Use of the stics crop model as a tool to inform vineyard zonages

Under standard wine tasting conditions, volatile organic compounds (VOCs) responsible for the wine’s bouquet progressively invade the glass headspace above the wine surface. Most of wines being complex water/ethanol mixtures (with typically 10-15 % ethanol by volume), gaseous ethanol is therefore undoubtedly the most abundant VOC in the glass headspace [1]. Yet, gaseous ethanol is known to have a multimodal influence on wine’s perception [2]. Of particular importance to flavor perception is the effect of ethanol on the release of aroma compounds into the headspace of the beverage [1].

Significant increases in anthropogenic carbon dioxide (CO2) emissions due to combustion of fossil fuels and intensive land management practices that release CO2 into the atmosphere have resulted in higher air temperatures due to the greenhouse effect.

In viticulture, terroir is a key concept that refers to an area and thus possesses a geographical dimension. Hence, zoning of viticultural terroir is an important issue

Une étude préliminaire des situations météorologiques journalières a été réalisée pour l’Afrique du Sud et pour les mois de février (période de maturation des raisins dans la Province occidentale du Cap), à l’image de la classification synoptique réalisée aux latitudes tempérées en France (Jones & Davis, 2000), afin d’étudier les relations entre le climat et la viticulture à des latitudes plus basses.

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.