Developing rootstocks adapted to environmental constraints constitutes a key lever for grapevine adaptation to climate change. In this context, Near Infrared Spectroscopy (NIRS) could be used as a high-throughput phenotyping technique to simplify the study of rootstocks in grafted situations. This study is an exploratory analysis to evaluate the potential of NIRS acquired on grafted tissues to reveal rootstock effects as well as the plasticity of combinations of scion/rootstock to better characterize these interactions.
Through the study of 25 combinations (5 scions times 5 rootstocks) in a dedicated experimental vineyard, we showed that NIRS obtained from grafted tissues capture rootstock and scion/rootstock interaction signals, up to 20% of the total variance at specific wavelengths.

In this study, a soil mapping methodology at subplot level (scale 1:5000) for vineyard soils was developed. The aim of this mapping method was to establish mapping units, which could be used as basic units for ‘terroir’ characterisation and vineyard management (precision viticulture).

Terpenes are responsible for flavors and aromas of grapes, however, they also protect from radiation, participate in biotic stress and antioxidant mechanisms. The phytohormone methyl jasmonate (MeJA) mediates many of these stress responses and has been associated with increased terpene content in berries. Here, we generated transcriptomic data of Vitis vinifera cv. ‘Gamay’ cells treated with MeJA (100 μM) and cyclodextrins (50 μM) to understand these responses. Ontology analysis revealed that up-regulated genes (URGs) were enriched in jasmonic acid biosynthesis and signaling terms, as expected. Inspection of transcription factors (TFs) among URGs allowed us to study uncharacterized TFs.

Dans le vignoble champenois, le risque thermique associé au gel des bourgeons au printemps et en hiver est très mal connu et ne peut être envisagé qu’à l’échelle locale, en raison d’une variabilité spatiale forte. L’objectif de l’étude est d’appréhender ce risque de façon fiable et pluri locale en utilisant le réseau de stations météos récemment implanté.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.