Lactiplantibacillus plantarum – A versatile tool for biological deacidification

To characterize in depth the enological productions according to the origin territories and to provide modern tools for the qualitative raising of the assorted typologies of wine produced, Veneto Agricoltura (the regional agency for the agriculture, forestry and food industry development), the Regional Government of Veneto (north-eastern Italy) and various Consortia of Producers have undertaken since 2002 a systematic classification of the viticultural territories by agro-ecological zoning to achieve a strategic project aimed to set Veneto as the first Italian region to have completed in a systematic and scientifically rigorous way the zoning of most of its Denomination of Origin areas.

Da anni la ricerca viticola sta orientando le sue attenzioni verso lo studio della vocazionalità degli ecosistemi viticoli, perché fulcro della produttività della vite e qualità dei suoi frutti.

The interest in understanding the water balance of terrestrial plants under drought has led to the creation of the isohydric/anisohydric terminology. The classification was related to an implication-driven framework, where isohydric plants maintain a constant and high leaf water potential through an early and intense closure of their stomata, hence risking carbon starvation. In contrast, anisohydric plants drop their leaf water potential to low values as soil drought is establishing due to insensitive stomata and thus risk mortality through hydraulic failure, albeit maximizing carbon intake. When applied to grapevines, this framework has been elusive, yielding discrepancies in the classification of different wine grape varieties around the world.

Context and Purpose of Study. Grapevine Shiraz disease (SD), which is associated with Grapevine Virus A (GVA), is one of the highly destructive diseases affecting Australian and South African vineyards.

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.