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This study aims to determine whether a significant decrease in airborne concentrations of Ambrosia pollen witnessed in the north-west of the Province of Milan in Northern Italy could be explained by environmental factors such as meteorology, or whether there is evidence to support the hypothesis that the decrease was related to the presence of large numbers of the oligophagous Ophraella communa leaf beetles that are used as a biological control agent against Ambrosia in other parts of the world. Airborne concentrations of Ambrosia, Cannabaceae and Urticaceae pollen data (2000-2013) were examined for trends over time and correlated with meteorological data. The amount of Ambrosia pollen recorded annually during the main flowering period of Ambrosia (AugustSeptember) was entered into linear regression models with meteorological data in order to determine whether the amount of airborne Ambrosia pollen recorded in 2013 was lower than would normally be expected based on the prevailing weather conditions. There were a number of significant correlations between concentrations of airborne Ambrosia, Cannabaceae and Urticaceae pollen, as well as between airborne pollen concentrations and daily and monthly meteorological data. The linear regression models greatly overestimated the amount of airborne Ambrosia pollen in 2013. The results of the regression analysis support the hypothesis that the observed decrease in airborne Ambrosia pollen may indeed be related to the presence of large numbers of O. communa in the Milan area, as the drastic decrease in airborne Ambrosia pollen in 2013 cannot be explained by meteorology alone.

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Crop yield determines economy by influencing prices on the trade market, and so accurate forecasts of the yield are important for planning various aspects of agricultural production. The main aim of this study is to construct a model for predicting walnut yield in an important walnut production area (the region of Novi Sad in Northern Serbia). Relationships between the amount of walnuts produced annually (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) and abiotic (e.g. meteorological) and biotic (e.g. airborne pollen data) factors were examined using Pearson correlation analysis. Walnut yield data were then entered into linear regression models with variables that had the highest correlations. The models were constructed using 10 years of data, and tested using 2 years of data not included in constructing the model. This paper has shown that walnut yield is greatly dependent on weather conditions, particularly during fertilisation and seed growth, but the amount of available airborne pollen also plays an important role. The introduction of the seasonal pollen index, as a proxy for the amount of pollen available for fertilisation, improved the performance of models predicting walnut yield.

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