Exogenous dsRNA applications to identify novel candidate susceptibility genes to downy mildew

Context and purpose of the study ‐ Recently early defoliation (ED) has been tested in several high‐ yielding grapevine varieties and sites aiming at reducing cluster compactness and hence, regulating yield and susceptibility to botrytis bunch rot infection. The reported results have been generally positive, encouraging growers to use this canopy management technique as an alternative for replacing the conventional time‐consuming cluster thinning and, simultaneously, as a sustainable practice to reduce the use of fungicides. However, ED increases berry sunburn risks and/or can induce carry‐over effects on vigor and node fruitfulness as shown in the two case studies reported in this work.

It is well documented that varietal aroma is an important parameter of wine quality. Chemical compounds responsible for wine varietal aroma are sourced from secondary grape metabolites. Until today little research is conducted on the influence of vine clone on the grape aromatic content of Greek grape varieties. Xinomavro (Vitis vinifera L.) is one of the most important Greek grape varieties, valuable for the wine industry of Northern Greece since it contributes to the production of PDO wine of Naoussa, Amindeo and Goumenissa.

In recent years, the application of plant protection unmanned aerial vehicle (UAV) in agricultural pest control has become more and more popular. However, there are few reports about the application of plant protection UAV for wine grapes, and there are no studies comparing the spraying effect of plant protection UAV with that of manual operation in vineyards. In this context, the objective of this study was to explore the feasibility of using plant protection UAV in vineyards instead of manual operations by evaluating the effectiveness of UAV spray in two common grape training systems in Northeast China.

The marked climate change impact on vine and grape development (phenology, sugar content, acidity …) is one of the manifestations of Genotype X Environment X Management interactions importance in viticulture. Some practices, such as irrigation, can mitigate the effect of water deficit on grape development, but warming is much more difficult to challenge. High temperatures tend to alter the acid balance of the fruit with a parallel increase in sugar concentration. In the long term, genetic improvement to select varieties better coping with temperature elevation appear as a good option to support sustainable viticulture. Nevertheless, the existing phenotypic diversity for grape quality components that are influenced by temperature is poorly understood, which jeopardizes breeding strategies.

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.