Can the use of rootstocks enhance terroir?

Using grapevine genetic diversity is one of the strategies to adapt viticulture to climate change. In this sense, assessing the plasticity of cultivars in their responses to environmental conditions is essential. For this purpose, the drought tolerance of Grenache, Tempranillo and Semillon cultivars grafted onto SO4 was evaluated at two experimental vineyards, one located in Valencia (Spain) and the other in Bordeaux (France). This was done by assessing gas exchange parameters, water relations and leaf hydraulic traits at the end of the season.

Climate change has become a major challenge for grape and wine production around the world

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.

Minervois is a vine region where the first detailed soil map was begun 30 years ago. In 2003, a new map was drawn plotting the soil-landscape associations. This map distinguishes 8 large soil units based on geology. The widest (called « marnes ») is the most complex : it is made of 57 sub-units, which leads to a high variability of the vine behaviour on this unit.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).