Kaio Olímpio das Graças Dias

This study evaluates enviromic models for predicting upland rice productivity in Brazil by integrating environmental and genetic data, finding that comprehensive environmental covariates improve model stability and accuracy.

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

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.

This study compares DNA array and Genotyping-by-Sequencing (GBS) for genomic prediction of soybean oil composition traits using BayesB and BRR models.

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 integrates environmental data with genetic data using machine learning to improve genomic prediction models for maize grain yield, showing increased predictive ability and computational efficiency.

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

The paper identifies haploids in popcorn using Navajo phenotype, seedling vigor, and ploidy level, finding that combining these methods reduces misclassification.

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