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

Pinot Blanc, an important grape variety grown in some mountain areas of Northern Italy such as South Tyrol over the last decades, with its cultivation covering 10.3% of the total vineyards, has compatible climatic conditions (e.g. heat requirements) which are normally found in the geographical areas of the mountain viticulture [1,2,3,4]. Climatic changes are hastening the growth of this variety at higher elevations, particularly for the production of high quality wine.

In this video recording of the IVES science meeting 2021, Sophie Tempère (Institut des Sciences de la Vigne et du Vin – ISVV, Université de Bordeaux) speaks about the recovery of olfactory capacity following a COVID-19 infection. This presentation is based on an original article accessible for free on IVES Technical Reviews.

Aim: This study aimed to isolate Oenococcus oeni in red wines from Yinchuan wine region in the East of Helan Mountain, China, and analysis their genetic diversity.

Methods and Results: Oenococcus oeni strains were isolated from Cabernet Sauvignon and Cabernet Gernischt wines of four

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.