Modeling the mite counts having overdispersion and excess values ofzero using zero-inflated generalized poisson regression

The aim of this study was to apply for zero-inflated generalized Poisson regression in the modelling of mite counts that include excess values of zero and overdispersion. The results of, as mean regression, overdispersion and zero-inflated regression, were determined in three stages. It was obtained that 33.33% (120 observations) of the total numbers of mite taken as a dependent variable to model had zero values. The overdispersion parameter range was detected to be quite high. It was determined that zero-inflated data and overdispersion had an important effect on mite counts (P< 0.01). The effects of region, month, year, varieties, temperature and humidity were found to be statistically significant on mite counts (P< 0.01). The number of eggs found in harmful mites (Aculus schlechtendali) in the Starking variety was relatively higher than in the Golden variety. The results displayed that the differences among regions and varieties regarding the number of eggs found in harmful mites were statistically significant (P<0.01).