Application of a spatial risk model of the crystalline spider mite (Oligonychus sp) to avocado crop damage using remote sensing
Aplicación de un modelo de riesgo espacial de la araña cristalina (Oligonychus sp.) al daño de cultivo de aguacate utilizando sensores remotos
| dc.creator | Báñez Aldave, Harry Wilson | |
| dc.creator | Cuesta-Herrera, Ledys | |
| dc.creator | López-Hernández, Juan Y. | |
| dc.creator | Andrades-Grassi, Jesús E. | |
| dc.creator | Torres-Mantilla, H | |
| dc.date | 2023-06-06T15:18:53Z | |
| dc.date | 2023-06-06T15:18:53Z | |
| dc.date | 2023 | |
| dc.date.accessioned | 2024-05-02T20:31:21Z | |
| dc.date.available | 2024-05-02T20:31:21Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/4844 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9275079 | |
| dc.description | The 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. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Revista Facultad Nacional de Agronomía Medellín, 76(2), 10309-10321 | |
| dc.subject | Univariate and multivariate geostatistics | |
| dc.subject | Crystal mite | |
| dc.subject | Kriging predicted and indicator | |
| dc.subject | Avocado | |
| dc.subject | Geostatistical simulation (en) | |
| dc.subject | Geoestadística univariante y multivariante | |
| dc.subject | Ácaro de cristalino | |
| dc.subject | Kriging predicto e indicado | |
| dc.subject | Aguacate | |
| dc.subject | Simulación geoestadística (es) | |
| dc.title | Application of a spatial risk model of the crystalline spider mite (Oligonychus sp) to avocado crop damage using remote sensing | |
| dc.title | Aplicación de un modelo de riesgo espacial de la araña cristalina (Oligonychus sp.) al daño de cultivo de aguacate utilizando sensores remotos | |
| dc.type | Article |