Reconocimiento geoedafológico para la zonificación vitivinícola de la D.O. Montilla-Moriles

Context and purpose of the study – Vine development, and hence management, depends on dynamic factors (climate, soil moisture, cultural practices etc.) whose impact can vary depending upon their temporal modalities.

Red wines can sometimes exhibit undesirable green, herbaceous, and vegetative aromas, negatively impacting their sensory profile and consumer acceptance.

Top quality sparkling wines (SW) are mostly produced using the traditional method that implies a second fermentation into the bottle[1]. That is the case of sparkling wines of reputed AOC such as Champagne, Cava or Franciacorta. However, it seems that the first SW was elaborated using the ancestral method in which only one fermentation takes place[2]. That is the case of the classical SW from the AOC Blanquette de Limoux[3]. In both cases, SW age in the bottle during some time in contact with lees favoring yeast’s autolysis[4]. There is a lot of information about traditional method but only few exists about ancestral method. The aim of this work was to compare SW made by the ancestral method with SW made by the traditional method.

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.

This work discusses the effects of soil and weather conditions on the grape composition of cv. Tempranillo in four different locations of Spain, during the 2008-2011 seasons.