Facteurs physiques et biologiques affectant la production viticole et vinicole de la région avec dénomination d’origine “Condado de Huelva” (SW d’Espagne)

Of biocontrol products and resistant grape varieties, synthetic pesticides are still widely used to control fungal diseases and protect vines from potential damage caused by pests. The use of pesticides is strictly regulated, and their use can sometimes lead to transfer from the grapes to the must and then into the wine. The study of pesticide residues in grapes and wines is commonly carried out by wine producers in order, among other things, to optimize treatment routes, check that products comply with regulations, and ultimately guarantee the food safety of the wine.

During the more than 190 years since the founding of the first ampelographic collection, the creation of a series of collections is attested on the territory of the Republic of Moldova, each operating in different historical periods and socio-economic conditions,

Aims: Viticulture practices linked with soil management, as cover crops and deficit irrigation, can help to regulate the vineyard behavior reducing in most cases plant vigor and modifying plant water and nutrient status, and as a consequence, grape yield and quality. Also, these practices can modify the soil biological activity mostly related to microbiome diversity and functionality.

Climate change has led to increased extreme weather events, such as severe droughts and intense rainfall, with regions like Alentejo and Algarve in Portugal, being particularly affected.

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.