Literature DB >> 25264219

Field evaluation of an expertise-based formal decision system for fungicide management of grapevine downy and powdery mildews.

Laurent Delière1,2, Philippe Cartolaro1,2, Bertrand Léger3, Olivier Naud4.   

Abstract

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.
© 2014 Society of Chemical Industry.

Entities:  

Keywords:  Vitis vinifera; crop protection; decision process; decision support system; disease management

Mesh:

Substances:

Year:  2014        PMID: 25264219     DOI: 10.1002/ps.3917

Source DB:  PubMed          Journal:  Pest Manag Sci        ISSN: 1526-498X            Impact factor:   4.845


  2 in total

1.  The impact of restrictions on neonicotinoid and fipronil insecticides on pest management in maize, oilseed rape and sunflower in eight European Union regions.

Authors:  Jonas Kathage; Pedro Castañera; José Luis Alonso-Prados; Manuel Gómez-Barbero; Emilio Rodríguez-Cerezo
Journal:  Pest Manag Sci       Date:  2017-10-13       Impact factor: 4.845

2.  Forecasting severe grape downy mildew attacks using machine learning.

Authors:  Mathilde Chen; François Brun; Marc Raynal; David Makowski
Journal:  PLoS One       Date:  2020-03-12       Impact factor: 3.240

  2 in total

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