Leonardo Lopes Bhering

Unraveling the inbreeding depression patterns in a self-pollinated eucalyptus population

This study examines inbreeding depression in a self-pollinated eucalyptus population by analyzing genomic and phenotypic data to estimate inbreeding depression and genetic parameters.

SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection

SimpleMating predicts and optimizes breeding crosses using genomic selection to maximize genetic gain while minimizing inbreeding in simulated maize breeding programs.

Trait prediction through computational intelligence and machine learning applied to soybean (Glycine max) breeding in shaded environments

This paper uses machine learning to predict soybean grain yield under different shading levels, finding plant height as the most predictive trait.

A data-driven approach for enhancing forest productivity by accounting for indirect genetic effects

The paper develops a method to enhance forest productivity by analyzing indirect genetic effects in clonal composites of Eucalyptus hybrids.