A. R. Gilmour

990 total citations
39 papers, 672 citations indexed

About

A. R. Gilmour is a scholar working on Genetics, Animal Science and Zoology and Agronomy and Crop Science. According to data from OpenAlex, A. R. Gilmour has authored 39 papers receiving a total of 672 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Genetics, 14 papers in Animal Science and Zoology and 11 papers in Agronomy and Crop Science. Recurrent topics in A. R. Gilmour's work include Genetic and phenotypic traits in livestock (25 papers), Ruminant Nutrition and Digestive Physiology (7 papers) and Animal Nutrition and Physiology (7 papers). A. R. Gilmour is often cited by papers focused on Genetic and phenotypic traits in livestock (25 papers), Ruminant Nutrition and Digestive Physiology (7 papers) and Animal Nutrition and Physiology (7 papers). A. R. Gilmour collaborates with scholars based in Australia, United States and South Africa. A. R. Gilmour's co-authors include Robert D. Anderson, A. L. Rae, N. M. Fogarty, J. B. van Wyk, S.W.P. Cloete, J.J. Olivier, Sharon Nielsen, B. R. Cullis, L. J. Cummins and G. M. Gaunt and has published in prestigious journals such as Biometrika, Theriogenology and Journal of Economic Entomology.

In The Last Decade

A. R. Gilmour

37 papers receiving 612 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
A. R. Gilmour Australia	14	385	299	144	84	81	39	672
A. L. Rae New Zealand	15	429 1.1×	317 1.1×	268 1.9×	107 1.3×	98 1.2×	47	805
JL Foulley France	12	758 2.0×	307 1.0×	217 1.5×	156 1.9×	228 2.8×	27	901
D. Sorensen United States	5	464 1.2×	119 0.4×	137 1.0×	130 1.5×	156 1.9×	7	551
A. Genizi Israel	14	441 1.1×	132 0.4×	49 0.3×	49 0.6×	407 5.0×	45	798
R. V. Large China	13	195 0.5×	291 1.0×	80 0.6×	65 0.8×	46 0.6×	30	481
Milena Kovač Slovenia	13	425 1.1×	157 0.5×	219 1.5×	78 0.9×	79 1.0×	58	693
W. R. Harvey United States	19	813 2.1×	518 1.7×	539 3.7×	144 1.7×	182 2.2×	70	1.4k
James Brown United States	9	319 0.8×	156 0.5×	161 1.1×	38 0.5×	69 0.9×	12	430
C.Y. Lin Canada	23	1.0k 2.6×	579 1.9×	441 3.1×	124 1.5×	268 3.3×	77	1.3k
M. Hossein Yazdi Iran	15	520 1.4×	321 1.1×	353 2.5×	216 2.6×	104 1.3×	46	855

Countries citing papers authored by A. R. Gilmour

Since Specialization

Citations

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

Fields of papers citing papers by A. R. Gilmour

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. R. Gilmour

This figure shows the co-authorship network connecting the top 25 collaborators of A. R. Gilmour. A scholar is included among the top collaborators of A. R. Gilmour 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 A. R. Gilmour. A. R. Gilmour is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Gilmour, A. R., et al.. (2023). Reaction-norm analysis of neonatal lamb mortality suggests heritability varies with cold-stress: an example in the Elsenburg Merino selection lines. Animal Production Science. 63(11). 1017–1030. 4 indexed citations

Greeff, J. C., L. J. E. Karlsson, A. C. Schlink, & A. R. Gilmour. (2017). Factors explaining the incidence of breech strike in a Mediterranean environment in unmulesed and uncrutched Merino sheep. Animal Production Science. 58(7). 1279–1288. 13 indexed citations

Afolayan, R. A., et al.. (2009). Preliminary genetic correlations of milk production and milk composition with reproduction, growth, wool traits and worm resistance in crossbred ewes. Small Ruminant Research. 82(1). 27–33. 19 indexed citations

Gilmour, A. R., et al.. (2009). ASReml Update. What's new in Release 3.00. 43 indexed citations

Eady, SJ, Hervé Garreau, & A. R. Gilmour. (2007). Heritability of resistance to bacterial infection in meat rabbits. Livestock Science. 112(1-2). 90–98. 15 indexed citations

Fogarty, N. M., et al.. (2007). The relationship of lamb growth from birth to weaning and the milk production of their primiparous crossbred dams. Australian Journal of Experimental Agriculture. 47(8). 899–899. 30 indexed citations

Cloete, S.W.P., et al.. (2006). Genetic parameters and trends for lamb behaviour.. 4 indexed citations

Gilmour, A. R., Declan Butler, B. R. Cullis, & Ron Thompson. (2006). ASReml 2 and SAMM 2 - mixed models for many occasions.. 3 indexed citations

Safari, E., A. R. Gilmour, K. D. Atkins, et al.. (2006). Wool and meat genetics - the joint possibilities.. Research Online (University of Wollongong). 54(1). 36–41. 13 indexed citations

10.

Holst, Peter Johannes, et al.. (2004). Grazing of pasture weeds by goats and sheep. 1. Nodding thistle (Carduus nutans). Australian Journal of Experimental Agriculture. 44(6). 547–547. 5 indexed citations

11.

Cloete, S.W.P., A. R. Gilmour, J.J. Olivier, & J. B. van Wyk. (2004). Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and liveweight in Merino lines divergently selected for multiple rearing ability. Australian Journal of Experimental Agriculture. 44(8). 745–754. 66 indexed citations

12.

Barchia, I., Grant A Herron, & A. R. Gilmour. (2003). Use of a Generalized Linear Mixed Model to Reduce Excessive Heterogeneity in Petroleum Spray Oil Bioassay Data. Journal of Economic Entomology. 96(3). 983–989. 7 indexed citations

13.

Ponzoni, R. W., et al.. (2003). Genetic parameters for weight, fat and eye muscle depth in South Australian Merino sheep.. 322–325. 7 indexed citations

14.

Cloete, S.W.P., A. R. Gilmour, J.J. Olivier, & J. B. van Wyk. (2003). Age trends in economically important traits of Merino ewes subjected to 10 years of divergent selection for multiple rearing ability. South African Journal of Animal Science. 33(1). 15 indexed citations

15.

Atkins, K. D., et al.. (1998). Prospects for across-flock genetic evaluation in Australian Merino sheep. Wool technology and sheep breeding. 46(1). 1 indexed citations

16.

Gilmour, A. R. & R. Thompson. (1998). Reformulated generalised linear (mixed) model aids multiple trait genetic evaluation with polychotomous calving ease. Rothamsted Repository (Rothamsted Repository). 1 indexed citations

17.

White, Ian, B. R. Cullis, A. R. Gilmour, & R. Thompson. (1998). Smoothing biological data with splines. Rothamsted Repository (Rothamsted Repository). 6 indexed citations

18.

McGuirk, Brian, et al.. (1995). The evaluation of Holstein Friesian sires for calving ease in the UK. Bulletin - International Bull Evaluation Service/Interbull bulletin. 4 indexed citations

19.

McGuirk, Brian, et al.. (1994). Evaluating UK beef bulls used in dairy crossing for calving ease and calf quality.. Proceedings of the World Congress on Genetics applied to Livestock Production. 165–168. 1 indexed citations

20.

Waddington, David, S. J. Welham, A. R. Gilmour, & R. Thompson. (1994). Comparisons of some GLMM estimators for a simple binomial model. Rothamsted Repository (Rothamsted Repository). 7 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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