Ivan Pocrnić

754 total citations
32 papers, 437 citations indexed

About

Ivan Pocrnić is a scholar working on Genetics, Plant Science and Animal Science and Zoology. According to data from OpenAlex, Ivan Pocrnić has authored 32 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Genetics, 12 papers in Plant Science and 6 papers in Animal Science and Zoology. Recurrent topics in Ivan Pocrnić's work include Genetic and phenotypic traits in livestock (29 papers), Genetic Mapping and Diversity in Plants and Animals (25 papers) and Genetics and Plant Breeding (11 papers). Ivan Pocrnić is often cited by papers focused on Genetic and phenotypic traits in livestock (29 papers), Genetic Mapping and Diversity in Plants and Animals (25 papers) and Genetics and Plant Breeding (11 papers). Ivan Pocrnić collaborates with scholars based in United Kingdom, United States and France. Ivan Pocrnić's co-authors include I. Misztal, Daniela Lourenço, Yutaka Masuda, Andrés Legarra, Breno Fragomeni, Gregor Gorjanc, Ole Fredslund Christensen, R. J. C. Cantet, R. Chris Gaynor and Zulma G. Vitezica and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Genetics.

In The Last Decade

Ivan Pocrnić

28 papers receiving 431 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ivan Pocrnić United Kingdom 12 414 219 70 48 39 32 437
Nasir Moghaddar Australia 12 404 1.0× 111 0.5× 54 0.8× 80 1.7× 29 0.7× 34 434
Christian Edel Germany 13 476 1.1× 166 0.8× 61 0.9× 104 2.2× 45 1.2× 31 525
Sebastián Munilla Argentina 10 239 0.6× 84 0.4× 40 0.6× 52 1.1× 36 0.9× 31 293
Sajjad Toghiani United States 11 239 0.6× 73 0.3× 86 1.2× 93 1.9× 17 0.4× 28 299
J Jacques J. J. Colleau France 12 498 1.2× 183 0.8× 121 1.7× 114 2.4× 22 0.6× 44 556
Mahlako L. Makgahlela South Africa 12 419 1.0× 97 0.4× 85 1.2× 149 3.1× 20 0.5× 32 465
Xiangdong Ding China 13 507 1.2× 222 1.0× 59 0.8× 84 1.8× 41 1.1× 24 545
Rafael Lara Tonussi Brazil 11 431 1.0× 82 0.4× 140 2.0× 112 2.3× 42 1.1× 15 467
Hassan Aliloo Australia 10 293 0.7× 75 0.3× 65 0.9× 91 1.9× 17 0.4× 21 314
Bayode O. Makanjuola Canada 8 267 0.6× 76 0.3× 79 1.1× 92 1.9× 14 0.4× 17 327

Countries citing papers authored by Ivan Pocrnić

Since Specialization
Citations

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

Fields of papers citing papers by Ivan Pocrnić

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ivan Pocrnić

This figure shows the co-authorship network connecting the top 25 collaborators of Ivan Pocrnić. A scholar is included among the top collaborators of Ivan Pocrnić 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 Ivan Pocrnić. Ivan Pocrnić 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.
Rochus, Christina M., Colin Price, & Ivan Pocrnić. (2025). Review: Assessing available genetic diversity estimates of rare breeds of livestock. animal. 19(11). 101669–101669.
2.
3.
Pocrnić, Ivan, et al.. (2024). Genomic Characterization of Local Croatian Sheep Breeds-Effective Population Size, Inbreeding & Signatures of Selection. Animals. 14(13). 1928–1928. 9 indexed citations
4.
Zumbach, B., et al.. (2024). Accounting for the nuclear and mito genome in dairy cattle breeding—A simulation study. SHILAP Revista de lepidopterología. 5(6). 572–576. 1 indexed citations
5.
6.
7.
Obšteter, Jana, Janez Jenko, Ivan Pocrnić, & Gregor Gorjanc. (2023). Investigating the benefits and perils of importing genetic material in small cattle breeding programs via simulation. Journal of Dairy Science. 106(8). 5593–5605. 1 indexed citations
8.
Pocrnić, Ivan, Jana Obšteter, R. Chris Gaynor, Anna Wolc, & Gregor Gorjanc. (2023). Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study. Frontiers in Genetics. 14. 1168212–1168212. 9 indexed citations
9.
Obšteter, Jana, et al.. (2023). A method for partitioning trends in genetic mean and variance to understand breeding practices. Genetics Selection Evolution. 55(1). 36–36. 2 indexed citations
10.
Zumbach, B., et al.. (2022). 301. Accounting for nuclear- and mito-genome in genetic evaluation and breeding of dairy cattle. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 1266–1269. 1 indexed citations
11.
Pocrnić, Ivan, et al.. (2022). Optimisation of the core subset for the APY approximation of genomic relationships. Genetics Selection Evolution. 54(1). 76–76. 6 indexed citations
12.
Pocrnić, Ivan, et al.. (2021). Temporal and genomic analysis of additive genetic variance in breeding programmes. Heredity. 128(1). 21–32. 19 indexed citations
13.
Gaynor, R. Chris, et al.. (2021). Novel combination of CRISPR-based gene drives eliminates resistance and localises spread. Scientific Reports. 11(1). 3719–3719. 22 indexed citations
14.
Pocrnić, Ivan, Daniela Lourenço, Yutaka Masuda, & I. Misztal. (2019). Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study. Genetics Selection Evolution. 51(1). 75–75. 31 indexed citations
15.
Legarra, Andrés, Ole Fredslund Christensen, I. Misztal, et al.. (2017). Metafounders are related to F st fixation indices and reduce bias in single-step genomic evaluations. Genetics Selection Evolution. 49(1). 34–34. 61 indexed citations
16.
Pocrnić, Ivan, et al.. (2017). 183 Optimum selection of core animals in the efficient inversion of the genomic relationship matrix. Journal of Animal Science. 95(suppl_4). 90–91. 2 indexed citations
17.
Pocrnić, Ivan, et al.. (2017). Technical note: Impact of pedigree depth on convergence of single-step genomic BLUP in a purebred swine population1. Journal of Animal Science. 95(8). 3391–3395. 12 indexed citations
18.
Pocrnić, Ivan, Daniela Lourenço, Yutaka Masuda, & I. Misztal. (2016). Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species. Genetics Selection Evolution. 48(1). 82–82. 60 indexed citations
19.
Andonov, Sreten, Daniela Lourenço, Breno Fragomeni, et al.. (2016). Accuracy of breeding values in small genotyped populations using different sources of external information—A simulation study. Journal of Dairy Science. 100(1). 395–401. 20 indexed citations
20.
Lourenço, Daniela, S. Tsuruta, Breno Fragomeni, et al.. (2016). 0303 Issues in commercial application of single-step genomic BLUP for genetic evaluation in American Angus. Journal of Animal Science. 94(suppl_5). 144–145. 2 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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