E. Lewis

4.1k total citations · 1 hit paper
60 papers, 1.7k citations indexed

About

E. Lewis is a scholar working on Agronomy and Crop Science, Genetics and Environmental Chemistry. According to data from OpenAlex, E. Lewis has authored 60 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Agronomy and Crop Science, 27 papers in Genetics and 10 papers in Environmental Chemistry. Recurrent topics in E. Lewis's work include Ruminant Nutrition and Digestive Physiology (45 papers), Reproductive Physiology in Livestock (27 papers) and Genetic and phenotypic traits in livestock (24 papers). E. Lewis is often cited by papers focused on Ruminant Nutrition and Digestive Physiology (45 papers), Reproductive Physiology in Livestock (27 papers) and Genetic and phenotypic traits in livestock (24 papers). E. Lewis collaborates with scholars based in Ireland, France and United Kingdom. E. Lewis's co-authors include M. O’Donovan, M. O’Donovan, T.M. Boland, M.H. Deighton, Paul W. O’Toole, Fiona Crispie, Paul D. Cotter, E. Kennedy, P. O’Kiely and Alexandre B. de Menezes and has published in prestigious journals such as Journal of Dairy Science, Agriculture Ecosystems & Environment and Journal of Animal Science.

In The Last Decade

E. Lewis

59 papers receiving 1.6k citations

Hit Papers

The rumen microbiome: a c... 2018 2026 2020 2023 2018 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The rumen microbiome: a crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency
    2018 265 citations E. Lewis et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
E. Lewis 1.2k 612 449 292 225 60 1.7k
Nicolás DiLorenzo 1.2k 1.0× 419 0.7× 499 1.1× 162 0.6× 173 0.8× 176 1.8k
M. O’Donovan 1.7k 1.4× 771 1.3× 430 1.0× 302 1.0× 155 0.7× 100 2.3k
D. J. Gibb 1.4k 1.1× 566 0.9× 707 1.6× 218 0.7× 117 0.5× 42 1.9k
Sylvie Giger‐Reverdin 951 0.8× 375 0.6× 513 1.1× 266 0.9× 169 0.8× 52 1.7k
J. Chiquette 1.4k 1.1× 326 0.5× 369 0.8× 351 1.2× 188 0.8× 35 1.8k
S.P. Washburn 1.1k 0.9× 731 1.2× 470 1.0× 164 0.6× 88 0.4× 40 1.7k
M. H. Poore 1.5k 1.2× 530 0.9× 458 1.0× 167 0.6× 165 0.7× 113 2.1k
M. O’Donovan 1.3k 1.1× 618 1.0× 376 0.8× 304 1.0× 74 0.3× 68 1.8k
Juha Nousiainen 1.9k 1.6× 880 1.4× 549 1.2× 320 1.1× 103 0.5× 77 2.4k
Tryon A Wickersham 1.2k 1.0× 454 0.7× 285 0.6× 158 0.5× 344 1.5× 108 1.8k

Countries citing papers authored by E. Lewis

Since Specialization
Citations

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

Fields of papers citing papers by E. Lewis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. Lewis. 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 E. Lewis. The network helps show where E. Lewis may publish in the future.

Co-authorship network of co-authors of E. Lewis

This figure shows the co-authorship network connecting the top 25 collaborators of E. Lewis. A scholar is included among the top collaborators of E. Lewis 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 E. Lewis. E. Lewis 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
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Delaby, Luc, et al.. (2019). Pasture allowance, duration, and stage of lactation—Effects on early and total lactation animal performance. Journal of Dairy Science. 102(10). 8986–8998. 7 indexed citations
3.
López‐Villalobos, N., S. McParland, E. Lewis, et al.. (2017). Genetics of alternative definitions of feed efficiency in grazing lactating dairy cows. Journal of Dairy Science. 100(7). 5501–5514. 35 indexed citations
4.
Minet, Eddy, S.F. Ledgard, Gary Lanigan, et al.. (2016). Mixing dicyandiamide (DCD) with supplementary feeds for cattle: An effective method to deliver a nitrification inhibitor in urine patches. Agriculture Ecosystems & Environment. 231. 114–121. 18 indexed citations
5.
Kennedy, E., et al.. (2015). Duration is important in the effect of pasture allowance restriction on subsequent milk production, in early lactation.. 110–112. 2 indexed citations
6.
Kennedy, E., et al.. (2015). Comparing the in vivo dry matter digestibility of perennial ryegrass in sheep and dairy cows.. 416–418. 2 indexed citations
7.
Kennedy, E., E. Lewis, J.P. Murphy, N. Galvin, & M. O’Donovan. (2015). Production parameters of autumn-calving cows offered either a total mixed ration or grazed grass plus concentrate during early lactation. Journal of Dairy Science. 98(11). 7917–7929. 6 indexed citations
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Buckley, F., Sinéad M. Waters, T.M. Boland, et al.. (2014). Gastrointestinal tract size, total-tract digestibility, and rumen microflora in different dairy cow genotypes. Journal of Dairy Science. 97(6). 3906–3917. 57 indexed citations
11.
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O’Donovan, M., C.T. Elliott, J. S. Bailey, et al.. (2014). The effect of dietary crude protein and phosphorus on grass-fed dairy cow production, nutrient status, and milk heat stability. Journal of Dairy Science. 98(1). 517–531. 28 indexed citations
13.
McParland, S., E. Kennedy, E. Lewis, et al.. (2014). Genetic parameters of dairy cow energy intake and body energy status predicted using mid-infrared spectrometry of milk. Journal of Dairy Science. 98(2). 1310–1320. 36 indexed citations
14.
Hennessy, D., et al.. (2013). Comparing drying protocols for perennial ryegrass samples in preparation for chemical analysis.. UKnowledge (University of Kentucky). 639–642. 5 indexed citations
15.
Vanrobays, Marie-Laure, Amélie Vanlierde, Frédéric Dehareng, et al.. (2013). Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows. Journal of Dairy Science. 91. 6 indexed citations
16.
McParland, S., Georgios Banos, B. McCarthy, et al.. (2012). Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows. Journal of Dairy Science. 95(12). 7225–7235. 43 indexed citations
17.
O’Neill, Bernadette, M.H. Deighton, F.J. Mulligan, et al.. (2011). Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production. Journal of Dairy Science. 94(4). 1941–1951. 103 indexed citations
18.
Burns, Gully, T. J. Gilliland, E. Lewis, et al.. (2010). Variability in composition of grass samples in a national testing system for Lolium perenne L.. 479–481. 1 indexed citations
19.
Deighton, M.H., et al.. (2010). Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period. Journal of Dairy Science. 93(10). 4976–4985. 86 indexed citations
20.
Lewis, E. & P. O. Brophy. (2005). The effect of floor type in farrowing crates on piglet welfare. Irish Journal of Agricultural and Food Research. 44(1). 69–81. 18 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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