Volatile compounds production during ripening of cv. “Sangiovese” grapes from different terroir

In Portugal, and particularly in the Demarcated Region of Douro (DDR), wine production has a great tradition, producing the unique and worldwide famous Port wine as well as other remarkably good table wines. In this study the impact of projected climate change to wine production is analysed for the DDR. A statistical grapevine yield model (GYM) is developed using climate parameters as predictors.

Context and purpose of the study. The quality of table grape clusters significantly affects consumer perception and market value, with berry size, texture, color, and overall appearance playing key roles.

PAs are compartmentalised within the grape berry, and differ in their composition and degree of extractability. Within each compartment, the CWM limits PA extraction firstly by its degree of permeability and secondly its ability to complex with PA molecules.

The two most deadly viruses infecting and threatening the productivity of grapevines worldwide are leafroll and red blotch viruses. There is no cure for viral diseases other than roguing the symptomatic vines and replacing them with certified vines derived from clean, virus-tested stocks.
Given that phloem plays a central role in virus infection, this study aimed to purge the virus by girdling the phloem of leafroll-infected vines at different phenological stages of infected grapevines. Phloem-girdling was performed on canes at veraison to varying regions between the proximal and distal clusters.

the phenological evolution is a crucial aspect of grapevine growth and development. Accurate detection of phenological stages can improve vineyard management, leading to better crop yield and quality traits. However, traditional methods of phenological tracking such as on-site observations are time-consuming and labour-intensive. This work proposes a scalable data-driven method to automatically detect key phenological stages of grapevines using satellite data. Our approach applies to vast areas because it solely relies on open and satellite data having global coverage without requiring any in-field data from weather stations or other sensors making the approach extensible to other areas.