Monitoring early rooting behavior of grapevine rootstocks: a 2D-imaging approach

“Sulphites” and mainly sulphur dioxide (SO2) is by far the most widely used additive (E-220/INS 220) in winemaking and likely the most difficult to replace. The well-known antioxidant, antioxidasic and antimicrobial properties of SO2 make this molecule a practically essential tool, not only in winemaking, but also in the production of other food products. The current trend in winemaking is the reduction of this unfriendly additive due to its negative effects on health and environmental. In particular, it could cause headaches and intolerance/allergic reactions in sensitive individuals. Wine is considered one of the major contributors of exposure of SO2 in the adult population, when this beverage is included in the diet.

The aroma of wine grapes is influenced by a complex metabolic network, with terroir factors, especially high temperatures, playing a critical role during berry development.

In sparkling wines, foam is a relevant aspect whose measurement method could affect the results. The shaking test (ST) is a simple method measuring foamability1,2

Rootstocks continue to serve as the first line of defence against phylloxera in viticulture; however, to mitigate the impacts of climate change, future viticulture will require novel, improved rootstock varieties. As conventional rootstock breeding is extremely time-consuming, modern genomics-assisted approaches are urgently needed.

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65