Climate effect on ripening process in Vitis vinifera, L. cv. Cencibel

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.

Wine cellars are affected by seasonally fluctuating workloads and face challenges especially in the harvest period connected to the required timely processing of the harvested grapes.

Botrytis cinerea is a fungus that causes common infection in grapes and other fruits. In winemaking, its presence can be both considered desirable in the case of noble rot infection or undesirable when grey rot is developed. This fungus produces an extracellular enzyme known as laccase which is able to cause oxidation of phenolic compounds present in must and wine, causing most of the times a decrease in its quality and problems during the winemaking process [1]. Material and methods: Three B. cinerea strains (B0510, VA612 and RM344) were selected and grown in a liquid medium adapted from one previously described [2]. The enzyme was isolated by tangential ultrafiltration of the culture medium using a QuixStand system equipped with a 30 KDa filtration membrane.

Fossil fuel combustion continues to drive increases in atmospheric carbon dioxide, consequently elevating the global annual mean temperature and specifically increasing the growing season temperatures in many of the world’s most important wine growing regions (IPCC 2014; Jones et al 2005). Grapes are sensitive to changes in growing season temperatures, and past models have shown a direct link between warming temperatures and earlier harvest dates (Cook and Wolkovich 2016). Globally, there have been shifts of 1-2 weeks for wine growing regions (Wolkovich et al 2017 and references within). The phenological shifts resulting from growing season temperature increases are documented internationally, and models predicting phenology using temperature are becoming more precise (Parker et al 2011).

AIM: Loop-mediated isothermal amplification (LAMP) [1] is a modern technology for fast and sensitive amplification of specific DNA sequences under isothermal conditions. Its simple handling and no need for dedicated equipment together with an evaluation of the amplification event by in-tube detection make this method advantageous and economically affordable for on-site investigations in the industry.