What is the best soil for Sangiovese quality wine?

Aim: In this work, we have optimized a robust methodology for investigating possible correlations between the phytochemical profile of wine and the terroir (including the climate), considering the specific wine-growing area. In particular, the untargeted metabolomic and sensorial profiles of Gutturnio DOC commercial wines (both still and “frizzante” types) from different production areas in the Piacenza province were determined. The geographical areas taken into consideration for this study consisted in Val Tidone, Val Nure and Val d’Arda.

[English version below]

L’objectif de l’étude est de mettre en évidence les différences rencontrées entre les vins Barbera d’Asti, qui sont produits en AOC. Celles-ci sont imputées aux terroirs caractérisés selon les facteurs pédologiques, climatiques, et qui conduisent à des différents potentiels viticoles et œnologiques. Il est proposé une individualisation des sous-zones.

Phenolic ripening represents a major interest for quality wine producers. Nevertheless, climatic or genotypical limitations can often prevent optimal maturation process. During winemaking seeds can be easily separated and technologically processed to improve their quality.

Warming trends over the winegrowing regions lead to an advance of grapevine phenology, diminution of yield and increased sugar content and must pH with a lower polyphenol content, especially anthocyanins. Canopy management practices are applied to control the source sink balance and improve the cluster microclimate to enhance berry composition. We hyphothesized that an early leaf removal might promote a delayed ripening through severe defoliation after fruitset; whereas, a late leaf removal at mid-ripening would reduce sugar accumulation.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.