From soil to canopy, the diversity of adaptation strategies  to abiotic constraints in grapevine

Extraction of anthocyanins and tannins have been studied for two grape varieties, Carignan and Grenache, two maturation levels and two vintages, in model solutions and in wines, using UHPLC-MS/MS in the MRM mode  and HPSEC.

Previous work from our laboratory has shown that both a purified toasted oak powder and extracts made from unoaked and oaked red wines influenced physiological parameters, metabolism and hepatic gene expression in high-fat fed C57/BL6J male mice (Luo et al., 2020).  Impacted pathways included glucose metabolism, liver fat accumulation, markers of chronic inflammation, and expression of the Gsta1 mRNA.  

The southern Côtes-du-Rhône vineyard shows a significant variety of ecological facets over the Lower Rhone Valley. Intending to characterize such a variety of “terroir “called vineyard situations, a spatial approach based on identification of soil landscapes has been initiated.

Current climate change is increasing inter- and intra-annual variability in atmospheric conditions leading to grapevine phenological shifts as well altered grape ripening and composition at ripeness. This study aims to (i) detect weather anomalies within a long-term time series, (ii) model grape ripening revealing altered traits in time to target specific ripeness thresholds for four Vitis vinifera cultivars, and (iii) establish empirical relationships between ripening and weather anomalies with forecasting purposes. The Day of the Year (DOY) to reach specific grape ripeness targets was determined from time series of sugar concentrations, total acidity and pH collected from a private company in the period 2009-2021 in North-Eastern Italy. Non-linear models for the DOY to reach the specified ripeness thresholds were assessed for model efficiency (EF) and error of prediction (RMSE) in four grapevine cultivars (Merlot, Cabernet Sauvignon, Glera and Garganega). For each vintage and cultivar, advances or delays in DOY to target specified ripeness thresholds were assessed with respect to the average ripening dynamics. Long-term meteorological series monitored at ground weather station by means of hourly air temperature and rainfall data were analyzed. Climate statistics were obtained and for each time period (month, bimester, quarter and year) weather anomalies were identified. A linear regression analysis was performed to assess a possible correlation that may exist between ripening and weather anomalies. For each cultivar, ripeness advances or delays expressed in number of days to target the specific ripening threshold were assessed in relation to registered weather anomalies and the specific reference time period in the vintage. Precipitation of the warmest month and spring quarter are key to understanding the effect of climate change on sugar ripeness. Minimum temperatures of May-June bimester and maximum temperatures of spring quarter best correlate with altered total acidity evolution and pH increment during the ripening process, respectively.

Implementing artificial intelligence (AI) in viticulture and enology is a rapidly growing field of research with an essential number of potential practical applications.