“Terroir” and climate change in Franconia / Germany

The quality of wine is difficult to define. This is most certainly accredited to everyone´s different perception of quality. Some of the indicators of high-quality wines are color complexity and balance. Color is one of the most crucial attributes of quality, not only for the obvious implications for their perception but also because they are indicators of other aspects related to its aroma and taste.

The South Western Cape of South Africa is exposed to strong southerly and south easterly synoptic winds during the growth period of the grapevine. The development of sea breezes in the afternoon is also a phenomenon associated with the ripening period of grapes cultivated in this coastal area. Wind is one of the environmental variables having the greatest spatial variation but the implications of regular exposure to wind for the performance of the grapevine has not yet been determined for vineyards in the South Western Cape. This study was initiated to meet this need.
The study was conducted in a hedge-trellised vineyard of Vitis vinifera L. cv Merlot with north east – south west row direction. Thirty experimental sites, each consisting of 14 vines, were identified as being exposed to wind or sheltered based on hand-held anemometer readings during the 2001/2002 season. Four stationary anemometers were strategically positioned between the thirty sites. Stomatal conductance and leaf temperature were measured with a PP systems porometer. Vegetative and yield measurements were performed during the 2002/2003 season. The t-test of equal variance was used to determine significant differences in measured parameters between exposed and sheltered grapevines.
Stomatal conductance and leaf area were significantly reduced by exposure to wind. This was associated with a significant reduction in the leaf area of primary shoots, related to shorter shoots, but a significant augmentation of secondary shoot leaf number and area. The number of bunches per vine and yield were also reduced for exposed vines. The berry potassium content was significantly increased for exposed grapevines.
This demonstrates that exposure to wind can result in significant within-vineyard, and potentially between-vineyard, variability in grapevine physiology, vegetative growth, yield and berry composition, with implications for wine style and quality.

Current changes of ecoclimatic indicators may cause significant variation in grapevine phenology and grape ripening. Climate change modifies several abiotic factors (e.g. temperature, sunlight radiation, water availability) during the grapevine growth cycle, having a direct impact on the phenological stages of the grapevine, modulating the metabolic profile of berries and activating the synthesis and accumulation of diverse compounds in the skin of berries, with consequences on the composition of the grapes.
The influence exerted by different meteorological conditions, during three consecutive years (2020-2022) on secondary metabolites such as the polyphenolic profile of Grignolino grapes was investigated. The samples were collected from three vineyards characterized by different microclimatic conditions mainly related to the vineyard aspect and to a different age of the plants.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.

Top quality sparkling wines (SW) are mostly produced using the traditional method that implies a second fermentation into the bottle[1]. That is the case of sparkling wines of reputed AOC such as Champagne, Cava or Franciacorta. However, it seems that the first SW was elaborated using the ancestral method in which only one fermentation takes place[2]. That is the case of the classical SW from the AOC Blanquette de Limoux[3]. In both cases, SW age in the bottle during some time in contact with lees favoring yeast’s autolysis[4]. There is a lot of information about traditional method but only few exists about ancestral method. The aim of this work was to compare SW made by the ancestral method with SW made by the traditional method.