<?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>1659-1321</journal-id>
<journal-title><![CDATA[Agronomía Mesoamericana]]></journal-title>
<abbrev-journal-title><![CDATA[Agron. Mesoam]]></abbrev-journal-title>
<issn>1659-1321</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1659-13212023000300004</article-id>
<article-id pub-id-type="doi">10.15517/am.2023.54430</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo predictivo de la severidad del tizón foliar por Cercospora kikuchii mediante variables meteorológicas]]></article-title>
<article-title xml:lang="en"><![CDATA[Predictive model of the severity of leaf blight by Cercospora kikuchii using meteorological variables]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lavilla]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martinez]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ivancovich]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz-Paleo]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional del Noroeste de la provincia de Buenos Aires  ]]></institution>
<addr-line><![CDATA[Pergamino Buenos Aires]]></addr-line>
<country>Argentina</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Tecnología Agropecuaria  ]]></institution>
<addr-line><![CDATA[Villa Tesei Buenos Aires]]></addr-line>
<country>Argentina</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional del Noroeste de la provincia de Buenos Aires  ]]></institution>
<addr-line><![CDATA[Pergamino Buenos Aires]]></addr-line>
<country>Argentina</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Nacional de Tecnología Agropecuaria  ]]></institution>
<addr-line><![CDATA[Pergamino Buenos Aires]]></addr-line>
<country>Argentina</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>34</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1659-13212023000300004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1659-13212023000300004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1659-13212023000300004&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Introducción. En el manejo integrado de enfermedades es importante incorporar elementos como umbrales de daño económico, monitoreo y sistemas de pronóstico de riesgo, los cuales constituyen herramientas para definir estrategias de control de las mismas. Objetivo. Desarrollar un modelo predictivo de la severidad del tizón foliar por Cercospora (TFC), mediante variables meteorológicas para el norte de la provincia de Buenos Aires, Argentina. Materiales y métodos. Se contó con datos de incidencia y severidad del TFC correspondientes a cinco ciclos productivos de soja (2013-2017) en Pergamino, Buenos Aires, relevados en distintos estados reproductivos R1 a R7. La variable dependiente fue la probabilidad de ocurrencia de niveles categorizados de la tasa de incremento (TI) de la severidad del TFC causado por C. kikuchii. Se registró diariamente la temperatura máxima y mínima, la precipitación y la humedad relativa. Se calculó el coeficiente de correlación no paramétrico de Kendall Tau-b entre los niveles categorizados binariamente de TI de la severidad del TFC y las variables meteorológicas. Resultados. Las variables meteorológicas con mayor correlación en relación a la TI del TFC fueron aquellas relacionadas con la humedad relativa (Días con humedad relativa (HR)&gt;76 % (DHR), días con temperatura máxima (Tmax) &lt;28 °C temperatura mínima (Tmin) &gt;15 °C y HR &gt;76 % (DHRT), días con Tmax &lt;28 °C, Tmin &gt;15 °C, lluvias &gt;0,5 mm y HR &gt;75 %(MOJro)). La inclusión de una variable térmica (en los días donde Tmax &lt;28 °C GDTmax: &#931;temperatura máxima-28 ºC) resultó importante para el ajuste del modelo predictivo. Conclusión. Se pudo desarrollar un modelo de predicción de la severidad del TFC que incluyó dos variables meteorológicas, una relacionada con los días de humedad relativa y otra térmica relacionada con un límite de temperatura máxima para el desarrollo de la enfermedad. Para validar y robustecer el modelo propuesto es necesario contar con más datos de severidad a través de los años.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Introduction. In the integrated management of diseases, it is important to incorporate elements such as economic damage thresholds, monitoring, and risk forecasting systems, which constitute tools to define d control strategies. Objective. To develop a predictive model of the severity of Cercospora leaf blight (TFC) using meteorological variables in the northern region of Buenos Aires Province, Argentina. Materials and methods. Data on the incidence and severity of TFC corresponding to five soybean production cycles (2013-2017) in Pergamino, Buenos Aires, at different reproductive stages (R1 to R7) were available. The dependent variable was the probability of occurrences of categorized levels of the rate of increase (TI) in TFC severity caused by C. kikuchii. Daily maximum and minimum temperature, precipitation, and relative humidity were recorded. The non-parametric Kendall Tau-b correlation coefficient was calculated between the binary categorized levels of TI in TFC severity and meteorological variables. Results. Meteorological variables with the highest correlation in relation to TFC TI were those related to relative humidity (Days with relative humidity (HR) &gt;76 % (DHR), days with maximum temperature (Tmax) &lt;28 °C, minimum temperature (Tmin) &gt;15 °C, and HR &gt;76 % (DHRT), days with Tmax &lt;28 °C, Tmin &gt;15 °C, rainfall &gt;0.5 mm, and HR &gt;75 %(MOJro)). The inclusion of a thermal variable (on days where Tmax &lt;28 °C, GDTmax: &#8721;maximum temperature-28 °C) was important for the adjustment of the predictive model. Conclusion. A predictive model for TFC severity was developed, which included two meteorological variables, one related to days of relative humidity and another thermal related to a maximum temperature threshold for disease development. To validate and strengthen the proposed model, it is necessary to have more severity data over the years.]]></p></abstract>
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<kwd lng="en"><![CDATA[pathology]]></kwd>
<kwd lng="en"><![CDATA[plague]]></kwd>
<kwd lng="en"><![CDATA[Glycine max]]></kwd>
</kwd-group>
</article-meta>
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