J. Jago

1.8k total citations
53 papers, 1.3k citations indexed

About

J. Jago is a scholar working on Agronomy and Crop Science, Small Animals and Animal Science and Zoology. According to data from OpenAlex, J. Jago has authored 53 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Agronomy and Crop Science, 29 papers in Small Animals and 20 papers in Animal Science and Zoology. Recurrent topics in J. Jago's work include Animal Behavior and Welfare Studies (29 papers), Effects of Environmental Stressors on Livestock (18 papers) and Milk Quality and Mastitis in Dairy Cows (17 papers). J. Jago is often cited by papers focused on Animal Behavior and Welfare Studies (29 papers), Effects of Environmental Stressors on Livestock (18 papers) and Milk Quality and Mastitis in Dairy Cows (17 papers). J. Jago collaborates with scholars based in New Zealand, Denmark and United States. J. Jago's co-authors include Paul Edwards, Christian Krohn, N. López‐Villalobos, C. Kamphuis, Lindsay R. Matthews, Xavier Boivin, K.L. Kerrisk, C.B. Glassey, Pablo Gregorini and Callum Eastwood and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Dairy Science and Journal of Animal Science.

In The Last Decade

J. Jago

53 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Jago New Zealand 23 757 726 647 542 149 53 1.3k
Birgit Fuerst‐Waltl Austria 22 744 1.0× 327 0.5× 487 0.8× 975 1.8× 72 0.5× 113 1.4k
K.L. Kerrisk Australia 18 455 0.6× 462 0.6× 565 0.9× 253 0.5× 142 1.0× 49 928
K. Svennersten‐Sjaunja Sweden 24 1.0k 1.3× 574 0.8× 652 1.0× 575 1.1× 303 2.0× 52 1.6k
B. Berglund Sweden 29 1.6k 2.2× 422 0.6× 584 0.9× 1.5k 2.7× 117 0.8× 74 2.1k
R. J. Esslemont United Kingdom 18 1.2k 1.6× 524 0.7× 419 0.6× 860 1.6× 95 0.6× 42 1.5k
Kerstin Barth Germany 16 353 0.5× 640 0.9× 404 0.6× 328 0.6× 86 0.6× 64 934
H. M. Burrow Australia 24 838 1.1× 711 1.0× 1.4k 2.1× 1.6k 3.0× 71 0.5× 85 2.4k
Giuseppe De Rosa Italy 27 516 0.7× 1.1k 1.5× 1.1k 1.6× 779 1.4× 165 1.1× 95 1.9k
Z.E. Barker United Kingdom 18 574 0.8× 1.2k 1.7× 742 1.1× 463 0.9× 106 0.7× 26 1.4k
Ute Knierim Germany 23 386 0.5× 1.5k 2.1× 1.3k 1.9× 660 1.2× 69 0.5× 106 2.0k

Countries citing papers authored by J. Jago

Since Specialization
Citations

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

Fields of papers citing papers by J. Jago

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Jago

This figure shows the co-authorship network connecting the top 25 collaborators of J. Jago. A scholar is included among the top collaborators of J. Jago 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 J. Jago. J. Jago 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.
Saunders, Karen, et al.. (2025). The development of a tool to assess cow quality of life based on system-level attributes across pastoral dairy farms. animal. 19(3). 101429–101429. 1 indexed citations
2.
Woodward, Simon, et al.. (2025). Regional heat stress maps for grazing dairy cows in New Zealand under climate change. Animal Production Science. 65(4). 1 indexed citations
3.
Zobel, Gosia, et al.. (2024). Updated method of estimating heat load for grazing dairy cattle. New Zealand Journal of Agricultural Research. 68(6). 1611–1631. 2 indexed citations
4.
Woodward, Simon, et al.. (2024). Identifying and predicting heat stress events for grazing dairy cows using rumen temperature boluses. SHILAP Revista de lepidopterología. 5(5). 431–435. 13 indexed citations
5.
Eastwood, Callum, B. Dela Rue, Paul Edwards, & J. Jago. (2022). Responsible robotics design–A systems approach to developing design guides for robotics in pasture-grazed dairy farming. Frontiers in Robotics and AI. 9. 914850–914850. 12 indexed citations
6.
7.
Edwards, Paul, J. Jago, & N. López‐Villalobos. (2013). Milking efficiency for grazing dairy cows can be improved by increasing automatic cluster remover thresholds without applying premilking stimulation. Journal of Dairy Science. 96(6). 3766–3773. 34 indexed citations
8.
Kamphuis, C., B. Dela Rue, G. A. Mein, & J. Jago. (2013). Development of protocols to evaluate in-line mastitis-detection systems. Journal of Dairy Science. 96(6). 4047–4058. 21 indexed citations
9.
Edwards, Paul, J. Jago, & N. López‐Villalobos. (2013). Short-term application of prestimulation and increased automatic cluster remover threshold affect milking characteristics of grazing dairy cows in late lactation. Journal of Dairy Science. 96(3). 1886–1893. 35 indexed citations
10.
11.
Jago, J., et al.. (2010). Effect of automatic cluster remover settings on production, udder health, and milking duration. Journal of Dairy Science. 93(6). 2541–2549. 48 indexed citations
12.
Clark, Cameron, Kimberly McLeod, C.B. Glassey, et al.. (2010). Capturing urine while maintaining pasture intake, milk production, and animal welfare of dairy cows in early and late lactation. Journal of Dairy Science. 93(5). 2280–2286. 32 indexed citations
13.
Gregorini, Pablo, Cameron Clark, J. Jago, et al.. (2009). Restricting time at pasture: Effects on dairy cow herbage intake, foraging behavior, hunger-related hormones, and metabolite concentration during the first grazing session. Journal of Dairy Science. 92(9). 4572–4580. 53 indexed citations
14.
Sun, Zhibin, Sandhya Samarasinghe, & J. Jago. (2009). Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks. Journal of Dairy Research. 77(2). 168–175. 47 indexed citations
15.
Kamphuis, C., et al.. (2008). Automatic Detection of Clinical Mastitis Is Improved by In-Line Monitoring of Somatic Cell Count. Journal of Dairy Science. 91(12). 4560–4570. 65 indexed citations
16.
Jago, J., et al.. (2004). Remote automatic selection of cows for milking in a pasture-based automatic milking system. Proceedings of the New Zealand Society of Animal Production. 64. 241–245. 13 indexed citations
17.
Jago, J., et al.. (2003). Dominance effects on the time budget and milking behaviour of cows managed on pasture and milked in an automated milking system. Proceedings of the New Zealand Society of Animal Production. 63. 120–123. 12 indexed citations
18.
Jago, J., et al.. (2002). An innovative farm system combining automated milking with grazing. Proceedings of the New Zealand Society of Animal Production. 62. 115–119. 23 indexed citations
19.
Jago, J., Lisa Matthews, T. E. Trigg, P.M. Dobbie, & J. J. Bass. (1999). The effect of immunocastration 7 weeks before slaughter on the behaviour, growth and meat quality of post-pubertal bulls. Animal Science. 68(1). 163–171. 5 indexed citations
20.
Jago, J., J. J. Bass, & Lisa Matthews. (1997). Evaluation of a vaccine to control bull behaviour. Proceedings of the New Zealand Society of Animal Production. 57. 91–95. 16 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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