A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

The Fruška Gora mountain is a traditional wine-growing region in Serbia situated in the Pannonian Basin. Due to such a position, the vicinity of the Danube River and the presence of concave configuration, it is suitable for grape production. This paper provides analyses of spatial variations in meteorological parameters and grape juice quality within Fruška Gora wine region over three consecutive vintages (2018-2020). The examined period can be defined as warm with cool nights during September (AVG 18,9°C; GDD 1918°C; CI 12°CF) and with the presence of mesoclimatic variability. The East part of the study area was somewhat drier and hotter compared to other parts of the region. The analyses of grape must samples (190 in total) of five cultivars (Cabernet-Sauvignon, Merlot, Chardonnay, Sauvignon blanc and Grašac (Welschriesling)) commonly grown across the region (19 sites), were performed using Fourier Transform Infrared Technology (FTIR). Among all cultivars, Sauvignon blanc was harvested first in the East area (DOY=246±5, GDD at harvest=1552±74, 22.2±0.7 °Brix), while the latest harvest was recorded for Cabernet-Sauvignon in the West (DOY=283±5, GDD at harvest=1936±187, 23.4±1.0 °Brix ). Both the red and white cultivars had higher acidity and YAN in the grape must if the vines were grown in the North and East compared to South and West areas. According to PCA analysis, Grašac showed the lowest variation in grape must chemical composition. Thus, the results confirm that Grašac is the most stable cultivar in Fruška Gora. All monitored cultivars reached technological fruit ripeness by the end of the growing season. However, it was difficult to reach full ripeness of red cultivars, mostly beacuse of uncoupling of technolocical and phenolic ripeness. Thus, Cabernet-Sauvignon had higher variations in GDD sums at harvest compared to other cultivars, which probably increased variations in grape must quality.

Microbes play key roles on crop nutrient availability via biogeochemical cycles, rhizosphere interactions with roots as well as on plant growth and health. Recent advances in technologies, such as High Throughput Sequencing Techniques, allowed to gain deeper insight on the structure of bacterial and fungal communities associated with soil, rhizosphere and plant phyllosphere. Over the past 10 years, numerous scientific studies have been carried out on the microbial component of the vineyard. Whether the soil or grape compartments have been taken into account, many studies agree on the evidence of regional delineations of microbial communities, that may contribute to regional wine characteristics and typicity. Some authors proposed the term “microbial terroir” including “yeast terroir” for grapes to describe the connection between microbial biogeography and regional wine characteristics. Many factors are involved in terroir including climate, soil, cultivar and human practices as well as their interactions. Studies considering “microbial terroir” greatly contributed to improve our knowledge on factors that shape the vineyard microbial structure and diversity. However, the potential impact of “microbial terroir” on wine composition has yet not received strong scientific evidence and many questions remain to be addressed, related to the functional characterization of the microbial community and its impact on plant physiology and grape composition, the origins and interannual stability of vineyard microbiota, as well as their impact on wine sensorial attributes. The presentation will give an overview on the role of microbiota as a terroir component and will highlight future perspectives and challenges on this key subject for the wine industry.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

Viticulture is facing two major changes – climate change and agroecological transition. In both cases, soil quality is seen as a lever to move towards a more sustainable viticulture. However, soil biological quality is little considered in the implementation of viticultural practices. Gascogn’Innov (2017-2022) is an Operational Group funded by the European Innovation Partnership for Agriculture. As such, it brings together winegrowers from the south-west of France, scientists, advisors and technicians, around a project focused on viticultural soil biological functioning and the design of technical routes more respectful toward soil heritage. To achieve this, the project aims to acquire references on the impact of viticultural practices on soil biology from a dynamic way, and to test a methodology to integrate information provided by the soil bioindicators to manage farming systems. A set of indicators of soil biological quality are evaluated in the project: microorganisms (bacteria and fungi abundance and diversity), fauna (abundance and diversity of nematodes and earthworms), physico-chemical characteristics, soil structure assessment and degradation rate of organic matter. Based on a network of 13 plots that have been subject to an initial diagnosis in 2017, several agronomical practices to restore soil fertility are experimented to redesign the cropping system (for instance plant cover, organic matter inputs, reduction of herbicides, mineral fertilizers). System redesign was made in collaboration by winegrowers and an interdisciplinary group of experts (agronomists, biologists). Several indicators are measured on vine and soil at each vintage to assess vine health and productivity. At the end of the project (2021), a final diagnosis was carried out. Gascogn’Innov allowed to create a regional database on the quality of wine-growing soils, which permitted to evaluate the effect of practices according to soil types. Especially, decreasing the intensity of tillage and increasing the duration and diversity of grass coverage tends to increase the abundance of all the organisms studied. This project confirmed the value of soil biological quality indicators to drive the sustainability of practices, but also highlighted the key-role of expertise, in both agronomy and soil biology, to help winegrowers understand and appropriate their soil quality diagnoses.