Radiation-associated effects on regulated deficit irrigation management in grapevine cv. Cabernet Sauvignon

The application of remote and proximal sensing in viticulture have been demonstrated as a fast and efficient method to monitor vegetative and physiological parameters of grapevines. The collection of these parameters could be highly valuable to derive information on associated yield and quality traits in the vineyard. However, to leverage the informative potential of the sensing systems, a series of preliminary evaluations should be carried out to standardize working protocols for the specific features of a winegrowing area (e.g., pedoclimate, topography, cultivar, training system). This work aims at evaluating remote and proximal sensing systems for their performance and suitability to provide information on the vegetative, physiological, yield and qualitative aspects of vines and grapes as a function of different training systems in the Valpolicella wine region (Verona, Italy).

The characterization of the plant is the obliged pathway between the environment and the product. The responses of the plant amplify or reduce the variations of the environment, while determining directly the type and the quality of the products. These results are inscribed inside the Viticultural Terroir Unit (VTU). VTU is the complex interaction between the Basic Terroir Unit or BTU (interaction mesoclimate x soil/subsoil), the genotype (variety x rootstock), the management system, the oenological technologies. Thus, at the most complex level, a global biological triptych is found again : environment (source) x plant (structure) = produced and exchanged substances.

Pergola veronese is the most important vine training system in Valpolicella area but Guyot in the last decades is diffusing. Rondinella is one of the three most important varieties

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.

The study analyzes a set of open innovation actions promoted by the innovation environments of the Instituto Federal do Rio Grande do Sul (IFRS), in cooperation with entities, companies in the sector and the Department of Innovation, Science and Technology of Rio Grande do Sul.