Incidence de la nature du sol et du cépage sur la maturation du raisin, à Saint Emilion, en 1995

Wildfires are becoming more common in many areas of the world that are also associated with wine grape production, especially the Pacific northwest United States, Australia and even some areas of France.

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.

Soil is important in the carbon cycle and the dynamics of greenhouse gases (CO2, CH4 and N2O). Key soil characteristics, such as organic matter content, texture, structure, pH and microbial activity, play a determining role in GHG emissions[1]. The objective of the study is to delimit different types of soil, with different soil management and to be able to verify the differences in CO2, CH4 and N2O emissions. The study was carried out in a vineyard of Bodegas Campo Viejo in Logroño (La Rioja), whose plant material is Vitis vinifera L. cv. Tempranillo.

The aim of this work was to reduce the alcohol content of Tempranillo wine. Tempranillo wines were produced by grapes harvested at different ripening dates (August 11 which was 21 oBrix and September 28 with 25 oBrix). At the second date, the Tempranillo wines were elaborated as follows: grapes were destemmed, crushed and collected into 50 L stainless-steel vats. Before preferementative maceration in cold, 50 % (M1) and 70 % (M2) of the must have been replaced by the same percentage of must from the first harvest. In addition, a control wine (C) was performed with only grapes from the second harvest.

Grapevine yield and fruit quality are two major drivers of input allocation and, ultimately, revenue for grape producers. Because yield and fruit quality vary substantially from year-to-year and within a single block, opportunities exist for optimization via precision management activities that could lead to more profitable and sustainable grape production. Here, we review recent advances in the techniques and technology used to measure, estimate, and forecast grapevine yield and fruit quality. First, we discuss direct “measurement” of yield and quality (i.e. ground-truth data generation), with an emphasis on potential for scalability and automation. Second, we discuss technology and techniques that do not directly measure yield and quality, but use correlated measurements for their estimation.