Modeling citrus huanglongbing data using a zero-inflated negative binomial distribution

• Published in: Acta scientiarum. Agronomy Vol. 38; no. 3; pp. 299 - 306
• Main Authors: Almeida, Eudmar Paiva de, Janeiro, Vanderly, Guedes, Terezinha Aparecida, Mulati, Fabio, Carneiro, José Walter Pedroza, Nunes, Willian Mario de Carvalho
• Format: Journal Article
• Language: English
• Published: Maringa Editora da Universidade Estadual de Maringá - EDUEM 2016; Eduem (Editora da Universidade Estadual de Maringá)
• Subjects: AGRONOMY; Binomial distribution; BLUP method; EM algorithm; mixed model; random effect; Statistical methods; Vectors (Biology); algoritmo EM; modelo misto; método BLUP; random effect; efeito aleatório; BLUP method; mixed model; EM algorithm
• ISSN: 1679-9275; 1807-8621; 1807-8621
• DOI: 10.4025/actasciagron.v38i3.28689

Zero-inflated data from field experiments can be problematic, as these data require the use of specific statistical models during the analysis process. This study utilized the zero-inflated negative binomial (ZINB) model with the log- and logistic-link functions to describe the incidence of plants with Huanglongbing (HLB, caused by Candidatus liberibacter spp.) in commercial citrus orchards in the Northwestern Parana State, Brazil. Each orchard was evaluated at different times. The ZINB model with random effects in both link functions provided the best fit, as the inclusion of these effects accounted for variations between orchards and the numbers of diseased plants. The results of this model show that older plants exhibit a lower probability of acquiring HLB. The application of insecticides on a calendar basis or during new foliage flushes resulted in a three times larger probability of developing HLB compared with applying insecticides only when the vector was detected.