Landscapes of Vines and Wines Patrimony – Stakes – Valorisation

Wine tasting has been shown to provide emotions to tasters (Coste et al. 2018). How will expertise impact this emotional response? Burnham and Skilleås (2012) reported that the cultural, experiential, and aesthetic competencies characterize an expert in wine compared to a novice. Although there is no consensual definition of an aesthetic experience, Burnham and Skilleås (2012) reported that aesthetic appreciation is “disinterested, normative for others and communicable” in comparison to sensory pleasure.

Les aires d’appellations de la Côte d’Or résultent d’une sélection humaine empirique, historique et évolutive en adéquation avec les facteurs naturels. Afin de comprendre quels facteurs naturels et humains agissent sur le caractère et l’évolution des terroirs des Côtes de Nuits et de Beaune, une méthodologie de recherche a été développée. Elle s’articule autour de deux axes, la caractérisation physique des lieux-dits viticoles et l’historicité de la qualité de ces lieux-dits. Le travail avec un S.I.G permet d’étudier l’évolution spatiale et temporelle de la qualité.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

Avec l’accroissement des surfaces viticoles conduites en Bio, la question de l’impact de la présence de résidus de cuivre (seul anti fongique autorisé dans l’UE dans ce cadre Règlementaire) sur le déroulement des fermentations et sur les qualités œnologiques et organoleptiques des vins s’est révélée de plus en plus cruciale.

Premium red wines are often aged in oak barrel. This widespread winemaking process is used, among others, to provide roundness and complexity to the wine. The study of wine evolution during barrel aging is crucial to better ensure control of wine quality.
¹H-NMR has already been proved to be an efficient tool to monitor winemaking process [1]. Indeed, it is a non-destructive technique, it requires a small amount of sample and a short time of analysis, yet it provides clues about several chemical families.