Role of climate on grape characteristics of “Moscato bianco” in Piemonte (Italy)

AIM: Since 2012 the Veneto Region regulation (north-east Italy) allowed wine production using 20 hybrid grapevine varieties selected for their high tolerance to downy mildew and powdery mildew. Characterized by vigour, high grape productivity and low pesticide use, these varieties are suitable to develop sustainable viticulture in mountain areas located at medium altitudes.

Over the years, microoxygenation (MOX) has become a popular vinification technique to improve wine sensory qualities. However, among the impacting factors reported

One of the main aspects of Climate Change is the increase of temperatures during summer and grape maturity period. Physiological processes are influenced by these high temperatures and result in grapes with higher sugar concentration, less acidity and less anthocyanin content among other quality changes. One strategy to deal with the climate change effects is the implementation of late winter pruning to alter the effect of high temperatures during key periods by delays in maturity time.

Le vigneron européen est de plus en plus à la recherche de la valorisation de son terroir par la personnalisation de la typicité de ses produits. Dans ce contexte, il est apparu depuis longtemps que la part des facteurs technologiques ou humains est d’une importance capitale face aux conditions de l’envirormement naturel. Le terroir se construit plus qu’il ne se subit.

Downy mildew, caused by Plasmopara viticola, is the most economically impactful disease affecting grapevines. This polycyclic pathogen triggers both primary and secondary infection cycles, resulting in significant yield losses when effective disease control measures are lacking. Over the winter, the pathogen survives by forming resting structures, the oospores, derived from sexual reproduction, which produce the inoculum for primary infections. To optimize grapevine downy mildew control and obtain the desired levels of production while minimizing chemical inputs, it is crucial to optimize the timeframe for fungicide application. Disease forecasting models are useful to identify the infection risk.