Volatile compounds production during ripening of cv. “Sangiovese” grapes from different terroir

Water deficit and salinity are increasingly affecting the viticulture and wine industry. These two stresses are intimately related; understanding the physiological and metabolic responses of grapevines to water deficit, salinity and combined stress is critical for developing strategies to mitigate the nega- tive impacts of these stresses on wine grape production. These strategies can include selecting more tolerant grapevine cultivars and graft combinations, improving irrigation management, and using soil amendments to reduce the effects of salinity. For this purpose, understanding the response of grape- vine metabolism to altered water balance and salinity is of pivotal importance.

Dans la region Nord-Est du Brésil, située à la Vallée du São Francisco, localiséee entre les paralleles 8-9º HS, la production de vins tropicaux a commencé il y a une vigntaine d’années. Dans cette région, il est possible d’avoir au minimum deux récoltes par an, car la moyenne de température est de 26 ºC, avec une pluviosité moyenne de 550 mm entre les mois de janvier-avril.

A.O.C. réservée aux viandes fraîches de bovins mâles ou femelles, nés, élevés et abattus dans une aire géographique définie (voir carte)

Chitin is the main structural component of a large number of organisms (i.e., mollusks, insects, crustaceans, fungi, algae), and marine invertebrates including crabs and shrimps.

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.