Dan Tulpan

2.3k total citations · 1 hit paper
74 papers, 1.2k citations indexed

About

Dan Tulpan is a scholar working on Animal Science and Zoology, Genetics and Molecular Biology. According to data from OpenAlex, Dan Tulpan has authored 74 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Animal Science and Zoology, 25 papers in Genetics and 18 papers in Molecular Biology. Recurrent topics in Dan Tulpan's work include Genetic and phenotypic traits in livestock (21 papers), Effects of Environmental Stressors on Livestock (17 papers) and Animal Behavior and Welfare Studies (14 papers). Dan Tulpan is often cited by papers focused on Genetic and phenotypic traits in livestock (21 papers), Effects of Environmental Stressors on Livestock (17 papers) and Animal Behavior and Welfare Studies (14 papers). Dan Tulpan collaborates with scholars based in Canada, United States and Switzerland. Dan Tulpan's co-authors include Mohsen Yoosefzadeh-Najafabadi, Milad Eskandari, Serge Léger, John Sulik, Hugh J. Earl, Miroslava Čuperlović‐Culf, Adrian S. Culf, Shadi Nayeri, Mehdi Sargolzaei and J.L. Ellis and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Dan Tulpan

66 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

2021 159 citations Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dan Tulpan Canada	19	439	310	241	200	131	74	1.2k
Weizheng Shen China	21	305 0.7×	57 0.2×	226 0.9×	64 0.3×	99 0.8×	76	1.1k
Beibei Xu China	13	311 0.7×	75 0.2×	88 0.4×	46 0.2×	78 0.6×	26	729
Daniel Smith Australia	17	90 0.2×	67 0.2×	218 0.9×	220 1.1×	105 0.8×	59	1.1k
Aqing Yang China	14	358 0.8×	52 0.2×	117 0.5×	30 0.1×	237 1.8×	22	727
Shujuan Zhang China	22	1.2k 2.8×	297 1.0×	83 0.3×	73 0.4×	125 1.0×	82	1.9k
Ritaban Dutta Australia	18	147 0.3×	73 0.2×	154 0.6×	68 0.3×	86 0.7×	62	1.4k
Abelardo Montesinos‐López Mexico	21	1.4k 3.3×	192 0.6×	49 0.2×	1.3k 6.7×	178 1.4×	82	2.2k
Xin Sun United States	22	566 1.3×	198 0.6×	293 1.2×	25 0.1×	233 1.8×	71	1.5k
R. Lacroix Canada	22	249 0.6×	22 0.1×	355 1.5×	434 2.2×	131 1.0×	64	1.3k
D. Ratcliff Australia	17	172 0.4×	112 0.4×	374 1.6×	77 0.4×	107 0.8×	44	1.6k

Countries citing papers authored by Dan Tulpan

Since Specialization

Citations

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

Fields of papers citing papers by Dan Tulpan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Tulpan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Fleming, A., et al.. (2025). Estimation of genetic parameters for milk mid-infrared-predicted methane production in Holstein dairy cattle. Journal of Dairy Science. 109(2). 1763–1774.

Fonseca, Pablo Augusto de Souza, Bayode O. Makanjuola, F. Miglior, et al.. (2025). A genome-wide association study on rumination time in first-lactation dairy cattle. Journal of Dairy Science. 108(7). 7297–7309. 1 indexed citations

Tedeschi, Luís O, Dan Tulpan, Hector M Menendez, & Ricardo Augusto Mendonça Vieira. (2025). Modeling complex systems in animal science: A pedagogical framework for new researchers. Revista Brasileira de Zootecnia. 54.

Tulpan, Dan, et al.. (2025). Prediction of Pellet Durability Index in a commercial feed mill using multiple linear regression with variable selection and dimensionality reduction. Journal of Animal Science. 103.

Tulpan, Dan, et al.. (2025). ASAS-NANP Symposium: mathematical modeling in animal nutrition: construction of supervised machine learning regression pipelines for livestock data modeling: a case studys. Journal of Animal Science. 104.

Schenkel, Flávio S., Christina M. Rochus, Hinayah Rojas de Oliveira, et al.. (2024). Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first-lactation Holstein cows. Journal of Dairy Science. 107(7). 4704–4713. 14 indexed citations

Tulpan, Dan. (2024). 458 Advanced computing and information technology to address challenges in livestock production. Journal of Animal Science. 102(Supplement_3). 295–296.

Tulpan, Dan. (2023). 65 Machine and Deep Learning Modelling Strategies for Body Weight Prediction of Cattle and Swine. Journal of Animal Science. 101(Supplement_3). 141–142. 1 indexed citations

Baes, Christine F., et al.. (2023). Genetic evaluation of heat tolerance in Holsteins using test-day production records and NASA POWER weather data. Journal of Dairy Science. 106(10). 6995–7007. 3 indexed citations

10.

Ma, Weihong, Qiang Bai, Dan Tulpan, et al.. (2023). A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data. Biosystems Engineering. 230. 171–190. 16 indexed citations

11.

Ellis, J.L., et al.. (2023). Comparison of imputation methods for missing production data of dairy cattle. animal. 17. 100921–100921. 9 indexed citations

12.

Tulpan, Dan, et al.. (2023). Review: When worlds collide – poultry modeling in the ‘Big Data’ era. animal. 17. 100874–100874. 7 indexed citations

13.

Ma, Weihong, Qifeng Li, Chunjiang Zhao, et al.. (2022). Multi-view real-time acquisition and 3D reconstruction of point clouds for beef cattle. Computers and Electronics in Agriculture. 197. 106987–106987. 23 indexed citations

14.

Yoosefzadeh-Najafabadi, Mohsen, Milad Eskandari, Sepideh Torabi, et al.. (2022). Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components. International Journal of Molecular Sciences. 23(10). 5538–5538. 32 indexed citations

15.

Baes, Christine F., et al.. (2022). Phenotypic analysis of heat stress in Holsteins using test-day production records and NASA POWER meteorological data. Journal of Dairy Science. 106(2). 1142–1158. 13 indexed citations

16.

Chud, T.C.S., Dagnachew Hailemariam, Hinayah Rojas de Oliveira, et al.. (2022). Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. Journal of Dairy Science. 105(10). 8257–8271. 14 indexed citations

17.

Wang, Zhuoyi, et al.. (2021). ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science. 99(2). 61 indexed citations

18.

Nayeri, Shadi, Mehdi Sargolzaei, & Dan Tulpan. (2019). A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews. 20(1). 31–46. 49 indexed citations

19.

Léger, Serge, et al.. (2019). Pairwise visual comparison of small RNA secondary structures with base pair probabilities. BMC Bioinformatics. 20(1). 293–293. 3 indexed citations

20.

Čuperlović‐Culf, Miroslava, Nandhakishore Rajagopalan, Dan Tulpan, & Michèle C. Loewen. (2016). Metabolomics and Cheminformatics Analysis of Antifungal Function of Plant Metabolites. Metabolites. 6(4). 31–31. 18 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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