Use of probabilistic expert elicitation for assessing risk of appearance of grape downy mildew
2019
Chen, Mathilde | Brun, Francois | Raynal, Marc | Debord, Christian | Makowski, David | Agronomie ; Institut National de la Recherche Agronomique (INRA)-AgroParisTech | AGroécologie, Innovations, teRritoires (AGIR) ; Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Institut Français de la Vigne et du Vin (IFV) | centre international de recherche sur l'environnement et le développement (CIRED) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École des hautes études en sciences sociales (EHESS)-AgroParisTech-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS) | ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016)
International audience
显示更多 [+] 显示较少 [-]英语. Grape downy mildew (GDM) is a major disease of grapevine and the date of appearance of its first symptoms is a determinant information for the protection of the vineyard. Probabilistic elicitation of experts has been used here to estimate this date. In 2017 and 2018, 29 experts were elicited to provide probability distributions of dates of GDM appearance between April and June, for different plots. The results of these elicitations show that the experts' forecasts and their uncertainty change over the season with possible consequences on the number of fungicide treatments. The elicited dates tend to be earlier at the beginning of the season and later at the end of the season, with an average difference of about 18 days. In April 2017 and 2018, most of the elicited dates are too early compared to observed dates of GDM symptom appearance. However, this bias becomes negligible in the month of May. Compared to qualitative scoring systems, our results indicate that probabilistic elicitation is a better tool for communicating expert judgments and their associated uncertainties in plant disease risk assessments and epidemiological alert bulletins.
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