A general phenological model for characterising grape vine flowering and véraison

Genomic selection (GS) has emerged as a transformative tool for accelerating breeding programs by predicting the genetic potential of individuals using genome-wide markers.

Knowledge of vine reaction to plant spacing under high potential soil conditions is restricted. This study was done to determine effects of vine spacing

The use of glutathione (GSH) in winemaking has been legitimated recently, according to OIV resolutions OENO 445-2015 and OENO 446-2015 a maximum dose of 20 mg/L is now allowed to use in must and wine. Several studies have proven the benefits of GSH, predominantly in Sauvignon blanc. Thus, oxidative coloration of must and wine is limited, aroma compounds such as volatile thiols are preserved, and the development of ageing flavors such as sotolon and 2-aminoacetophenone is impeded. The protective effect may be explained by the high affinity of GSH to bind o-quinones which are formed during phenolic oxidation and which are known to initiate browning and other oxidative changes. Some researchers have proposed the hydroxycinnamic acid to GSH ratio (HGR) as an indicator of oxidation susceptibility of must and could show that lower ratios yielded lighter musts.

The selection of a suitable grape variety (Vitis vinifera L.) for specific viticulture and oenology objectives is a considerable challenge in the context of climate change.

Grapevine yield and fruit quality are two major drivers of input allocation and, ultimately, revenue for grape producers. Because yield and fruit quality vary substantially from year-to-year and within a single block, opportunities exist for optimization via precision management activities that could lead to more profitable and sustainable grape production. Here, we review recent advances in the techniques and technology used to measure, estimate, and forecast grapevine yield and fruit quality. First, we discuss direct “measurement” of yield and quality (i.e. ground-truth data generation), with an emphasis on potential for scalability and automation. Second, we discuss technology and techniques that do not directly measure yield and quality, but use correlated measurements for their estimation.