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This call focuses on advancing breeding techniques and practices to enhance multi-stress tolerance in crops, addressing challenges from climate change, reduced agricultural inputs, and emerging stressors such as pests, diseases, and abiotic factors. The project aligns with the European Green Deal and EU climate and biodiversity strategies, targeting sustainable agricultural practices and food security. Key objectives include the identification and development of resilient crop varieties, fostering biodiversity-friendly practices, and integrating cutting-edge technologies such as AI, computational modelling, and 'omics' data for evaluating crop performance under real-life conditions. Projects are expected to collaborate with existing Horizon Europe initiatives, focusing on comprehensive solutions that promote agroecological diversity, soil health, and crop resilience. The indicative budget per project is 7.000.000 €, with a total available budget of 14.000.000 €.
Opening: 06-05-2025
Deadline(s): 17-09-2025
Data provided by Ghent University
This funding opportunity represents a pre-agreed draft that has not yet been officially approved by the European Commission. The final, approved version is expected to be published in the first quarter of 2025. This draft is provided for informational purposes and may be used to preliminarily form consortia and develop project ideas, but it is offered without any guarantees or warranties.
Expected Outcome
• Enhanced knowledge of traits for multi-stress tolerance available to researchers and breeders
• Identification and use of local varieties adapted to multi-stress conditions
• Improved evaluation capacity for multi-stress effects on crops
• Accelerated development of resilient crop varieties for diverse environmental conditions
• Recommendations on variety performance and practices available for advisors and farmers.
Scope
• Advance breeding for multi-stress tolerance in crops
• Identify and utilise local varieties with high plasticity to enhance agro-biodiversity
• Develop agro-ecological practices to promote sustainable, biodiversity-friendly cropping systems
• Integrate advanced technologies such as AI and computational modelling for G x E x M analysis
• Create location-specific strategies for breeding and cropping system improvement.