Effects of stress memory on grapevine resilience in response to recurrent drought and recovery events 

Hydrogen sulfide (H2S) has a significant impact on wine aroma attributes and wine quality when present at concentrations above its aroma threshold of 1.1 to 1.6 μg/L.

Microbial terroir is increasingly linked to wine typicity, yet the chemical mechanisms connecting regional microbiomes to the wine phenotype remain incompletely defined.

The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield and quality parameters and determine their spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways responsible for different grape quality traits behave in zones diverging for one or the other parameter. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated by a standard Normalized Difference Vegetation Index (NDVI) mapping approach, employing UAV platform, accompanied by detailed ground-truthing (e.g. vegetative, yield, and berry ripening compositional parameters) that was applied in 14 spots in the vineyard. Berries from different spots were additionally investigated by microarray gene expression analysis, performed at five time points from fruit set to full ripening.

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.

When a winemaker considers which closure to use for a specific wine, they must evaluate several factors, including the wine’s expected longevity and its shelf life after leaving the cellar.