Harvest dates – temperature relationships and thermal requirements of winegrape varieties in Greece: observed and future climate responses

Aims: The aims of this study were (1) to measure the vertical temperature gradient in the vine canopy in parcels with different vineyard floor management practices and (2) to analyze the factors influencing this gradient. The objective was to investigate whether the increase of trunk height could be an adaptation strategy to reduce air temperature in the bunch zone in a context of climate change. 

Aged red wines possess phenolic composition very different from young ones due to the transformations among native grape phenolics and the formation of new polymeric polyphenols during aging process.

Grapevine (vitis spp.) Is one of the major and most economically important fruit crops worldwide. Unlike other cropping systems, viticulture has ancient historical connections with the development of human culture and with the socio-cultural background of grape-growing areas. The vitis genus is characterised by high levels of genetic diversity, as result of natural genetic mutations, which are common in grapevines and further assisted by ongoing vegetative propagation.

The implementation of food safety assurance systems in wineries involves ensuring that the wines produced do not pose a risk to consumer health and are therefore free from harmful substances, such as those that may be incorporated during the production process (pesticides, additives, etc.), allergens or mycotoxins.

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.