homogeneous zones by intersecting several partial zonings of major factors that influence vineyard growth. Each of them follows specific process from their corresponding disciplines. Soil zoning specifically refers to a Soil Resource Inventory map that has traditionally been generated by conventional soil mapping methods. These methods have shortcomings in reaching fine cartographic and categorical details and involve significant expenses, which undermines their applicability. A new framework named Digital Soil Mapping has introduced quantitative models by statistical techniques to establish soil-landscape relationships and is able to provide intensive scale cartography. In the present study, a microzoning at 1:10.000 scale is generated from an initial zoning, where the conventional soil map with polytaxic map units is replaced by a new one from digital techniques that disaggregates them. The comparison between the zonings considers a quantitative evaluation of capability for each Homogeneous Terroir Unit by means of the Viticultural Quality Index and its categorization based on its distribution by map. The spatial intersection of both maps gives rise to a confusion matrix in which the flows of class variations after the substitution are assessed. The results show a five-fold increase in the number of Homogeneous Terroir Units identified and a larger differentiation among them, evidenced by a wider range in the capability index distribution. Both elements are accompanied by an increase in the detection of areas of higher potential within previously undervalued uniform zones.These features are a direct effect of the improvements brought by Digital Soil Mapping techniques and would verify the advantages of their implementation in the Integrated Terroir zoning. Eventually, such new highly detailed terroir units would benefit precision viticulture and sustainable management practices.

To digitally record and present all the origins of Austrian wines in the same perfect and clear way was the motivation for the Austrian Wine Marketing Board (Austrian Wine) to start with the project in 2018. In June 2021 the results were presented to the public in an online viewer showing all the designations of Austrian wine, available at https://austrianvineyards.com in a largely barrier-free manner. The online viewer provides tailored individual maps fitted to the respective zoom level. The smallest unit of wine-origins in Austria is called Ried and is displayed in a plot-specific manner highlighting areas under vine. Information on the Ried include administrative district, winegrowing municipality, cadastral municipality, large collective vineyard site, specific winegrowing region, generic winegrowing region, winegrowing area and, in many cases, an illustrative picture. Complementary data on the size, elevation (minimum-maximum), orientation (in 8 sectors plus flat) and gradient (minimum, maximum, average) are based on the area under vine according to the EU’s Integrated Administration and Control System. Additional information covers climate data. The diagrams are taken from the monthly breakdown of data in the annals of the Central Institute for Meteorology and Geodynamics, Austria provide a display of values for air temperature, precipitation, and sunshine hours for the reference year and the long-term average. Seasonal aggregated data on temperature, precipitation, and sunshine hours complete the display. Short descriptions with emphasis on geology and soil, field name in historical maps, etymology of the denomination, and main planted variety complements the available information for the main designations in the online viewer. These descriptions are compiled by winegrowers, geologists, historians, and journalists. All the information and data can be extracted to a pdf-file. Printed vineyard maps are also available. Missing content regarding wine origins in Styria will be completed in winter 2021/22.

Root system architecture (RSA) is important for soil exploration and edaphic resources acquisition by the plant, and thus contributes largely to its productivity and adaptation to environmental stresses, particularly soil water deficit. In grafted grapevine, while the degree of drought tolerance induced by the rootstock has been well documented in the vineyard, information about the underlying physiological processes, particularly at the root level, is scarce, due to the inherent difficulties in observing large root systems in situ. The objectives of this study were to determine genetic differences in the root architectural traits and their relationships to water uptake in two Vitis rootstocks genotypes (RGM, 140Ru) differing in their adaptation to drought. Young rootstocks grafted upon the Riesling variety were transplanted into cylindrical tubes and in 2D rhizotrons under two conditions, well watered and moderate water stress. Root traits were analyzed by digital imaging and the amount of transpired water was measured gravimetrically twice a week. Root phenotyping after 30 days reveal substantial variation in RSA traits between genotypes despite similar total root mass; the drought-tolerant 140Ru showed higher root length density in the deep layer, while the drought-sensitive RGM was characterised by shallow-angled root system development with more basal roots and a larger proportion of fine roots in the upper half of the tube. Water deficit affected canopy size and shoot mass to a greater extent than root development and architectural-related traits for both 140Ru and RGM, suggesting vertical distribution of roots was controlled by genotype rather than plasticity to soil water regime. The deeper root system of 140Ru as compared to RGM correlated with greater daily water uptake and sustained stomata opening under water-limited conditions but had little effect on above-ground growth. Our results highlight that grapevine rootstocks have constitutively distinct RSA phenotypes and that, in the context of climate change, those that develop an extensive root network at depth may provide a desirable advantage to the plant in coping with reduced water resources.

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.

Because terroir and cultivar are drivers of wine quality, is essential to investigate theirs effects on polyphenolic profile before promoting the implantation of a red minority variety in a specific area. This work, included in MINORVIN project, focuses in the polyphenolic profile of 7 red grapevines minority varieties of Vitis vinifera L. (Morate, Sanguina, Santafe, Terriza Tinta Jeromo Tortozona Tinta) and Tempranillo) from six typical viticulture Spanish areas: Aragón (A1), Cataluña (A2), Castilla la Mancha (A3), Castilla –León (A4), Madrid (A5) and Navarra (A6) of 2020 season. Polyphenolic substances were extracted from grapes. 35 compounds were identified and quantified (mg subtance/kg fresh berry) by HPLC and grouped in anthocyanins (ANT) flavanols (FLAVA), flavonols (FLAVO), hydroxycinnamic (AH), benzoic (BA) acids and stilbenes (ST). Antioxidant activity (AA, mmol TE /g fresh berry) was determined by DPPH method. The results were submitted to a two-way ANOVA to investigate the influence of variety, area and their interaction for each polyphenolic family and cluster analysis was used to construct hierarchical dendrograms, searching the natural groupings among the samples. Sanguina (A3) had the most of total polyphenols while Tempranillo (A5) those of ANT. Sanguina (A2) and (A3) reached the highest values of FLAVO, FLAVA and AA. These two last samples had also the maximum of AA. The effect cultivar and area were significant for all polyphenolic families analyzed. A high variability due to variety (>50%) was observed in FLAVA and the maximum value of variability due to growing area was detected in AA (86.41%), ANT and FLAVO (51%); the interaction variety*zone was significant only for ANT, FLAVO, EST and AA. Finally, dendrograms presented five cluster: i) Sanguina (A2); ii) Sanguina (A3); iii) Tempranillo (A5); iv) Tempranillo (A3); Terriza (A3,A5), Morate (A5,A6); v) Santafé (A1,A6); Tortozona tinta (A1,A3,A6); Tinta Jeromo (A3,A4).