Yield monitoring can provide the winegrowers with information for precise production inputs during the season, thereby, ensuring the best possible harvest. Yield estimation is currently achieved through an intensive process that is destructive and time-consuming. However, remote sensing provides a group of proximal technologies and techniques for a non-destructive and less time-consuming method for yield monitoring.The objective of this study was to analyse three different approaches, for measuring grapevine yield close to harvest.

The online monitoring of fermentative aromas provides a better understanding of the effect of temperature on the synthesis and the loss of these molecules. During fermentation, gas and liquid phase concentrations as well as losses and total productions of volatile compounds can be followed with an unprecedented acquisition frequency of about one measurement per hour. Access to instantaneous production rates and total production balances for the various volatile compounds makes it possible to distinguish the impact of temperature on yeast production (biological effect) from the loss of aromatic molecules due to a physical effect³.

Age-related macular degeneration (AMD) that is the main cause of visual impairment and blindness in Europe which is characterized by damages in the central part of the retina, the macula. This degenerative disease of the retina is mainly due to the molecular mechanism involving the production and secretion of vascular endothelial growth factor (VEF). Despite therapeutic advances thanks

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.

Increasingly pronounced climate changes, including prolonged drought periods, pose a significant challenge to the cultivation of table grape varieties.