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Application of a spatial risk model of the crystalline spider mite (Oligonychus sp) to avocado crop damage using remote sensing

dc.contributor.authorBáñez Aldave, Harry Wilson
dc.contributor.authorCuesta-Herrera, Ledys
dc.contributor.authorLópez-Hernández, Juan Y.
dc.contributor.authorAndrades-Grassi, Jesús E.
dc.contributor.authorTorres-Mantilla, H
dc.date.accessioned2023-06-06T15:18:53Z
dc.date.available2023-06-06T15:18:53Z
dc.date.issued2023
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/4844
dc.description.abstractThe avocado is one of the most consumed foods in the world and it is affected by the mite Oligonychus sp., which affects the generation of chlorophyll by the plant, resulting in a decrease in productivity. Given the economic importance of the avocado, a spatial statistical methodology was used to analyze the risk of a pest in its crops. A total of 202 observations of a 1.1 ha avocado farm were used to measure the number of mites per leaf in the area of Barranca, Perú. Predictive geostatistical methods and indicators were applied. A Spherical semivariogram was adjusted to estimate a Univariate Ordinary Kriging, covariates such as vegetation indicators and geomorphometric variables were used to improve the spatial resolution of the covariates and geostatistical simulation was used and linear co-regionalization models were adjusted with which pest predictions were made with co-Kriging. Finally, the predictions were transformed into a risk model using Kriging Indicator. The results obtained show that the mite presents a stationary process in second order with spatial dependence of less than 10 m, in which univariante Ordinary Kriging was the most efficient. Despite the results, the linear co-regionalization models are consistent, but the geostatistical simulation was not enough to improve the predictions. Covariate data should be incorporated at a higher level of detail and small-scale variations should be analyzed. It is suggested to incorporate covariate data with a higher level of detail and analyze small-scale variations.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceRevista Facultad Nacional de Agronomía Medellín, 76(2), 10309-10321es_CL
dc.subjectUnivariate and multivariate geostatisticses_CL
dc.subjectCrystal mitees_CL
dc.subjectKriging predicted and indicatores_CL
dc.subjectAvocadoes_CL
dc.subjectGeostatistical simulation (en)es_CL
dc.subjectGeoestadística univariante y multivariantees_CL
dc.subjectÁcaro de cristalinoes_CL
dc.subjectKriging predicto e indicadoes_CL
dc.subjectAguacatees_CL
dc.subjectSimulación geoestadística (es)es_CL
dc.titleApplication of a spatial risk model of the crystalline spider mite (Oligonychus sp) to avocado crop damage using remote sensinges_CL
dc.title.alternativeAplicación de un modelo de riesgo espacial de la araña cristalina (Oligonychus sp.) al daño de cultivo de aguacate utilizando sensores remotoses_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias Básicases_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionScieloes_CL
dc.ucm.indexacionOtroes_CL
dc.ucm.urirevistas.unal.edu.co/index.php/refame/article/view/102479es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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