Michael A. Brown

2.8k total citations
122 papers, 2.3k citations indexed

About

Michael A. Brown is a scholar working on Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics and Genetics. According to data from OpenAlex, Michael A. Brown has authored 122 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Agronomy and Crop Science, 31 papers in Ecology, Evolution, Behavior and Systematics and 21 papers in Genetics. Recurrent topics in Michael A. Brown's work include Ruminant Nutrition and Digestive Physiology (36 papers), Plant and fungal interactions (22 papers) and Genetic and phenotypic traits in livestock (16 papers). Michael A. Brown is often cited by papers focused on Ruminant Nutrition and Digestive Physiology (36 papers), Plant and fungal interactions (22 papers) and Genetic and phenotypic traits in livestock (16 papers). Michael A. Brown collaborates with scholars based in United States, China and United Kingdom. Michael A. Brown's co-authors include A.H. Brown, W.G. Jackson, Helen Thompson, Hongbin Liu, Jianfei Zhao, Shiyan Qiao, Xiaoya Zhang, Patrick J. Starks, Gary L. Emmert and Ahmed A. Shabana and has published in prestigious journals such as PLoS ONE, Water Research and Journal of The Electrochemical Society.

In The Last Decade

Michael A. Brown

115 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael A. Brown United States 25 607 494 448 424 333 122 2.3k
Yves Beckers Belgium 31 272 0.4× 169 0.3× 113 0.3× 792 1.9× 383 1.2× 136 2.5k
S. J. Welham United Kingdom 29 420 0.7× 418 0.8× 230 0.5× 400 0.9× 273 0.8× 93 3.0k
Rodrigo Labouriau Denmark 30 448 0.7× 159 0.3× 83 0.2× 412 1.0× 208 0.6× 106 2.4k
Edzard van Santen United States 27 156 0.3× 465 0.9× 281 0.6× 508 1.2× 253 0.8× 210 2.6k
Stephen D. Kachman United States 35 1.3k 2.2× 284 0.6× 219 0.5× 681 1.6× 1.7k 5.2× 159 4.8k
Jérôme Bindelle Belgium 33 290 0.5× 163 0.3× 316 0.7× 479 1.1× 780 2.3× 160 3.4k
Tryon A Wickersham United States 23 454 0.7× 131 0.3× 95 0.2× 1.2k 2.8× 344 1.0× 108 1.8k
David R. Davies United Kingdom 29 458 0.8× 359 0.7× 70 0.2× 2.2k 5.2× 461 1.4× 76 3.3k
R. E. Pitt United States 29 282 0.5× 164 0.3× 80 0.2× 1.1k 2.7× 202 0.6× 82 2.8k

Countries citing papers authored by Michael A. Brown

Since Specialization
Citations

This map shows the geographic impact of Michael A. Brown'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 Michael A. Brown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael A. Brown more than expected).

Fields of papers citing papers by Michael A. Brown

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael A. Brown. 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 Michael A. Brown. The network helps show where Michael A. Brown may publish in the future.

Co-authorship network of co-authors of Michael A. Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Michael A. Brown. A scholar is included among the top collaborators of Michael A. Brown 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 Michael A. Brown. Michael A. Brown 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.
Emmert, Gary L., et al.. (2025). A low-cost, high-sensitivity 3D printed fluorescence detector. The Analyst. 150(12). 2536–2544.
3.
Emmert, Gary L., et al.. (2023). A low-cost automated titration system for colorimetric endpoint detection. The Analyst. 148(9). 2133–2140. 3 indexed citations
4.
Emmert, Gary L., et al.. (2023). Automated system for performing pH-based titrations. Instrumentation Science & Technology. 51(5). 574–590. 1 indexed citations
5.
Emmert, Gary L., et al.. (2022). Low-cost automated pipetting system using a single board computer and 3D-printing. Instrumentation Science & Technology. 51(4). 355–370. 3 indexed citations
6.
Jiao, Ting, Jian Wu, D. P. Casper, et al.. (2021). Feeding Sheep Cobalt and Oregano Essential Oil Alone or in Combination on Ruminal Nutrient Digestibility, Fermentation, and Fiber Digestion Combined With Scanning Electron Microscopy. Frontiers in Veterinary Science. 8. 639432–639432. 12 indexed citations
7.
Zhang, Lei, Ling Liu, Defa Li, et al.. (2016). Effects of variety and storage duration on the nutrient digestibility and the digestible and metabolisable energy content of maize fed to growing pigs. Archives of Animal Nutrition. 71(1). 67–80. 7 indexed citations
8.
Budge, Giles E., David Garthwaite, Andrew Crowe, et al.. (2015). Evidence for pollinator cost and farming benefits of neonicotinoid seed coatings on oilseed rape. Scientific Reports. 5(1). 12574–12574. 64 indexed citations
9.
Gao, Fengqin, et al.. (2014). Evaluation of Processing Technology for Triarrhena sacchariflora (Maxim.) Nakai for Ethanol Production. PLoS ONE. 9(12). e114399–e114399. 4 indexed citations
10.
Burke, J.M., S. W. Coleman, C. C. Chase, et al.. (2010). Interaction of breed type and endophyte-infected tall fescue on milk production and quality in beef cattle1. Journal of Animal Science. 88(8). 2802–2811. 16 indexed citations
11.
Brown, Michael A., et al.. (2010). Different oilseed supplements alter fatty acid composition of different adipose tissues of adult ewes. Meat Science. 85(3). 542–549. 33 indexed citations
12.
Brown, Michael A., et al.. (2009). On-line monitoring of nine haloacetic acid species at the μgL−1 level using post-column reaction-ion chromatography with nicotinamide fluorescence. Analytica Chimica Acta. 654(2). 133–140. 13 indexed citations
14.
Brown, Michael A., Sarah Clark Miller, & Gary L. Emmert. (2007). On-line purge and trap gas chromatography for monitoring of trihalomethanes in drinking water distribution systems. Analytica Chimica Acta. 592(2). 154–161. 24 indexed citations
15.
Brown, Michael A., et al.. (2001). Genotype x environment interactions in milk yield and quality in Angus, Brahman, and reciprocal-cross cows on different forage systems.. Journal of Animal Science. 79(7). 1643–1643. 27 indexed citations
16.
Brown, A.H., Z. B. Johnson, R. B. Simpson, et al.. (1994). Relationship of horn fly to face fly infestation in beef cattle. Journal of Animal Science. 72(9). 2264–2269. 9 indexed citations
17.
Brown, A.H., et al.. (1993). Breed Means for Face Fly Count and Estimate of Heritability of Beef Cattle Resistance to the Face Fly. The Professional Animal Scientist. 9(1). 24–30. 3 indexed citations
18.
Nutting, David F., et al.. (1992). Serum amylase activity and calcium and magnesium concentrations in young cattle grazing fescue and Bermuda grass pastures. American Journal of Veterinary Research. 53(5). 834–839. 8 indexed citations
19.
Fox, M., et al.. (1991). A case of Eimeria gilruthi infection in a sheep in northern England. Veterinary Record. 129(7). 141–142. 4 indexed citations
20.
Fuhrmann, Daniel R., Michael A. Brown, Michael I. Miller, et al.. (1987). Data acquisition system for maximum‐likelihood analysis of electron microscopic autoradiographs. Journal of Electron Microscopy Technique. 7(3). 199–204. 4 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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