AOC Saint-Romain, Hautes-Côtes-de-Beaune, Burgundy: analysis of a “terroir”

El tema que me corresponde tratar en esta mini conferencia sobre “Caracterización vitivinícola de las Malvasías en Canarias”, es por razones obvias la parte que atañe a la Isla de La Palma.

The use of oenological tannins during winemaking has been mostly studied for improving colour traits and stability on red wines. Their effectiveness mainly depends on the tannin composition, grape variety and winemaking approach [1].

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Water resource is a major limiting factor impacted by climate change that threatens grapevine production and quality. Understanding the ecophysiological mechanisms involved in the response to water deficit is crucial to select new varieties more drought tolerant. A major bottleneck that hampers such advances is the lack of methods for measuring fine functioning traits on thousands of plants as required for genetic analyses. This study aimed at investigating how water deficit affects the trade-off between carbon gains and water losses in a large panel representative of the Vitis vinifera genetic diversity. 250 genotypes were grown under 3 watering scenarios (well-watered, moderate and severe water deficit) in a high-throughput phenotyping platform.

One cultivar could produce distinct wines with typical properties and qualities different depending on its cultivated and its mesoclimatic conditions.