Characterization and modelling of water flow on vineyard soil. Effect of compaction and grass cover

In 2010, the OIV adopted a resolution that defines ‘terroir’. The OIV definition understands terroir as the result of the interactions between the physical specificities of a space and human labor, with an emphasis on the subsequently produced collective knowledge (OIV-VITI 333-2010); by doing so, it alludes to the social and cultural dimensions of terroir.

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.

Cover crops are planted in vineyards for multiple benefits including soil conservation, weed management, regulation of grapevine vegetative growth

AIM This work aims to investigate fluorescence spectroscopy as a tool to assess grape homogenates to discriminate between samples of varying maturities and to develop an index to objectively characterise the level of grape heterogeneity present in any given vineyard.

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