Observation and modeling of climate at fine scales in wine-producing areas

The perception of wine quality is determined by the assessment of multiple sensory stimuli, including aroma, taste, mouthfeel and visual aspects. With so many different parameters contributing to the overall perception of wine quality, it is important to consider the contribution of all metabolites in a wine when attempting to relate composition to quality.

Etant lié au sol et aux conditions atmosphériques, le statut hydrique influence la physiologie de la vigne d’une part, mais joue aussi un role important en ce qui concerne la qualité du raisin et donc du vin d’autre part. Nous avons mesuré, dans la région de Stellenbosch, le statut hydrique sur des pieds de Sauvignon Blanc non irrigués, implantés sur 2 terroirs différents, l’un froid, l’autre plus chaud.

L’objectif de l’étude était de connaître le rôle du climat sur les aspects phénologiques du cépage « Moscato bianco » dans les différentes zones de production du vin Moscato d’Asti aocg en Piemonte (Italie) et ses effets sur l’époque de vendange. La représentation cartographique ( échelle 1 :25000) de exposition, altitude, climat, index

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.

Row orientation and canopy management are essential for high quality grapevine production. Microclimatic conditions of the leaves and fruits can be influenced by the canopy geometry. Remote sensing is a very promising tool to describe vegetative growth and physiological behavior of vineyards. However, the correlation between remotely sensed data and in situ field measurements has been described scarcely in the scientific literature so far. The aim of the study was to correlate remotely sensed data obtained with Unmanned Aerial Vehicle (UAV) with in situ field measurements to describe canopy structure.