Panya Sae‐Lim

739 total citations
26 papers, 517 citations indexed

About

Panya Sae‐Lim is a scholar working on Aquatic Science, Genetics and Nature and Landscape Conservation. According to data from OpenAlex, Panya Sae‐Lim has authored 26 papers receiving a total of 517 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Aquatic Science, 18 papers in Genetics and 9 papers in Nature and Landscape Conservation. Recurrent topics in Panya Sae‐Lim's work include Aquaculture Nutrition and Growth (18 papers), Genetic and phenotypic traits in livestock (16 papers) and Fish Ecology and Management Studies (9 papers). Panya Sae‐Lim is often cited by papers focused on Aquaculture Nutrition and Growth (18 papers), Genetic and phenotypic traits in livestock (16 papers) and Fish Ecology and Management Studies (9 papers). Panya Sae‐Lim collaborates with scholars based in Norway, Netherlands and Finland. Panya Sae‐Lim's co-authors include Antti Kause, H.A. Mulder, Hans Komen, Bjarne Gjerde, Hanne Marie Nielsen, Marie Lillehammer, James E. Parsons, Kyle E. Martin, J.A.M. van Arendonk and Ingrid Olesen and has published in prestigious journals such as PLoS ONE, Aquaculture and Journal of Animal Science.

In The Last Decade

Panya Sae‐Lim

24 papers receiving 500 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Panya Sae‐Lim Norway 12 308 257 161 75 68 26 517
Anastasia Bestin France 15 333 1.1× 352 1.4× 116 0.7× 60 0.8× 62 0.9× 25 580
Wagdy Mekkawy Egypt 14 234 0.8× 208 0.8× 97 0.6× 39 0.5× 68 1.0× 41 510
Cheryl Quinton Canada 14 345 1.1× 270 1.1× 190 1.2× 66 0.9× 119 1.8× 27 583
Kyle E. Martin United States 13 282 0.9× 467 1.8× 123 0.8× 50 0.7× 63 0.9× 23 625
Mahmoud Rezk Netherlands 13 534 1.7× 266 1.0× 250 1.6× 76 1.0× 138 2.0× 22 706
Tuija Paananen Finland 11 501 1.6× 329 1.3× 333 2.1× 48 0.6× 63 0.9× 16 667
Dimitrios Chatziplis Greece 14 157 0.5× 347 1.4× 59 0.4× 27 0.4× 54 0.8× 40 480
María E. López Chile 15 248 0.8× 467 1.8× 110 0.7× 51 0.7× 103 1.5× 26 667
Ricardo Pérez-Enríquez Mexico 17 373 1.2× 441 1.7× 224 1.4× 203 2.7× 221 3.3× 58 893
M.N. Siti Azizah Malaysia 12 379 1.2× 95 0.4× 221 1.4× 84 1.1× 205 3.0× 23 617

Countries citing papers authored by Panya Sae‐Lim

Since Specialization
Citations

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

Fields of papers citing papers by Panya Sae‐Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Panya Sae‐Lim. 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 Panya Sae‐Lim. The network helps show where Panya Sae‐Lim may publish in the future.

Co-authorship network of co-authors of Panya Sae‐Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Panya Sae‐Lim. A scholar is included among the top collaborators of Panya Sae‐Lim 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 Panya Sae‐Lim. Panya Sae‐Lim 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
2.
Sae‐Lim, Panya, et al.. (2025). Genomic prediction accuracy of growth in Atlantic salmon (Salmo salar) when genotype-by-environment interaction is present. Aquaculture. 603. 742391–742391. 1 indexed citations
3.
Sae‐Lim, Panya, et al.. (2024). Duration, day, and hour postmortem influence the color of farmed Atlantic salmon: Insights into phenotypic measurements. Aquaculture. 596. 741739–741739. 1 indexed citations
4.
Sae‐Lim, Panya, et al.. (2024). Genome-wide association identifies genomic regions influencing fillet color in Northwest Atlantic salmon (Salmo salar Linnaeus 1758). Frontiers in Genetics. 15. 1402927–1402927. 1 indexed citations
5.
Tang, Sureerat, et al.. (2024). Genetic diversity, heritability, and estimated breeding values for growth of domesticated Asian seabass Lates calcarifer from Thailand. Aquaculture and Fisheries. 10(4). 576–585. 4 indexed citations
7.
Sae‐Lim, Panya, Hooi Ling Khaw, Hanne Marie Nielsen, et al.. (2019). Genetic variance for uniformity of body weight in lumpfish (Cyclopterus lumpus) used a double hierarchical generalized linear model. Aquaculture. 514. 734515–734515. 4 indexed citations
8.
Sae‐Lim, Panya, Jette Jakobsen, & H.A. Mulder. (2018). Uniformity in birth weight is heritable in Norwegian White Sheep. Socio-Environmental Systems Modeling. 278. 1 indexed citations
9.
Sae‐Lim, Panya, et al.. (2018). Effects of tank color on the growth, stress responses, and skin color of snakeskin gourami (Trichogaster pectoralis). Aquaculture International. 26(2). 659–672. 22 indexed citations
10.
Sae‐Lim, Panya, Antti Kause, H.A. Mulder, & Ingrid Olesen. (2017). BREEDING AND GENETICS SYMPOSIUM: Climate change and selective breeding in aquaculture1. Journal of Animal Science. 95(4). 1801–1812. 41 indexed citations
11.
Sae‐Lim, Panya, et al.. (2017). A comparison of nonlinear mixed models and response to selection of tick-infestation on lambs. PLoS ONE. 12(3). e0172711–e0172711. 12 indexed citations
12.
Sae‐Lim, Panya, Antti Kause, Marie Lillehammer, & H.A. Mulder. (2017). Estimation of breeding values for uniformity of growth in Atlantic salmon (Salmo salar) using pedigree relationships or single-step genomic evaluation. Genetics Selection Evolution. 49(1). 33–33. 32 indexed citations
13.
Sae‐Lim, Panya, Antti Kause, Matti Janhunen, et al.. (2015). Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments. Genetics Selection Evolution. 47(1). 46–46. 48 indexed citations
14.
Bangera, Rama, Hanne Marie Nielsen, Panya Sae‐Lim, et al.. (2015). Genotype by Environment Interaction for Growth in Atlantic Cod (Gadus morhua L.) in Four Farms of Norway. Journal of Marine Science and Engineering. 3(2). 412–427. 11 indexed citations
15.
Sae‐Lim, Panya & Piter Bijma. (2015). Comparison of designs for estimating genetic parameters and obtaining response to selection for social interaction traits in aquaculture. Aquaculture. 451. 330–339. 5 indexed citations
16.
Sae‐Lim, Panya, H.A. Mulder, Bjarne Gjerde, et al.. (2015). Genetics of Growth Reaction Norms in Farmed Rainbow Trout. PLoS ONE. 10(8). e0135133–e0135133. 15 indexed citations
17.
Komen, Hans, et al.. (2014). Genetic Analysis of Shape in Trout, using image analysis. Socio-Environmental Systems Modeling. 2 indexed citations
18.
Sae‐Lim, Panya, Hans Komen, Antti Kause, & H.A. Mulder. (2014). Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models. Genetics Selection Evolution. 46(1). 16–16. 29 indexed citations
19.
Sae‐Lim, Panya, Antti Kause, H.A. Mulder, et al.. (2013). Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study1. Journal of Animal Science. 91(12). 5572–5581. 59 indexed citations
20.
Sae‐Lim, Panya, Hans Komen, Antti Kause, et al.. (2012). Enhancing selective breeding for growth, slaughter traits and overall survival in rainbow trout (Oncorhynchus mykiss). Aquaculture. 372-375. 89–96. 24 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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