William Dale Beavis

This paper identifies mega-environments in soybean breeding using historical field trial data and integrates genetic and non-genetic factors to analyze genotype by environment interactions.

The paper evaluates models to estimate genetic gain in soybean seed yield from multi-environment trials and finds all models are biased, recommending multiple models for accurate estimation.

The paper evaluates breeding strategies for soybean to balance short-term genetic gains with long-term diversity preservation using genomic and phenotypic selection methods.

This study compares genomic prediction methods for ordinal traits like IDC tolerance, finding that Random Forest provides the best decision metrics for breeding decisions.

This study evaluates the effects of selection methods on genetic response in soybean recurrent selection over 40 cycles, identifying interactions that influence short-term gains and long-term genetic potential.