Spatial suitability analysis for site selection of vineyards using biophysical models and computational intelligence

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.

Production, demand, and brand awareness of dealcoholized wine (<0.5% v/v) is steadily increasing worldwide. However, there have been few studies to date investigating and comparing the different physical processes for dealcoholizing wine.

The vinification by Carbonic maceration (CM) involves the process whereby the whole bunches are subjected to anaerobic conditions during several days. In this anaerobic condition, the grape endogenous enzymes begin an intracellular fermentation. This situation favors that whole grapes split open and release their juice into the tank, increasing the liquid phase that is fermented by yeasts [1]. Then, two types of wines are obtained; one from the free-run liquid in the tank (FCM) and other from the liquid after pressing the whole grape bunches (PCM). PCM wines are recognized as high quality young wines because their fruity and floral aromas[2] that although they are very intense at the end of the winemaking they gradually disappear during conservation.

Three compounds, 3-mercaptohexanol (3MH), 3-mercaptohexyl-acetate (3MHA) and 4-mercapto-4-methylpentan-2-one (4MMP), also known as varietal thiols, have been identified to contribute positively to wine aroma and are responsible for the distinct gooseberry, grapefruit, guava and box tree character found in Sauvignon blanc wines. Certain volatile thiol compounds though, can cause off-aromas of onion, garlic, rubber and rotten egg, this group of molecules is known as reductive sulphur compounds (RSC). This study looks into how the addition of sulphur-compounds to Sauvignon blanc juice contributes to the varietal thiol (3MH and 3MHA) concentration and reductive sulphur compound concentration in South African Sauvignon blanc wine.

Low temperatures are required for the acquisition and maintenance of bud cold tolerance, which are necessary for grapevines to survive freezing temperatures in winter.