Insect dynamics depend on temperature patterns, and therefore, global warming may lead to increasing frequencies and intensities of insect outbreaks. The aim of this work was to analyze the dynamics of the olive fruit fly, Bactrocera oleae (Rossi), in Tuscany (Italy). We profited from long-term records of insect infestation and weather data available from the regional database and agrometeorological network. We tested whether the analysis of 13 years of monitoring campaigns can be used as basis for prediction models of B. oleae infestation. We related the percentage of infestation observed in the first part of the host-pest interaction and throughout the whole year to agrometeorological indices formulated for different time periods. A two-step approach was adopted to inspect the effect of weather on infestation: generalized linear model with a binomial error distribution and principal component regression to reduce the number of the agrometeorological factors and remove their collinearity. We found a consistent relationship between the degree of infestation and the temperature-based indices calculated for the previous period. The relationship was stronger with the minimum temperature of winter season. Higher infestation was observed in years following warmer winters. The temperature of the previous winter and spring explained 66 % of variance of early-season infestation. The temperature of previous winter and spring, and current summer, explained 72 % of variance of total annual infestation. These results highlight the importance of multiannual monitoring activity to fully understand the dynamics of B. oleae populations at a regional scale.

Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus’ ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements.

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