Experimental vinification of withered grapes of Vitis vinifera “Muscat of Alexandria”

Adrenaline (epinephrine) belongs to catecholamine class. It is a neurotransmitter and both a hormone which is released by the sympathetic nervous system and adrenal medulla in response to a range of stresses in order to regulate blood pressure, cardiac stimulation, relaxation of smooth muscles and other physiological processes. Adrenaline exhibits an effective antioxidant capacity (1). However, adrenalin is capable to auto-oxidation and in this case it generates toxic reactive oxygen intermediates and adrenochrome. Under in vitro conditions, auto-oxidation of adrenaline occurs in an alkaline medium (2).

Developing a sustainable agricultural production system and acquiring the full potential of land resources requires employing land-use assessment. This entails knowledge of the climate, soil, and topography of the area of interest.

Vine growth circumstances are becoming warmer and drier because of climate change. Higher temperatures advance ripening to a point in the season less conducive to the production of fine wine, while drought reduces yields (Van Leeuwen et al., 2019). Several wine-producing regions around the world have already recognized threats to their viticultural viability (Santos et al., 2020). An economical and cost-effective strategy for adaptation is the employment of late-ripening, drought-resistant plant material (varieties, clones, and rootstocks).

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.

Vine water status is one of the most influential factors in grape vigor, yield, and quality (Ojeda et al., 2002; Guilpart et al., 2014). Severe water deficits during the first stage of crop development (bud break to fruit set) impact yield in the current year and the following year. While during grape ripening, water availability impacts berry size, grape composition, and health status. Therefore, a correct assessment of plant water status allows for proper water management with an impact on grape yield and composition (McClymont et al, 2012; Pereyra et al., 2022).