VITOUR – The European World Heritage Vineyards

A total of 39 study sites from 11 commercial vineyards located in two traditional growing areas of Northern Italy were identified for airborne hyperspectral acquisition in summer 2009 with the Aisa-EAGLE Airborne Hyperspectral Imaging Sensor.

Grape quality potential for wine production is strongly influenced by environmental parameters such as climate and agronomic factors such as rootstock. Several studies underline the effect of rootstock on vegetative growth of the scions [1] and on berry composition [2, 3] with an impact on wine quality. Rootstocks are promising agronomic tools for climate change adaptation and in most grape-growing regions the potential diversity of rootstocks is not fully used and only a few genotypes are planted. Little is known about the effect of rootstock genetic variability on the aromatic composition in wines; thus further investigations are needed.

Global warning increases evaporative demand and accelerates grapevine phenology. As a consequence, the ripening phase shifts to warmer and drier periods. This results in lower acidity and higher sugar levels in berries, yielding too alcoholic wines with altered organoleptic properties. Agrivoltaics, which combines crop and renewable energy production on the same land using photovoltaic panels, emerged as a promising innovation to counteract these impacts by partially shading the plants.

Aims: Wine perceived quality lies on a number of different factors. Among these, sensory features, which are in turn dependent on chemical composition, play a primary role. There is traditionally a great emphasis on producing wines that have specific sensory profiles, particularly aroma, that reflect identity features connected to the place and the variety of origin. In the case of high quality

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.