Effect of vineyard management strategy on the nutritional status of irrigated « Tempranillo » vineyards grown in semi-arid areas

Color is one of the key elements for the marketing of rosé wines due to their packaging in transparent bottles. Their broad color range is due to the presence of pigments belonging to phenolic compounds extracted from grapes or formed during the wine-making process. However, the mechanisms responsible for such diversity are poorly understood. The few investigations performed on rosé wines showed that their phenolic composition is highly variable, close to that of red wines for the darkest rosés but very different for light ones [1]. Moreover, large variations in the extent of color loss taking place during fermentation have been reported but the mechanisms involved and causes of such variability are unknown.

Usually the winemaker consider the grapeberry maturity as an actor of the wine quality. Grape seed are frequently used as a marker to assess the grape maturity. The first aim of this study is to obtain a better understanding of the impact of grape seed maturity on the grape seed and grape berry composition.

Trunk diseases and esca in particular, represent a major threat to the sustainability of the vineyards. The percentages of unproductive vines in a plot could vary from 4% to over 20 % depending on local conditions and vintages.

Sensory analysis is an essential component of oenology, offering valuable insights into wine quality that influence decision-making in viticulture and winemaking.

The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield and quality parameters and determine their spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways responsible for different grape quality traits behave in zones diverging for one or the other parameter. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated by a standard Normalized Difference Vegetation Index (NDVI) mapping approach, employing UAV platform, accompanied by detailed ground-truthing (e.g. vegetative, yield, and berry ripening compositional parameters) that was applied in 14 spots in the vineyard. Berries from different spots were additionally investigated by microarray gene expression analysis, performed at five time points from fruit set to full ripening.