Joint project

Funder: Federal Ministry of Food and Agriculture

Period: 2023-2026

URI: https://www.julius-kuehn.de/ex_anwendung/FoPro/projectprofile.php?id=3248&lang=de

Abstract: 
Winter oilseed rape suffered from severe crop losses in many regions of Germany and Europe in recent years as a result of repeated dry and hot spring and summer weather patterns. New, resilient cultivars are essential to overcome yield losses in the face of these challenges. However, targeted breeding to improve yield stability under drought stress is extremely difficult due to the low heritability of this trait complex and depends very much on high-quality recording of relevant target traits in large breeding populations. STRESS-STOPP pursues the hypothesis that an extremely accurate recording of previously ignored, drought-stress-associated 'physiological phenotypes' as well as related genotype-by-environment interactions in suitable breeding populations, using machine learning methods for pattern recognition in performance-relevant phenotype, genotype and environmental data, can lead to increased selection accuracy for the yield security of winter oilseed rape in drought-stress environments.