A.O.C. taureau de Camargue

Wine complexity is considered a multidimensional yet equivocal sensory percept. This project uncovered sensory attributes Australian Chardonnay wine consumers associate with Chardonnay wine complexity
and correlations between expert and consumer perceived wine complexity and preference. A
wine consumer test examined 6 Australian Chardonnay wines of three complexity levels designated low (LC1&2), medium (MC1&2), and high (HC1&2) by an expert panel (n = 8) using a benchtop sensory task. Consumers (n = 81) rated their perceived liking using a 9-point hedonic scale; wine complexity with a 5-point scale anchored “low”, “low-medium”, “medium”, “medium-high”, and “high” and lastly, profiled the wines using Rate-All-That-Apply (RATA). Psychographic segmentation with the Fine Wine Instrument
(FWI) generated three segments; Wine Enthusiasts (WE n=29), Aspirants (ASP n=40) and No- Frills (NF n=12).

Changes in the rainfall and temperature patterns affect the increase of drought periods becoming one of the major constraints to assure agricultural and crop resilience in the Mediterranean regions. Beside the adaptation of agricultural practices, also the microbial compartment associated to plants should be considered in the crop management. It is known that the microbial community change according to several factors such as soil composition, agricultural management system, plant variety and rootstock.

The vine is colonized by a multitude of micro-organisms (fungi, bacteria, oomycetes) mainly coming from the microbial reservoir constituted by the soil. These microorganisms have positive or negative effects on the vine (protection against pathogens, resistance to abiotic stress, nutrition, but also triggering of diseases) (Fournier, Pellan et al. 2022). In addition to these functional roles, they respond quickly to environmental changes (climate, cultural practices) which could make them good bioindicators of the functioning of the wine ecosystem.

Gli autori presentano gli ultimi risultati delle ricerche dei DIAF sulla meccanizzazione delle operazioni colorali in zone di difficile accesso e transitabilità quali le aree marginali, i terreni terrazzati e altre realtà agricole caratterizzate da spazi estremamente ristretti (vivaismo, orticoltura, ecc.).

Viticulture is entangled with weather and climate. Therefore, areas currently suitable for grape production can be challenged by climate change. Winegrowers in Italy already experiences the effect of climate change, especially in the form of warmer growing season, more frequent drought periods, and increased frequency of weather extremes.
The aim of this study is to investigate the impact of climate variability and change on grape yield in Italy to provide winegrowers the information needed to make their business more sustainable and resilient to climate change. We computed a specific range of bioclimatic indices, selected by the International Organisation of Vine and Wine (OIV), and correlated them to grape yield data. We have worked in collaboration with some wine consortiums in northern and central Italy, which provided grape yield data for our analysis.
Using climate variables from the E-OBS dataset we investigate how the bioclimatic indices changed in the past, and the impact of this change on grape productivity in the study areas. The climate impact on productivity is also investigated by using high-resolution convection-permitting models (CPMs – 2.2 horizontal resolution), with the purpose of estimating productivity in future emission scenarios. The CPMs are likely the best available option for this kind of impact studies since they allow a better representation of small-scale processes and features, explicitly resolve deep convection, and show an improved representation of extremes. In our study, we also compare CPMs with regional climate models (RCMs – 12 km horizontal resolution) to assess the added value of high-resolution models for impact studies. Further development of our study will lead to assessing the future suitability for vine cultivation and could lead to the construction of a statistical model for future projection of grape yield.