David Ertl

2.3k total citations
20 papers, 1.3k citations indexed

About

David Ertl is a scholar working on Plant Science, Agronomy and Crop Science and Hematology. According to data from OpenAlex, David Ertl has authored 20 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Plant Science, 3 papers in Agronomy and Crop Science and 3 papers in Hematology. Recurrent topics in David Ertl's work include Phytase and its Applications (7 papers), Plant Micronutrient Interactions and Effects (5 papers) and Biodiesel Production and Applications (3 papers). David Ertl is often cited by papers focused on Phytase and its Applications (7 papers), Plant Micronutrient Interactions and Effects (5 papers) and Biodiesel Production and Applications (3 papers). David Ertl collaborates with scholars based in United States, Germany and Austria. David Ertl's co-authors include Victor Raboy, Jinrui Shi, Hongyu Wang, Jan Hazebroek, Robert Meeley, K. A. Young, Andrew T. Bauman, Kevin A. Young, Pushpalatha P. N. Murthy and William F. Sheridan and has published in prestigious journals such as Nature Biotechnology, PLANT PHYSIOLOGY and The Plant Journal.

In The Last Decade

David Ertl

18 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Ertl United States 12 1.2k 297 230 160 135 20 1.3k
Irene Ockenden Canada 7 587 0.5× 128 0.4× 99 0.4× 128 0.8× 74 0.5× 20 665
John A. Dorsch United States 9 729 0.6× 243 0.8× 97 0.4× 187 1.2× 111 0.8× 10 813
U. Konietzny Germany 19 1.7k 1.5× 398 1.3× 944 4.1× 422 2.6× 277 2.1× 31 1.9k
Ameny Farhat Tunisia 17 431 0.4× 56 0.2× 255 1.1× 56 0.3× 63 0.5× 26 618
Xueliang Ren China 14 553 0.5× 55 0.2× 232 1.0× 48 0.3× 44 0.3× 41 681
I. Motzok Canada 15 191 0.2× 116 0.4× 73 0.3× 135 0.8× 42 0.3× 46 631
Magdalena Zielińska‐Dawidziak Poland 13 219 0.2× 74 0.2× 87 0.4× 141 0.9× 15 0.1× 44 563
M. E. Muhrer United States 10 244 0.2× 95 0.3× 56 0.2× 227 1.4× 17 0.1× 20 592
Michela Landoni Italy 21 609 0.5× 34 0.1× 274 1.2× 55 0.3× 28 0.2× 47 892
Dominique Neuville France 10 128 0.1× 125 0.4× 192 0.8× 189 1.2× 12 0.1× 23 453

Countries citing papers authored by David Ertl

Since Specialization
Citations

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

Fields of papers citing papers by David Ertl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Ertl

This figure shows the co-authorship network connecting the top 25 collaborators of David Ertl. A scholar is included among the top collaborators of David Ertl 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 David Ertl. David Ertl 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.
Adak, Alper, Seth C. Murray, Jode W. Edwards, et al.. (2025). Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients. The Plant Genome. 18(3). e70078–e70078. 1 indexed citations
2.
Wallace, Jason G., James C. Schnable, Judith M. Kolkman, et al.. (2023). Yield prediction through integration of genetic, environment, and management data through deep learning. G3 Genes Genomes Genetics. 13(4). 26 indexed citations
3.
Tuggle, Christopher K., Nicole Scott, Jennifer Clarke, et al.. (2023). 175 Building the Tools to Solve the Genome to Phenome Puzzle in Agriculture. Journal of Animal Science. 101(Supplement_2). 25–26.
4.
Tuggle, Christopher K., Nicole Scott, Jennifer Clarke, et al.. (2023). 72 The AG2pi Vision for Resources in Agricultural Genomics and Phenomics: How Asas Can Contribute. Journal of Animal Science. 101(Supplement_3). 50–51.
5.
Ickes, Andrew, et al.. (2013). Blend Ratio Optimization of Fuels Containing Gasoline Blendstock, Ethanol, and Higher Alcohols (C3-C6): Part II - Blend Properties and Target Value Sensitivity. SAE technical papers on CD-ROM/SAE technical paper series. 1. 7 indexed citations
6.
7.
Ickes, Andrew, et al.. (2013). Blend Ratio Optimization of Fuels Containing Gasoline Blendstock, Ethanol, and Higher Alcohols (C3-C6): Part I - Methodology and Scenario Definition. SAE technical papers on CD-ROM/SAE technical paper series. 1. 16 indexed citations
8.
Wallner, Thomas, et al.. (2013). Meeting RFS2 Targets with an E10/E15-like Fuel - Experimental and Analytical Assessment of Higher Alcohols in Multi-component Blends with Gasoline. SAE international journal of fuels and lubricants. 6(3). 691–701. 2 indexed citations
9.
Kaindl, Hermann, et al.. (2011). Robot-Supported Cooperative Work: A Shared-Shopping Scenario. 1–10. 6 indexed citations
10.
Shi, Jinrui, Hongyu Wang, Bailin Li, et al.. (2007). Embryo-specific silencing of a transporter reduces phytic acid content of maize and soybean seeds. Nature Biotechnology. 25(8). 930–937. 241 indexed citations
11.
Shi, Jinrui, et al.. (2005). The maize low‐phytic acid 3 encodes a myo‐inositol kinase that plays a role in phytic acid biosynthesis in developing seeds. The Plant Journal. 42(5). 708–719. 170 indexed citations
12.
Shi, Jinrui, et al.. (2003). The Maize Low-Phytic Acid Mutant lpa2 Is Caused by Mutation in an Inositol Phosphate Kinase Gene. PLANT PHYSIOLOGY. 131(2). 507–515. 185 indexed citations
13.
Veum, T. L., D.R. Ledoux, Victor Raboy, & David Ertl. (2001). Low-phytic acid corn improves nutrient utilization for growing pigs.. Journal of Animal Science. 79(11). 2873–2873. 55 indexed citations
14.
Ledoux, D.R., et al.. (2000). Effects of low phytic acid corn on phosphorus utilization, performance, and bone mineralization in broiler chicks. Poultry Science. 79(10). 1444–1450. 77 indexed citations
15.
Raboy, Victor, Kevin A. Young, Andrew T. Bauman, et al.. (2000). Origin and Seed Phenotype of Maize low phytic acid 1-1 and low phytic acid 2-1 . PLANT PHYSIOLOGY. 124(1). 355–368. 351 indexed citations
16.
Ertl, David, K. A. Young, & Victor Raboy. (1998). Plant Genetic Approaches to Phosphorus Management in Agricultural Production. Journal of Environmental Quality. 27(2). 299–304. 136 indexed citations
17.
Ertl, David & Nick Dale. (1997). The Metabolizable Energy of Waxy vs. Normal Corn for Poultry. The Journal of Applied Poultry Research. 6(4). 432–435. 14 indexed citations
18.
Ertl, David, et al.. (1993). Comparative Trial in Volunteers to Investigate Possible Ethanol-Ranitidine Interaction. Annals of Pharmacotherapy. 27(7-8). 841–845. 10 indexed citations
19.
Fehr, W. R., et al.. (1985). Iron‐Deficiency Chlorosis of Soybean Cultivars Injured by Plant Cutoff and Defoliation1. Crop Science. 25(1). 21–23. 11 indexed citations
20.
Ertl, David & W. R. Fehr. (1985). Agronomic Performance of Soybean Genotypes from Glycine max ✕ Glycine soja Crosses1. Crop Science. 25(4). 589–592. 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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