Conservation: the best valorisation strategy for wine growing areas

Color is one of the main qualitative parameters of a wine. As a matter of fact, immediately after having opened a bottle of wine, color, even before aroma and taste, is the first sensorial parameter to be evaluated by the consumer It can change according to various factors depending on the characteristics of the grapes or on the different production and storage processes.

Rapid and accurate quantification of grape berry phenolics, anthocyanins and tannins, and identification of grape varieties are both important for effective quality control of harvesting and initial processing for wine making. Current reference technologies including High Performance Liquid Chromatography (HPLC) can be rate limiting and too complex and expensive for effective field operations

Consumer demands for environmentally friendly products, including wine, are constantly increasing.

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.

One of the main aspects of Climate Change is the increase of temperatures during summer and grape maturity period. Physiological processes are influenced by these high temperatures and result in grapes with higher sugar concentration, less acidity and less anthocyanin content among other quality changes. One strategy to deal with the climate change effects is the implementation of late winter pruning to alter the effect of high temperatures during key periods by delays in maturity time.