“Compost Application in the Vineyard: Effects on Soil Nutrition and Compaction”

SO2 play a major role in wine stability and evolution during its aging and storage. Winemaking without SO2 is a big challenge for the winemakers since the lack of SO2 affects directly the wine chemical evolution such as the aromas compounds as well as the phenolic compounds. During the red wine aging, phenolic compounds such as anthocyanin, responsible of the red wine colour, and tannins, responsible of the mouthfeel organoleptic properties of wine, evolved quickly from the winemaking process to aging [1]. A lot of new interaction and molecules occurred lead by oxygen[2] thus the lack of SO2 will induce wine properties changes. Nowadays, the phenolic composition of the wine without added SO2 have not been clearly reported.

Wine is one of the most representative components of Mediterranean diet. Moderate wine intake together with food, has been positively correlated with reduced risk of many chronic diseases. This beneficial effect seems to be ascribed to elevated polyphenolic content of wine [1]. Traditional approaches for the identification of wine biomarkers consumption include targeted metabolomics that focuses on the quantification of well-defined metabolites, losing a valuable information about a massive number of compounds. On the other hand, untargeted metabolomics can disclose a large quantity of signals corresponding to potential biomarkers in a single analysis with high sensitivity and resolution.

In grapevine, phenology from bud break to berry maturation, depends on temperature and water availability. Increases in average temperatures accelerates initiation of bud break, exposing newly formed shoots to detrimental environmental stresses. It is therefore essential to identify genotypes that could delay phenology in order to adapt to the environment. The use of different rootstocks has been applied to change scion’s characteristics, to adapt and resist to abiotic and biotic stresses[1].

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

The under-trellis zone of vineyards is a sensitive area through which vines cover a significant portion of their nutrient and water needs. Mechanical and chemical methods are applied to suppress competing and tall-growing weeds to ensure optimal vine growth conditions. In addition to higher operating costs and depending on the soil conditions, these practices might lead to a long-term reduction in soil fertility and biodiversity. The presented study aims to analyse the suitability and interspecies competition of a selected green cover mixture of five local herbaceous species as potential green cover mixture in the under-trellis area of Lower Austrian vineyards.