Autologistic model of spatial pattern of phytophthora epidemic in bell pepper: effects of soil variables on disease presence

The autologistic model is a flexible model for predicting presence or absence of disease in an agricultural field, based on soil variables, while taking spatial correlation into account. In the autologistic model, the log odds of disease in a particular quadrat are modeled as a linear combination of...

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Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 1997-06, Vol.2 (2), p.131-156
Main Authors: Gumpertz, M.L. (North Carolina State University, Raleigh, NC.), Graham, J.M, Ristaino, J.B
Format: Article
Language:English
Subjects:
CAPSICUM ANNUUM
DISEASE INCIDENCE
Disease models
DISTRIBUCION ESPACIAL
DISTRIBUTION SPATIALE
ENFERMEDADES FUNGOSAS
Epidemics
Epidemiology
FUNGAL DISEASES
INCIDENCE
Logistic regression
MALADIE FONGIQUE
MATHEMATICAL MODELS
METHODE STATISTIQUE
METODOS ESTADISTICOS
MODELE MATHEMATIQUE
Modeling
MODELOS MATEMATICOS
Parametric models
PHYTOPHTHORA
Plant diseases
Soil water
Soil water content
SPATIAL DISTRIBUTION
Spatial models
STATISTICAL METHODS
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Summary:The autologistic model is a flexible model for predicting presence or absence of disease in an agricultural field, based on soil variables, while taking spatial correlation into account. In the autologistic model, the log odds of disease in a particular quadrat are modeled as a linear combination of disease presence or absence in neighboring quadrats and the soil variables. Neighboring quadrats can be defined as adjacent quadrats within a row, quadrats in adjacent rows, quadrats two rows away, and so forth. The regression coefficients give estimates of the increase in odds of disease if neighbors within a row or in adjacent rows show disease symptoms; thus, we obtain information about the degree of spread in different directions. The coefficients for the soil variables give estimates of the increase in odds of disease as soil water content or pathogen population density increase. In this problem, the soil variables may also be highly correlated over quadrats, and disease incidence in within-row neighbors may be highly correlated with disease incidence in adjacent-row neighbors. This collinearity makes estimation and interpretation of the parameters of the autologistic model more difficult. We discuss fitting the autologistic model and tools for evaluating the aptness of the model.
ISSN:1085-7117
1537-2693
DOI:10.2307/1400400