Caractérisation du terroir en Espagne : méthodologie de l’évaluation et de la validation

Organic wine production and consumption is one of the sustainable practices contributing to a number of sustainable development goals (SDGs).

This project aims on monitoring the plant development and comparison of the effects of various training systems on vine fertility and physiological processes.

Dating back to the early domestication period of grapevine (Vitis vinifera L.), expansion of human activity led to the creation of thousands of modern day genotypes that serve multiple purposes such as table and wine consumption. They also encompass a strong phenotypic diversity. Presently, viticulture faces various challenges, which include threatening climatic change scenarios and an historical track record of genetic erosion. Paritularly with regards to wine varieties, there is a pressing need to characterize the extant genetic diversity of modern varieties, as a means to delvier knowledge-based solutions under a rapidly evolving scenario, that may enable improved yields and profiles, resistance to pathogens, and increased resilience to climate change.

Wine tourism has transformed from a leisure activity into a crucial part of the tourist experience, significantly contributing to rural tourism’s expansion in italy. It has witnessed a notable surge in popularity in recent years, evolving as a key motivator for travel (antonioli corigliano, 2002; brunori & rossi, 2000; città del vino & censis servizi, 2011; garibaldi, 2018; 2019a; 2020; montanari, 2009; romano & natilli, 2009). The allure of wine tourism, driven by sensory experiences and cultural immersion, continues to attract a diverse group of tourists. The economic impact is substantial, with events and festivals contributing approximately €2.5 billion annually.

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.