Merging two genomes: a holistic approach to disentangle rootstock-mediated drought and recovery responses

Wineries of Beaumes-de-Venise area make their best red wines with grapes from the “Triassic terroir”. This « terroir » is characterized by soils from the Triassic period. These specific soils are complex and quite heterogeneous. They originate from an eventful geological history to keep in mind to understand soils geographical distribution.

In the context of climate change and for the sustainable exploitation of Mediterranean vineyards, it is necessary to use new strategies to adapt to the new climatic conditions.

The terroir is today the most important factor of production and development in the wine sector. In a context where the commercial challenge is taking place all over the place, the distinction between traditional and “new” producing countries is not only a geographical, cultural and technical counter position but also, and above all, a legal one. Indeed, the system of standards present in the “old world” (plantation rights, production decrees, yields per hectare, etc.) which may represent, in the short term on the global market, constraints to development and product innovation must become an opportunity. But threats become opportunities, if we work, from the vine to the market, via communication, more on the elements of difference than on those of affinity.

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.

Studying the impact of reducing alcohol techniques in the chemical composition of the aromatic profile of rosé Tempranillo wines from the spanish region of Castilla-La Mancha INTRODUCTION: In the last decades there has been an increseasing demand of wines with low or non-existing alcohol concentration due to the negative effects that alcohol has in health. In spite of that, there are not laws that protect these products, and there is also a great difficulty in the elaboration of these type of wines due to the increasing temperatures because of climate change. This is why the oenological industry has made great advances in the development of different techniques that could satisfy consumers’ demands without modifying wine quality. The most used techniques have been post-fermentative ones.