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Glutathione is a natural tripeptide composed of l-glutamate, l-cysteine and glycine, found in various foods and beverages. In particular, glutathione can be found in its reduced (GSH) or oxidized form (GSSG) in must, wine or yeasts¹. Numerous studies have highlighted the importance of GSH in wine quality and aging potential². During winemaking, especially during aging on lees, GSH helps prevent the harmful effects of oxidation on the aroma of the wine³. Nevertheless, the amounts of GSH/GSSG present in wine lees are often unknown and the choice of operating conditions (quantity of lees and aging time) remains empirical.

Wine viticulture, being firmly linked to the vine-terroir relationship, has always encountered significant bottlenecks to genetic innovation. Nonetheless, the development of new breeding strategies leading to the selection of stress resilient genotypes is urgent, especially in viticulture, where it would allow reducing the use of chemical treatments adopted to control fungal diseases. Genome editing represents an extremely promising breeding technique. Unfortunately, the well-known recalcitrance of several wine grape cultivars to in vitro regeneration strongly limits the exploitation of this approach, which to our knowledge has so far been developed on table grape genotypes with high regeneration potential.

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.

Aim: Global climate change affects regional climates and hold implications for wine growing regions worldwide (Jones, 2007, 2015; van Leeuwen and Darriet, 2016). The prospect of 21st century climate change consequently is one of the major challenges facing the wine industry (Keller, 2010).