Phenolic composition of Xinomavro (vitis vinifera L.cv.) grapes from different regions of Greece

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).

Grapevine (Vitis vinifera L.) is a globally cultivated crop and economically significant, particularly in the wine industry (Varela et al., 2024). Climate change is already affecting vineyards and is expected to worsen (Averbeck et al., 2019; Dupuis and Knoepfel, 2011).

Climatic variability studies at fine scale have been developed in recent years with the reduction of material cost and the development of competitive miniaturized sensors. This work is forming part the LIFE-ADVICLIM project, of which one of the objectives is to model spatial temperature variability at vineyard scale. In the Bordeaux pilot site, a large network of data loggers has been set up to record temperature close to the vine canopy. The reduced distance between plant foliage and measurement equipment raises specific issues and leads to an increased rate of outliers compared to data retrieved from classical weather stations. Some of these were detected during data analysis, but others could not be easily identified. The present study aims to address the issue of data quality control and provide recommendations for data processing in climatic studies at fine scale.

Accurate and early yield estimation in vineyards is essential for the effective management of resources and informed decision-making in viticulture.

Over the past two decades, sustainable vineyard management practices have become increasingly important as the wine industry is facing critical challenges, including climate change, biodiversity loss, and soil degradation.