Characteristics of some Montefalco Sagrantino vineyards through polyphenolic components

Flavonoids, including flavonols, anthocyanins, and tannins, are important contributors to grape and wine quality, and their biosynthesis is strongly influenced by bunch microclimate.

The bioprotection of musts or grapes is a strategy for limiting sulfiting during winemaking and more specifically at pre-fermentative step. The most preconized yeasts in bioprotection mainly belong to Metschnikowia pulcherrima and Torulaspora delbrueckii species. While previous studies have demonstrated that bioprotectant non-Saccharomyces strains were able to protect musts and wines against microbial spoilage as well as sulfites, they cannot protect must against oxidation which appears to be the main limit of this practice.

Wineries of Beaumes-de-Venise area make their best red wines with grapes from the “Triassic terroir”. This « terroir » is characterized by soils from the Triassic period. These specific soils are complex and quite heterogeneous. They originate from an eventful geological history to keep in mind to understand soils geographical distribution.

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.

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