Use of the stics crop model as a tool to inform vineyard zonages

The effects of climate change produce an increase in sugar concentration and a decrease in acidity, without reaching the optimum grape phenolic maturity [1]. The aim of this work was to characterize 25 white grape varieties

In the highly competitive global wine market, Burgundy has a long-established reputation to maintain. The vine and wine sector in Burgundy is based on a five-level ranking of AOC (Appellation d’Origine Contrôlée) wines and of the plots where the grapes are grown.

Tannin, anthocyanins and their reaction products play a major role in the quality of red wines. They contribute to their sensory characteristics, particularly colour and astringency. Grape tannins and anthocyanins are extracted during red wine fermentation. However, their concentration and composition change over time, due to their strong chemical reactivity1. It is also well known that yeasts influence the wine phenolic content, either through the release of metabolites involved in the formation of derived pigments1, or through polyphenol adsorption2,3.

Introduction increasing sustainability is essential and a societal challenge, requiring fundamental changes in behaviour and attitudes. This applies to both producers and consumers. For the wine industry in particular, such a change is a major challenge. An eip-agri research project is evaluating the introduction of a returnable glass system in the german wine industry as a key solution for increasing sustainability. Given the need for change associated with a returnable system, the project is theoretically grounded in systems innovation, as this approach provides solutions for complex, transformative change.

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.