Dan Tulpan

2.3k citations
74 papers · 1.2k · 1 hit paper · h-index 19

Impact in

Papers in

    • Effects of Environmental Stressors on Livestock 17
    • Meat and Animal Product Quality 10
    • Animal Nutrition and Physiology 7
    • Genetic and phenotypic traits in livestock 21

Dan Tulpan

66 papers receiving 1.2k citations

Dan Tulpan's Hit Papers

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean 2021 · 159 citations
1590+1+3Years since publication50100150

Peers

Dan Tulpan
Comparison fields: 5 of 127
  • Animal Science and Zoology 241
  • Small Animals 124
  • Plant Science 439
  • Analytical Chemistry 93
  • Agronomy and Crop Science 76
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Citations per field
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Citations per year

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-authors

The 25 scholars most cited alongside Dan Tulpan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Tulpan Line = papers co-authored together Dan Tulpan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean
Hit paper breakdown →
2021159
2 201180
3 202161
4 202057
5 202156
6 201949
7 201849
8 202045
9 202143
10 202137
11 201637
12 200533
13 202232
14 200524
15 202223
16 202223
17 200922
18 201320
19 201618
20 201418

About Dan Tulpan

Dan Tulpan is a scholar working on Animal Science and Zoology, Genetics, Molecular Biology, Small Animals and Plant Science, having authored 74 papers that have together received 1.2k indexed citations. Recurring topics across this work include Genetic and phenotypic traits in livestock (21 papers), Effects of Environmental Stressors on Livestock (17 papers), Animal Behavior and Welfare Studies (14 papers), Meat and Animal Product Quality (10 papers), Animal Nutrition and Physiology (7 papers), Advanced biosensing and bioanalysis techniques (7 papers), Soybean genetics and cultivation (6 papers) and DNA and Biological Computing (6 papers). The work is most often cited by research in Animal Science and Zoology (241 citations), Small Animals (124 citations), Plant Science (439 citations), Analytical Chemistry (93 citations) and Agronomy and Crop Science (76 citations). Dan Tulpan has collaborated with scholars based in Canada, United States and Switzerland. Frequent co-authors include Milad Eskandari, Mohsen Yoosefzadeh-Najafabadi, Serge Léger, Hugh J. Earl, John Sulik, Miroslava Čuperlović‐Culf, Adrian S. Culf, Shadi Nayeri, Mehdi Sargolzaei and J.L. Ellis. Their work appears in journals such as Journal of Animal Science, Journal of Dairy Science, BMC Bioinformatics, Computers and Electronics in Agriculture and animal.

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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