Unidad Posgrado Facultad Ingeniería Agronómica

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Author: search.filters.author.Carranza Arévalo, Galo EduardoSubject: search.filters.subject.AGRONOMIA

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    Predicción de los efectos del cambio climático sobre el daño potencial de una especie de cogollero en el cultivo de maíz (Zea mays) en la provincia de Tungurahua
    (2020-09) Carranza Arévalo, Galo Eduardo; Vásquez Freytez, Carlos Luis
    The fall army worm, Spodoptera frugiperda (Lepidoptera: Noctuidae) is a pest species native to America and is distributed throughout the continent. Because of climate change, this species has recently become an invasive pest in Africa and Asia, posing a serious threat to corn cultivation. In the present study, an attempt to design a predictive model on the impact of climate change on the level of damage and incidence of the fall armyworm was made in the Ecuadorian Andean region. The model was based on establishing multiple and simple correlations between climatic factors (temperature, precipitation, and relative humidity) and the incidence and severity of the pest, using R language. According to the regression analysis, no significant correlation was detected. between climatic variables and damage and incidence of S. frugiperda. However, the severity level showed a quadratic trend with the climatic variables, being more pronounced with relative humidity and temperature, while the effect of precipitation was milder, the incidence showed a quadratic relationship with relative humidity with increases between humidity levels between 78 and 84%, but in relation to temperature, the incidence showed a tendency to decrease with decreasing temperature. While with precipitation, a very slight trend was observed to increase with precipitation levels around 200 mm / month. Based on the results obtained, it is suggested to evaluate predictive models that consider evaluations of climatic variables (minimum and maximum temperatures, precipitation) differentiating at various times of the day (morning, afternoon, night), in order to be able to carry out more adjusted models and also repeat this analysis considering data from the Amazonia and Costa region in order to verify the fit of the models proposed in this investigation