Hannah Worral

This study identifies genomic and transcriptomic factors contributing to salinity tolerance in dry pea using genetic mapping and transcriptomic analysis of diverse germplasm sets.

The paper evaluates a multi-trait multi-environment genomic prediction model for improving yield and protein content in field peas under stress and non-stress conditions using breeding lines from a pulse crop program.

This paper develops an automated seed quality assessment tool for dry peas using imaging and deep learning, achieving high accuracy and efficiency compared to traditional methods.

The paper estimates effective population size (Ne) in pea using linkage disequilibrium, finding higher Ne in the USDA diversity panel compared to NDSU breeding lines.

This study shows that multi-trait genomic selection with sparse phenotyping improves prediction accuracy for seed mineral concentrations in pea compared to univariate genomic selection.

The paper evaluates genomic prediction models on pea germplasm to improve selection efficiency using SNP markers and phenotypic data.