Influenza delle componenti climatiche e pedologiche sulla variabilità dei contenuti polifenolici in alcuni ambienti vitati della DOCG Sagrantino di Montefalco

Grapevine is grown grafted in most of the world largely because of Phylloxera. Rootstocks not only provide tolerance to Phylloxera, but also ensure the supply of water and mineral nutrients to the scion. Rootstocks are an important means of adaptation to environmental conditions if we want to conserve the typical features of the currently used scion genotypes. To aid this adaptation, we can exploit the large diversity of rootstocks used worldwide. To fully explore this existing rootstock diversity, this work benefits from the unique GreffAdapt vineyard, in which four scion genotypes were studied onto 55 commercial rootstocks in three blocks. The aim of this study was to characterise rootstock regulation of scion mineral status and how it relates to scion development.

In recent years, sustainability has become a key focus in agriculture, including viticulture.

Red wines felt as astringent and bitter generally show high content of tannins due to grape phenolic compounds’ extraction in the maceration process. Among different enological practices, mannoproteins have been shown to improve the mouthfeel of red wines (1) and the color (2,3). In this work, we evaluated the effect of mannoproteins on the mouthfeel profile of Sangiovese wines obtained by excessive tannin extraction.

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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.