Samuel B. Fernandes

955 total citations
32 papers, 551 citations indexed

About

Samuel B. Fernandes is a scholar working on Genetics, Plant Science and Agronomy and Crop Science. According to data from OpenAlex, Samuel B. Fernandes has authored 32 papers receiving a total of 551 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Genetics, 20 papers in Plant Science and 5 papers in Agronomy and Crop Science. Recurrent topics in Samuel B. Fernandes's work include Genetic Mapping and Diversity in Plants and Animals (19 papers), Genetics and Plant Breeding (11 papers) and Genetic and phenotypic traits in livestock (10 papers). Samuel B. Fernandes is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (19 papers), Genetics and Plant Breeding (11 papers) and Genetic and phenotypic traits in livestock (10 papers). Samuel B. Fernandes collaborates with scholars based in United States, Brazil and United Kingdom. Samuel B. Fernandes's co-authors include Patrick J. Brown, Alexander E. Lipka, Kaio Olímpio das Graças Dias, Daniel Furtado Ferreira, Andrew D. B. Leakey, Roberto Lozano, Edward S. Buckler, Michael A. Gore, Ravi Valluru and Nonoy Bandillo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and PLANT PHYSIOLOGY.

In The Last Decade

Samuel B. Fernandes

29 papers receiving 545 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Samuel B. Fernandes United States 12 412 283 122 82 65 32 551
Meredith T. Hanlon United States 11 687 1.7× 99 0.3× 125 1.0× 52 0.6× 44 0.7× 17 746
Jagdeep Singh Sidhu United States 12 531 1.3× 89 0.3× 105 0.9× 67 0.8× 27 0.4× 25 610
Xiaoqing Yu United States 13 497 1.2× 306 1.1× 91 0.7× 80 1.0× 12 0.2× 14 601
M. Laza Philippines 17 767 1.9× 179 0.6× 166 1.4× 37 0.5× 66 1.0× 24 845
Jared Crain United States 14 481 1.2× 175 0.6× 233 1.9× 45 0.5× 29 0.4× 26 633
R. Lafitte Philippines 19 1.3k 3.1× 322 1.1× 175 1.4× 156 1.9× 120 1.8× 28 1.4k
Agostino Fricano Italy 11 264 0.6× 80 0.3× 114 0.9× 62 0.8× 38 0.6× 21 379
Émilie Millet France 10 525 1.3× 222 0.8× 112 0.9× 57 0.7× 51 0.8× 19 633
Prabin Bajgain United States 15 470 1.1× 227 0.8× 203 1.7× 82 1.0× 25 0.4× 39 652
Tohru Yamagishi Japan 13 497 1.2× 85 0.3× 71 0.6× 91 1.1× 50 0.8× 25 576

Countries citing papers authored by Samuel B. Fernandes

Since Specialization
Citations

This map shows the geographic impact of Samuel B. Fernandes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Samuel B. Fernandes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel B. Fernandes more than expected).

Fields of papers citing papers by Samuel B. Fernandes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel B. Fernandes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Samuel B. Fernandes. The network helps show where Samuel B. Fernandes may publish in the future.

Co-authorship network of co-authors of Samuel B. Fernandes

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel B. Fernandes. A scholar is included among the top collaborators of Samuel B. Fernandes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Samuel B. Fernandes. Samuel B. Fernandes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Norsworthy, Jason K., et al.. (2025). Rice cultivar tolerance to preemergence- and postemergence-applied fluridone. Weed Technology. 39.
3.
Brye, Kristofor R., et al.. (2024). Near-Surface Soil Chemical Properties as Affected by Cover Crops Over Time in the Lower Mississippi River Valley. Agricultural Sciences. 15(9). 1035–1056. 1 indexed citations
4.
5.
Alves‐Pereira, Alessandro, et al.. (2024). Genome-wide association insights into the genomic regions controlling vegetative and oil production traits in Acrocomia aculeata. BMC Plant Biology. 24(1). 1125–1125. 1 indexed citations
6.
Vieira, Caio Canella, et al.. (2024). Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials. Theoretical and Applied Genetics. 137(8). 189–189. 5 indexed citations
7.
Dias, Luíz Antônio dos Santos, et al.. (2024). Realized genetic gain with reciprocal recurrent selection in a Eucalyptus breeding program. Tree Genetics & Genomes. 20(6). 1 indexed citations
8.
Srivastava, Vibha, et al.. (2024). Beat the heat: Breeding, genomics, and gene editing for high nighttime temperature tolerance in rice. Current Opinion in Plant Biology. 82. 102659–102659. 1 indexed citations
9.
Bhering, Leonardo Lopes, Rodrigo Silva Alves, Elizabete Keiko Takahashi, et al.. (2023). A novel strategy to predict clonal composites by jointly modeling spatial variation and genetic competition. Forest Ecology and Management. 548. 121393–121393. 5 indexed citations
10.
Daniels, Mike, et al.. (2023). Potassium losses in runoff from cotton production fields. Agronomy Journal. 115(4). 1666–1677. 6 indexed citations
11.
Fernandes, Samuel B., et al.. (2022). Assessment of two statistical approaches for variance genome-wide association studies in plants. Heredity. 129(2). 93–102. 2 indexed citations
12.
Fernandes, Samuel B., Terry Casstevens, Peter J. Bradbury, & Alexander E. Lipka. (2022). A multi‐trait multi‐locus stepwise approach for conducting GWAS on correlated traits. The Plant Genome. 15(2). 10 indexed citations
13.
Lozano, Roberto, Élodie Gazave, J. Santos, et al.. (2021). Comparative evolutionary genetics of deleterious load in sorghum and maize. Nature Plants. 7(1). 17–24. 55 indexed citations
14.
Pignon, Charles P., Samuel B. Fernandes, Ravi Valluru, et al.. (2021). Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. PLANT PHYSIOLOGY. 187(4). 2544–2562. 35 indexed citations
15.
Fernandes, Samuel B., et al.. (2021). Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping. PLANT PHYSIOLOGY. 187(3). 1462–1480. 51 indexed citations
16.
Ferguson, John N., Samuel B. Fernandes, Brandon Monier, et al.. (2021). Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. PLANT PHYSIOLOGY. 187(3). 1481–1500. 61 indexed citations
17.
Adhikari, Pragya, Samuel B. Fernandes, Alexander E. Lipka, et al.. (2020). Genetic variation associated with PPO-inhibiting herbicide tolerance in sorghum. PLoS ONE. 15(10). e0233254–e0233254. 6 indexed citations
18.
Santos, J., Samuel B. Fernandes, Scott McCoy, et al.. (2019). Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. G3 Genes Genomes Genetics. 10(2). 769–781. 30 indexed citations
19.
Owings, Amanda C., Samuel B. Fernandes, Marcus O. Olatoye, et al.. (2019). Population Structure Analyses Provide Insight into the Source Populations Underlying Rural Isolated Communities in Illinois. Human Biology. 91(1). 31–31. 1 indexed citations
20.
Fernandes, Samuel B., Kaio Olímpio das Graças Dias, Daniel Furtado Ferreira, & Patrick J. Brown. (2017). Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum. Theoretical and Applied Genetics. 131(3). 747–755. 122 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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