Deliere, L., Cartolaro, Philippe, Léger, Bertrand, Naud, O., Unité Mixte de Recherche en Santé Végétale (INRA/ENITA) (UMRSV), Institut National de la Recherche Agronomique (INRA)-École Nationale d'Ingénieurs des Travaux Agricoles - Bordeaux (ENITAB)-Institut des Sciences de la Vigne et du Vin (ISVV), Université de Bordeaux (UB), Technologies pour la sécurité et les performances des agroéquipements (UR TSAN), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
International audience; BACKGROUND In France, viticulture accounts for 20% of the phytochemicals sprayed in agriculture, and 80% of grapevine pesticides target powdery and downy mildews. European policies promote pesticide use reduction, and new methods for low-input disease management are needed for viticulture. Here, we present the assessment, in France, of Mildium®, a new decision support system for the management of grapevine mildews. RESULTS A 4 year assessment trial of Mildium has been conducted in a network of 83 plots distributed across the French vineyards. In most vineyards, Mildium has proved to be successful at protecting the crop while reducing by 30–50% the number of treatments required when compared with grower practices. CONCLUSION The design of Mildium results from the formalisation of a common management of both powdery and downy mildews and eventually leads to a significant fungicide reduction at the plot scale. It could encourage stakeholders to design customised farm-scale and low-chemical-input decision support methods.