Characterization of different clone candidates of xinomavro according to their phenolic composition

En Anjou, une méthode de caractérisation des terroirs viticoles a été développée. Elle utilise un modèle de terrain basé sur la profondeur de sol et son degré d’argilisation. Le modèle concerne des terrains issus principalement de roches mères métamorphiques et éruptives du Massif Armoricain. Cet outil de caractérisation des terroirs viticoles nécessite d’être adapté lorsqu’il s’agit d’ensembles géologiques très différents, en particulier sur sols d’apport et de roches mères tendres et poreuses du Bassin Parisien. Une meilleure compréhension de la réserve hydrique des sols apparaît être un critère important de l’interaction entre le milieu et la plante.

Wine perception results from the interaction between the wine and its intrinsic and extrinsic characteristics and the experience [1], background and beliefs of the consumer [2,3]. Among all of the factors affecting wine perception, in this study we focused on culture and cognitive processes, working under the hypothesis that higher familiarity with wines would induce higher perceived quality. Furthermore, we hypothesised that culture would influence the verbalisation of wine properties associated with the different experiences of consumers from different cultures.

The intricate relationship between the necrotrophic pathogen Botrytis cinerea and grape berries (Vitis vinifera spp.) can lead to the development of either the desirable noble rot (NR) or the unfavourable grey rot (GR), depending on the prevailing weather conditions.

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.

Temperature extremes affect grapevine physiology, as well as grape quality and production. In most grape growing regions, frost or heat wave events are rare and as such conducting a risk analysis using robust statistics makes the use of long term daily data necessary.