Discriminant value of soil properties for terroir zoning

AIM: To investigate chemical and sensory drivers of regional typicity of Cabernet Sauvignon from different geographical regions of Australia.

In 2001, the Hungarian Ministry of Agriculture and Rural Development designated the Institute of Geodesy, Cartography and Remote Sensing (FÖMI) to elaborate a Geographic Information System (GIS) supported Vineyard Register (VINGIS) in Hungary. The basis of this work was a qualification methodology (vineyard and wine cellar cadastre system) dating back to several decades, however, in the 1980s and 1990s the available geographical maps and information technology did not provide enough accuracy for an overall evaluation of viticultural areas. The reason for the VINGIS elaboration and development was an obligation resulting from the EU membership to ensure the agricultural subsidies for the wine–viticulture sector.

In this audio recording of the IVES science meeting 2022, Thibaut Verdenal (Agroscope, Pully, Switzerland) speaks about managing nitrogen balance in cover-cropped vineyard. This presentation is based on an original article accessible for free on OENO One.

SO₂ play a major role in the stability and wine during storage. Nowadays, the reduction of chemical input during red winemaking and especially the removing SO₂ is a growing expectation from the consumers. Winemaking without SO₂ is a big challenge for the winemakers since the lack of SO₂ affects directly the wine chemical evolution such as the phenolic compounds as well as its microbiological stability.

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.