Interaction among grapevine cultivars (Sangiovese, Cabernet-Sauvignon and Merlot) and site of cultivation in Bolgheri (Tuscany)

3-Sulfanylhexan-1-ol (3SH) is a key impact odorant of white wines such as Sauvignon Blanc. In particular, the varietal characters of Sauvignon Blanc, especially from Marlborough NZ, are strongly influenced by the concentrations of 3SH

Context and purpose of the study. Cultivation techniques are widely recognized for their significant impact on the aroma profile of grapes and wines.

The type of wine is determined by environmental, plant materials and human factors. These factors are numerous and interact together, which makes it difficult to determine the hierarchy of their effects

Le lien fonctionnel entre typicité et terroir a été étudié en prenant en compte deux dimensions importantes : le type de sol et la date de vendanges. Ces deux facteurs sont, à des degrés divers

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.