A vine physiology-based terroir study in the AOC-Lavaux region in Switzerland

The vine is colonized by a multitude of micro-organisms (fungi, bacteria, oomycetes) mainly coming from the microbial reservoir constituted by the soil. These microorganisms have positive or negative effects on the vine (protection against pathogens, resistance to abiotic stress, nutrition, but also triggering of diseases) (Fournier, Pellan et al. 2022). In addition to these functional roles, they respond quickly to environmental changes (climate, cultural practices) which could make them good bioindicators of the functioning of the wine ecosystem.

Sauvignon blanc was first planted in Marlborough, New Zealand in the mid-1970s. Since that time, Marlborough has gained an international reputation by producing the definitive wine style of that grape variety.

Several winemaking operations, such as filtration, pumping, and racking, are known to potentially facilitate the incorporation of atmospheric O2 into the wine. Control of grape must oxidation is one key aspect in the management of white wine aroma expression, color stability and shelf-life extension. On the one hand, controlled must oxidation may help to remove highly reactive phenolic compounds, which otherwise could contribute to premature oxidation. And on the other hand, in certain cases of extreme protection of the must from O2 (e.g. pressing under inert atmosphere), it can help to preserve varietal aromas and natural must antioxidants.

Much research has sought to define the complex contribution of terroir (varieties x site x cultural practices) on wine composition. This investigation applied recent advances in chemometrics to determine relative contributions of vine growth, berry maturity and site mesoclimate to wine composition and sensory profiles of Shiraz and Cabernet Sauvignon for two vintages.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages.