Identifying physiological and genetic bases of grapevine adaptation to climate change with maintained quality: Genome diversity as a driver for phenotypic plasticity  (‘PlastiVigne’ project)

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The Txakoli, a white wine produced in the Basque Country (North of Spain), has recently gained popularity due to wine quality improvement and increase in both acreages of production and wine consumption. The aim of this study was to characterize the chemical and microbiological differences between Txakoli wines made with grapes from different sites.

The interaction between wine and landscapes is of an unsuspected richness. On the one side, the vineyards form part of the landscapes which they model. On the other side, the wines are related in their perception to the image of a region, a landscape and are at the origin of a cultural richness.

Visual clarity in wines is crucial for commercial purposes [1]. Potential protein haze in white wines remains a constant concern in wineries, commonly addressed using bentonite [2].

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.