Haplotype-Resolved genome assembly of the Microvine

Evidence indicates that climate change affects the environment, human health, and well-being via drought, increasing greenhouse effect, and climatic catastrophes. As the wine sector is also negatively affected by climate change, the role of climate change mitigation and adaptation policies is important in wine production. One example of an adaptation policy is the implementation of grapevine genetics (duchene, 2016), while organic farming may be used as an approach to mitigate the consequences of climate change (vinci et al., 2022). To this end, the european commission’s objective is to reach the european green deal target of at least 25% of the european union’s agricultural land under organic farming by 2030.

C’è grande attenzione al rapporto tra zonazione e marketing. Mi sembra però che ci sia anco­ra oggi un salto fra le pratiche di analisi del terreno e di deterrninazione di quello che potremo definire “cru” e quello che può essere la sua utilizzazione rispetto ai consumatori finali.

Winemaking is a globally significant industry in the field of food technology (218 mhL of wine estimated for 2024 harvest) [1], which activity produces tons of by-products annually, including pomace (pulp, stems, seeds, skins), lees, organic acids, CO2, and water [2].

Phenolic compounds play a central role in sensory characteristics of wine, such as colour, mouthfeel, flavour and determine its shelf life. Furthermore, the major non-enzymatic wine oxidation process is due to the catalytic oxidation of phenols in quinones. Due their importance, during the years have been developed different analytical methods to monitor the concentration of phenols in wine, such as Folin-Ciocalteu method, spectrophotometric techniques and HPLC. These methods can also be used to follow some oxidation-related chemical transformations.

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.