Remote sensing and radiometric techniques applied to vineyards in two regions of Rio Grande do Sul, Brazil

Pruning vine-shoots are a viticulture waste that have been traditionally poorly exploited in relation to its chemical minority composition related to phenolic and volatile compounds. In this line, toasted vine-shoots supposes a proposal of enological tool to use to modulate the chemical and sensorial profile of wines. From a phenolic point of view, when vine-shoots are used during winemaking mainly influence to increase the flavanols and stilbenes content, mostly trans-resveratrol, as also an increasing in the sweet tannins and decreasing the green character and total anthocyanins, changing the violet for garnet colour.

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.

Understanding the impact of rootstocks on grapevine water relations is crucial to face climate change maintaining vineyard productivity and sustainability.

Grapevine rootstocks play a pivotal role in influencing scion vigor, yield, and fruit quality, making their selection critical for sustainable vineyard management.

Some wine producers, in good years, can produce free-SO2 red wines and decide to add the minimum amount of sulphur dioxide only at bottling. To manage this addition