Ripening of Mencía grape cultivar in different edaphoclimatic situations (D.O. Ribeira Sacra, Spain)

Phenolic compounds are important quality indicators in red wine. A large number of polyphenols play an important role in wine development, contributing to the colour and the sensory perception of the wines. Anthocyanins are the pigments responsible for the colour in young red wines while tannins are the principal contributors to the bitterness and the astringency of the wines. Wine polyphenols are considered more complex molecules than grape phenolics, due to the enormous number of chemical reactions which take place during the entire winemaking process and storage, forming more stable compounds.

Agroecology is an integrated approach that simultaneously applies ecological and social concepts and principles to redesign and manage food and agricultural systems, promoting agroecosystems with the necessary biological, socio-economic, and institutional diversity and alignment to support greater efficiency. Thus, several studies have been carried out at promoting the adoption of more agroecological practices among farmers and a wider audience concerning soil conservation and health maintenance.

The work was aimed at comparing some analytical methods used to characterize oenological tannins and the measure of oxygen consumption rate (OCR), in order to provide oenologists with a rapid method to test the antioxidant capacity of tannin based products and a tool to choose the best suited product for each purpose.

The main challenge faced by viticulture is to improve the quality of the wines, adapting them to the new consumer demands that demand wines with lower alcohol content and greater freshness. In the last 30 years, a clear modification has been observed in the composition of the grape due to climate change

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.