Possible Reduction Method Of Volatile Acid Content And Polyphenols Of Tokaj Aszú Wines With The Aid Of Citosan Bactericid Wine-Treatments

Monitoring vine water status is a major challenge for vineyard management because it influences both yield and harvest quality. It is also a challenge at the territorial scale for identifying periods of high water restriction or zones regularly impacted by water stress. This information is of major importance for defining collective strategies, anticipating harvest logistic or applying for irrigation authorisation. At this spatial scale, existing tools and methods for monitoring vine water status are few and often require strong assumptions (e.g. water balance model). This paper proposes to consider a collaborative collection of observations by winegrowers and wine industry stakeholders (crowdsourcing) as an interesting alternative. Indeed, it allows the collection of a large number of field observations while pooling the collection effort. However, the feasibility of such a project and its interest in monitoring vine water status at regional scale has never been tested. The objective of this article is to explore the possibility of making a regional map of vine water status based on crowdsourcing observations. It is based on the study of the free mobile application ApeX-Vigne, which allows the collection of observations about vine shoot growth. This information is easy to collect and can be considered, under certain conditions, as a proxy for vine water status. This article presents the first results obtained from the nearly 18,000 observations collected by winegrowers and wine industry stakeholders during 2019, 2020 and 2021 seasons. It presents the vine shoot growth maps obtained at regional scale and their evolution over the three vintages studied. It also proposes an analysis of the factors that favoured the number of observations collected and those that favoured their quality. These results open up new perspectives for monitoring vine water status at a regional scale but above they provide references for other crowdsourcing projects in viticulture.

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.

There is a growing trend on the transition from conventional to agroecological management of vineyards. However, the impact of practices, such as reduced-tillage, organic fertilization and cover crops, is not well-understood regarding the soil microbial diversity, and its relationship with the soil physicochemical properties in the subsurface depth near the rooting zone. Soil bacterial diversity is an important contributor towards plant health, productivity and response to environmental stresses. A field experiment was conducted by sampling subsurface soil bacterial community (NGS and qPCR) near to the root zone of Macabeu and Xarel·lo vineyards, located at the Penedes. 3 organic (ECO) and 3 conventional (CON) vineyards, with more than 10 years of respective management were sampled (n=5 each plot). ECO practices did not affect bacterial and fungal abundance but increased significantly the ammonium oxidizing bacteria and alpha-diversity (Inv.Simpson). Interestingly beta-diversity was significantly affected by the management strategy. ANOSIM-tests revealed a significative effect of the management (ecological vs conventional) and plot, on the soil microbial structure (ASV abundance). Main phyla depicted were Proteobacteria, Actinobacteria and Acidobacteria, whose relative abundances were not affected by the management. EdgeR assay revealed a significant increase of Cyanobacteria and decrease of Gemmatimonadetes and Firmicutes phyla in ECO. Interestingly, the grapevine variety was not correlated with the soil microbial community structure. Mantel-test revealed an important correlation (Spearman) of some physicochemical parameters with the soil microbiota structure, in order of importance: texture, EC, pH Ca/Mg, Mg/P, K+, Mg2+, Ca2+, SO42-, and OM. N-NH4 and NTK, which were higher in the ECO managed soils, did not correlated significantly with the soil microbiome population. The results revealed the importance of combining a deep physicochemical characterization of each replicate with the microbial diversity assessment to gain better insights on the relationship between soil microbiome and vineyard management.

The Barossa zone is arguably one of the most well-recognised wine producing regions in Australia and internationally; known mainly for the production of its distinct Shiraz wines. However, within the broad Barossa geographical delimitation, a variation in terroir can be perceived and is expressed as sensorial and chemical profile differences between wines. This study aimed to explore the sub-division classification across the Barossa region using chemical and sensory measurements. Shiraz grapes from 4 different vintages and different vineyards across the Barossa (2018, n = 69; 2019, n = 72; 2020, n = 79; 2021, n = 64) were harvested and made using a standardised small lot winemaking procedure. The analysis involved a sensory descriptive analysis with a highly trained panel and chemical measurement including basic chemistry (e.g. pH, TA, alcohol content, total SO2), phenolic composition, volatile compounds, metals, proline, and polysaccharides. The datasets were combined and analysed through an unsupervised, clustering analysis. Firstly, each vintage was considered separately to investigate any vintage to vintage variation. The datasets were then combined and analysed as a whole. The number of sub-divisions based on the measurements were identified and characterised with their sensory and chemical profile and some consistencies were seen between the vintages. Preliminary analysis of the sensory results showed that in most vintages, two major groups could be identified characterised with one group showing a fruit-forward profile and another displaying savoury and cooked vegetables characters. The exploration of distinct profiles arising from the Barossa wine producing region will provide producers with valuable information about the regional potential of their wine assisting with tools to increase their target market and reputation. This study will also provide a robust and comprehensive basis to determine the distinctive terroir characteristics which exist within the Barossa wine producing region.

Winegrowing regions recognized as protected designations of origin (PDOs) are closely tied to well defined geographic locations with a specific set of pedoclimatic attributes and strictly regulated by legal specifications. However, climate change is increasingly threatening these regions by changing local conditions and altering winegrowing processes. The vulnerability to these changes is largely heterogenous across different winegrowing regions because it is determined by individual characteristics of each region, including the capacity to adapt to new climatic conditions and the sensitivity to climate change, which depend not only on natural, but also socioeconomic and legal factors. Accurate vulnerability assessments therefore need to combine information about adaptive capacity and climate change sensitivity with projected exposure to new climatic conditions. However, most existing studies focus on specific impacts neglecting important interactions between the different factors that determine climate change vulnerability. Here, we present the first comprehensive vulnerability assessment of European wine PDOs that spatially combines multiple indicators of adaptive capacity and climate change sensitivity with high-resolution climate projections. We found that the climate change vulnerability of PDO areas largely depends on the complex interactions between physical and socioeconomic factors. Homogenous topographic conditions and a narrow varietal spectrum increase climate change vulnerability, while the skills and education of farmers, together with a good economic situation, decrease their vulnerability. Assessments of climate change consequences therefore need to consider multiple variables as well as their interrelations to provide a comprehensive understanding of the expected impacts of climate change on European PDOs. Our results provide the first vulnerability assessment for European winegrowing regions at high spatiotemporal resolution that includes multiple factors related to climate exposure, sensitivity, and adaptive capacity on the level of single winegrowing regions. They will therefore help to identify hot spots of climate change vulnerability among European PDOs and efficiently direct adaptation strategies.