<?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>2007-0934</journal-id>
<journal-title><![CDATA[Revista mexicana de ciencias agrícolas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Mex. Cienc. Agríc]]></abbrev-journal-title>
<issn>2007-0934</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-09342018000904341</article-id>
<article-id pub-id-type="doi">10.29312/remexca.v0i21.1535</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelos para predecir precipitación probabilística en Tabasco México generados con información publicada]]></article-title>
<article-title xml:lang="en"><![CDATA[Models to predict probabilistic precipitation in Tabasco, Mexico generated with published information]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Romantchik Kriuchkova]]></surname>
<given-names><![CDATA[Eugenio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arteaga-Ramírez]]></surname>
<given-names><![CDATA[Ramón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cervantes Osornio]]></surname>
<given-names><![CDATA[Rocío]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez-Peña]]></surname>
<given-names><![CDATA[Mario A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma Chapingo Departamento de Irrigación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>11</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>11</month>
<year>2018</year>
</pub-date>
<volume>9</volume>
<numero>spe21</numero>
<fpage>4341</fpage>
<lpage>4354</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-09342018000904341&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-09342018000904341&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-09342018000904341&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las estaciones climatológicas generalmente presentan datos perdidos en sus registros, lo que complica realizar estudios probabilísticos en este caso de la precipitación. Pero existe información publicada que se obtiene para tal fin, por lo que, el objetivo fue generar modelos para predecir precipitación probabilística en el estado de Tabasco con información publicada. Se contó con información publicada de forma gráfica de 19 estaciones en el estado de Tabasco, de éstas se tomó la precipitación media y se generó la precipitación probabilística a los niveles de 80, 60, 40 y 20%, se utilizó el modelo lineal simple y se generaron cuatro modelos para estimar la precipitación probabilística a los niveles indicados en función de la precipitación media con los datos de 17 estaciones, las otras dos se utilizaron en la validación de los modelos. Para definir la bondad predictiva de éstos, se utilizó la raíz cuadrada del cuadrado medio del error (RCCME). Los cuatro modelos generados presentaron buen ajuste, ya que sus coeficientes de determinación fueron de 0.959, 0.985, 0.991 y 0.97, en los niveles de probabilidad de 80, 60, 40 y 20% respectivamente. Los valores de la RCCME variaron de 4.6 a 27.7 mm lo que indica que los modelos son buenos pronosticadores.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The weather stations usually present data lost in their records, which complicates probabilistic studies in this case of precipitation. But there is published information obtained for this purpose, so the objective was to generate models to predict probabilistic precipitation in the state of Tabasco with published information. There was information published graphically of 19 stations in the state of Tabasco, of these the average precipitation was taken and the probabilistic precipitation was generated at levels of 80, 60, 40 and 20%, the simple linear model was used and four models were generated to estimate the probabilistic precipitation at the indicated levels based on the average rainfall with data from 17 stations, the other two were used in the validation of the models. To define the predictive goodness of these, the square root of the mean square of the error (RCCME) was used. The four generated models presented good adjustment, since their coefficients of determination were 0.959, 0.985, 0.991 and 0.97, in the probability levels of 80, 60, 40 and 20% respectively. The values of the RCCME varied from 4.6 to 27.7 mm which indicates that the models are good predictors.]]></p></abstract>
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<kwd lng="es"><![CDATA[estimación de precipitación]]></kwd>
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<kwd lng="es"><![CDATA[raíz cuadrada del cuadrado medio del error y zonificación de cultivos]]></kwd>
<kwd lng="en"><![CDATA[precipitation estimation]]></kwd>
<kwd lng="en"><![CDATA[linear model]]></kwd>
<kwd lng="en"><![CDATA[square root of the mean error square and crop zoning]]></kwd>
</kwd-group>
</article-meta>
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