Rootstock x environment interaction shapes shoot system phenotypic variation in grafted ‘Chambourcin’

Since 1998, the GiESCO (previously named GESCO: Groupe d’Etude des Systèmes de COnduite de la vigne) has provided the scientific community with relevant contributions to the study of terroirs. Here is a synthesis of the main terroir-related fields and the major ideas the GiESCO has developed: Basic Terroir Unit and climate, Vine Ecophysiology and microclimate – moderate drought, Vineyard heterogeneity and new technologies, Viticultural Terroir Unit and canopy management, Terroir – Territory and man.

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.

AIM: The refractive index is a basic optical property of materials and a key tool for the determination of major components in musts, such as sugars

Cultivar and rootstock selection are two well-known strategies for adapting vine production in challenging environments. Despite the vast diversity of rootstocks and cultivars, their effective contribution to grapevine sustainable development and acclimation to changing growing conditions remains an open question. The use of robust and prompt monitoring tools can allow a powerful screening of the water status of the vineyard before considering a further detailed characterization. This study leveraged new tools to monitor the stomatal conductance (gs), transpiration rate (E), and quantum efficiency of photosystem II (ᶲPSII) throughout a season, from pre-veraison to after-harvest.

Vines are exposed to environmental conditions that cause abiotic stress on the plants (drought, nutrient and mineral deficits, salinity, etc.). Polyamines are growth regulators involved in various physiological processes, as in abiotic plant stress responses. Stressful conditions can modify grape’s composition, and in this work, we have focused on studying the effect of abiotic stress on the composition of polyamines and amino acids in grapes. In addition, the effect of grape variety on these compounds has been studied.