Metrology applied to agrometeorology and forecasting models for agro-food sciences technologies

Meteorological data have many applications in the prevention of natural adversities of climate origin, in programming and addressing the human activities, in the use of resources and in territorial planning. Local agrometeorological stations provide useful data in forecasting models to support management decisions, to contain costs and to reduce the impact of farming activities on environment. Agrometeorology can also play a significant role in reducing the negative impacts caused by pests and diseases. An appropriate, preferably integrated, pest management system using meteorological and microclimatological information can reduce pre and post-harvest losses appreciably. Despite the considerable improvement of technologies and instrumentations, the analytical methodologies need a robust metrological assessment for the measurements of meteorological parameters in agricultural and food science field. Metrology, for instance, can be usefully applied in support of epidemiological forecasting models for vineyard diseases, such as grapevine downy mildew (Plasmopara viticola), one of the most important disease affecting viticulture strictly depending by temperature, humidity and rain. In order to relate the meteorological quantities with the biological cycle of the pathogen, several forecasting models have been developed. These provide information on progress and evolution of the infections, on the best time to make or not treatment in the field, all depending on the meteorological data. However, these models do not consider the quality of input data, usually collected from sensors not calibrated or calibrated without traceability and without inclusion of measurement uncertainties. The success of any investigation or study depends upon the availability of reliable data. Meteorological data should be collected under standard conditions in accordance with established practices, both for observations and for the exposure of instruments. There is a need for testing various types of sensors, their calibration and to evaluate the measurement uncertainty related the meteorological quantities gathered from automatic weather stations (AWS), in order to improve cultivation disease predictions and reduce the use of chemicals in agriculture. The main objectives of this research were to achieve a metrological systemic approach applied to agrometeorological studies to implement the traceability of weather measurements. In the first part of the PhD work, both non-calibrated and calibrated AWS were installed in a vineyard, evaluating the uncertainty in meteorological measurements and improving epidemiological forecasting models, to optimize the use of pesticides with a positive impact on the environment, health and crops management.