Grape ripening is a critical phenophase during which many metabolites driving wine quality are accumulated in berries. Major changes in berry composition include a rapid increase in sugar and a decrease in malic acid content and concentration. Its duration is highly variable depending on grapevine variety, climatic parameters, soil type and management practices.

Many compounds of interest in wine are difficult to analyze since they are present in very small quantities or they are unstable. The need for reliable data led scientists to develop complex method in order to overcome the analytical difficulties and provide accurate quantitative data for grape or wine characterization.

AIM: ‘Nebbiolo’ (Vitis vinifera L.) is one of the most ancient and prestigious Italian grape cultivars. It is renowned for its use in producing monovarietal high-quality red wines, such Barolo and Barbaresco. Wine quality and value can be heavily modified if cultivars other than those allowed are employed.

Climate change challenges differently wine growing systems, depending on their biophysical, sociological and economic features. Therefore, there is a need to locally design and evaluate adaptation strategies combining several technical options, and considering the local opportunities and constraints (e.g. water access, wine typicity). The case study took place in a typical and heterogeneous Mediterranean vineyard of 1,500 ha in the South of France. We developed a participatory modeling approach to (1) conceptualize local climate change issues and design spatially explicit adaptation strategies with stakeholders, (2) numerically evaluate their effects on phenology, yield and irrigation needs under the high-emissions climate change scenario RCP 8.5, and (3) collectively discuss simulation results. We organized five sets of workshops, with in-between modeling phases. A process-based model was developed that allowed to evaluate the effects of six technical options (late varieties, irrigation, water saving by reducing canopy size, adjusting cover cropping, reducing density, and shading) with various distributions in the watershed, as well as vineyard relocation. Overall, we co-designed three adaptation strategies. Delay harvest strategy with late varieties showed little effects on decreasing air temperature during ripening. Water constraint limitation strategy would compensate for production losses if disruptive adaptations (e.g. reduced density) were adopted, and more land got access to irrigation. Relocation strategy would foster high premium wine production in the constrained mountainous areas where grapevine is less impacted by climate change. This research shows that a spatial distribution of technical changes gives room for adaptation to climate change, and that the collaboration with local stakeholders is a key to the identification of relevant adaptation. Further research should explore the potential of adaptation strategies based on soil quality improvement and on water stress tolerant varieties.

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions.