J. M. Young

1.1k total citations
38 papers, 740 citations indexed

About

J. M. Young is a scholar working on Animal Science and Zoology, Small Animals and Genetics. According to data from OpenAlex, J. M. Young has authored 38 papers receiving a total of 740 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Animal Science and Zoology, 19 papers in Small Animals and 10 papers in Genetics. Recurrent topics in J. M. Young's work include Meat and Animal Product Quality (22 papers), Animal Behavior and Welfare Studies (18 papers) and Animal Nutrition and Physiology (14 papers). J. M. Young is often cited by papers focused on Meat and Animal Product Quality (22 papers), Animal Behavior and Welfare Studies (18 papers) and Animal Nutrition and Physiology (14 papers). J. M. Young collaborates with scholars based in United States, China and Bulgaria. J. M. Young's co-authors include Jack C. M. Dekkers, Xin Sun, David Newman, Weijie Cai, Suneel Kumar Onteru, Danielle M. Gorbach, Max F. Rothschild, Steven M. Lonergan, Dorian J. Garrick and E. Huff‐Lonergan and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Dairy Science.

In The Last Decade

J. M. Young

36 papers receiving 719 citations

Peers

J. M. Young

ASZ

GENET

E.A. Navajas Uruguay

A. Albera Italy

T. D. Pringle United States

Marjeta Čandek‐Potokar Slovenia

Martin Škrlep Slovenia

Keigo KUCHIDA Japan

L. Pannier Australia

Juan M. García Casco Spain

Ken Prusa United States

V. Bonfatti Italy

E.A. Navajas Uruguay

J. M. Young

646 ×1.5

ASZ

459 ×1.6

GENET

142 ×0.9

82 ×0.6

250 ×2.1

Citations per year, relative to J. M. Young J. M. Young (= 1×) peers E.A. Navajas

Countries citing papers authored by J. M. Young

Since Specialization

Citations

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

Fields of papers citing papers by J. M. Young

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. M. Young

This figure shows the co-authorship network connecting the top 25 collaborators of J. M. Young. A scholar is included among the top collaborators of J. M. Young 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. M. Young. J. M. Young 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

Shelver, Weilin L., et al.. (2024). Comparison of immunoassay and LC-tandem mass spectrometry analyses of ractopamine in hog oral fluid. Food Additives & Contaminants Part A. 41(2). 162–174. 3 indexed citations

Young, J. M., et al.. (2023). Evaluating the effect of a mild cycling heating period on leukocyte coping capacity in growing pigs. SHILAP Revista de lepidopterología. 4.

Shi, Yinyan, Xiaochan Wang, Md Saidul Borhan, et al.. (2021). A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies. Food Science of Animal Resources. 41(4). 563–588. 61 indexed citations

Ahmed, Mohammed Raju, et al.. (2021). Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology. Applied Sciences. 11(10). 4588–4588. 13 indexed citations

Young, J. M., et al.. (2020). The effects of flunixin meglumine and hoof trimming on lying behavior, locomotion, and milk production in lame and nonlame lactating dairy cows. Journal of Dairy Science. 103(6). 5422–5430. 4 indexed citations

Young, J. M., et al.. (2020). A ramp in nursery housing affects nursery pig behavior and speeds loading of market hogs. Applied Animal Science. 36(4). 574–581. 4 indexed citations

Sun, Xin, et al.. (2018). Predicting pork loin intramuscular fat using computer vision system. Meat Science. 143. 18–23. 40 indexed citations

Sun, Xin, et al.. (2018). Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Science. 140. 72–77. 69 indexed citations

Wagner, Sarah, et al.. (2017). Short communication: Behavioral evaluation of the analgesic effect of flunixin meglumine in lame dairy cows. Journal of Dairy Science. 100(8). 6562–6566. 15 indexed citations

10.

Sun, Xin, et al.. (2016). 143 Predicting pork color scores using machine vision and support vector machine technologies. Journal of Animal Science. 94(suppl_2). 67–67. 1 indexed citations

11.

Young, J. M., Rob Bergsma, E.F. Knol, John F. Patience, & Jack C. M. Dekkers. (2016). Effect of selection for residual feed intake during the grow/finish phase of production on sow reproductive performance and lactation efficiency1. Journal of Animal Science. 94(10). 4120–4132. 18 indexed citations

12.

Sun, Xin, et al.. (2015). Prediction of pork color attributes using computer vision system. Meat Science. 113. 62–64. 39 indexed citations

13.

Boddicker, Nicholas, et al.. (2015). Response of pigs divergently selected for residual feed intake to experimental infection with the PRRS virus. Livestock Science. 177. 132–141. 36 indexed citations

14.

Newman, David, et al.. (2014). Effect of Season, Transport Length, Deck Location, and Lairage Length on Pork Quality and Blood Cortisol Concentrations of Market Hogs. Animals. 4(4). 627–642. 4 indexed citations

15.

Ros‐Freixedes, Roger, Suneel Kumar Onteru, J. M. Young, et al.. (2013). Relationship between gilt behavior and meat quality using principal component analysis. Meat Science. 96(1). 264–269. 9 indexed citations

16.

Onteru, Suneel Kumar, Danielle M. Gorbach, J. M. Young, et al.. (2013). Whole Genome Association Studies of Residual Feed Intake and Related Traits in the Pig. PLoS ONE. 8(6). e61756–e61756. 100 indexed citations

17.

Young, J. M., Weijie Cai, & Jack C. M. Dekkers. (2010). Effect of selection for residual feed intake on feeding behavior and daily feed intake patterns in Yorkshire swine1. Journal of Animal Science. 89(3). 639–647. 66 indexed citations

18.

Gabler, Nicholas K, J. M. Young, Weijie Cai, et al.. (2010). Effects of selection for decreased residual feed intake on composition and quality of fresh pork1. Journal of Animal Science. 89(1). 192–200. 55 indexed citations

19.

Fan, B., Weijie Cai, J. M. Young, et al.. (2009). Identification of genetic markers associated with residual feed intake and meat quality traits in the pig. Meat Science. 84(4). 645–650. 70 indexed citations

20.

McFerran, J., Helen Cahill, J. M. Young, & Charles Wright. (1971). Isolation of infectious bronchitis virus from newborn chicks and dead-in-shell embryos. Veterinary Record. 89(21). 560–561. 4 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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