Rootstock-scion contributions to seasonal water and light use diversity under field conditions

In the Cognac region in France, Ugni blanc is the most planted grape variety (98% of the 80 500 ha). This vine region is in expansion due to the success of the associated well-known brandy and the need of high grape yield to guarrantee the production of base wine for distillation. About 2 to 3000 ha are newly planted each year and rootstocks are one powerfull tool for vineyard adaptation to soil or climate change. As rootstocks ensure water and mineral nutrient supplies to the scion, it is important to better understand their effect on berry compostionnal parameters such as sugars and nitrogen compounds, which are the main precursors for fermentary aroma metabolites, the latter being quality markers for Cognac after distillation.

Air temperature is the key driver of grapevine phenology and a significant environmental factor impacting yield and quality for a winegrape growing region. In this study the optimal downscaled CMIP5 ensemble for computing thegrowing season average temperature (GST) viticulture climate classification index was determined to spatially compute on a decadal basis predictions of the GST climate index and the grapevine sugar ripeness (GSR) model for Pinot Noir throughout the Willamette Valley (WV) American Viticultural Area (AVA). Forecasts for average temperature and a 220 g/L target sugar concentration level were computed using daily Localized Constructed Analogs (LOCA) downscaled CMIP5 historic and Representative Concentration Pathways (RCP) future climate projections of minimum and maximum daily temperature. We explore spatiotemporal trends of the GST climate classification index and Pinot Noir specific applications of the GSR phenology model for the WV AVA. Spatiotemporal computations of the GST climate index and Pinot Noir specific applications of the GSR model enable the opportunity to explore relationships between their computed values with one intent being to provide updated GST ranges that better align with current temperature-based modeling understanding of Pinot Noir grapevine phenology and the viticultural application of LOCA CMIP5 climate projections for the WV AVA. The Pinot Noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot Noir production is currently at or near its peak value in the upper 80s to lower 90s of this century.

Wine vinegars have a tangy flavor and are versatile in cooking. They’ve been used since the neolithic period and are now used as microbial inhibitors and acidifiers. They’re low in calories, have antioxidants, and have a long shelf life, but quality may decrease after opening. The objective of this study focuses on the physical-chemical, sensory, and emotional characterization of wine vinegar samples from the douro demarcated region and la rioja. In total, 22 samples of wine vinegar were analyzed at the time of opening.

Lugana Protected Designation of Origin (PDO) wines are made from Turbiana grapes. The aroma of Lugana wines results from the combined contribution of esters, terpenes, norisprenoids, sulfur compounds and the benzenoid methyl salicylate. This study aims to investigate how volatile aroma compounds are affected by different nitrogen supplies and yeast strains. Wines were produced with a standard protocol with 2021 Turbiana grapes with two different yeasts Zymaflore Delta e Zymaflore X5 (Laffort, France).During the alcoholic fermentation of the must, when H2S appeared, additions of various nitrogen supply were made: inorganic nitrogen, organic nitrogen, a mix of inorganic and organic nitrogen and organic nitrogen with an addition of pure methionine. During wine fermentation, a daily measurement of hydrogen sulfide was carried out.

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.