Textural classification of soils in rice-growing areas in Colombiadoi:10.7910/DVN/WKAOILHarvard Dataverse2015-11-241Da Silva, Mayesse; Rodriguez, Maryory; Majin, Marvin; Chirinda, Ngonidzashe, 2015, "Textural classification of soils in rice-growing areas in Colombia", https://doi.org/10.7910/DVN/WKAOIL, Harvard Dataverse, V1Textural classification of soils in rice-growing areas in ColombiaClasificación textural de los suelos en las áreas arroceras de Colombiadoi:10.7910/DVN/WKAOILDa Silva, MayesseRodriguez, MaryoryMajin, MarvinChirinda, NgonidzasheInternational Center for Tropical Agriculture2015-05Project 1-b/c (01554)Harvard DataverseInternational Center for Tropical AgricultureCIAT Data and Research MethodsMwanzia, Leroy2015-11-19Earth and Environmental SciencesSoil textureDigital soil mappingCartographyLatin America and the CaribbeanSoilsDigital soil mapping<strong>English</strong> <p> The Digital Soil Mapping (DSM) approach was used to generate maps of the soil texture (sand, silt, and clay) in Colombia, based on the soil geomorphology. The soil units were differentiated by land shapes using a STRM 90-m DEM, and stratified by geopedological units from the Agustín Codazzi Geographic Institute (IGAC). The digital maps of soil texture were generated using the fuzzy logic approach, which enabled determining statistically the value limits of percent-slope classes, moisture index and standardized height on each of the soil units. Data inherited from IGAC’s soil profiles were used to determine the gauge values for the mapping model (80% of total data) for sand, stilt, and clay, as well as to validate (20% of total data) based on the DSM approach. The root mean squared error (RMSE) was 21, 12, and 15% for sand, stilt, and clay, respectively, and these values are the same as the standard deviation of the field data. The map of texture classes was generated with the information on sand, stilt, and clay. </p> <strong>Español</strong> <p> Se usó el enfoque de Mapeo Digital del Suelo (MDS) para generar los mapas de textura del suelo (arena, limo y arcilla) para Colombia basado en la geomorfología del suelo. Las unidades del suelo fueron diferenciadas por formas del terreno usando un DEM SRTM de 90 m de resolución y además, estratificadas por unidades geopedológicas del Instituto Geográfico Agustín Codazzi (IGAC). Los mapas digitales de textura del suelo fueron generados usando el enfoque de lógica difusa, donde se determinaron estadísticamente los límites de los valores de las clases de porcentaje de pendiente, índice de humedad y altura normalizada en cada una de las unidades de suelo. Se usaron datos heredados de perfiles de suelos de IGAC, para determinar los valores de calibración del modelo de mapeo (80% del total de los datos) para arena, limo y arcilla, así como para validar (20% del total de los datos) según el enfoque de MDS. La raíz del error medio cuadrado (RMSE) fue 21, 12 y 15 % para arena, limo y arcilla respectivamente, valores que son iguales a la desviación estándar de los datos de campo. Con la información de arena, arcilla y limo se generó el mapa de clases de textura. </p>2015-052015-052015-052015-05ColombiaGeospatial DataGeographic DataSoil DataMapRaster imagesData inherited from soil profiles (Source: IGAC)Whilst utmost care has been taken CIAT and data authors while collecting and compiling the data, the data is however offered "as is" with no express or implied warranty. In no event shall the data authors, CIAT, or relevant funding agencies be liable for any actual, incidental or consequential damages arising from use of the data. <BR/><BR/> By using the CIAT Dataverse, the user expressly acknowledges that the Data may contain some nonconformities, defects, or errors. No warranty is given that the data will meet the user's needs or expectations or that all nonconformities, defects, or errors can or will be corrected. <BR/><BR/> The user should always verify actual data; therefore the user bears all responsibility in determining whether the data is fit for the user’s intended use. The user of the data should use the related publications as a baseline for their analysis whenever possible. Doing so will be an added safeguard against misinterpretation of the data. Related publications are listed in the metadata section of the Dataverse study.<a rel="license" href="http://creativecommons.org/licenses/by/4.0/" target="_blank"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br /> These data and documents are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank"> Creative Commons Attribution 4.0 International license.</a> You may copy, distribute and transmit the data as long as you acknowledge the source through proper <a href="http://best-practices.dataverse.org/data-citation/" target="_blank">data citation</a>.Agustín Codazzi Geographic Institute (IGAC). "Estudios de Suelos y Capacidad de Uso de 23 Departamentos, escala 1:100000."Instituto Geografico Agustin Codazzi (IGAC). Mapa de Geopedología de Colombia. http://sigotn.igac.gov.co/sigotn/default.aspx DEM SRTM de 90 m de resolución1. Clasificacion textural.jpgSoil Texture map in .JPEG format :::: Textura de suelo map en formato de .JPEG. image/jpeg2. Clasificación textural.zipSoil Texture map in .tiff format as a zip file.application/zip3. Clasificacion textural.tifSoil Texture map in .tiff format :::: Textura de suelo map en formato de .tiff. image/tiff3. Clasificacion textural.tif.aux.xmlSoil Texture map in .tiff format :::: Textura de suelo map en formato de .tiff. text/xml3. Clasificacion textural.tif.ovrSoil Texture map in .tiff format :::: Textura de suelo map en formato de .tiff. image/tiff3. Clasificacion textural.tif.vat.dbfSoil Texture map in .tiff format :::: Textura de suelo map en formato de .tiff. application/octet-stream3. Clasificacion textural.tif.vat.dbf.xmlSoil Texture map in .tiff format :::: Textura de suelo map en formato de .tiff. text/xml