Towards a better understanding of the root system diversity and plasticityin young grafted vines using 2D imaging and 3D modelling tools

Overarching surveys of QTL (Quantitative Trait Loci) studies in both model plants and staple crops have facilitated the access to information and boosted the impact of existing data on plant improvement activities. Today, the grapevine community is ready to take up the challenge of making the wealth of QTL information F.A.I.R.. To ensure that all valuable published data can be used more effectively, the myriad of identified QTLs have to be captured, standardised and stored in a dedicated public database.
As an outcome of the GRAPEDIA initiative, QTL-dedicated experts from around the world have gathered to compile the grapevine QTLome: the complete information (e.g., map positions, associated phenotypes) describing all experimentally supported QTLs for a specific trait.

Practices in viticulture have greatly evolved in the last five decades. There were three objectives: improvement in the quality of the products, reduction in the production costs through mechanization

Castilla y León is the third longest region in the European Union, having more than 85.000 vineyard hectares.

Grapevine (Vitis spp.) is a globally significant fruit crop, and enhancing its agronomic and oenological traits is crucial to meet changing agricultural conditions and consumer demands. Conventional breeding has played a key role in domesticating grapevine varieties, but it is a time-consuming process to develop new cultivars with desirable traits for cultivation.
New plant breeding techniques (NpBTs) offer a potential revolution in grapevine cultivation, and genome editing has shown promise for targeted mutagenesis. The success of these biotechnological approaches relies on efficient in vitro regeneration protocols, particularly through somatic embryogenesis (SE).

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.