The use of local knowledge relating to vineyard performance to identify viticultural terroirs in Stellenbosch and surrounds

AIM: Wine consumption in the last fifteen years showed a decrease in Europe [1]. New alternatives of wines appeared on the market. Those beverages are obtained by blending wines and fruit juices or flavoring wines with artificial or natural aromas and have medium alcohol content (from 8 to 10.5%) [2]. Recently, an innovative fruit wine has been proposed obtained by co-fermenting grape must and kiwi juice [3] whose potential attractiveness to consumers should be exploited. However, differences in product acceptability and perception, as well as the individuals’ willingness to consume and pay could change in function of subjects socio-demographic characteristics. The target group selected is represented by young adults (18-35 years old) consumption groups.

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.

In this work, results about the composition in polyphenols and polyamines in important wine-grape cultivars from the Emilia-Romagna region are presented. Spectrophotometric and HPLC analyses suggest that especially coloured berries are particularly rich of antioxidant species (stilbenes and catechins). Potential allergenic capability of biogenic amines was also characterized.

Nel corso del 2009, in alcuni vigneti allevati ad alberata in provincia di Caserta (Italia), è stata avviata una ricerca per valutare la variabilità genetica della popolazione del vitigno ‘Asprinio’, la condizione sanitaria delle piante e le caratteristiche del vino sia rispetto alla forma di allevamento (alberata tradizionale e controspalliera) che all’altezza della fascia produttiva.

Climate change presents a challenge for agriculture worldwide. Yet, crop productivity is negatively impacted by abiotic hazards such as high temperatures and water deficit.