The key role of vineyard parcel in modifying flavor compounds of Cabernet Sauvignon grapes

The transition to a decarbonized economy requires companies to adopt mitigation measures. The wine sector is one of the most affected by climate change and, therefore, interested in its mitigation. The question is how this process develops. To address this, we build on a previous study [1], which identified different types of Spanish wineries based on their sustainability approach.

This paper presents is the create, the study and amplographic
description the new white Greek table variety grapes “Aztec”, created in 2013 by breeder P. Zamanidis at
the Athens vineyard of the Institute of Olive, Subtropical Plants and Vine.

Sustainability is becoming an increasingly present challenge, whether due to an increase in the level of perception and demand from consumers and stakeholders or the rise of events linked to climate change, which directly impacts agricultural-based sectors such as the vine and wine industry.

Wine aroma quality is a complex interplay of factors like terroir, vinification techniques, that modulate aroma compound composition.

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.