2008
DOI: 10.1590/s0103-90162008000500014
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Abstract: The citrus sudden death (CSD) disease affects dramatically citrus trees causing a progressive plant decline and death. The disease has been identified in the late 90's in the main citrus production area of Brazil and since then there are efforts to understand the etiology as well as the mechanisms its spreading. One relevant aspect of such studies is to investigate spatial patterns of the occurrence within a field. Methods for determining whether the spatial pattern is aggregated or not has been frequently use… Show more

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Cited by 7 publications
(1 citation statement)
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“…The autologistic model was adapted by Krainski et al (2008) from the R statistical environment for statistical analysis (R Development Core Team, 2013) to account for the temporal patterns that occur when estimating the probability of disease in a tree at an arbitrary location as a function of the disease status of the neighbouring trees at another time. Comparing a sequence of models with different covariates for disease status within and across rows at current and previous observation times allows detection of the relevant spatial and temporal patterns that were associated with the spread of PFD (Krainski et al, 2008). Using this model, covariates were built for each assessment date by considering the following: (i) the disease status of the immediate neighbour trees within the row, (ii) in the column (across row), and (iii) in both.…”
Section: Spatiotemporal Autologistic Analysismentioning
confidence: 99%
“…The autologistic model was adapted by Krainski et al (2008) from the R statistical environment for statistical analysis (R Development Core Team, 2013) to account for the temporal patterns that occur when estimating the probability of disease in a tree at an arbitrary location as a function of the disease status of the neighbouring trees at another time. Comparing a sequence of models with different covariates for disease status within and across rows at current and previous observation times allows detection of the relevant spatial and temporal patterns that were associated with the spread of PFD (Krainski et al, 2008). Using this model, covariates were built for each assessment date by considering the following: (i) the disease status of the immediate neighbour trees within the row, (ii) in the column (across row), and (iii) in both.…”
Section: Spatiotemporal Autologistic Analysismentioning
confidence: 99%