Germano Costa‐Neto

This study identifies convergent genomic and gene-level constraints in grasses adapting to extreme environments using phylogenomic data and large language models.

The paper identifies a conserved cis-regulatory code across 589 grass species and finds that motif conservation declines rapidly but remains significant over evolutionary time, with adaptive changes linked to ecological transitions.

The paper examines nitrogen dynamics in Andropogoneae grasses, finding annual species conserve nitrogen while perennials vary in their impact on nitrification and nitrate loss.

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 enhances multi-year genomic prediction of wheat breeding data by integrating environmental associations (EPA) to improve genotype-by-environment interaction predictions.

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.

This paper introduces E-GP, an enviromic assembly method that improves genomic prediction accuracy for yield plasticity by integrating environmental data and optimizing field trials.

The paper optimizes genomic prediction training sets using enviromic data and genetic algorithms, increasing genetic gains by 142% per dollar invested.

EnvRtype provides a software toolkit for integrating enviromics data into quantitative genomics to improve whole-genome prediction accuracy across different environments.