Characterization and modelling of water flow on vineyard soil. Effect of compaction and grass cover

The current climate changes are directly threatening the balance of the vineyard at harvest time. The maturation period of the grapes is shifted to the middle of the summer, at a time when radiation and air temperature are at their maximum. In this context, the implementation of corrective practices becomes problematic. Unfortunately, our knowledge of the climate effect on the quality of different grape varieties remains very incomplete to guide these choices. During the Innovine project, original experiments were carried out on Syrah to study the combined effects of normal or high air temperature and varying degrees of exposure of the berries to the sun. Berries subjected to these different conditions were sampled and analyzed throughout the maturation period. Several quality characteristics were determined, including anthocyanin content. The objective of the experiments was to investigate which climatic determinants were most important for anthocyanin accumulation in the berries. Temperature and irradiance data, observed over time with a very thin discretization step, are called functional data in statistics. We developed the procedure SpiceFP (Sparse and Structured Procedure to Identify Combined Effects of Functional Predictors) to explain the variations of a scalar response variable (a grape berry quality variable for example) by two or three functional predictors (as temperature and irradiance) in a context of joint influence of these predictors. Particular attention was paid to the interpretability of the results. Analysis of the data using SpiceFP identified a negative impact of morning combinations of low irradiance (lower than about 100 μmol m−2 s−1 or 45 μmol m−2 s−1 depending on the advanced-delayed state of the berries) and high temperature (higher than 25oC). A slight difference associated with overnight temperature occurred between these effects identified in the morning.

Using viticultural and enological techniques to increase aromatics in white wine is a prized yet challenging technique for commercial wine producers. Equally difficult are challenges encountered in hastening phenolic maturity and thereby increasing color intensity in red wines. The ability to alter organoleptic and visual properties of wines plays a decisive role in vintages in which grapes are not able to reach full maturity, which is seen increasingly more often as a result of climate change. A new, yeast-based product on the viticultural market may give the opportunity to increase sensory properties of finished wines. Manufacturer packaging claims these yeast derivatives intensify wine aromas of white grape varieties, as well as improve phenolic ripeness of red varieties, but the effects of this application have been little researched until now. The current study applied the yeast derivative, according to the manufacture’s instructions, to the leaves of both neutral and aromatic white wine varieties, as well as on structured red wine varieties. Chemical parameters and volatile aromatics were analyzed in grape musts and finished wines, and all wines were subjected to sensory analysis by a tasting panel. Collective results of all analyses showed that the application of the yeast derivative in the vineyard showed no effect across all varieties examined, and did not intensify white wine aromatics, nor improve phenolic ripeness and color intensity in red wine.

Vitis vinifera L. ‘Gewürztraminer’ is a historical grape variety of Alto Adige (Südtirol), Italy, which is widely grown in the area of Tramin an der Weinstraße, but is also grown globally. It produces highly aromatic wines that are strongly influenced by the terroir of the vineyard sites where they are grown. This study looked at musts and young wines from ‘Gewürztraminer’ grapes harvested in seven distinct vineyards near Tramin and then processed at Cantina di Termeno, minimizing winemaking protocol variability. Samples were profiled using bidimensional gas chromatography–time-of-flight mass spectrometry, liquid chromatography coupled to electrochemical detection, and near-IR spectrometry. The data were subjected to Principle Component Analysis and Hierarchical Clustering Analysis. Sensory discriminant testing was undertaken using the sorting method with a semi-trained panel, and the data were processed using Multidimensional Scaling. Seven must/wine pairs could be distinguished based on their untargeted volatilome profiles and on sensory evaluation. As expected, there were greater differences in the volatile compounds between the wines than between the musts. The wines from vineyards 4 and 5 were nonetheless quite homogenous in terms of chemical and sensory analyses, as were the wines from vineyards 1 and 3. For the phenolic profile, differences were noted between the musts and wines of vineyards 2, 3, and 4, but the musts from vineyards 5 and 7 were similar. Sensory analysis showed the wines from vineyards 6 and 7 to be distinct from the rest. These results reinforce that the composition of ‘Gewürztraminer’ musts and wines is strongly determined by vineyard site, even in a small geographic area with high variability of the terroir (soil and microclimate), and that these differences are apparent in the flavours and aromas of the finished wines. Further confirmation would require a larger sample of wines, preferably from several vintages.

Soil is a reservoir of microorganisms playing important roles in biogeochemical cycles and interacting with plants whether in the rhizosphere or in the root endosphere. The composition of the microbial communities thus impacts the plant health. Rhizodeposits (such as sugar, organic and amino acids, secondary metabolites, dead root cells …) are released by the roots and influence the communities of rhizospheric microorganisms, acting as signaling compounds or carbon sources for microbes. The composition of root exudates varies depending on several factors including genotypes. As most of the cultivated grapevines worldwide are grafted plants, the aim of this study was to explore the influence of rootstock and scion genotypes on the microbial communities of the rhizosphere and the root endosphere. The work was conducted in the GreffAdapt plot (55 rootstocks x 5 scions), in which the 275 combinations have been planted into 3 blocks designed according to the soil resistivity. Samples of roots and rhizosphere of 10 scion x rootstock combinations were first collected in May among the blocks 2 and 3. The quantities of bacteria, fungi and archaea have been assessed in the rhizosphere by quantitative PCR, and by cultivable methods for bacteria and fungi. The communities of bacteria, fungi and arbuscular mycorrhizal fungi (AMF) was analyzed by Illumina sequencing of 16S rRNA gene, ITS and 28S rRNA gene, respectively. The level of mycorrhization was also evaluated using black ink coloration of newly formed roots harvested in October. The level of bacteria, fungi and archaea was dependent on rootstock and scion genotypes. A block effect was observed, suggesting that the soil characteristics strongly influenced the microorganisms from the rhizosphere and root endosphere. High-throughput sequencing of the different target genes showed different communities of bacteria, fungi and AMF associated with the scion x rootstock combinations. Finally, all the combinations were naturally mycorrhized. The root mycorrhization intensity was influenced by the rootstock genotype, but not by the scion one. Altogether, these results suggest that both rootstock and scion genotypes influence the rhizosphere and root endophytic microbiomes. It would be interesting to analyze the biochemical composition of the rhizodeposition of these genotypes for a better understanding of the processes involved in the modulation of these microbiomes. Moreover, crossing our data with the plant agronomic characteristics could provide insights into their roles on plant fitness.

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.