<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0187-5779</journal-id>
<journal-title><![CDATA[Terra Latinoamericana]]></journal-title>
<abbrev-journal-title><![CDATA[Terra Latinoam]]></abbrev-journal-title>
<issn>0187-5779</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de la Ciencia del Suelo A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-57792023000100129</article-id>
<article-id pub-id-type="doi">10.28940/terra.v41i0.974</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Covariables Ambientales que Definen los Principales Grupos de Suelo en México]]></article-title>
<article-title xml:lang="en"><![CDATA[Environmental Covariates that Define the Main Soil Groups in Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bonilla-Gaviño]]></surname>
<given-names><![CDATA[Cristina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fernández-Reynoso]]></surname>
<given-names><![CDATA[Demetrio S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cajuste-Bontemp]]></surname>
<given-names><![CDATA[Lenom]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez-Ayala]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Texcoco  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>41</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-57792023000100129&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-57792023000100129&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-57792023000100129&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: La variabilidad de los suelos depende de la interacción de covariables ambientales que intervienen en su formación. En México, se registran 25 grupos de suelo de los 32 que incluye la Base Referencial Mundial (BRM). Este estudio identifica el orden de importancia de 11 covariables ambientales, utilizando el modelo no paramétrico basado en un algoritmo de aprendizaje automático supervisado denominado random forest, el cual caracteriza 19 grupos de suelo que incluyen el 99.2% del territorio nacional. Las covariables que se incluyeron fueron, curvatura, densidad de drenaje, distancia al cauce más cercano, geología, índice de aridez, índice de humedad topográfica, índice de posición topográfica, índice de vegetación de diferencia normalizada (NDVI), radiación, rugosidad y temperatura. Los resultados mostraron un total de 100 árboles de clasificación, con una precisión global de 81.83% del modelo a través de random forest y un valor de Kappa de 0.80, expresado como muy bueno. La precisión de disminución promedio mostró que, las cinco covariables analizadas más importantes que clasifican los 19 grupos de suelo son, índice de posición topográfica, índice de aridez, curvatura, radiación y densidad de drenaje.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Summary: The variability of soils depends on the interaction of environmental covariates involved in their formation. In Mexico, 25 out of the 32 soil groups included in the World Reference Base (WRB) are recorded. This study identified the importance order of eleven environmental covariates that using the non-parametric model based on a supervised machine learning algorithm called random forest, which characterize 19 soil groups covering 99.2% of the national territory. The covariates included were, curvature, drainage density, distance to the nearest stream, geology, aridity index, topographic humidity index, topographic position index, NDVI, radiation, roughness, and temperature. The results showed 100 classification trees, with accuracy of 81.83% of the random forest model and 0.80 of Kappa, which is considered as very good. The mean decrease accuracy, showed that, the five most important covariates for classifying the 19 analyzed soil groups are topographic position index, aridity index, curvature, radiation, and drainage density.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[bosque aleatorio]]></kwd>
<kwd lng="es"><![CDATA[kappa]]></kwd>
<kwd lng="es"><![CDATA[precisión]]></kwd>
<kwd lng="es"><![CDATA[WRB]]></kwd>
<kwd lng="es"><![CDATA[variabilidad]]></kwd>
<kwd lng="en"><![CDATA[random forest]]></kwd>
<kwd lng="en"><![CDATA[kappa]]></kwd>
<kwd lng="en"><![CDATA[precision]]></kwd>
<kwd lng="en"><![CDATA[WRB]]></kwd>
<kwd lng="en"><![CDATA[variability]]></kwd>
</kwd-group>
</article-meta>
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