Selection of Interpolators to Predict Populations of Tibraca limbativentris in Irrigated Rice

• Autor(a) principal: Pasini,Mauricio Paulo Batistella
• Data de Publicação: 2021
• Outros Autores: Engel,Eduardo, Lúcio,Alessandro Dal’Col, Nora,Sabrina Lago Dalla
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Archives of Biology and Technology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132021000100224
• topic: precision; Oryza sativa; stink bug; geostatistics

Abstract Kriging is a method that estimates values in places not sampled from different interpolators, therefore, widely used to predict the spatial distribution of organisms. However, the different interpolators may vary in performance depending on the organism under study or the area evaluated. The aimed study to compare the ordinary kriging and inverse of distance weighted interpolation methods, applied to the spatial distribution of population density of Tibraca limbativentris in irrigated rice. This study was carried out in Santa Maria, RS, Brazil, in two fields with areas of 1.3 ha and 6.2 ha, respectively. Seven evaluations of the population density of T. limbativentris were carried out, corresponding to the period from sowing to maturation. In these areas the adults of T. limbativentris were quantified and the sum used for the statistical and geostatistical analysis. The sample population of T. limbativentris was submitted to different semivariograms, which were selected through cross-validation. The sample population of T. limbativentris was submitted to different semivariograms, selected by means of cross-validation. Once selected, semivariograms were used in both tested interpolation methods. From the results it was concluded that the ordinary kriging interpolation method performed better in all evaluations performed in both areas. Therefore, we recommend its use for estimating the population density and spatial distribution of T. limbativentris in the irrigated rice throughout the crop phenology. Using appropriate interpolation methods, localized management can be used, reducing costs for controlling this pest and increasing the sustainability of the environment.