Under-vine cover crops: impact on weed development, yield and grape composition

The aim of this study was to evaluate how the different tank materials could affect the chemical and sensory characteristics of a Sangiovese red wine during one-year aging.

When someone watches a hilly landscape, the image beauty creates emotions and frames of mind not easily forgettable, but sometimes man’s intervention by means of soil movement and reduction of the natural biodiversity can significantly modify the landscape and consequently the above-mentioned emotions. One speculates if sensory appreciation of a wine may be strongly affected by psychological factor: landscape beauty.

Malic acid has a strong impact on wine pH and the contribution of fermenting yeasts to modulate its concentration has been intensively investigated in the past. Recent advances in yeast genetics have shed light on the unexpected property of some strains to produce large amounts of malic acid (“acidic strains”) while most of the wine starters consume it during the alcoholic fermentation. Being a key metabolite of the central carbohydrate metabolism, malic acid participates to TCA and glyoxylate cycles as well as neoglucogenesis. Although present at important concentrations in grape juice, the metabolic fate of malic acid has been poorly investigated.

One of the effects of warming trends is the advance of budburst, increasing the frequency of spring frost-related damage. In April 2021, a severe frost event affected central and northern italian viticulture. In a cv. Barbera vineyard located in the Colli Piacentini wine district, after such occurrence, vines were tracked and growth of primary bud shoots (PBS), secondary bud shoots (SBS), and suckers (SK) was monitored, as well as their fruitfulness and fruit composition. Vine performances were then compared to those of the previous year, when no post-budburst freezing temperatures occurred. The goal of the study was to evaluate the efficacy of SBS in restoring yield loss due to PBS injuries and analyze respective contribution to fruit composition.

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.