Matheus Dalsente Krause

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 introduces GIS-FA, a method integrating GIS, factor-analytic models, and envirotyping to predict cultivar performance in untested environments and support decision-making.

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 uses Bayesian probability methods to analyze genotype-by-environment interactions in plant breeding, improving cultivar recommendation by quantifying adaptation and stability probabilities.