Efectos del deshojado y de su combinación con el aclareo de Racimos en los componentes básicos de la producción y del Mosto, sobre cv. Tempranillo en la D.O. Ribera del Duero

Geological influences on terroir development

Due to the global demand for terroir wines, the winemaking industry has focused attention on exploiting the local yeast microflora of each wine growing region to express the regional character and enhance the sensory profile of wines such as varietal typicity and aroma complexity. The objective of the present study was to isolate and compare the indigenous strains of Saccharomyces cerevisiae present in different vineyards in the Mesogeia – Attiki wine region (Greece), evaluate their impact on chemical composition and sensory profile of Savatiano wines and select the most suitable ones for winemaking process.

In a previous zoning program (1998-2002), climatic and pedological factors were able to distinguish 14 terroir within the Soave DOC area where wine characteristics are well recognizable. Nevertheless, in the past vinegrowers identified several vineyards where a better quality of the grapes and wines could be obtained. So, « beyond the zonation » will aim to suggest a new methodology to characterise the Cru, starting with 15 vineyards that were selected in the Soave Classico DOC area. In the year 2005, a meteorological station was positioned in each vineyard and temperature data were collected; because of the limited area of investigation, only 3 rain sensors were set up.

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.

Fungus-resistant grape varieties (frgv) are an important field of research in viticulture, as they represent a way of reducing the use of copper-containing pesticides and thus minimising the environmental impact. The literature suggests that resistant grape varieties are a promising solution to the problem of using copper-containing pesticides in viticulture and that their quality has improved in recent years. However, there are still challenges in the acceptance and dissemination of FRGV by wine producers and consumers.