Modeling as a vineyard quality prediction tool
Abstract
Bayesian networks are widely used for knowledge representation and reasoning under uncertainty in natural resource management. We propose here a probabilistic model able to assess the quality of a vineyard. There is a rising interest for this methodology as tool for ecological and agronomic modelling. Our model takes into account the geographical area, chemical and physical properties of the soil, agronomic variables and water resources. This version is based on a previous model that was designed to assess the quality of large wine areas (ABBAL et al., 2016). In this new version, we added the local drought index or soil water reservoir (W0), the vineyard balance, the vigor, the yield and the kind of wine expected (early, regular, long conservation). Furthermore, vine varieties have been ranked into four quality classes taking into account Huglin index associated with the level of their ability to elaborate top wines. This model assessing the potential quality of a vineyard is a real decision-making tool for a professional who wishes to quantify the qualitative potential of a vineyard. The appropriation of this model at the level of the wine sector will provide solutions for most current and future problems and at the same time will help to improve the model itself by testing it in as many situations as possible.
Issue: GiESCO 2017
Type: Extended abstract
Format: Oral
Authors
1 INRA, UMR 1083, Science for Enology, bat. 28, 2 Place Viala, 34060 Montpellier, France
2 INRA, Unité Expérimentale de Pech Rouge, 11430 Gruissan, France
3 SupAgro, 2, Place Viala, 34060 Montpellier, France
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Keywords
vineyard quality, Bayesian network, expert data, wine quality