Les motivations du vigneron en quête de l’expression “terroir”

Vitis vinifera L. is the most widely cultivated Vitis species which includes numerous cultivars. Owing to their superior quality of grapes, these cultivars were long considered the only suitable for the production of fine wines. However, the lack of resistance genes in V. vinifera against major grapevine pathogens, requires for its cultivation frequent spraying with large amount of fungicides. Thus, the search for alternative and more sustainable methods to control the grapevine pathogens have brought the breeders to focus their attention on other Vitis species. In fact, wild Vitis genotypes present multiple resistance traits against pathogens, such as powdery mildew, downy mildew and phylloxera.

The mechanization of pruning and harvesting in vineyards has increased the risk of soil compaction. To reclaim soil properties or avoid this degradation process, it is crucial to properly manage the soil organic matter, and the application of compost derived from the vines themselves is a strategy to achieve this. The objective of this study was to evaluate the properties of soil treated with different doses of compost applied both on the vine row and the inter rows of a Vitis vinifera crop.

Limited information is available on grape wall-derived polymeric structure/composition and how this changes during fermentation. Commercial winemaking operations use enzymes that target the polysaccharide-rich polymers of the cell walls of grape tissues to clarify musts and extract pigments during the fermentations. In this study we have assessed changes in polysaccharide composition/ turnover throughout the winemaking process by applying recently developed cell wall profiling approaches to both wine and pomace polysaccharides. The methods included gas chromatography for monosaccharide composition (GC-MS), infra-red (IR) spectroscopy and comprehensive microarray polymer profiling
(CoMPP) using cell wall probes.

Merlot grapevine is the second wine cultivar most planted in the world and especially in the Bordeaux wine region. This cultivar has many advantages in producing high quality wine; however, in the last decade, climate change has increased the sugar concentration in berries at harvest and shortened the maturation cycle. If this has been up to now a great opportunity to improve wine quality profile, we are touching the tipping point. High sugar concentration at harvest induces high alcool content in wine which can negatively impact wine quality. There are many viticultural and oenological practices possible to limit this effect. In this study we focus on plant material through intra-varietal diversity of Merlot cultivar.

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.