Water relations, growth and yield of grapevines in Portugal’s Douro wine region

The aim of this ongoing study is to evaluate the degree of adaptability of grapevine scion:rootstock combinations to different conditions of water constraint. Here we present results from the young vine development phase, using three scenarios of water constraint that were implemented from planting. The experimental vineyard was established in 2020 and the data presented will cover the 2021/2022 and 2022/2023 seasons. The experiment consisted of the cultivars Pinotage (PIN), Shiraz (SHI) and Cabernet Sauvignon (CAB), grafted on two rootstocks, Richter 110 (R110) and USVIT-8-7 (US87).

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.

NIR spectroscopy has widely been tested in viticulture as powerful alternative to traditional analytical methods in the field of quality evaluation. NIR instruments have been used for assessing must and wine quality features in several works, but little information regarding their application on whole berries for polyphenol determination is available.

Winemaking generates a significant amount of waste. Grape marc, the main solid residue, constitutes 20-25% of the pressed grapes and approximately 8-9 million tons are produced globally each year.

Recent EU regulations allow the use of carboxymethylcellulose (CMC) as a stabilization agent in wine. We tested CMC in bases for sparkling wines, which must be stabilized before the second fermentation that raises alcohol concentration by 1,5%.