<?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>2224-7920</journal-id>
<journal-title><![CDATA[Revista de Producción Animal]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. prod. anim.]]></abbrev-journal-title>
<issn>2224-7920</issn>
<publisher>
<publisher-name><![CDATA[Ediciones Universidad de Camagüey]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2224-79202019000100011</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Eficiencia técnica en granjas lecheras de la Sierra Andina mediante modelación con redes neuronales]]></article-title>
<article-title xml:lang="en"><![CDATA[Technical Efficiency on Dairy Farms in Sierra Andina Using Neural Network Modelling]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Torres-Inga]]></surname>
<given-names><![CDATA[Carlos S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Crespo]]></surname>
<given-names><![CDATA[Gonzalo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guevara-Viera]]></surname>
<given-names><![CDATA[Raúl]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Narváez-Terán]]></surname>
<given-names><![CDATA[Jhonny]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Serpa-García]]></surname>
<given-names><![CDATA[Víctor Guillermo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guzmán-Espinoza]]></surname>
<given-names><![CDATA[Clelia Kathrine]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guevara-Viera]]></surname>
<given-names><![CDATA[Guillermo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguirre de Juana]]></surname>
<given-names><![CDATA[Ángel J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Cuenca Facultad de Ciencias Agropecuarias ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Secretaría de Educación Superior, Ciencia, Tecnología e Innovación Proyecto Prometeo ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<volume>31</volume>
<numero>1</numero>
<fpage>11</fpage>
<lpage>17</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2224-79202019000100011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2224-79202019000100011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2224-79202019000100011&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes: El objetivo del trabajo fue estimar la eficiencia en la producción lechera de 1 168 casos en la Sierra Sur Andina Ecuatoriana, mediante la aplicación de un modelo de redes neuronales con per-ceptrón multicapa.  Métodos: Los casos fueron tomados de fuentes secundarias provistas por el instituto oficial de estadísticas nacionales del Ecuador para el año 2016. Las variables seleccionadas para el modelo fueron: producción total de leche el día de ayer (P) como variable dependiente y número total de ganado vacuno (GV), el total de trabajadores en el terreno (E) además de la superficie total a cargo de la persona productora (S) como variables independientes. Los criterios de selección de las variables fueron: la existencia de datos por cada caso y el impacto de ellas en la variable dependiente.  Resultados: La eficiencia promedio fue del 8,11 %, donde la cantidad de casos detectados con eficiencia &gt; 0,70 fueron en total 11 (0,9 % de la muestra). Posteriormente, los casos estudiados se clasificaron en tres grupos en función de la eficiencia calculada: Grupo 1 (eficiencia &#8804; 0,4), Grupo 2 (eficiencia &gt; 0,4 hasta &#8804; 0,7) y Grupo 3 (eficiencia &gt; 0,7).  Conclusiones: Al compararlos se encontraron diferencias estadísticas (P &lt; 0,01) para las variables producción total de leche al año de la granja, y además de otras variables como: total de trabajadores en el terreno, área de la granja, total de vacas, total de unidades de ganado vacuno, partos, vacas preñadas y vacas servidas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Aim: The aim of this work was to estimate the efficiency of milk production in 1 168 cases in Ecuadoran Sierra Sur Andina, with the implementation of neural networks with multilayer perceptrons.  Materials and Methods: These cases were collected from secondary samples provided by the Official Institute of National Statistics of Ecuador, in 2016. The variables chosen for the model were total milk production on the previ-ous day (P), as dependent variable; and total cattle heads (CH), total laborers in the field (E), and total surface at-tended by laborer (S), as independent variables. The selection criteria were the existence of data from individual cas-es, and their impact on the dependent variable.  Results: The average efficiency was 8.11 %, from which the total cases detected efficiently (&gt; 0.70) accounted for 11 (0.9 % of the sample). Later, the cases studied were classified into three groups, depending on the efficiency calculated: Group 1 (&#8804; 0.4 efficiency); Group 2 (&gt; 0.4 - &#8804; 0.7 efficiency); and Group 3 (&gt; 0.7 efficiency).  Conclusion: A comparison produced several statistical differences (P &lt; 0.01) for variables total milk production/year on the farm, total field laborers, farm size, total cows, total cattle heads, calvings, pregnant cows, and served cows.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[bovinos lecheros]]></kwd>
<kwd lng="es"><![CDATA[fronteras de producción]]></kwd>
<kwd lng="es"><![CDATA[perceptrón multicapas]]></kwd>
<kwd lng="es"><![CDATA[modelación]]></kwd>
<kwd lng="en"><![CDATA[dairy bovines]]></kwd>
<kwd lng="en"><![CDATA[production frontiers]]></kwd>
<kwd lng="en"><![CDATA[multilayer perceptron]]></kwd>
<kwd lng="en"><![CDATA[modelling]]></kwd>
</kwd-group>
</article-meta>
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