Luc Janss

This study predicts single cross performance in perennial ryegrass using genomic models, finding high predictive accuracy for dry matter yield and nutritive quality traits.

The paper introduces an automated method for measuring seminal root angles from images and validates its accuracy against manual measurements.

This paper introduces SparCEV and SparXCC to estimate cross-correlations between microbiome OTUs and external variables, showing they outperform naive transformations in certain scenarios.

This study uses multi-omics data from grass hybrids to identify gene and metabolite networks associated with agronomic traits, finding conserved hubs but limited predictive power of network features.

This study maps agronomic trait genetics and identifies selection signatures in faba bean germplasm using genome-wide association studies and population structure analysis.

This study uses random regression models to analyze spatio-temporal genomic data in ryegrass, finding improved prediction accuracy for nutritive quality traits but not dry matter yield.

This study compares genetic gain and accuracy of genomic selection (GS) versus phenotypic selection in perennial ryegrass breeding programs, finding GS achieves significantly higher genetic gain.