Effects of water and nitrogen uptake, and soil temperature, on vine development, berry ripening and wine quality of Cabernet-Sauvignon, Cabernet franc and Merlot (Saint-Emilion, 1997)

The valley of Caravelí (Peru) received the first vine plants in colonial times and the tradition of cultivation is maintained thanks to its terroir and artisanal techniques.

Nitrogen (N) supply strongly influences vine productivity and berry composition, matching availability and uptake requirements of vines during the growing season is essential to optimize vine nutrition. The nutritional status of grapevines is commonly assessed by the determination of petiole nutrient concentrations at flowering. The reserve N could also be an earlier indicator for grapevine N status, this work aimed to assess how the petiole levels relate to these perennial N reserves.

Mediterranean viticulture is increasingly exposed to more frequent extreme conditions such as heat waves. These extreme events co-occur with low soil water content, high air vapor pressure deficit and high solar radiant energy fluxes and result in leaf and berry sunburn, lower yield, and berry quality, which is a major constraint for the sustainability of the sector. Grape growers must find ways to proper and effectively manage heat waves and extreme canopy and berry temperatures. Irrigation to keep soil moisture levels and enable adequate plant turgor, and convective and evaporative cooling emerged as a key tool to overcome this major challenge. The effects of irrigation on soil and plant water status are easily quantifiable but the impact of irrigation on soil and canopy temperature and on heat convection from soil to cluster zone remain less characterized. Therefore, a more detailed quantification of vineyard heat fluxes is highly relevant to better understand and implement strategies to limit the effects of extreme weather events on grapevine leaf and berry physiology and vineyards performance. Low-cost sensor technologies emerge as an opportunity to improve monitoring and support decision making in viticulture. However, validation of low-cost sensors is mandatory for practical applicability. A two-year study was carried in a vineyard in Alentejo, south of Portugal, using low-cost thermal cameras (FLIR One, 80×60 pixels and FLIR C5, 160×120 pixels, 8-14 µm, FLIR systems, USA) and pocket thermohygrometers (Extech RHT30, EXTECH instruments, USA) to monitor grapevine and soil temperatures. Preliminary results show that low-cost cameras can detect severe water stress and support the evaluation of vertical canopy temperature variability, providing information on soil surface temperature. All these thermal parameters can be relevant for soil and crop management and be used in decision support systems.

Studying the impact of reducing alcohol techniques in the chemical composition of the aromatic profile of rosé Tempranillo wines from the spanish region of Castilla-La Mancha INTRODUCTION: In the last decades there has been an increseasing demand of wines with low or non-existing alcohol concentration due to the negative effects that alcohol has in health. In spite of that, there are not laws that protect these products, and there is also a great difficulty in the elaboration of these type of wines due to the increasing temperatures because of climate change. This is why the oenological industry has made great advances in the development of different techniques that could satisfy consumers’ demands without modifying wine quality. The most used techniques have been post-fermentative ones.

The assessment of wine authenticity is of great importance for consumers, producers and regulatory agencies to guarantee the geographical origin of wines and grape variety as well. Since mid-infrared (MIR) spectroscopy with chemometrics represent a suitable tool to ascertain the wine composition, including features associated with the polyphenolic compounds, the aim of this study was to generate MIR spectra of red wines to be exploited for classification of red wines based on the relationship between grape variety and wine composition. Several multivariate data analyses were used, including Principal Component Analysis (PCA), Discriminant Analysis (DA), Support Vector Machine (SVM), and Soft Intelligent Modelling of Class Analogy (SIMCA).