M.A. Crowe

8.0k total citations
161 papers, 6.0k citations indexed

About

M.A. Crowe is a scholar working on Agronomy and Crop Science, Genetics and Animal Science and Zoology. According to data from OpenAlex, M.A. Crowe has authored 161 papers receiving a total of 6.0k indexed citations (citations by other indexed papers that have themselves been cited), including 123 papers in Agronomy and Crop Science, 85 papers in Genetics and 40 papers in Animal Science and Zoology. Recurrent topics in M.A. Crowe's work include Reproductive Physiology in Livestock (121 papers), Genetic and phenotypic traits in livestock (78 papers) and Ruminant Nutrition and Digestive Physiology (28 papers). M.A. Crowe is often cited by papers focused on Reproductive Physiology in Livestock (121 papers), Genetic and phenotypic traits in livestock (78 papers) and Ruminant Nutrition and Digestive Physiology (28 papers). M.A. Crowe collaborates with scholars based in Ireland, United Kingdom and United States. M.A. Crowe's co-authors include Bernadette Earley, P. Lonergan, J. F. Roche, J Roche, Niamh Forde, W. J. Enright, A.C.O. Evans, M.G. Diskin, Marijke E. Beltman and Patrick E. Duffy and has published in prestigious journals such as International Journal of Molecular Sciences, Genome Research and Journal of Dairy Science.

In The Last Decade

M.A. Crowe

160 papers receiving 5.8k citations

Author Peers

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

Author Last Decade Papers Cites
M.A. Crowe 4.1k 3.0k 1.7k 1.4k 1.3k 161 6.0k
M.G. Diskin 4.9k 1.2× 3.6k 1.2× 1.1k 0.6× 442 0.3× 1.7k 1.2× 142 6.3k
H. Bollwein 2.8k 0.7× 1.5k 0.5× 690 0.4× 644 0.4× 1.5k 1.1× 226 4.5k
M.C. Lucy 7.3k 1.8× 5.4k 1.8× 2.2k 1.3× 997 0.7× 1.7k 1.3× 200 9.4k
P.M. Fricke 5.7k 1.4× 4.9k 1.7× 1.4k 0.8× 740 0.5× 1.7k 1.3× 127 6.4k
Jeffrey S. Stevenson 5.6k 1.3× 4.7k 1.6× 1.5k 0.9× 747 0.5× 1.0k 0.8× 205 6.6k
R.J. Mapletoft 5.7k 1.4× 4.4k 1.5× 1.1k 0.6× 351 0.2× 3.6k 2.7× 275 7.2k
R.L.A. Cerri 3.9k 0.9× 3.0k 1.0× 1.5k 0.9× 703 0.5× 809 0.6× 107 4.5k
L. Badinga 2.4k 0.6× 1.5k 0.5× 856 0.5× 217 0.2× 741 0.6× 58 3.1k
Michael J. D’Occhio 2.0k 0.5× 1.4k 0.5× 668 0.4× 396 0.3× 731 0.5× 130 3.1k
P. Humblot 2.3k 0.6× 1.9k 0.7× 438 0.3× 414 0.3× 1.5k 1.1× 167 3.9k

Countries citing papers authored by M.A. Crowe

Since Specialization
Citations

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

Fields of papers citing papers by M.A. Crowe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M.A. Crowe

This figure shows the co-authorship network connecting the top 25 collaborators of M.A. Crowe. A scholar is included among the top collaborators of M.A. Crowe 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 M.A. Crowe. M.A. Crowe 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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Pascottini, Osvaldo Bogado, et al.. (2024). Perspectives in cattle reproduction for the next 20 years – A European context. Theriogenology. 233. 8–23. 4 indexed citations
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Cheng, Zhangrui, C.P. Ferris, M.A. Crowe, et al.. (2023). Hepatic Global Transcriptomic Profiles of Holstein Cows According to Parity Reveal Age-Related Changes in Early Lactation. International Journal of Molecular Sciences. 24(12). 9906–9906. 3 indexed citations
5.
Tang, Lijing, Thomas Lopdell, В. А. Петров, et al.. (2023). An organism-wide ATAC-seq peak catalog for the bovine and its use to identify regulatory variants. Genome Research. 33(10). 1848–1864. 10 indexed citations
6.
Beltman, Marijke E., et al.. (2023). Association between maternal growth in the pre‐conception and early gestational period of nulliparous dairy heifers with anti‐Müllerian hormone in their female offspring. Reproduction in Domestic Animals. 59(1). e14498–e14498. 6 indexed citations
7.
Wathes, D.C., Zhangrui Cheng, Mazdak Salavati, et al.. (2021). Relationships between metabolic profiles and gene expression in liver and leukocytes of dairy cows in early lactation. Journal of Dairy Science. 104(3). 3596–3616. 28 indexed citations
8.
Grelet, Clément, Phuong N. Ho, J.E. Pryce, et al.. (2021). Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows’ Dry Matter Intake. Animals. 11(5). 1316–1316. 12 indexed citations
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Atashi, H., Mazdak Salavati, Jenne De Koster, et al.. (2020). Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows. Journal of Dairy Science. 103(7). 6392–6406. 5 indexed citations
11.
Grelet, Clément, Éric Froidmont, Leslie Foldager, et al.. (2020). Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation. Journal of Dairy Science. 103(5). 4435–4445. 33 indexed citations
12.
Atashi, H., Mazdak Salavati, Jenne De Koster, et al.. (2020). A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach. Animals. 10(3). 500–500. 12 indexed citations
13.
O’Sullivan, Michael, S.T. Butler, K.M. Pierce, et al.. (2019). Reproductive efficiency and survival of Holstein-Friesian cows of divergent Economic Breeding Index, evaluated under seasonal calving pasture-based management. Journal of Dairy Science. 103(2). 1685–1700. 33 indexed citations
14.
Atashi, H., Mazdak Salavati, Jenne De Koster, et al.. (2019). Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows. Journal of Animal Breeding and Genetics. 137(3). 292–304. 46 indexed citations
15.
Marchitelli, Cinzia, Federica Signorelli, F. Napolitano, et al.. (2018). Milk biomarkers to evaluate health status of mammary gland in high producing dairy cattle. Open Repository and Bibliography (University of Liège). 1 indexed citations
16.
Grelet, Clément, Amélie Vanlierde, Miel Hostens, et al.. (2018). Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. animal. 13(3). 649–658. 56 indexed citations
17.
Crowe, M.A., Miel Hostens, & G. Opsomer. (2018). Reproductive management in dairy cows - the future. Irish Veterinary Journal. 71(1). 1–1. 99 indexed citations
18.
Pokharel, Kisun, Jaana Peippo, Mervi Honkatukia, et al.. (2018). Integrated ovarian mRNA and miRNA transcriptome profiling characterizes the genetic basis of prolificacy traits in sheep (Ovis aries). BMC Genomics. 19(1). 104–104. 37 indexed citations
19.
Beltman, Marijke E., S. Walsh, M.J. Canty, Patrick E. Duffy, & M.A. Crowe. (2014). Hormonal composition of follicular fluid from abnormal follicular structures in mares. Research in Veterinary Science. 97(3). 488–490. 3 indexed citations
20.
Sunderland, S., M.A. Crowe, M.P. Boland, J Roche, & James J. Ireland. (1994). Selection, dominance and atresia of follicles during the oestrous cycle of heifers. Reproduction. 101(3). 547–555. 183 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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