Characterization of berry softening and sugar accumulation dynamics in a slow-ripening genotype and its response to abscisic acid treatments

Natural factors such as the environment in which the vine is grown play an important role in the quality of the wine. If you want to produce a good wine, it is indeed essential to produce quality grapes. To do this, we must enhance and optimize the terroir effect which, for the moment, plays a role that is not very well known. It is therefore essential, for example, to have scientifically established and well quantifiable relationships in order to have the system of areas of controlled origin accepted. R. Morlat (1989) and G. Seguin (1970) have already carried out studies on the role of certain soil factors on grape quality. In particular, they showed the importance of soil temperature and water content.

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.

Sauvignon blanc was first planted in Marlborough, New Zealand in the mid-1970s. Since that time, Marlborough has gained an international reputation by producing the definitive wine style of that grape variety.

En la región vitivinícola con D.O. Montilla-Moriles (Córdoba) la variabilidad geologico-petrográfica de los terrenos es grande (ROLDÁN GARCÍA y DIVAR RODRÍGUEZ, 1988 a; roldán garcía et al.

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