Geochemistry of Vrbničko Polje (Croatia) winegrowing site

The recovery of ancestral or minority vine varieties has been gaining great interest in recent years, among other reasons because it is likely that some of these varieties, due to the fact that they are found in relict areas, have a greater potential for adaptation to external factors (biotic or abiotic) and can minimize the effects that climate change is causing in viticulture. Varieties that can be grown at altitude are currently being sought to combat rising temperatures and prolonged extreme drought conditions. In Catalonia, the Pyrenean expansion of vineyard cultivation is documented from the 10th century and has been related to the “small climatic optimum” (9th-12th centuries) and also to seigniorial power.[1] But different adverse climatic periods and the arrival of Phylloxera by the late 19th century made many of these crops disappear.[2]

With the development of naturality expectations, wines produced without any addition of sulfur dioxide (SO₂) become very popular for consumers and such wines are increasingly present on the market. Recent studies also showed that Bordeaux red wines without added SO₂ could be differentiated from a sensory point of view from similar wines produced with SO₂¹. Thus, the aim of the current study was to characterize from a sensory point of view, specific aromas of wines without added SO₂ and to identify compounds involved.

Recycled organic mulch within the row in vineyard floor management has become an interesting ecological strategy to adapt the crop to climate change consequences in semi-arid regions.
This study aimed to assess the impact of three recycled organic mulches [straw (STR), grape pruning debris (GPD), and spent mushroom compost (SMC)] and two conventional soil management practices [herbicide (HERB) and under-row tillage (TILL)] on vegetative vigour (NDVI), production (kg/plant), and physiological parameters (δ13C in grapes and leaf gas exchange during four grapevine phenology stages). Additionally, temperature and water soil parameters were collected at three soil depths. Data was collected during the 2021 and 2022 grapevine growing seasons in La Rioja, Spain.

The increased intensity and frequency of heatwaves, coupled with prolonged periods of drought, are a significant threat to viticulture worldwide. During these conditions the more exposed leaves can show visible symptoms of heat damage. We monitored the functionality of photosystem II (PSII) in the field to better understand the impact of heatwaves on canopy performance. A factorial experiment was established in summer 2023 using Shiraz grapevines in the Barossa valley of South Australia, involving water-stressed and well-watered vines.

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