Viticulture, landscapes and the marketing of our wine

Malolactic fermentation (MLF) is a desired process in red and acidic white wines, after alcoholic fermentation (AF), carried out by the lactic acid bacterium (LAB) Oenococcus oeni. The advantages are an increase of pH, microbiological stabilization and organoleptic improvement of the final wine. However, the presence of stress factors such as ethanol, low pH, high total SO2, lack of nutrients and presence of inhibitors, could affect the successful completion of MLF [1]. Changes in amino acid composition and deficiencies in peptides after AF, showed that MLF can be delayed, signaling its importance for bacterial growth and L-malic acid degradation during MLF [2].

Root architecture (RSA), the spatial-temporal arrangement of a root system in soil, is essential for edaphic resources acquisition by the plant, and thus contributes largely to its productivity and adaptation to environmental stresses, particularly soil water deficit. In grafted grapevine, while the degree of drought tolerance induced by the rootstock has been well documented in the vineyard, information about the underlying physiological processes, particularly at the root level, is scarce, due to the inherent difficulties in observing large root systems in situ. The aims of this study were (i) to determine the phenotypic differences in traits related to root distribution and morphology along the substrate profile in different Vitis rootstocks during early growth, (ii) to assess the plasticity of these traits to soil water deficit and (iii) to quantify their relationships with plant water uptake.

The addition of fungal chitosan in wine is allowed since 2009 to release some spoilage microorganisms such as Brettanomyces bruxellensis (OIV/OENO 338A/2009; EC 53/2011). This yeast is able to produce volatil phenols and is responsible of organoleptic deviations compromising quality and typicality of red wines [1]. Despite the fact that fungal chitosan is highly renewable, no toxic and non-allergenic, its use remains marginal because this treatment is relatively recent (compare to sulphites treatment) and information are contradictory between different studies described in literature. For all these reasons,

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.

The gustatory balance of dry wines is centered on three flavors, sourness, bitterness and sweetness. Even if certain compounds were already identified as contributing to sweetness, some taste modifications remain largely unexplained1,2. Some empirical observations combined with sensory analyzes have shown that an increase of wine sweetness occurs during post-fermentation maceration³. This step is a key stage of red winemaking during which the juice is left in contact with the marc, that contains the solid parts of the grape (seeds, skins and sometimes stems). This work aimed to identify a new taste-active compound that contributes to this gain of sweetness.