Denial of the wine-growing landscape

The mapping of spatial variability in vineyards offers the potential to implement zonal management strategies with the aim to optimize economic benefits and increase sustainability by managing natural resources, such as water used for irrigation, more optimally. This study characterized the (natural) variability in plant water status in a commercial Cabernet Sauvignon block, using remote sensing techniques, and identified the impact of this variability on the yield, and juice and wine composition. From the field data collected over two growing seasons, we demonstrated that remote sensing techniques are a practical and powerful tool for mapping spatial variability within vineyard blocks.

In the actual scenario of climate change, optimization of water usage is becoming critical in sustainable viticulture. Most of the current approaches to assess grapevine water status and drive irrigation scheduling are either destructive, time and labour consuming and monitor a small, limited number of plants. This work presents a novel methodology using a contactless, miniaturized, low-cost NIR spectrometer to monitor the vineyard water status variability from a moving vehicle, to provide reliable information towards precision irrigation.

The quality of wine is assessed, among other things, by its color, which is mainly due to its anthocyanin content. These pigments are polyphenols that give red, purple and blue hues depending on the relative proportion of anthocyanins produced by the action of flavonoid 3’5′ hydroxylase (delphinidin-3-glucoside, petunidin-3-glucoside, malvidin-3-glucoside) or flavonoid 3′ hydroxylase (cyanidin-3-glucoside, peonidin-3-glucoside). To study the genes involved in this biosynthetic pathway, we focused on Vitis vinifera cv. Tannat, known for producing wines with higher anthocyanin content and darker purple color compared to most red grape varieties. In this work, we have performed RNA-Seq analysis of skins during berry development, taking green and red berries at 50% veraison as separate samples, as an experimental strategy to focus on the differential expression of genes of interest.

In recent years, consumer demand for products without chemical additives increased, becoming a priority for the wine sector. SO₂ is widely used for its multiple properties including antiseptics, antioxidants and antioxidasics and the strategy of bioprotection in winemaking represents now an alternative to this chemical additive. In oenology, results have highlighted the interest of bioprotection to limit the development of microorganisms like Hanseniaspora uvarum and thus reduce the doses of sulphite. Indeed, this species is considered because of its acetic acid and methyl butyl acetate production, the latter can cover the varietal character of wines.

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