Alexandre Bryan Heinemann

The paper introduces an R-based GIS function to define Target Population of Environments for enviromics studies in plant breeding, enhancing GxE interaction analysis.

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.

This paper develops an enviromic prediction approach using GAM to analyze climate impacts on common bean yield in Brazil, identifying key environmental drivers and optimal conditions for different regions and seasons.

The paper uses machine learning to predict environmental quality for irrigated rice in Brazil by clustering yield data and analyzing genotype-environment interactions.