Evaluation of the site index model for viticultural zoning

The advent of precision viticulture (PV) has allowed to address problems related to spatial and temporal variability at the within-field scale. Nowadays, several remote and proximal sensing solutions allow description of the existing variability at different temporal and ground resolution through extremely robust soil, vigor, yield, and grape quality maps. In parallel, numerous studies have described grapevine performances within the homogeneous zones and identified soil as main driver of variability. There is a broad consensus that different vigor zones within the same plot may show differential canopy growth, yield and fruit composition, depicting diverse enological potentials and cultural needs.

Climate change is increasing the frequency and severity of environmental hazards (e.g., prolonged drought), and even non-extreme climate events (e.g., a period of slightly warmer temperatures) can lead to extreme impacts when they occur simultaneously with other (non-extreme) events.

1,1,6-trimethyl-1,2-dihydronaphtelene (TDN) evokes the odor of “petrol” in wine, especially in the variety Riesling. Increasing UV-radiation due to climate change intensifies formation of carotenoids in the berry skins and an increase of TDN-precursors1. Exploring new viticultural and oenological strategies to limit TDN formation in the future requires precise knowledge of TDN thresholds in different matrices. Thresholds reported in the literature vary substantially between 2 µg/L up to 20 µg/L2,3,4 due to the use of different methods. As Riesling grapes are used for very different wine styles such as dry, sweet or sparkling wines, it is essential to study the impact of varying ethanol and carbonation levels.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

Exposure to solar radiation affects berry composition through photomorphogenesis or changes in temperature. Flavonol synthesis is upregulated by UV‐B radiation