Autologistic model with an application to the citrus "sudden death" disease
Autor(a) principal: | |
---|---|
Data de Publicação: | 2008 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500014 |
Resumo: | 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 used. However it is possible to further explore and describe the data by means of adopting an explicit model to discriminate and quantify effects by attaching parameters to covariates which represent aspects of interest to be investigated. One alternative involves autologistic models, which extend a usual logistic model in order to accommodate spatial effects. In order to implement such model it is necessary to take into account the reuse of data to built spatial covariates, which requires extensions in methodology and algorithms to assess the variance of the estimates. This work presents an application of the autologistic model to data collected at 11 time points from citrus fields affected by CSD. It is shown how the autologistic model is suitable to investigate diseases of this type, as well as a description of the model and the computational aspects necessary for model fitting. |
id |
USP-18_19e1177dc480cc1ff533b8b37d317b62 |
---|---|
oai_identifier_str |
oai:scielo:S0103-90162008000500014 |
network_acronym_str |
USP-18 |
network_name_str |
Scientia Agrícola (Online) |
repository_id_str |
|
spelling |
Autologistic model with an application to the citrus "sudden death" diseasespatial statisticsplant diseasebinary response variablepseudolikelihoodbootstrapThe 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 used. However it is possible to further explore and describe the data by means of adopting an explicit model to discriminate and quantify effects by attaching parameters to covariates which represent aspects of interest to be investigated. One alternative involves autologistic models, which extend a usual logistic model in order to accommodate spatial effects. In order to implement such model it is necessary to take into account the reuse of data to built spatial covariates, which requires extensions in methodology and algorithms to assess the variance of the estimates. This work presents an application of the autologistic model to data collected at 11 time points from citrus fields affected by CSD. It is shown how the autologistic model is suitable to investigate diseases of this type, as well as a description of the model and the computational aspects necessary for model fitting.Escola Superior de Agricultura "Luiz de Queiroz"2008-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500014Scientia Agricola v.65 n.5 2008reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162008000500014info:eu-repo/semantics/openAccessKrainski,Elias TeixeiraRibeiro Junior,Paulo JustinianoBassanezi,Renato BeozzoFranciscon,Luzianeeng2008-09-16T00:00:00Zoai:scielo:S0103-90162008000500014Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2008-09-16T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Autologistic model with an application to the citrus "sudden death" disease |
title |
Autologistic model with an application to the citrus "sudden death" disease |
spellingShingle |
Autologistic model with an application to the citrus "sudden death" disease Krainski,Elias Teixeira spatial statistics plant disease binary response variable pseudolikelihood bootstrap |
title_short |
Autologistic model with an application to the citrus "sudden death" disease |
title_full |
Autologistic model with an application to the citrus "sudden death" disease |
title_fullStr |
Autologistic model with an application to the citrus "sudden death" disease |
title_full_unstemmed |
Autologistic model with an application to the citrus "sudden death" disease |
title_sort |
Autologistic model with an application to the citrus "sudden death" disease |
author |
Krainski,Elias Teixeira |
author_facet |
Krainski,Elias Teixeira Ribeiro Junior,Paulo Justiniano Bassanezi,Renato Beozzo Franciscon,Luziane |
author_role |
author |
author2 |
Ribeiro Junior,Paulo Justiniano Bassanezi,Renato Beozzo Franciscon,Luziane |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Krainski,Elias Teixeira Ribeiro Junior,Paulo Justiniano Bassanezi,Renato Beozzo Franciscon,Luziane |
dc.subject.por.fl_str_mv |
spatial statistics plant disease binary response variable pseudolikelihood bootstrap |
topic |
spatial statistics plant disease binary response variable pseudolikelihood bootstrap |
description |
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 used. However it is possible to further explore and describe the data by means of adopting an explicit model to discriminate and quantify effects by attaching parameters to covariates which represent aspects of interest to be investigated. One alternative involves autologistic models, which extend a usual logistic model in order to accommodate spatial effects. In order to implement such model it is necessary to take into account the reuse of data to built spatial covariates, which requires extensions in methodology and algorithms to assess the variance of the estimates. This work presents an application of the autologistic model to data collected at 11 time points from citrus fields affected by CSD. It is shown how the autologistic model is suitable to investigate diseases of this type, as well as a description of the model and the computational aspects necessary for model fitting. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500014 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500014 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-90162008000500014 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.65 n.5 2008 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936461040746496 |