Irrigation and terroir: two opposite concepts? Point of view of international experts and french consumers

Grappa is the most important italian spirit and its production includes elements of history, tradition, and culture of the transalpine country. In accordance with EU laws, grappa is obtained from the fermentation and distillation of the pomace, eventually added with fermentation lees and water. Grappa is one of the richest fruit distillates in volatile compounds that confer to the product its characteristic flagrance. The aroma is largely due to the volatile compounds present in the raw materials, in particular alcohols, esters and carbonyl compounds formed during the alcoholic fermentation, but also to grape aromas such as terpenols and norisoprenoids, that confers grappa the distinctive floral scents.

Understanding how different pedoclimatic conditions interact with vine and berry physiology, and subsequently impact wine quality, is paramount for an good valorization of viticultural terroirs and can help to optimize mitigation strategies in the face of global warming

The integration of digital social networks (DSN) has profoundly transformed communication practices within the wine industry, reorganizing the dynamics of prescription and marketing.

This work aimed at setting up a multivariate and geostatistical methodology to map natural terroir units of the viticultural areas at the province scale (1:125,000).

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.