Bio‐metaethics viticulture proposed by the Giesco. Direct charter with producers. Example of evaluation of training systems

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.

Vineyard yield forecasting is a key issue for vintage scheduling and optimization of winemaking operations. High errors in yield forecasting can be found in the wine industry, mainly due to the high spatial variability in vineyards, strong dependency on historical yield data, insufficient use of agroclimatic data and inadequate sampling methods

Differences in wine flavour proceed primarily from grape quality. Environmental factors determined by the climate, soil and training systems modify many grape and wine quality traits. Metabolic profiling based on proton nuclear magnetic resonance (1H-NMR) spectra has been proved to be useful to study multifactorial effects of the vine environment on intricate grape quality traits. The capacity of this method to discriminate the environmental effects on wine has to be demonstrated.

La D.O. Montilla-Moriles, situada en el sur de la provincia de Córdoba, corresponde a una de las zonas de mayor interés dentro de la vitivinicultura andaluza. Las formaciones de suelos

The projected rise in mean air temperatures together with the frequency, intensity, and length of heat waves in many wine-growing regions worldwide will deeply impact grape berry development and quality. Several studies have been conducted and a large set of molecular data was produced to better understand the impact of high temperatures on grape berry development and metabolism[1]. According to these data, it is highly likely that the metabolomic dynamics could be strongly modulated by heat stress (HS).