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Mobilization of Olive GenRes through pre-breeding activities to face the future challenges and development of an intelligent interface to ensure a friendly information availability for end users. (GEN4OLIVE)

The GEN4OLIVE project is coordinated by the Cordoba University, and involves 16 interdisciplinary and transdisciplinary partners from 7 different countries: Spain, Morocco, France, Germany, Italy, Greece and Turkey. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101000427.

As its acronym shows, GEN4OLIVE aims to leverage olive Genetic Resources (GenRes) by bringing them to a higher level closer to breeders and markets. The overall goal of GEN4OLIVE is to accelerate the mobilization of olive GenRes and to foster pre-breeding activities by (1) developing a smart and user-friendly interface that will implement Artificial Intelligence utilities; and (2) enhancing breeders and growers' participation through the implementation of two open calls for supporting pre-breeding activities and breeding plans.

GEN4OLIVE will develop collective pre-breeding activities aiming to in-depth characterize more than 500 worldwide varieties and 1000 wild and ancient genotypes around 5 topics: climate change, pests and diseases, production and quality, and modern planting systems. After integrating all results in the GEN4OLIVE interface, breeders and other end-users will have an effective tool for speeding up all kind of breeding programs. The combination of pre-breeding results with modern ICTs will enable the access of end-users to this valuable information.

Universidad de Córdoba

- Assessment of end users' needs, and definition of evaluation protocols for working under harmonised and comparable techniques among partners.
- Cluster and link with the previous and on-going projects in olive sector.
- Exploration and characterization of the genetic resources of domesticated, wild and the ancient olives in Mediterranean basin.
- Evaluation of Environment X Genotype interaction and the determination of climate change effect in olive-growing sector.
- Building an intelligent interface based on machine learning and big data processing technology, which will be able to provide an easy and ready-to-use information to end users.
- Foster the involvement of SMEs in the pre-breeding activities through two open calls (Financial Support to Third Parties).
- Enhance the inter-actor cooperation: co-creation, capacity-building and results sharing.

UCO is the coordinator of this proposal and it will hold the Project Coordinator role, main responsible of the project. It will provide an academic multidisciplinary profile to the Consortium, being responsible of WP1, WP3, WP4, WP10. The main role in the aforenamed WPs is:
- The coordination and definition of the common and harmonized protocols for carrying out the technical work under comparable methodologies.
- Characterization of olive varieties that's belong to UCO´s GB and of the wild genotypes collected in Spanish area.
- Development of tools such as bio-markers for accelerating the breeding process.
- Definition of the most urgent breeding lines to be performed.
- Preparation of the database with images of olive cultivars´ pits and diseases symptoms to develop the machine learning tools.
- Coordination of the consortium work.

D1.2. Definition of the harmonised and common protocols for carrying out all the analysis and characterization.
D3.1. Intermediate report about the olive varieties resilience to adverse environmental conditions.
D3.3. Preliminary classification of olive varieties according to their production and quality.
D3.4. Final report about the resilience olive varieties to adverse environmental conditions.
D3.6. Final classification of olive varieties according to their production and quality.
D3.7. Intermediate report about the olive varieties classification according to their resilience against Verticillium disease, susceptibility scale.
D3.9. Final determination of the susceptibility/resistance scale of olive varieties to Verticillium disease.
D4.2. Preliminary report about the candidate gene markers related to climate resilience characters (resilience to abiotic stress) and juvenility period.
D4.5. A final report: candidate gene markers related to climate resilience characters (resilience to abiotic stress) and juvenility period.
D6.2. Complete dataset on 22 olive and of olive varieties Morphological pits descriptors.
D8.5. Report on GEN4OLIVE harmonised protocols and results' capitalization by partners and/or stakeholders.
D9.4. A complete report with the publicized scientific papers and protected results (if any).
D9.5. Communication, Dissemination and Results Exploitation Plans and last Updates.
D10.1. Innovation Management Plan.
D10.2. Project Management Manual including Quality Assurance Plan and Data Management Plan.
D10.3. Consortium meetings minutes.
D11.1. H - Requirement No. 1
D11.2. POPD - Requirement No. 2
D11.3. NEC - Requirement No. 4
D11.4. EPQ - Requirement No. 5
D11.5. OEI - Requirement No. 6

- Activities will enhance the status of genetic resources and increase effectiveness of conservation efforts, in particular in Europe.
- To improve tools to display user-friendly information on accessions and their characteristics.
- To speed up the introduction of useful characteristics from GenRes into breeding.
- To promote the delivery of new varieties which are fit for purpose as regards changing environmental / climatic conditions and consumer demands.
- In the long-term activities will allow tapping into the vast potential of GenRes more effectively in order to meet current and future needs of food security, the delivery of non-food products from primary production and support the different functions of forestry.


Code PAIDI: AGR-157

Diego Barranco Navero. Coordinator. 

Universidad de Córdoba

Budget of Andalusian group: € 1,660,772.50

Keywords: Pre-breeding, breeding, varieties, olive genetic resources, germplasm banks, wild olives, ancient olives, genotyping, phenotyping, progenitors, smart interface, machine learning, AI
Duration: 48 months. October, 1th 2020 to September, 30th 2024
Project cost: € 7,385,558.36