Monitoring of microbial biomass to characterise vineyard soils

The reproducibility of elemental profile in wines produced across multiple vintages has been previously reported using grapes from a single scion clone of Vitis vinifera L. cv. Pinot noir. The grapevines were grown on fourteen different vineyard sites, from Oregon to southern California in the U.S.A., which span distances from approximately hundreds of meters to 1450 km, while elevations range from near sea level to nearly 500 m. In addition, sensorial (i.e. aroma, taste, and mouthfeel) and chemical (i.e. polyphenolic and volatile) differences across the different vineyard sites have also been observed among these wines at two aging time points. While strong evidence exists to support that grapes grown in different regions can produce wines with unique chemical and sensorial profiles, even when a single clone is used, the understanding of growing site characteristics that result in this reproducible differentiation continues to emerge. One hypothesis is that the elemental profile that a vineyard site imparts to the grape berries and the resulting wine is an important contributor to this differentiation in chemistry and sensory of wines. For example, various classes of enzymes that catalyze the formation of key aroma compounds or their precursors require specific metals. In this work, we begin to report correlations between elemental and volatile aroma profiles of site-specific Pinot noir wines, made under standardized winemaking conditions, that have been previously shown to be distinguished separately by these chemical analyses.

There is currently a trend towards the production of wines with low alcohol content. To achieve this, grapes with low sugar content must be used. There are techniques at the vineyard level that can delay ripening and avoid excessive sugar accumulation without, a priori, affecting the final polyphenol content. Postveraison shoot trimming (PVST) is experimentally evaluated for these purposes, but its impact under Uruguayan climatic conditions with high interannual variability is not known. The aim of this work is to assess the PVST in Tannat and Merlot cultivars and their impact on yield components, plant balance and berry primary composition. In this study, two commercial vineyards of 10 years old Tannat and Merlot (grafted on SO4) at Canelones Department were selected. During the 2020-201 growing season, grapevines were submitted to PVST when grapes reached 15º Brix. In a randomized block, trimmed (T) and control (C) plants were evaluated with three repetitions each cultivar. Evaluation of the evolution of primary berry composition during ripening, measurement of yield components and plant balance were performed. For both cultivars, PVST did not affect yield components. Merlot reached 5.4 kg per plant and Tannat 7.1 kg, with not statistical significance between treatments. However, statistical differences were observed in terms of plant balance. In Merlot Ravaz Index reached a difference of 5.3 (12.0 in T and 6.7 in C) meanwhile Tannat reached 3.5 of statistical difference (13.7 in T and 10.2 in C). The tendency to imbalance for the treated plants had an impact on the final grape composition. Merlot grapes showed statistical difference in final total acidity (0.3 g of difference between treatments) while treatments impact final sugar content on Tannat grapes (10.0 g of difference between treatments). Further studies are needed to assess the impact of different canopy management techniques in our conditions.

Late spring frost is a major challenge for various winegrowing regions across the world, its occurrence often leading to important yield losses and/or plant failure. Despite a significant increase in minimum temperatures worldwide, the spatial and temporal evolution of spring frost risk under a warmer climate remains largely uncertain. Recent projections of spring frost risk for viticulture in Europe throughout the 21st century show that its evolution strongly depends on the model approach used to simulate budburst. Furthermore, the frost damage modelling methods used in these projections are usually not assessed through comparison to field observations and/or frost damage reports.
The present study aims at comparing frost risk projections simulated using six spring frost models based on two approaches: a) models considering a fixed damage threshold after the predicted budburst date (e.g BRIN, Smoothed-Utah, Growing Degree Days, Fenovitis) and b) models considering a dynamic frost sensitivity threshold based on the predicted grapevine winter/spring dehardening process (e.g. Ferguson model). The capability of each model to simulate an actual frost event for the Vitis vinifera cv. Chadonnay B was previously assessed by comparing simulated cold thermal stress to reports of events with frost damage in Chablis, the northernmost winegrowing region of Burgundy. Models exhibited scores of κ > 0.65 when reproducing the frost/non-frost damage years and an accuracy ranging from 0.82 to 0.90.
Spring frost risk projections throughout the 21st century were performed for all winegrowing subregions of Bourgogne-Franche-Comté under two CMIP5 concentration pathways (4.5 and 8.5) using statistically downscaled 8×8 km daily air temperature and humidity of 13 climate models. Contrasting results with region-specific spring frost risk trends were observed. Three out of five models show a decrease in the frequency of frost years across the whole study area while the other two show an increase that is more or less pronounced depending on winegrowing subregion. Our findings indicate that the lack of accuracy in grapevine budburst and dehardening models makes climate projections of spring frost risk highly uncertain for grapevine cultivation regions.

The aim of this study was to evaluate the effects of different soil types on the chemical composition of Mediterranean red wines, through untargeted and targeted 1H-NMR metabolomics. One milliliter of raw wine was analyzed by means of a Bruker Avance II 400 spectrometer operating at 400.15 MHz. The spectra were recorded by applying the NOESYGPPS1D pulse sequency, to achieve water and ethanol signals suppression. No modification of the pH was performed to avoid any chemical alteration of the matrix. The generation of input variables for untargeted analysis was done via bucketing the spectra. The resulting dataset was preprocessed prior to perform unsupervised PCA, by means of MetaboAnalyst web-based tool suite. The identification of compounds for the targeted analysis was performed by comparison to pure compounds spectra by means of SMA plug-in of MNova 14.2.3 software. The dataset containing the concentrations (%) of identified compounds was subjected to one-way analysis of variance (ANOVA) to highlight significant differences among the wines. The untargeted analysis, carried out through the PCA, revealed a clear differentiation among the wines. The fragments of the spectra contributing mostly to the separation were attributed to flavonoids, aroma compounds and amino acids. The targeted analysis leaded to the identification of 68 compounds, whose concentrations were significant different among the wines. The results were related to soils physical-chemical analysis and showed that: 1) high concentrations of flavan-3-ols and flavonols are correlated with high clay content in soils; 2) high concentrations of anthocyanins, amino acids, and aroma compounds are correlated with neutral and moderately alkaline soil pH; 3) low concentrations of flavonoids and aroma compounds are correlated with high soil organic matter content and acidic pH. The 1H-NMR metabolomic analysis proved to be an excellent tool to discriminate between wines originating from grapes grown on different soil types and revealed that soils in the Mediterranean area exert a strong impact on the chemical composition of the wines.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.