Riqualificazione dell’antica “Terra di Lavoro” attraverso il rilancio della cultivar Abbuoto

Grapevine (vitis spp.) Is one of the major and most economically important fruit crops worldwide. Unlike other cropping systems, viticulture has ancient historical connections with the development of human culture and with the socio-cultural background of grape-growing areas. The vitis genus is characterised by high levels of genetic diversity, as result of natural genetic mutations, which are common in grapevines and further assisted by ongoing vegetative propagation.

Post-harvest grape berries dehydration/withering are worldwide applied to produce high-quality sweet and dry wines (e.i., Vin Santo, Tokaji, Amarone della Valpolicella). Temperature and water loss impact grape metabolism [1] and are key variables in modulating the production of grape compounds of oenological interest, such as Volatile Organic Compounds (VOCs), secondary metabolites responsible for the aroma of the final wine.
The aim of this research was to assess the impact of post-harvest dehydration on free and glycosylated VOCs of two Italian red wine grapes, namely Nebbiolo and Aleatico, dehydrated in tunnel under controlled condition (varied temperature and weight-loss, at constant humidity and air flow). From these grapes Sforzato di Valtellina Passito DOCG and Elba Aleatico Passito DOCG, respectively.

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.

Franconia which is a “cool climate” winegrowing region is well known for its fruity white wines. The most common grape cultivars are Silvaner and Mueller-Thurgau.

La valanga di fango che si è abbattuta nel Salemitano e nell’Avellinese, provocando decine di vittime, è stata causata in larga misura dalle insufficienti opere idrauliche e dalla manca­ta manutenzione di antiquati canali idrici.