Efecto de la cota sobre el potencial enológico de tres varietales tintos en el sur de Tenerife

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.

Land use, plays a relevant role in the climatic system. It endows means for agriculture practices thus contributing to the food supply. Since climate and land are closely intertwined through multiple interface processes, climate change may lead to significant impacts in land use. In this study, 1-km observational gridded datasets are used to assess changes in the Köppen–Geiger and Worldwide Bioclimatic (WBCS)

The study of climate is relevant as an element conditioning the typicity of a product, its quality and sustainability over the years. The grapevine development and growth and the final grape and wine composition are closely related to temperature, while climate components vary at mesoscale according to topography and/or proximity to large bodies of water. The objective of this work is to assess the mesoclimate of the Atlantic region of Uruguay and to determine the effect of topography and the ocean on temperature and consequently on Tannat grapevine behavior.

One of the major issues for the wine sector is the impact of climate change linked to the increasing temperatures which affects physicochemical parameters of the grape varieties planted in Bordeaux vineyard and consequently, the quality of wine. In some varietals, the attenuation of their fresh fruity character is accompanied by the accentuation of dried-fruit notes [1]. As a new adaptive strategy on climate change, some winegrowers have initiated changes in the Bordeaux blend of vine varieties [2]. This study intends to explore the fruitiness in wines produced from grape varieties adapted to the future climate of Bordeaux. 10 commercial single–varietal wines from 2018 vintage made from the main grape varieties in the Bordeaux region (Cabernet franc, Cabernet-Sauvignon and Merlot) as well as from indigenous grape varieties from the Mediterranean basin, such as Cyprus (Yiannoudin), France (Syrah), Greece (Agiorgitiko and Xinomavro), Portugal (Touriga Nacional) and Spain (Garnacha and Tempranillo), were selected among 19 samples using sensory descriptive analyses. Both sensory and instrumental analyses were coupled, to investigate their fruity aroma expression. For sensory analysis, samples were prepared from wine, using a semi preparative HPLC method which preserves wine aroma and isolates fruity characteristics in 25 specific fractions [3,4]. Fractions of interest with intense fruity aromas were sensorially selected for each wine by a trained panel and mixed with ethanol and microfiltered water to obtain fruity aromatic reconstitutions (FAR) [5]. A free sorting task was applied to categorize FAR according to their similarities or dissimilarities, and different clusters were highlighted. Instrumental analysis of the different FAR and wines demonstrated variations in their molecular composition. Results obtained from sensory and gas chromatography analysis enrich the knowledge of the fruity expression of red wines from “new” grape varieties opening up new perspectives in wine technology, including blending, thus providing new tools for producers.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.