Mining microbiome data to identify antagonists of grapevine downy mildew (Plasmopara viticola)

Context and purpose of the study. Red Blotch disease, an affliction caused by the Grapevine red blotch-associated virus (GRBaV), represents a formidable challenge for grape growers and winemakers in prominent viticultural regions around the world.

Climate change will require the adaptation of agricultural systems and among the different means of adaptation, changing plant material is a promising strategy. In viticulture, different levels of diversity are currently exploited: clonal and varietal diversity for rootstocks and scions. A huge quantity of research aims to evaluate different genotypes in different environmental conditions to identify which ones are the best adapted and the most tolerant to future environmental conditions.

Climatic variability studies at fine scale have been developed in recent years with the reduction of material cost and the development of competitive miniaturized sensors. This work is forming part the LIFE-ADVICLIM project, of which one of the objectives is to model spatial temperature variability at vineyard scale. In the Bordeaux pilot site, a large network of data loggers has been set up to record temperature close to the vine canopy. The reduced distance between plant foliage and measurement equipment raises specific issues and leads to an increased rate of outliers compared to data retrieved from classical weather stations. Some of these were detected during data analysis, but others could not be easily identified. The present study aims to address the issue of data quality control and provide recommendations for data processing in climatic studies at fine scale.

The knowledge about the molecular fraction associated to white wines oxidative stability is still poorly understood.

New and developing technologies, that provide sensors and the software systems for using and interpreting them, are becoming pervasive through our lives and society. From smart phones to cars to farm machinery, all contain a range of sensors that are monitored automatically with intelligent software, providing us with the information we need, when we need it. This technological revolution has the potential to monitor all aspects of vineyard activity, assisting growers to make the management choices they need to achieve the outcomes they want. For example, a future vineyard may possess automated imaging that generates a three dimensional model of the vine canopy, highlighting differences from the desired structure and how to use canopy management to improve fruit composition, or generates maps with yield estimates and measurements of berry composition throughout the growing season.