Optimizing disease management in the Rioja wine region: a study on Erisiphe necator and the Gubler-Thomas model

The production of grated vines is a complex process from grafting to final sorting in nurseries. To reach the market, grafted grapevines must meet three criteria by law in France: resistance to a manual graft union test (named thumb test), a minimum number of three roots and a woody shoot of at least 2 cm long.

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.

We evaluate wine ratings by comparing data from two crowdsourcing platforms – Vivino, which aggregates the opinions of a large number of wine lovers, and Global Wine Medal Rating, which aggregates the scores from more than 1030 international wine competitions since 2020.

Context description and research question: an increasing number of farmers are considering the impact of conservation practices on soil health to guide sustainable management of vineyards. Understanding impacts of soil management on soil organic carbon (SOC) is one lever for adoption of agroecological practice with potential to help maintain or improve soil health while building SOC stocks to mitigate climate change (Amelung et al., 2020).

One of the main sensory characteristics of red wine is astringency, which can be defined as drying, puckering and roughing of the oral cavity after the exposure to tannin-rich wines. Tannins are the main responsible for the intensity of the sensation as well for the qualitative aspects of astringency. However, the total intensity of the sensation is not sufficient to fully characterize red wine astringency. Thirty-three different subqualities (Gawel et al. 2001) had been generated to describe the complexity of this multi perceptual phenomenon, which includes both tastes, tactile, and flavor sensations. So, how to feel tannins during tasting? In this study, we used a sensory method that combine the training for astringency subqualities with touch-standards and the CATA questions, usually applied in consumer science, to evaluate the astringency subqualities of different typologies of Sangiovese: commercial and experimental wines. Sangiovese wine represents a good model for the study of astringency because it is generally characterized by a high content of low and high molecular weight proantocyanidins. Commercial wines differed for percentage of Sangiovese (80-100 %) grapes used in winemaking and for designation (Toscana TS, Chianti Classico CH, Chianti Riserva CR, Morellino di Scansano MS). The astringency profile of wines changed as the percentage of Sangiovese increased. Positive subqualities as velvet, soft, mouthcoat, and rich highly characterized the Sangiovese wine belonging to TS and CR designations. Moreover, the astringency subqualities related to blending or wood aging, represented the drivers of quality of commercial Sangiovese wines.