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Evaluation of EPI forecasting model with inclusion of uncertainty in input value and traceable calibration (Contributo in atti di convegno)

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Evaluation of EPI forecasting model with inclusion of uncertainty in input value and traceable calibration (Contributo in atti di convegno) (literal)

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Francesca Sanna1, Quirico Antonio Cossu2, Guido Roggero3, Simone Bellagarda3, Roberto Deboli1, Andrea Merlone3 (2014)
Evaluation of EPI forecasting model with inclusion of uncertainty in input value and traceable calibration
in XVII CONVEGNO NAZIONALE DI AGROMETEOROLOGIA AIAM Ruolo dell'agrometeorologia nelle nuove politiche agricole, Roma, 10-12 giugno 2014
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Francesca Sanna1, Quirico Antonio Cossu2, Guido Roggero3, Simone Bellagarda3, Roberto Deboli1, Andrea Merlone3 (literal)

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1 IMAMOTER-CNR - Istituto per le Macchine Agricole e Movimento Terra - Consiglio Nazionale Ricerche, Strada delle Cacce, 73, 10135, Torino (TO). 2 ARPAS Sardegna - Agenzia Regionale per la Protezione dell'Ambiente della Sardegna, Via Rockefeller 58-60 - 07100 Sassari (SS). 3 INRiM - Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce, 91, 10135, Torino (TO). (literal)

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Evaluation of EPI forecasting model with inclusion of uncertainty in input value and traceable calibration (literal)

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Abstract

Phytopathologies affecting viticulture, such as grapevine downy mildew (Plasmopara viticola), are depending on meteorological trend. Diseases are controlled with the use of fungicides, which has considerable economic costs, negative effects on environment, human health and wine quality. This strategy should be reconsidered through introducing methods to reduce the use of chemicals. In order to act promptly, it is necessary to know the correct incubation period and an accurate knowledge of meteorological parameters such as temperature, humidity and precipitation is required. This project is an example of metrological approach applied to agrometeorological studies. The aims are: improvement of the meteorological observations in field by applying a metrological approach and calibration methods; evaluation of the uncertainties; implementation of traceability in meteorological measurements; and improvement of the forecasting models by inclusion of measurement uncertainties in the input values. (literal)

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