Samuel B. Fernandes Source Confirmed
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
University of Arkansas at Fayetteville
faculty
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Biography and Research Information
OverviewAI-generated summary
Samuel B. Fernandes' research focuses on the application of machine learning and advanced phenotyping techniques to understand plant genetics and improve crop traits. His work investigates the genetic basis of water use efficiency (WUE) in sorghum and maize, utilizing methods such as optical topometry and thermal imaging to rapidly phenotype stomatal patterning and closure. Fernandes employs genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) to identify quantitative trait loci (QTL) and genes associated with these traits.
His recent publications also explore the comparative evolutionary genetics of deleterious load in sorghum and maize, as well as methods for distinguishing true from spurious pleiotropy in GWAS. Fernandes has also used machine learning to integrate genetic and environmental data for predicting maize grain yield across multiple environments. He collaborates with researchers at the University of Arkansas at Fayetteville, including Kristofor R. Brye, Mike Daniels, Caio Canella Vieira, and Igor Kuivjogi Fernandes.
Metrics
- h-index: 14
- Publications: 66
- Citations: 928
Selected Publications
- High‐Throughput Screen of <scp>NPQ</scp> in Sorghum Shows Highly Polygenic Architecture of Photoprotection (2026) DOI
- Replication Data for: Nonlinear Genomic Selection Index Accelerates Multi-Trait Crop Improvement (2025) DOI
- Fluridone use in furrow-irrigated rice: Palmer amaranth control and crop response (2025) DOI
- Genomic prediction and association mapping of early season flood tolerance in soybean (2025) DOI
- Biochar type and rate effects on greenhouse gas emissions from furrow‐irrigated rice (2025) DOI
- Improving Multi-Trait Genomic Prediction Efficiency Through The Incorporation Of Synthetic Traits Selected Based on Co-heritability (2025) DOI
- Optimizing population simulations to accurately parallel empirical data for digital breeding (2025) DOI
- High throughput screen of NPQ in sorghum shows highly polygenic architecture of photoprotection (2025) DOI
- Realized genetic gain with reciprocal recurrent selection in a Eucalyptus breeding program (2024) DOI
- Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay (2024) DOI
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions (2024) DOI
- Genome-Wide Association Insights into the Genomic Regions Controlling Oil Production Traits in <i>Acrocomia aculeata</i> (neotropical native palm) (2024) DOI
- Importance of genetic architecture in marker selection decisions for genomic prediction (2023) DOI
- A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition (2023) DOI
- Linking genetic and environmental factors through marker effect networks to understand trait plasticity (2023) DOI
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