Temperature-based phenology modelling for the grapevine 

Today, the concept of precision vine management (or site-specific viticulture) has a great relevance. It is based on the practice of a different management in relation to the different features of the crop site. In this way, all practices should be adapted to the land spatial variability and should be linked to the real needs of vines.

Genetic variants known to produce single residue missense mutations have been associated with phenotypic traits of commercial interest in grapevine. This is the case of the K284N substitution in VviDXS1 associated with muscat aroma, or the R197L in VviAGL11 causing stenospermocarpic seedless grapes. The impact of such mutations on protein structure, stability, dynamics, interactions, or functional mechanism can be studied by computational methods, including our pyDock scoring, previously developed. For this, knowledge on the 3D structure of the protein and its complexes with other proteins and biomolecules is required, but such knowledge is not available for virtually none of the proteins and complexes in grapevine.

Aroma is considered one of the most important factors in determining the quality and character of wine. The relationship between wine character and its volatile composition is recognized by several researchers worldwide. Since these compounds influence the sensory perceptions of consumers, both volatile composition and sensory properties are essential in determining wine aroma characteristics.

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 current levels of greenhouse gas emissions are expecting to provoke a change on the environmental conditions which, among others, will include a rise of global mean surface temperature and an increment of atmospheric CO2 levels (IPCC, 2014), known as climate change. The response of grapevine (Vitis vinifera L.), one of the most important crops in Europe, from both a cultural and economic point of view, is not completely understood yet and the studies considering the interaction between factors are scarce. Besides, the potential variety of responses among somatic variants needs to be studied in order to be exploited in the avoidance of undesired traits linked to climate change (Carbonell‐Bejerano et al., 2015).