Lilia Levy Häner

This paper introduces a deep learning model that integrates phenomic, genomic, and environmental data to predict wheat yield across diverse environments, showing improved performance over traditional genomic models.

This study shows that plasticity in ear density drives overyielding in wheat mixtures through complementarity effects, with tillering under shade as a key breeding target.

The paper uses XAI and IML methods on 30 years of wheat yield data to identify climate variables affecting yields and their interactions with genotype.

This study shows that wheat cultivar mixtures stabilize baking quality by buffering weather effects, rather than improving it overall.

This study evaluates the performance and stability of wheat variety mixtures across multiple traits, finding that mixtures generally outperform pure stands and offer improved quality stability.