Does the sustainability perception depend on the Terroir?

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Bushfires are a common occurrence throughout Australia and their incidence is predicted to both rise and increase in severity due to climate change. Many of these bushfires occur in areas close to wine regions, which receive different levels of exposure to smoke. Wine produced from smoke-affected grapes are characterised by unpalatable smoky aromas such as “burning rubber”, “smoked meats” and “burnt wood”. These smoke tainted wines are unprofitable and result in significant financial losses for winegrowers.

The objective of the present work is to assess yeast effects on the development of wine varietal aroma throughout aging and on wine longevity.

Three independent experiments were carried out; two fermenting semi-synthetic musts fortified with polyphenols and aroma precursors extracted from Tempranillo (1) or Albariño (2) grapes and with synthetic precursors of polyfunctional mercaptans (PFMs), and a third in which a must, mixture of 6 different grape varieties was used. In all cases, fermentations were carried out by different Saccharomyces cerevisiae strains and one S. kudriavzevii, and the obtained wines were further submitted to anoxic accelerated aging to reproduce bottle aging. The volatile profile of the wines was analyzed using several chromatographic procedures, in order to provide a comprehensive evaluation of wine aroma. Aroma compounds analyzed included fermentation volatile metabolites, grape-derived aroma compounds including PFMs, and Strecker aldehydes (SA).

Results revealed that the effects of yeast on wine aroma throughout its self-life extend along three main axes:

1. A direct or indirect action on primary varietal aroma and on its evolution during wine
aging.

2. The direct production of SA during fermentation and/or their delayed formation by producing the required reagents (amino acids + dicarbonyls) for Strecker degradation
during anoxic aging.

3. Producing acids (leucidic, branched acids) precursors to fruity esters. More specifically, and leaving aside the infrequent de novo formation, the action of the different strains of yeast on primary varietal aroma takes four different forms:

1.- Speeding the hydrolysis of aroma precursors, which leads to early aroma formation without changing the amount of aroma formed. In the case of labile molecules, such as linalool, the enhancement of young wine aroma implies a short-living wine. 2.- Metabolizing the aroma precursor, reducing the amounts of aroma formed, which can be of advantage for negative aroma compounds, such as TDN or guaiacol; 3.- Transforming grape components into aroma precursors, increasing the amounts of aroma formed, as for ethyl cinnamate, leucidic acid or vinylphenols; 4.- Forming reactive species such as vinylphenols able to destroy varietal polyfunctional mercaptans.

Overall, it can be concluded that the yeast carrying alcoholic fermentation not only influences fermentative wine aroma but also affects to the wine varietal aroma, to its evolution during aging and to the development of oxidative off-odors

Dividing agrarian exploitations into microfields is a problem that influences the modern viticulture in a very important way. The aim of this work is the study of the influence of Geology and Geomorphology in agricultural structures

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.