Withering of the ‘Moscato giallo’ grapes under covered space

The correlations between vine water deficit cumulated over the ripening period of grapes, assessed by ΔC13 in must sugar, and the main analytic variables of grapes are significant. As a result ΔC13 is a useful tool in zoning homogeneous areas according to their technological qualities when harvesting.

Grapevine (Vitis vinifera) is amongst the world’s most cultivated fruit crops, and of global and economic significance, producing a wide variety of grape-derived products, including wine, and table grapes. The genus Vitis, encompassing approximately 70 naturally occurring inter-fertile species, exhibits extensive genetic and phenotypic diversity, highlighted by the global cultivation of thousands of predominantly Vitis vinifera cultivars. Despite the importance of harnessing its naturally occurring genetic diversity to pursue traits of interest, especially considering the continued and growing demand for sustainable high-quality grape production, the systematic characterization of available functional genetic variants remains limited.

Since the renewal of the French vineyard after the Phylloxera crisis, the panorama of cultivated varieties has dramatically changed. This current genetic erosion is due to the increasing interest

The phenolic component of red wine is responsible for important elements of flavor and mouthfeel, and thus quality of the finished wine. Additionally, many of these phenolics have been associated with health benefits such as reduction of the risk of developing cardiovascular disease, cancer, osteoporosis and preventing Alzheimer’s disease. While the origins, concentrations, and chemistries of the phenolics in a finished red wine are well known, the fundamental mechanisms and kinetics of extraction of these phenolics from grape skins and seeds during red wine fermentation are poorly understood. This lack of knowledge regarding the extraction mechanisms of phenolics during red wine fermentation makes informed manipulations of the finished wine’s phenolic composition difficult.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages.