Il turismo del vino: dalla logica individuale a quella di distretto

Les fromages d’Appellation d’Origine Contrôlée (AOC) représentent un enjeu économique important pour la filière laitière (11 % des fromages produits en France sont des fromages d’AOC, et dans certaines régions de montagne, cette proportion dépasse 50 %). Les spécificités de ces fromages et leurs liaisons avec les caractéristiques du terroir constituent un système complexe où interagissent en particulier la technologie fromagère et les caractéristiques des laits (composition chimique en particulier). Ces dernières dépendent elles-mêmes des caractéristiques des animaux (origine génétique, facteurs physiologiques, état sanitaire) et de leur mode de conduite (alimentation, hygiène, traite…) (fig. 1). L’influence de ces facteurs de production (alimentation et type d’animal en particulier) sur les caractéristiques des fromages est fréquemment mise en avant par les fromagers, sur la base d’observations empiriques. Il existe cependant très peu de travaux expérimentaux sur le sujet, en raison, entre autres, de la difficulté de séparer correctement les effets propres de ces facteurs d’amont de ceux liés à la technologie fromagère.

Fungus resistant grape varieties (FRGV or PIWI) offer many benefits such as less pesticide use or premium prices for enhanced sustainability. Still, winemakers are concerned about inferior wine quality. This study evaluates how European wine consumers assess wines made from new FRGVs in comparison to traditional V. vinifera varieties. Most of them were grown in the same vineyard. Four white (Calardis Blanc, Muscaris, Sauvignac, Cabernet Blanc) und three red (Satin Noir, Cabernet Cortis, Laurot) FRGV were compared to Riesling, Sauvignon blanc, Muskateller, Cab. Sauvignon and Merlot. For each FRGV, different styles were vinified using standardized protocols.

Estimation of vineyard leaf area index (LAI) is an important aspect for the winegrowers. However, tracking and monitoring are difficult tasks due to time constraints. Satellite and unmanned aerial vehicle (UAV) imaging have become a practical monitoring method for LAI. Nevertheless, for a proper LAI determination, the image’s spatial resolution is a key factor, since low-resolution images are incapable of distinguishing between adjacent vines due to the large area covered in each pixel, this leads to misinterpretation or generalisation of vineyard information.

Agriculture plantations are complex systems whose performance critically depends on the execution of several types of tasks with precise timing and efficiency to respond to different external factors. This is particularly true for orchards like vineyards, which need to be strictly monitored and regulated, as they are sensitive to diverse types of pests, and climate conditions. In these environments, managing and optimally scheduling the available work force and resources is not trivial and is usually done by teams of senior managers based on their experience. In this regard, having a baseline schedule could help them in the decision process and improve their results, in terms of time and resources spent.

Genetic variants known to produce single residue missense mutations have been associated with phenotypic traits of commercial interest in grapevine. This is the case of the K284N substitution in VviDXS1 associated with muscat aroma, or the R197L in VviAGL11 causing stenospermocarpic seedless grapes. The impact of such mutations on protein structure, stability, dynamics, interactions, or functional mechanism can be studied by computational methods, including our pyDock scoring, previously developed. For this, knowledge on the 3D structure of the protein and its complexes with other proteins and biomolecules is required, but such knowledge is not available for virtually none of the proteins and complexes in grapevine.