Nonlinear models for describing the Citrus Variegated Chlorosis in groves of two counties at northwestern Paraná state, Brazil - doi: 10.4025/actasciagron.v32i4.11625

In Brazil, the production of sweet oranges has been threatened by the Citrus Variegated Chlorosis (CVC) incited by the gram-negative bacterium Xylella fastidiosa (Wells). Commercial citrus groves in two counties at the Northwestern Paraná state were evaluated to estimate the disease progression by u...

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Detalles Bibliográficos
Autores: Ueda, Clara Matiko, Yamamoto, Akemi Yamagata, Nunes, William Mário de Carvalho, Scapim, Carlos Alberto, Guedes, Terezinha Aparecida
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2010
País:Brasil
Institución:Universidade Estadual de Maringá (UEM)
Repositorio:Acta Scientiarum. Agronomy (Online)
Idioma:portugués
inglés
OAI Identifier:oai:periodicos.uem.br/ojs:article/11625
Acceso en línea:http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/11625
Access Level:acceso abierto
Palabra clave:growth curves
nonlinear models
Citrus Variegated Chlorosi.
Fitopatologia
Descripción
Sumario:In Brazil, the production of sweet oranges has been threatened by the Citrus Variegated Chlorosis (CVC) incited by the gram-negative bacterium Xylella fastidiosa (Wells). Commercial citrus groves in two counties at the Northwestern Paraná state were evaluated to estimate the disease progression by using parameterizations of nonlinear models. Groves of Citrus sinensis Osbeck, variety “Pêra”, “Valência”, “Natal” and “Folha Murcha” had all the plants evaluated for the presence of disease symptoms. Thereafter, different parameterizations of the Logistic and Gompertz models were fitted to these data. The goodness of fit was evaluated by the intrinsic (IN) and parameter-effects (PE) curvatures of Bates and Watts, the bias of Box and the Hougaard measures of skewness. In Loanda, the best model was the Fermi-Dirac, and in Nova Esperança the data were best fitted to the parameterization L5, which is also a parameterization from the Logistic model.