Distribuição espacial do anel vermelho (Bursaphelenchus cocophilus) e da resinose (Thielaviopsis paradoxa) em coqueiro

Silva, Carina Melo da; Macambira, Laís Carvalho; Mercês, Érika de Paula Ramos das; Silva, Gisele Barata da; Lins, P. M. P.; Carvalho, Eudes de Arruda

doi:10.5039/agraria.v11i3a5385

Cited by 2 publications

(4 citation statements)

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“…The aggregated distribution model is the one that best suits the spatial behavior of the disease, as it shows the foci of incidence in the form of concentric areas that tend to expand in all directions according to the disease's population growth (Bastos et al, 2019). Studies by Silva et al (2016), found an aggregate distribution with moderate spatial dependence for the red ring disease (caused by Bursaphelenchus cocophilus) and strong spatial dependence for resinosis incidence (caused by Thielaviopsis paradoxa), diseases that affect coconut trees in the study region.…”

Section: Spatial Dependencementioning

confidence: 78%

“…The Surfer (v.11) software was used to interpolate data. It is a grid-based mapping progr interpolates irregularly spaced XYZ data into a regularly spaced grid, allowing the adjustm interpolation and grid parameters, identifying the spatial continuity of the data with variograms mod a function of the degree of spatial dependence between samples (Golden Software, 2014 semivariograms were adjusted based on the determination index (R 2 ), to choose the best mod function of the mean square of the error, standard error of prediction, and the autocorrelation betw data (Seidel & Oliveira, 2016;Silva et al, 2016).…”

Section: Mappingmentioning

confidence: 99%

“…Moreove dependence indices [SDI = C₀ / (C₀ + C₁ )] were generated, classified according to intervals that of the data with variograms modeled as a function of the degree of spatial dependence between samples (Golden Software, 2014). The semivariograms were adjusted based on the determination index (R 2 ), to choose the best model as a function of the mean square of the error, standard error of prediction, and the autocorrelation between the data (Seidel & Oliveira, 2016;Silva et al, 2016).…”

Section: Krigingmentioning

confidence: 99%

“…Pest attacks and diseases greatly damage coconut cultivation (Ferreira, Warwick, & Siqueira, 2018). Currently, several phytosanitary problems affect coconut trees in the world (including Brazil): Lethal Yellowing (phytoplasmas of the 16Sr DNA IV group), Resinosis (Thielaviopsis paradoxa), Red palm mite (Raoiella indica Hirs), Red ring disease (Bursaphelenchus cocophilus), Coconut mite (Aceria guerreronis Keifer), among others (Barroso et al, 2019;Bonnot, Franqueville, & Lourença, 2010;Carvalho et al, 2021;(Rezende, Melo, Oliveira, & Gondim, 2016;Silva et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Geostatistical modeling of “lethal coconut palm crown atrophy”, a new disease in the State of Pará

Gama¹,

Farias²

2021

Semina: Ciênc. Agrár.

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’Lethal Coconut Palm Crown Atrophy’ (LCCA) is a rapidly spreading disease in Brazil, capable of quickly killing coconut trees and threatening the commercial exploration of this plant. The objective of this work was to characterize the spatial and temporal distribution pattern of LCCA in green dwarf coconut commercial plantation areas, located the municipality of Santa Izabel, mesoregion of Northeastern Pará, Brazil. Surveys were carried out at monthly intervals between January 2014 and December 2018, checking for plants with LCCA-characteristic symptoms. Geostatistics was applied to perform spatial-temporal disease estimates based on semivariogram modeling and preparation of ordinary kriging maps. These spatial estimates are conducted through interpolations that characterize data variability in the area. The spherical model yielded the best fit to the spatial distribution of the disease, as it presented the best coefficient of determination (R²), with the range varying between 14m and 45m. The Spatial Dependence Index (SDI) was moderate in the evaluations carried out between 2014 and 2017 (in the 0.26-0.64 range), but not in 2018, when it was strong (0.23). The values of the clustering intensity of LCCA-symptomatic plants were estimated in non-sampled points. The spherical fit model of the data indicates an aggregated distribution pattern, shown by aggregation patches in the plantation, graded by values of dissemination intensity. The kriging maps allowed the observation that the disease expands between plants in the same line, suggesting the possibility of the presence of a short-range vector.

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Section: Spatial Dependencementioning

confidence: 78%

Section: Mappingmentioning

confidence: 99%

Section: Krigingmentioning

confidence: 99%