Stephen Conroy

642 total citations
24 papers, 423 citations indexed

About

Stephen Conroy is a scholar working on Genetics, Animal Science and Zoology and Agronomy and Crop Science. According to data from OpenAlex, Stephen Conroy has authored 24 papers receiving a total of 423 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Genetics, 17 papers in Animal Science and Zoology and 10 papers in Agronomy and Crop Science. Recurrent topics in Stephen Conroy's work include Genetic and phenotypic traits in livestock (19 papers), Effects of Environmental Stressors on Livestock (9 papers) and Ruminant Nutrition and Digestive Physiology (8 papers). Stephen Conroy is often cited by papers focused on Genetic and phenotypic traits in livestock (19 papers), Effects of Environmental Stressors on Livestock (9 papers) and Ruminant Nutrition and Digestive Physiology (8 papers). Stephen Conroy collaborates with scholars based in Ireland, Canada and France. Stephen Conroy's co-authors include D.P. Berry, M. McGee, M. J. Drennan, D.A. Kenny, T. Pabiou, A.R. Cromie, Craig P. Murphy, David Kenny, Roy D. Sleator and Sinéad M. Waters and has published in prestigious journals such as Bioresource Technology, Scientific Reports and Journal of Animal Science.

In The Last Decade

Stephen Conroy

24 papers receiving 415 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stephen Conroy Ireland 12 256 227 195 49 38 24 423
Roger Hegarty Australia 8 210 0.8× 149 0.7× 221 1.1× 73 1.5× 26 0.7× 15 340
Josie B. Garner Australia 11 178 0.7× 335 1.5× 134 0.7× 74 1.5× 85 2.2× 17 499
Amélie Vanlierde Belgium 14 472 1.8× 405 1.8× 531 2.7× 70 1.4× 21 0.6× 32 711
Roberta Carrilho Canesin Brazil 13 318 1.2× 239 1.1× 499 2.6× 94 1.9× 30 0.8× 59 678
N. Krattenmacher Germany 11 307 1.2× 115 0.5× 224 1.1× 29 0.6× 23 0.6× 21 381
Rodrigo Silva Goulart Brazil 14 103 0.4× 233 1.0× 190 1.0× 29 0.6× 51 1.3× 37 389
Soumya Dash India 8 169 0.7× 209 0.9× 154 0.8× 25 0.5× 36 0.9× 21 359
K. Nichols Netherlands 13 117 0.5× 77 0.3× 297 1.5× 32 0.7× 71 1.9× 26 383
Mitsuru KAMIYA Japan 13 71 0.3× 244 1.1× 152 0.8× 29 0.6× 42 1.1× 36 388
Javier Fernández Álvarez Spain 12 188 0.7× 69 0.3× 84 0.4× 35 0.7× 24 0.6× 35 279

Countries citing papers authored by Stephen Conroy

Since Specialization
Citations

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

Fields of papers citing papers by Stephen Conroy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen Conroy

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Conroy. A scholar is included among the top collaborators of Stephen Conroy 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 Stephen Conroy. Stephen Conroy 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.
Pabiou, T., Deirdre C. Purfield, D.P. Berry, et al.. (2024). Exploring definitions of daily enteric methane emission phenotypes for genetic evaluations using a population of indoor-fed multi-breed growing cattle with feed intake data. Journal of Animal Science. 102. 2 indexed citations
2.
Crowley, Sean, et al.. (2024). Associations between a range of enteric methane emission traits and performance traits in indoor-fed growing cattle. Journal of Animal Science. 102. 4 indexed citations
3.
Cerrone, Federico, Binbin Zhou, Luc Avérous, et al.. (2023). Pseudomonas umsongensis GO16 as a platform for the in vivo synthesis of short and medium chain length polyhydroxyalkanoate blends. Bioresource Technology. 387. 129668–129668. 12 indexed citations
4.
5.
Sleator, Roy D., et al.. (2021). Phenotypic and genetic associations between feeding behavior and carcass merit in crossbred growing cattle. Journal of Animal Science. 99(12). 1 indexed citations
6.
Sleator, Roy D., et al.. (2021). PSVI-20 Extent of genetic variation in feeding behavior and genetic associations with performance and feed efficiency in crossbred growing cattle. Journal of Animal Science. 99(Supplement_3). 233–234. 3 indexed citations
7.
Berry, D.P., et al.. (2020). Inter-animal genetic variability exist in organoleptic properties of prime beef meat. Meat Science. 173. 108401–108401. 9 indexed citations
8.
Prendiville, R., F. Buckley, E. Kennedy, et al.. (2020). The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasture-based diets. animal. 14(11). 2288–2297. 28 indexed citations
9.
Conroy, Stephen, et al.. (2020). Feed and production efficiency of young crossbred beef cattle stratified on a terminal total merit index1. Translational Animal Science. 4(3). txaa106–txaa106. 7 indexed citations
10.
11.
Sleator, Roy D., et al.. (2020). Large variability in feeding behavior among crossbred growing cattle. Journal of Animal Science. 98(7). 11 indexed citations
12.
Murphy, Craig P., et al.. (2019). Feed efficiency and carcass metrics in growing cattle1. Journal of Animal Science. 97(11). 4405–4417. 33 indexed citations
13.
Pabiou, T., et al.. (2019). Potential exists to change, through breeding, the yield of individual primal carcass cuts in cattle without increasing overall carcass weight1. Journal of Animal Science. 97(7). 2769–2779. 17 indexed citations
14.
Pabiou, T., et al.. (2019). Factors associated with the weight of individual primal cuts and their inter-relationship in cattle. Translational Animal Science. 3(4). 1593–1605. 11 indexed citations
15.
McClure, Matthew, Stephen Conroy, David Kenny, et al.. (2018). GWAS and eQTL analysis identifies a SNP associated with both residual feed intake and GFRA2 expression in beef cattle. Scientific Reports. 8(1). 14301–14301. 48 indexed citations
16.
Conroy, Stephen, et al.. (2018). Variance component estimation of efficiency, carcass and meat quality traits in beef cattle. Proceedings of the World Congress on Genetics Applied to Livestock Production. 912. 4 indexed citations
17.
18.
Berry, D.P., Stephen Conroy, T. Pabiou, & A.R. Cromie. (2017). Animal breeding strategies can improve meat quality attributes within entire populations. Meat Science. 132. 6–18. 41 indexed citations
19.
Conroy, Stephen, M. J. Drennan, D.A. Kenny, & M. McGee. (2009). The relationship of live animal muscular and skeletal scores, ultrasound measurements and carcass classification scores with carcass composition and value in steers. animal. 3(11). 1613–1624. 32 indexed citations
20.
Conroy, Stephen, M. J. Drennan, M. McGee, et al.. (2009). Predicting beef carcass meat, fat and bone proportions from carcass conformation and fat scores or hindquarter dissection. animal. 4(2). 234–241. 42 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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