Soil management of interrow spacing as an important factor to protect the vineyard soils from runoff and erosion under the Mediterranean climate

In the context of global warming, water scarcity is becoming an increasing issue worldwide. However, the reference method to characterize vine water deficit is based on water potential measurement, which is a destructive and discontinuous method. The current climatic context emphasizes the need for more precise and more continuous vineyard water use measurements in order to optimize irrigation and vine water deficit monitoring.

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.

The traditional methods of grape extraction of enochemical compounds use very often mechanical energy by pistons such as the pigeage or mechanical energy produced by must (delestage, pumping over). Recent trend by winemaker is trying to introduce in the fermentation tank, whole berry grape to avoid even minimal oxidation. Unfortunately, the use of the traditional mechanical techniques aforementioned, very often do not guarantee the optimal extraction with residual sugars in the marc. Use of resonance waves (airmixingtm) and a slight overpressure by CO2 (adcftm) permit to work on whole berry guaranteeing the perfect extraction.

The Ribera del Duero Designation of Origin (DO) has acquired great recognition during the last decades, being considered one of the highest quality wine producing regions in the world. This DO has grown from 6,460 ha of vineyards officially registered in 1985 to approximately 21,500 ha in 2013. The total grape production stands at around 90 million kg, with an average yield that approaches nearly 4,500 kg/ha. Most vineyards are cultivated under rainfed conditions.

This study aims to investigate the effects of different conditions of grape processing or fermentation on the aroma profile of New Zealand white wines.