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

AIM: Wine consumption in the last fifteen years showed a decrease in Europe [1]. New alternatives of wines appeared on the market. Those beverages are obtained by blending wines and fruit juices or flavoring wines with artificial or natural aromas and have medium alcohol content (from 8 to 10.5%) [2]. Recently, an innovative fruit wine has been proposed obtained by co-fermenting grape must and kiwi juice [3] whose potential attractiveness to consumers should be exploited. However, differences in product acceptability and perception, as well as the individuals’ willingness to consume and pay could change in function of subjects socio-demographic characteristics. The target group selected is represented by young adults (18-35 years old) consumption groups.

In the context of climate change, excessive bitterness and astringency in wines have become increasingly prevalent. While variety selection and viticultural practices offer long-term solutions, they require considerable time before yielding practical results. In contrast, fining remains an accessible and immediate tool for winemakers.

In this audio recording of the IVES science meeting 2022, Gonzaga Santesteban (Department of Agronomy, Biotechnology and Food Science, Public University of Navarra (UPNA), Pamplona, Navarra, Spain) speaks about cover crops in viticulture. This presentation is based on 2 original articles accessible for free on OENO One.

The elemental composition (the ionome) of grape leaves is an important indicator of nutritional
health, but its genetic architecture has received limited scientific attention. In this study, we
analyzed the leaf ionome of 131 interspecific F1 hybrid progeny from a Vitis rupestris (♀) X Vitis
riparia (♂) cross. The progeny were replicated in New York, South Dakota, Southwest Missouri ad Central Missouri, and the concentration of 20 elements were measured in their leaves at
three different phenological stages during the growing season. In leaves collected at the apical node at anthesis, elemental concentrations correlated in a consistent manner (p < 0.05) across all four geographic locations. In subsequent phenological stages, elemental ratios in the apical-node leaves remained consistent across the South Dakota and New York sites, but not across the Missouri sites. In leaves collected at the basal and middle nodes, correlations varied greatly across all locations.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.