The grapesim model: a model to better understand the complex interactions between carbon and nitrogen cycles in grapevines

In 2020, 12% of all bottled German wines were aromatized, which may increase further due to rising popularity of dealcoholized wines. As sealing polymers of a bottling line absorb aroma compounds and may release them into regular wines in the next filling¹, this unintentional carry-over bears the risk to violate the legal ban of any aromatization of regular wine. However, following EU legislation, German food control authorities accept a technical unavoidable transfer of aroma compounds, if this is of no sensory significance.

Catechins, a subclass of flavonoids widely found in plants and plant-based foods and beverages such as wine and tea, not only exhibit significant antioxidant properties [1], as extensively documented in the literature, but can also inhibit amyloid protein aggregation [2], a key process implicated in the onset of neurodegenerative diseases such as Parkinson’s, Alzheimer’s, and Huntington’s.

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.

Understanding grapevine responses to increasing atmospheric CO2 (aCO2) concentrations is crucial for assessing the impact of climate change on viticulture. Previously, at the VineyardFACE (Free Air Carbon dioxide Enrichment) experiment in Geisenheim, leaf gas exchange measurements were made as Vitis vinifera cv. Cabernet Sauvignon established from planting (2014 to 2016) under aCO2 or elevated CO2 (eCO2, aCO2 + 20%) concentrations. Contrary to many preceding observations with grapevines and other perennial plant species the young vines showed an increased intrinsic water use efficiency (WUEi) that was mainly associated with an increase in net assimilation (A) rather than a decrease in stomatal conductance (gs) under eCO2.

In Brazil, the regulation of pesticide residues is guided by the National Health Surveillance Agency (ANVISA) and the Ministry of Agriculture and Livestock (MAPA), emphasizing the importance of monitoring pesticide levels in agricultural products to protect consumer health.