Exploiting somaclonal variability to increase drought stress tolerance in grapevine 

Changes in the rainfall and temperature patterns affect the increase of drought periods becoming one of the major constraints to assure agricultural and crop resilience in the Mediterranean regions. Beside the adaptation of agricultural practices, also the microbial compartment associated to plants should be considered in the crop management. It is known that the microbial community change according to several factors such as soil composition, agricultural management system, plant variety and rootstock.

As one of the most important crop plants in the world, securing grapevine yields and production quality is a top priority.

ine nitrogen deficiency can negatively influence the aroma profile and ageing potential of white wines. Canopy management can alter vine microclimate, affect the nitrogen availability and influence the response of leaf senescence. Increasing the nitrogen availability to vines can increase the Yeast Assimilable Nitrogen (YAN) levels in harvested fruit and wine. Studies show that foliar nitrogen and sulphur applications at véraison, on low YAN Sauvignon blanc grapes have an effect on the level of amino acids (Jreij et al. 2009) and on S-containing compounds such as glutathione and thiols (Lacroux et al. 2008), which in turn can influence the formation of major volatiles and the aroma profile of the wine.

Brandy is a spirit drink made from “wine spirit” (<86% Alcohol by Volume – ABV; high levels of congeners and they are mainly less volatile than ethanol), it may be blended with a “wine distillate” (<94.8%ABV; low levels of congeners and these are mainly more volatile than ethanol), as long as that distillate does not exceed a maximum of 50% of the alcoholic content of the finished product[1]. Brandy must be aged for at least 6 months in oak casks with <1000L of capacity. During ageing, changes occur in colour, flavour, and aroma that improve the quality of the original distillate.

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.