Identifying New Zealand Sauvignon blanc terroirs

Precision nutrient management in viticulture can be addressed on the basis of a spatial characterization of within‐vineyard vine

Yeast cell wall components are valuable biotechnological tools with applications in oenology and beyond [1], [2].

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

Viticulture is threatened by the environmental modification caused by climate change. Higher temperatures determine an acceleration of the ripening process, which can be detrimental to wine quality. In the mediterranean area, heat waves are also increasingly frequent, with consequent blocking of the vegetative activity of the vines and increased susceptibility to sunburn damage. thus, adaptation strategies are necessary to reduce stress and improve the quality of grape production. Amongst the various techniques available, shading nets represent an interesting alternative for their effects on canopy microclimate (i.e., reduction of photosynthetic activity, improvement of water use efficiency, and slowing down in the ripening process).

The interaction between cultivar and growing environment is the base of wine quality and typicality. In recent time the behaviour of different clones within the same cultivar became another fundamental factor influencing the enological result. In order to clarify cultivar/clone/environment relations, a trial was carried out in 2008 studying the performances of three clones of ‘Nebbiolo’, grown in different environments: south-east Piedmont (hilly and characterized by a loamy and alkaline soil) and north-east Piedmont (a plain area characterized by a sandy and acidic soil).