The use of microwaves during the maceration of Cabernet Sauvignon wines for improving their chromatic characteristics

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

Crops are facing increasing biotic and abiotic stress pressures due to global changes. However, trade-off mechanisms between these stresses and the underlying physiological processes are still poorly understood, especially in perennial crop species. To better understand these trade-offs, we studied the effect of drought on grapevine (Vitis vinifera) physiology and esca-related wood fungal communities. Esca is a vascular disease caused by a community of wood-infecting pathogenic fungi, and characterized by trunk necrosis, leaf scorch symptoms, yield losses, and mortality.

Wine flavanols are extracted from grape skin and seeds along red winemaking. Potentially, eight flavan-3-ol subunits may be present as monomers or as tannins constituents, being these catechin, epicathechin, gallocatechin, epigallocatechin end the gallates of the mentioned units. In this work the flavanol profiles of grape skins and seeds before (grapes) and after (pomace) red winemaking were studied together with the one in the corresponding wines. The trials were made over two vintages in Vitis vinifera cv. Tannat, Syrah and Marselan from Uruguay.

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. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.