Caractérisation du terroir en Espagne : méthodologie de l’évaluation et de la validation

The microbial diversity during spontaneous grape must fermentation has a determinant influence on the chemical composition and sensory properties of wine. Therefore, yeast diversity is an important target to better understand wine regionality. Hence, the aim of this study was to isolate, identify, and characterize the yeast core microbiota in grape must during the early stage of lab-scale spontaneous fermentation of withered grapes to produce Amarone della Valpolicella wine (Verona, Italy).

In grapevine, phenology from bud break to berry maturation, depends on temperature and water availability. Increases in average temperatures accelerates initiation of bud break, exposing newly formed shoots to detrimental environmental stresses. It is therefore essential to identify genotypes that could delay phenology in order to adapt to the environment. The use of different rootstocks has been applied to change scion’s characteristics, to adapt and resist to abiotic and biotic stresses[1].

Climate change is increasing crop evapotranspiration and reducing water availability, especially in the Mediterranean area.

Multiple sclerosis (MS) is a multifactorial autoimmune disease associating demyelination and axonal degeneration developing in young adults and affecting 2–3 million people worldwide. Plant polyphenols endowed with many therapeutic benefits associated with anti-inflammatory and antioxidant properties represent highly interesting new potential therapeutic strategies. We recently showed the safety and high efficiency of grape seed extract (GSE), a complex mixture of polyphenolics compounds comprising notably flavonoids and proanthocyanidins, in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS.

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.