Classification of the wine-growing environment of Central Mancha (Spain). First works

Context and purpose of the study ‐ The aim of this study is the examination of wines of 9 different clones of a Greek grape variety Xinomavro, (ΧE1, X19, X22, X28, ΧE2 X30, X31, X35, X36, X37), with regards to their phenolic and anthocyanin content and chemical composition.

Wine consumers generally demand wines having a perception of softer tannins and less ripe, having a heaviness and richness on palate (full-body wine) with a limpid and stable color. However, polyphenol
(tannins)-rich wines have been also correlated with unpleasant taste properties such as astringency and
bitterness when perceived at high intensities. Modulating these unpleasant properties could be important for consumer’s approval of wines.

The aim of this work was to evaluate the effect of preharvest application in Monastrell berries using two different types of applications: conventional treatments

Usually the winemaker consider polyphenols from the grape berry as an actor of the wine quality. There are frequently consider as a marker of grape maturity. It is commonly known that winemaker consider tannins and anthocyanins as main polyphenol actors for winemaking practices and wine quality. Here we will focus on the characterisation of lignins in grape seeds. Previous studies suggest that the seed is lignified [1], which could explain the change in colour of the seed when it reaches maturity and thus provide a reliable indicator for describing the maturity stage in the seed.

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.