Extreme canopy management for vineyard adaptation to climate change: is it a good idea?

Grape canes are a non-recycled byproduct of wine industry (1-5 tons per hectare per year) containing valuable phytochemicals of medicine and agronomical interest. Resveratrol and wine polyphenols are known to exert a plethora of health-promoting effects including antioxidant capacity, cardioprotection, anticancer activity, anti-inflammatory effects, and estrogenic/antiestrogenic properties (Guerrero et al. 2009). Additionally, resveratrol is a major phytoalexin produced by plants in response to various stresses and promotes disease resistance (Chang et al. 2011). Our project aims to develop polyphenol-rich grape cane extracts to fight phytopathogenic or clinically relevant fungi. We initiate the project with the development of analytical methods to analyze resveratrol mono- and oligomers (dimers, trimers and tetramers) from grape canes and we evaluate their potential activity against clinically relevant opportunistic fungal pathogens (Houillé et al. 2014).

The grape quality is affected by the canopy manipulation. Water management is a fundamental tool for controlling reproductive growth

Understanding how different pedoclimatic conditions interact with vine and berry physiology, and subsequently impact wine quality, is paramount for an good valorization of viticultural terroirs and can help to optimize mitigation strategies in the face of global warming

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

Beihong and Beimei were bred as winemaking cultivars released by Institute of Botany, the Chinese Academy of Sciences in 2008. The cultivars are selected from the population of ‘Muscat Hamburg’ (Vitis vinifera) ×V. amurensis. They are extended to most provinces in North of China because they have strong resistance to cold and disease and need not be buried in soil in winter. To better understand the effect of cropload on volatile compounds during wine-making, we surveyed volatiles composition and content of different cropload level in 3-years-old ‘Beihong’ and ‘Beimei’ vines which planted in east foot of Helan mountain of Ningxia (EHN).