Molecular characterization of a variegated grapevine mutant cv Bruce’s Sport

The foundation of wine production lies in the use of high-quality grapes. To produce wines that meet the highest standards, a fast and reliable analytical assessment of grape quality is essential. Many wineries currently employ Fourier-Transform Middle-Infrared Spectroscopy (FTIR) for this purpose.

The selection, characterization, and recruitment of autochthonous yeast strains to drive the alcoholic fermentation process is a highly researched practice because it allows the differentiation of the organoleptic properties of wines, assuring process standardization, reducing fermentation times and improving the quality and safety of the final products [1, 2]. Sparkling wines are “special wines” obtained by secondary fermentation of the base wine. ​In the traditional method (Champenoise method), the re-fermentation takes place in the bottle after the addition to the base wine of the so-called tirage solution. This step, also known as prise de mousse, is followed by an aging period characterized by the release of compounds from the yeast cells that affect the organoleptic properties of the final product. The use of autochthonous yeasts as starter cultures for secondary fermentation is one of the recent innovations proposed to enhance and differentiate these wines’ sensory quality [3,4]. Apulia is the second Italian wine-producing region, and its productive chain is now going through a qualitative evolution by implementing the employment of innovative approaches to exalt the peculiar properties of regional wines.

Climate change presents a significant challenge for actual viticulture. In this context, recovering minority grape varieties can be a crucial strategy to ensure resilience, particularly those capable of maintaining quality and aromatic complexity under water stress.

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).

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.