<?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>S2007-09342021000100127</article-id>
<article-id pub-id-type="doi">10.29312/remexca.v12i1.2552</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Revisión: modelos de crecimiento y rendimiento de maíz en escenarios de cambio climático]]></article-title>
<article-title xml:lang="en"><![CDATA[Review: corn growth and yield models under climate change scenarios]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Noriega-Navarrete]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salazar-Moreno]]></surname>
<given-names><![CDATA[Raquel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Cruz]]></surname>
<given-names><![CDATA[Irineo Lorenzo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma Chapingo Posgrado en Ingeniería Agrícola y Uso Integral del Agua ]]></institution>
<addr-line><![CDATA[ Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>02</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>02</month>
<year>2021</year>
</pub-date>
<volume>12</volume>
<numero>1</numero>
<fpage>127</fpage>
<lpage>140</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-09342021000100127&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-09342021000100127&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-09342021000100127&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El cambio climático (CC) afecta las condiciones meteorológicas actuales y repercute negativamente en el rendimiento de maíz, particularmente de temporal. Para estimar los efectos del CC en la productividad, se han utilizado modelos de simulación de crecimiento bajo diferentes escenarios de cambio climático. En este artículo se hace una revisión sobre modelos implementados globalmente, durante el periodo 2006 a 2019; a través, de Scopus y Google Académico. Los modelos reportados son mecanicistas, dinámicos y estocásticos, como DSSAT-CERES-Maize, APSIM-Maize, CropSyst, AquaCrop, EPIC-Maize, CropWat InfoCrop y WOFOST. Las simulaciones en diversos escenarios reportan disminución del rendimiento de maíz en África Subsahariana (78%), China (70%), Latinoamérica (61%) y Medio Oriente (45%), e incrementos en la Unión Europea (71%), Cinturón Maicero Estadounidense (57%), Medio Oriente (45%) e India (44.5%). En México, se tienen estimaciones de incremento en los rendimientos de maíz desde 5 a 22% considerando los efectos de la fertilización carbónica, y reducciones de hasta de 49.3% bajo otras condiciones. Se requiere profundizar en estudios sobre efectos de CC en las diferentes regiones del país, e implementar modelos que puedan utilizarse para el diseño de políticas y estrategias de adaptación y mitigación, ante los efectos negativos del CC en la agricultura mexicana.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Climate change (CC) affects the current meteorological conditions and negatively affects the yield of corn, particularly during the rainy season. To estimate the effects of CC on productivity, growth simulation models have been used under different climate change scenarios. This article reviews the models implemented globally, during the period 2006 to 2019, through Scopus and Google Scholar. The reported models are mechanistic, dynamic and stochastic, such as DSSAT-CERES-Maize, APSIM-Maize, CropSyst, AquaCrop, EPIC-Maize, CropWat InfoCrop, and WOFOST. Simulations in various scenarios report decreased corn yields in Sub-Saharan Africa (78%), China (70%), Latin America (61%) and the Middle East (45%), and increases in the European Union (71%), Belt American Corn (57%), Middle East (45%) and India (44.5%). In Mexico, there are estimates of increases in corn yields from 5 to 22% considering the effects of carbonic fertilization, and reductions of up to 49.3% under other conditions. It is necessary to deepen studies on CC effects in the different regions of the country and implement models that can be used to design adaptation and mitigation policies and strategies, given the negative effects of CC in Mexican agriculture.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Biosistemas]]></kwd>
<kwd lng="es"><![CDATA[DSSAT-CERES-Maize]]></kwd>
<kwd lng="es"><![CDATA[modelación matemática]]></kwd>
<kwd lng="en"><![CDATA[Biosystems]]></kwd>
<kwd lng="en"><![CDATA[DSSAT-CERES-Maize]]></kwd>
<kwd lng="en"><![CDATA[mathematical modeling]]></kwd>
</kwd-group>
</article-meta>
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