Bayesian forecasting of grape moth emergence

Wine production is an important industry in Western Switzerland, but vineyards must contend with grape vine and grape berry moths, the larval stages of which feed on grape flower buds and berries. This paper proposes a stochastic model for prediction of the emergence times of the moths, in order to aid treatment aimed at their control. Observed moth counts are treated as a realization of an inhomogeneous Poisson process whose probabilities are related to the distribution of time to emergence. We use a hierarchical model to account for the variability of this distribution between sites and years. Maximum likelihood and Bayesian fitting of the model to data are described, the latter using Markov chain Monte Carlo simulation, and the Bayesian approach is used for forecasting in future years. Grape vine moth flights proved much more variable than grape berry moth flights among sites and years; depending on the site, the flights of the two species can overlap or be completely distinct. Credibility intervals are provided for each site for a random future year, and the predictions can be updated and refined once 15-20% of the flight for a new year has emerged. The paper ends with discussion of the role of such models in similar applications.