometrics, Geoderma, 324, 131–137, https://doi.org/10.1016/j.geoderma.2018.03.009, 2018.
Silatsa, F. B. T., Yemefack, M., Tabi, F. O., Heuvelink, G. B. M., and Leenaars, J. G. B.: Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon, Geoderma, 367, 13, https://doi.org/10.1016/j.geoderma.2020.114260, 2020.
SISLAC.: Sistema de Información de Suelos de Latinoamérica – SISLAC, http://www.sislac.org/# (last access: 2 October 2017), 2013.
Varón-Ramírez, V. M., Araujo-Carrillo, G. A., and Guevara Santamaría, M. A.: Colombian soil texture: building a spatial ensemble model, Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, 2022.
Short summary
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (
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