Early detection project – make a GTD infection visible without disease symptoms

Climate change is altering water balances, thereby compromising water availability for crops. In grapevine, the strategic selection of genotypes more tolerant to soil water deficit can improve the resilience of the vineyard under this scenario. Previous studies demonstrated that root anatomical and morphological traits determine vine performance under water deficit conditions. Therefore, 13 ungrafted rootstock genotypes, 6 commercial (420 A, 41 B, Evex 13-5, Fercal, 140 Ru y 110 R), and 7 from new breeding programs (RG2, RG3, RG4, RG7, RG8, RG9 and RM2) were evaluated in pots during 2021 and 2022.

The way to manage the vineyard soils has certainly changed in Spain during the last years. Traditionally, the vineyards were tilled, but this growing technique has been replaced in some vineyards by the bare soil with herbicide

Although some wine production processes still rely on post-production evaluation and off-site laboratory analysis, the new winemaking industry is aware of a need for a better knowledge of the process to improve the properties of the final product. Thus, more and more wineries are interested in incorporating quality-by-design (QbD) strategies instead of postproduction testing because of the possibility to early detect deviations in fermentation or any other wine process. This would allow to detect unwanted situations and eventually to ‘readjust’ the process, thus minimizing rejects.

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 agri-food industry is constantly searching for ingredients of high functional value, healthy and of natural origin. One species of particular interest is Vitis vinifera, due to its recognized antioxidant potential. Among the grape varieties, one group possesses these antioxidant compounds not only in the skin, but also in its pulp: Teinturier. The red grape has traditionally been used for color correction purposes in winemaking, however, its high antioxidant content transforms it into a raw material of high potential for new formulations of ingredients and foods for the health and wellness market.