Influence de la nutrition potassique sur le manque d’acidité des vins issus du cépage Negrette

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 nutrient reserves in the grapevine perennial structure perform a critical role in supplying the grapevine with nutrients

Context and purpose of the study. Cultivation techniques are widely recognized for their significant impact on the aroma profile of grapes and wines.

Interest and willing-ness to buy alcohol-free wines by customers is increasing for several years [1]. Due to the rising relevance of dealcoholised wines it is the objective of this study to contribute to a better understanding of the flavor variation among dealcoholised wines and to explore enological measures, how to improve final quality.
First a range of commercial, alcoholfree white wines were analysed by the holistic sensory method projective mapping, including a question for hedonic acceptance. Based on the combination of a non-target-HS-SPME-GC/MS analysis with sensory analysis we obtained a clustering of the wines into three groups.

Viticulture is facing two major challenges – climate change and agroecological transition. The soil plays a pivotal role in these transition processes. Therefore, soil quality and adequate soil management are key levers for an ecologically and economically sustainable viticulture. Over the last 15 years, numerous studies evidenced strong effects of viticultural practices on the soil physical, chemical and biological quality. However, to date a global analysis providing a comprehensive overview of the ecological impacts of viticultural practices on soil biological quality is missing.