Hyperspectral imaging and machine learning for monitoring grapevine physiology

Grape cane is a viticultural by-product that is currently underused or not used at all. Therefore, it bears a high potential for valorization due to the presence of anti-microbially active stilbenoids, being biologically relevant for plant defense. These compounds are highly interesting for applications in the agricultural sector as well as for the food and feed industry.

The quality of a wine is largely related to the balance between its sourness, bitterness and sweetness. Recently, molecules coming from grapes have been showed to notably contribute to sweet taste of dry wines. To study the viticultural and oenological parameters likely to affect their concentration, their quantification appears of high interest and subsequently requires powerful analytical techniques. Therefore, a new method using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) was developed and validated to quantify epi-DPA-3′-O-β-glucopyranoside acid (epi-DPA-G) and astilbin, sweet molecules identified in wine. Three gradients were tested on five different C18 columns (Hypersil Gold, HSS T3, BEH, Syncronis and Kinetex).

Climate change and its consequences are becoming an increasing challenge for viticulture. The breeding of new grapevine varieties that are better adapted to the changing conditions offers a possible solution.

Aims: This study aims to: (1) characterize the agroclimatic conditions of the Portuguese Denominations of Origin, using a compound index that combines thermal and soil water balance conditions and a high-resolution climatic dataset (~1 km spatial resolution); (2) categorize the main grapevine varieties as a function of this compound index.

Stomatal traits determine grapevine water use, carbon supply, and water stress, which directly impact yield and berry chemistry. Breeding for stomatal traits has the strong potential to improve grapevine performance under future, drier conditions, but the trait values that breeders should target are unknown. We used a functional-structural plant model developed for grapevine (HydroShoot) to determine how stomatal traits impact canopy gas exchange, water potential, and temperature under historical and future conditions in high-quality and hot-climate California wine regions (Napa and the Central Valley). Historical climate (1990-2010) was collected from weather stations and future climate (2079-99) was projected from 4 representative climate models for California, assuming medium- and high-emissions (RCP 4.5 and 8.5). Five trait parameterizations, representing mean and extreme values for the maximum stomatal conductance (gmax) and leaf water potential threshold for stomatal closure (Ψsc), were defined from meta-analyses. Compared to mean trait values, the water-spending extremes (highest gmax or most negative Ysc) had negligible benefits for carbon gain and canopy cooling, but exacerbated vine water use and stress, for both sites and climate scenarios. These traits increased cumulative transpiration by 8 – 17%, changed cumulative carbon gain by -4 – 3%, and reduced minimum water potentials by 10 – 18%. Conversely, the water-saving extremes (lowest gmax or least negative Ψsc) strongly reduced water use and stress, but potentially compromised the carbon supply for ripening. Under RCP 8.5 conditions, these traits reduced transpiration by 22 – 35% and carbon gain by 9 – 16% and increased minimum water potentials by 20 – 28%, compared to mean values. Overall, selecting for more water-saving stomatal traits could improve water-use efficiency and avoid the detrimental effects of highly negative canopy water potentials on yield and quality, but more work is needed to evaluate whether these benefits outweigh the consequences of minor declines in carbon gain for fruit production.