Landscape marketing and landscape reality: what is the relationship? The case of the Loire Valley vineyards

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.

Chitosan is a biopolymer industrially obtained from the deacetylation of chitin, the second most abundant polysaccharide on earth, after cellulose. It is extracted from various terrestrial and marine resources, including insects, grasshoppers, shrimps, crabs, lobsters, squids, and fungi. chitosan has a polycationic character due to the free amine groups along its chemical backbone, and depending on its deacetylation degree (DD) and molecular weight (MW), it shows variable properties that differ from those of other natural polysaccharides.

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.

Maintaining stable yields and quality over time is a major challenge for the wine industry. Within the context of climate change, the choice of the rootstock is an important lever for adapting to current and future climatic conditions. Within a vineyard, the choice of the rootstock depends on the environmental conditions, the scion variety and the objectives of production. Many experimental data on the performances of rootstock already exist and can guide our decision-making.

Plant water stress affects grape (Vitis vinifera L.) berry composition and is variable in space due to variations in the physical environment at the growing site. Could we use water status maps as a sensitive tool to discriminate between harvest zones?