Roberto Fritsche‐Neto

This paper uses Bayesian causal forests to estimate the causal effect of nitrogen levels on maize traits, finding that environmental factors largely drive trait variation while genomic background influences baseline levels.

This study evaluates how marker density and minor allele frequency affect genomic selection accuracy in grapevine using machine learning models and conventional methods.

This study performs a GWAS on a diverse grapevine collection to identify genetic determinants of berry size and cluster architecture traits, finding significant SNPs and candidate genes.

The paper identifies a major locus controlling late leaf rust resistance in raspberries using GWAS and copula graphical models, and determines the optimal disease severity evaluation stage.

The paper optimizes multi-environment trials in the US Rice belt by integrating climate-soil models and economic factors to reduce trial locations without sacrificing accuracy.

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

This study uses hyperspectral and genomic data to classify plant growth-promoting bacteria inoculation status and predict growth traits in maize, finding that hyperspectral data better predict shoot dry mass and stalk diameter while genomic data better predict plant height.

This paper develops a machine learning approach for predicting rubber tree stem circumference using molecular markers, achieving higher accuracy than traditional models in a single-environment scenario.

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 identifies a heterochromatic knob locus on chromosome 9 associated with earlier flowering time in maize through genome size and flowering time measurements and association analysis.

This study develops a low-cost greenhouse-based high-throughput phenotyping platform to assess maize growth under plant growth-promoting bacteria inoculation, showing moderate to high genomic heritability and correlation with manual measurements.

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.

The paper introduces CV-α, a validation method for genomic prediction that improves precision and enables statistical tests by reducing residual dependence compared to traditional methods.

This study shows that reducing genotyping marker density in cross-pollinated crops decreases predictive accuracy over generations, suggesting that higher marker density is better for long-term breeding.

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

This study compares genomic selection models for rubber trees, finding that multi-environment models with nonlinear and linear kernels improve prediction accuracy and genetic gain compared to single-environment models.

The paper develops a method to convert tetraploid GBS data into diploidized genotype calls and evaluates its performance using Urochloa hybrids.