Viticulture, landscapes and the marketing of our wine

Aim of the study was to investigate the metabolomic differences between Chilean Cabernet Sauvignon wines, divided according to their quality in two main groups: “Varietal” and “Premium”, and to point out metabolites tentative markers of their chemical signature and sensorial quality. Initially, 150 (50 x 3 biological replicates) experimental wines were produced by the same semi-industrial process, which covered 8 different Chilean valleys. The wine classification made by experts, divided the wines into two major groups (“Varietal” and “Premium”) and four subgroups (two for each major group). All the samples were analyzed according to a robust LC-MS based untargeted work-flow (Arapitsas et al 2018), and the proposed minimum reporting standards for chemical analysis of the Metabolomics Standards Initiative (Sumner et al 2007)

The ongoing climate change results in increasing mean air temperature, which is manifested by weather extremes or sudden changes between drought and local heavy rainfalls. These changing conditions are especially challenging for the established grapevine varieties growing under cool climate conditions due to an increased biotic infection pressure. Thus, the scope of most grapevine breeding programs is the selection of mildew fungus-resistant and climatic adapted grapevines with balanced, healthy yield and outstanding wine quality. Since no resistances or candidate genes have yet been described for Botrytis bunch rot (BBR), physical-mechanical traits like berry size and thick, impermeable berry cuticles phenotyped with high-throughput sensors represent novel effective parameters to predict BBR.

The interest in sustainable and ecologic agricultural practices in grapevine has grown significantly in recent years in the context of ecological transition. Organic mulches are treatments that support the circular economy and positively affect the soil and the plant. They are an alternative to herbicides and other conventional practices since they may influence soil moisture, erosion, structure and weed control. However, their effects on the soil and must microbiota remain unknown.

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.

Aims: The current study seeks to demonstrate that premium Shiraz wines from different Australian geographic indications (GI) can be distinguished by their volatile compound composition.