Mapping and tracking canopy size with VitiCanopy

Chitin is the main structural component of a large number of organisms (i.e., mollusks, insects, crustaceans, fungi, algae), and marine invertebrates including crabs and shrimps.

One of the limitations of vineyards in warm areas is the loss of wine quality due to higher temperatures during the grape ripening period. In order to adapt the vineyards to these new climatic conditions, a possible solution is to delay the ripening process of the grapes towards periods with milder temperatures, by means of management practices and thus improve the quality of the fruit and the wine produced. The technique of severe shoot pruning (SSP) has proven useful in achieving this objective.

Le système des appellations d’origine aux Etats-Unis était adopté en 1978 et est entré en vigueur en 1983. Jusqu’à présent, 146 aires viticoles avaient été établies dans 26 états.

Originaire du bassin lémanique, le chasselas est l’emblème de la viticulture suisse. Pour autant, les surfaces de chasselas n’ont cessé de diminuer, passant de 6’585 hectares en 1986 à près de 3’600 aujourd’hui, reflet d’une baisse de consommation. Une récente étude a cherché à comprendre les raisons de ce désintérêt. Réalisée dans

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.