The regulation of ABA-induced anthocyanin accumulation in grape berry

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.

Achieving disease resistance in grapevine (Vitis vinifera) without compromising plant growth is a significant challenge for viticulture. Astrategy to reduce susceptibility to Downy Mildew (DM) is through dual inactivation of the salicylic acid (SA) hydroxylases VviDMR6-1 and VviDMR6-2 by gene editing, which leads to elevated endogenous SAlevels and enhanced immunity.

L’aire d’AOC Ribeira Sacra s’étend sur plus de 200 km au large des versants escarpés du Miño et du Sil, dans la Galice (Espagne).

Plant-pathogen interactions can trigger extensive epigenetic modifications in the host genome.

In viticulture, climate change significantly impacts the plant’s development and the quality and characteristics of wines. These variations are often observed over short distances in a wine-growing region and are linked to local features (slope, soil, seasonal climate, etc.). The high spatial variability of climate caused by local factors is often of the same order or even higher than the temperature increase simulated by the different IPCC scenarios.