A Viticultural Terroir in Brazil: Change and continuity

Dans de précédents travaux sur le zonage “GRANDE ZONAZIONE” (GZ) (“Grand Zonage”), on a traité, entre autre, de la “GRANDE FILIERA” (GF) (Grande filière) où parmi les 54 descripteurs prévus pour lire et évaluer par exemple un zonage, sont compris aussi la Communication

A 3-year zoning study promoted by the Cooperative Winery Valpolicella (Negrar, Verona, Italy) was carried out on a wine territory of about 500 ha.

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.

Acidity of grape berries is lowered due to climate changes (1), resulting in musts and wines with higher pHs. These higher pHs induce microbiological instability

Climate change (CC) is altering grape/wine composition, challenging wine sensory quality. Rising temperatures increase grape sugar levels, with higher wine ethanol (EtOH) contents, reduce total acidity (TA) converging with increased pH and lead to the accumulation of CC odorous markers such as γ-nonalactone (γ-C9) and massoia lactone (ML).