An overview of geological influences on South African vineyards

Wine quality depends on grape biochemical constituents, including sugars, organic acids, amino acids, and bound and free aroma compounds, which are influenced by vineyard location and environmental factors such as temperature and precipitation [1].

The conventional wisdom of vintners is that alkalinity, and thus less sour and more rounded taste, are enhanced in wine and grapes challenged by low-nutrient soils.

In the bottom of Val d’Illasi (Verona province), one of the major valleys which passes through the Lessini mountains, viticulture is widely extended. In the territory belonging to Illasi and Tregnago villages, which includes ca. 1100 ha of vineyards, devoted to produce Soave and Valpolicella DOC wines, an experimental survey was conducted on a network of twenty five reference 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.

This report has investigated the ripening characteristics of Vitis vinifera grapes Cabernet Franc, Merlot, Sangiovese and Syrah in two consecutive vintages (2006 and 2007), in order to evaluate the adaptation from these recently varieties planted in São Joaquim town, Santa Catarina State, Brazil.