A general phenological model for characterising grape vine flowering and véraison

Initial results are presented of research into the relationship between climate variability and viticulture in New Zealand vineyards. Atmospheric modelling and analytical tools are being developed to improve adaptation of viticultural practices and grape varieties to current and future climate.

Yeast protein extracts (ypes) have flocculating properties, allowing clarification of musts and wines. They are already authorized by oiv for fining purposes with a maximum dosage limit of 60 g/hl for red wines, and 30 g/hl for musts, white and rosè wines. The extraction of ypes from the cytoplasm of yeasts (saccharomyces spp) cells is defined by the resolution oiv oeno 452-2012, that indicate also some specification of the final product.

Today winegrowers involved in mechanical winter pruning are applying this viticultural technique on two main training systems, the free cordon, appearing to be the more efficient, and the trellised vertical shoot positioning (VSP) system. The main reasons for maintaining the trellis are generally due to common habits in vineyard management, risk of wind damage for the shoots, or risk of decrease in photosynthesis potential. The aim of the study was to assess the effects of the two training systems on vine. In addition, different nitrogen fertilization levels were applied on the two systems to evaluate the best combination to achieve yield and grape quality.

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 wine metabolome through multiple targeted methods is complicated and limitative; since grapes, yeasts, bacteria, oxygen, enological techniques and wine aging collaborate to deliver one of the richest metabolomic fingerprint.