Origin of unpleasant smelling sulphur compounds during wine fermentation

The OIV[i] defines terroir as a concept referring to an area in which collective knowledge of the interactions between the physical and biological environment (soil, topography, climate, landscape characteristics and biodiversity features) and vitivinicultural practices develops, providing distinctive wine characteristics. Those are perceptible in the taste of wine, which drives consumer preference and, therefore, wine’s value in the marketplace. Geographical indications (GI) are recognized regulatory constructs formalizing and protecting the nexus between wine taste and the terroir generating it. Despite considering updates, GIs do not consider the nexus as a dynamic one and do not anticipate change, namely of climate. Being climate a fundamental feature of terroir, it strongly impacts wine characteristics, such as taste. According to IPCC[ii], many widespread, rapid and unprecedented changes of climate occurred, some being irreversible over hundreds to thousands of years. Climatic shifts and atmospheric-driven extreme events have been widely reported worldwide. Recent climatic trends are projected to strengthen in upcoming decades, whereas extremes are expected to increase in frequency and intensity, forcing wines away from GI definitions. Geographical shifts of viticultural suitability are projected, often moving into regions and countries different from current ones. Some authors propose adaptation in viticulture, winemaking and product innovation. We show evidence of climate changing wine characteristics in the Douro valley, home of 270-year-old Port GI. We discuss herein resist or adapt stances for when climate changes the nexus between terroir and wine characteristics. Using the MED-GOLD[iii] dashboard, a tool allowing for easy visual navigation of past and future climates, we demonstrate how policymakers can identify future moments, throughout the 21st century under different emission scenarios, when GI specifications will likely need updates (e.g., boundaries, varieties) to reduce climate-change impacts.

Sixty five % of the agricultural area of the Basque Country located in the DO Ca Rioja corresponds to vineyards. More than 40% of it has an average slope greater than 10%, which makes it sensitive to erosive processes. Furthermore, it is foreseeable that extreme weather events (storms, hail, extreme heat and cold, etc.) will be favored due to climate change. Cover cropping can mitigate this risk, and therefore the objective of this work is to evaluate the impact that a vegetable cover has on the agronomic behavior of the vineyard, the quality of the grape and soil erosion. For this, a trial has been carried out with a Graciano variety vineyard with a slope between 10% -20% during the years 2020 and 2021. Conventional tillage management in the area has been compared (4-6 passes per year of tillage machinery) versus spontaneous vegetation cover management in the vineyard. This implies not tilling and allowing the grass of the land to colonize the range between the lines of vines, controlling their height through 1-3 mowing passes per year, always trying to affect the surface of the land as little as possible. The vegetative growth, yield and quality of the grape and wine was measured. Furthermore, erosion has been measured using Gerlasch boxes. The yield was lower in the second year of the trial in the cover crop treatment, but erosion was significantly reduced.

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.

Vineyards are complex agro-ecosystems with high spatial and temporal variability. An efficient training system may counteract the adverse effects of this variability. Moreover, considering the climate change issues, choosing an efficient training system that enhances water use and protects the vines from radiative thermal stress has become a priority for the farmers. A multidisciplinary approach that assesses the soil-crop-yield-wine relationships of vineyards in a distributed and holistic way could bring added knowledge on the behavior of the different training systems. This ongoing research aimed to implement a multidisciplinary approach to study the behavior of “Aglianico del Vulture” grapevines trained with two different systems: a spurred cordon (SC) and an “Alberello in parete” (AL), grown in a high-quality wine production area of Basilicata region (Italy). The approach merged several methods and scales of soil, ecophysiology, must/wine quality, and spectral data collection to assess the influence of the training system. Homogeneous zones (HZs) in both training systems were defined through a procedure based on geomorphological classification, unmanned aerial vehicles (UAV) images analysis, and a traditional soil survey supported by geophysical scanning. During the 2021 season, TDR probes monitored soil water content, while grapevine health status was assessed using eco-physiological measurements (LWP, chlorophyll content, PSII photosynthetic efficiency, LAI, and point-based field spectroscopy). These grapevine in-vivo measurements validated the spectral vegetation indexes (NDVI, RENDVI, CVI, and TVI) derived from the UAV multispectral imagery, which monitored the grapevine status in a distributed and non-invasive way. Grape yield, quality of berries, must and wine were measured to assess the effects of the training systems. The first experimental year results showed the variability of the vineyards and revealed relationships among soil parameters, crop characteristics, and vegetation indices of the SC and AL training systems. This multidisciplinary study could bring new insights into the vineyard training system’s effects on grape yield and wine quality.

This work is a first step to make a map of vineyard soils. The characterization of the soils of the Protected Designation of Origin (D.P.O.) Valdepeñas will allow to group the studied profiles according to their physico-chemical characteristics and the concentrations of most relevant chemical elements. 90 soil profiles were analysed throughout the territory and the soils were sampled and described according to FAO (2006) and classified according to and Soil Taxonomy (2014). All samples were air dried, sieved and some physico-chemical parameters were determined following standard protocols. Also, major and trace elements were analysed by X-ray fluorescence. The statistically study was made using the SPSS program. Trend maps were made using the ArcGIS program. The studied soils have the following average properties: pH, 8.3; electrical conductivity, 0,20 dS/m (low); clay, 18.8% (medium) and CaCO3, 17.1% (high). In the study for the major elements. The major elements of these soils are Si, followed by Ca and Al, with an average content of 203.7 g/kg, 105.5 g/kg and 74.0 g/kg respectively. On the other hand, 27 trace elements have been studied. Of all of them, it can be highlighted the average values of Ba (361.8 mg/kg), Sr (129.3 mg/kg), Rb (83.4 mg/kg), V (74.2 mg/kg) and Ce (70.6 mg/kg). Ba, V and Ce values are higher and the values of Sr and Rb are lower to those found in the literature. The discriminant analysis shows a percentage of grouping of 91%. The content of chemical elements together with the physico-chemical characteristics allows grouping the soils in 4 group according to their order in the classification to Soil Taxonomy; due to the importance of the Calcisols in Castilla-La Mancha, it has been decided to establish them as their own group even if they do not appear in Soil Taxonomy classification.