David Pinnow

455 total citations
26 papers, 335 citations indexed

About

David Pinnow is a scholar working on Plant Science, Inorganic Chemistry and Agronomy and Crop Science. According to data from OpenAlex, David Pinnow has authored 26 papers receiving a total of 335 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Plant Science, 6 papers in Inorganic Chemistry and 4 papers in Agronomy and Crop Science. Recurrent topics in David Pinnow's work include Plant Virus Research Studies (12 papers), Peanut Plant Research Studies (11 papers) and Agricultural pest management studies (7 papers). David Pinnow is often cited by papers focused on Plant Virus Research Studies (12 papers), Peanut Plant Research Studies (11 papers) and Agricultural pest management studies (7 papers). David Pinnow collaborates with scholars based in United States, Puerto Rico and South Africa. David Pinnow's co-authors include Brandon Tonnis, Ming Li Wang, G. A. Pederson, Roy N. Pittman, Noelle A. Barkley, C. Corley Holbrook, Charles Y. Chen, A. G. Gillaspie, H. T. Stalker and Paul L. Raymer and has published in prestigious journals such as PLoS ONE, Journal of Agricultural and Food Chemistry and Crop Science.

In The Last Decade

David Pinnow

26 papers receiving 321 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 Pinnow United States 10 273 107 100 35 29 26 335
Surendra S. Manohar India 16 728 2.7× 272 2.5× 166 1.7× 12 0.3× 19 0.7× 37 761
Vinay Sharma India 12 375 1.4× 33 0.3× 82 0.8× 7 0.2× 5 0.2× 38 438
Hongwei Xun China 12 355 1.3× 4 0.0× 215 2.1× 23 0.7× 4 0.1× 23 465
Huiting Luo China 9 96 0.4× 4 0.0× 115 1.1× 20 0.6× 7 0.2× 20 300
A. Kemal Seçkin Türkiye 11 20 0.1× 9 0.1× 96 1.0× 15 0.4× 12 0.4× 27 333
V. Jimeno Spain 8 30 0.1× 25 0.2× 42 0.4× 23 0.7× 22 0.8× 10 450
Hariom Kushwaha India 8 245 0.9× 2 0.0× 235 2.4× 29 0.8× 8 0.3× 11 354
Shanzhi Lin China 13 188 0.7× 3 0.0× 239 2.4× 57 1.6× 82 2.8× 29 361
J.V. Visentainer Brazil 11 51 0.2× 10 0.1× 31 0.3× 11 0.3× 6 0.2× 15 395
Ai͏̈da Jalloul France 11 440 1.6× 2 0.0× 163 1.6× 9 0.3× 7 0.2× 16 491

Countries citing papers authored by David Pinnow

Since Specialization
Citations

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

Fields of papers citing papers by David Pinnow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Pinnow

This figure shows the co-authorship network connecting the top 25 collaborators of David Pinnow. A scholar is included among the top collaborators of David Pinnow 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 Pinnow. David Pinnow 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.
Tonnis, Brandon, Charles Y. Chen, Xianran Li, et al.. (2022). Evaluation of variability in seed coat color, weight, oil content, and fatty acid composition within the entire USDA‐cultivated peanut germplasm collection. Crop Science. 62(6). 2332–2346. 2 indexed citations
2.
Wang, Ming Li, Charles Y. Chen, Brandon Tonnis, et al.. (2018). Changes of Seed Weight, Fatty Acid Composition, and Oil and Protein Contents from Different Peanut FAD2 Genotypes at Different Seed Developmental and Maturation Stages. Journal of Agricultural and Food Chemistry. 66(14). 3658–3665. 30 indexed citations
3.
Bhattarai, Gehendra, Ainong Shi, Jun Qin, et al.. (2017). Association analysis of cowpea mosaic virus (CPMV) resistance in the USDA cowpea germplasm collection. Euphytica. 213(10). 6 indexed citations
4.
Wang, Ming Li, Michael A. Grusak, Charles Y. Chen, et al.. (2016). Seed Protein Percentage and Mineral Concentration Variability and Their Correlation with Other Seed Quality Traits in the U.S. Peanut Mini-Core Collection. Peanut Science. 43(2). 119–125. 8 indexed citations
5.
Wang, Ming Li, Pawan Khera, Manish K. Pandey, et al.. (2015). Genetic Mapping of QTLs Controlling Fatty Acids Provided Insights into the Genetic Control of Fatty Acid Synthesis Pathway in Peanut (Arachis hypogaea L.). PLoS ONE. 10(4). e0119454–e0119454. 70 indexed citations
6.
7.
Wang, Ming Li, Charles Y. Chen, Brandon Tonnis, et al.. (2013). Oil, Fatty Acid, Flavonoid, and Resveratrol Content Variability and FAD2A Functional SNP Genotypes in the U.S. Peanut Mini-Core Collection. Journal of Agricultural and Food Chemistry. 61(11). 2875–2882. 62 indexed citations
8.
Wang, Mingli, Zhanguo Xin, Brandon Tonnis, et al.. (2012). Evaluation of Sweet Sorghum as a Feedstock by Multiple Harvests for Sustainable Bioenergy Production. 2(4). 122–137. 14 indexed citations
9.
Pinnow, David, et al.. (2012). Seed Dormancy Variability in the U.S. Peanut Mini-Core Collection. 5(3). 84–95. 13 indexed citations
10.
11.
Wang, Ming Li, J. Bradley Morris, Brandon Tonnis, et al.. (2011). Screening of the Entire USDA Castor Germplasm Collection for Oil Content and Fatty Acid Composition for Optimum Biodiesel Production. Journal of Agricultural and Food Chemistry. 59(17). 9250–9256. 21 indexed citations
12.
Morris, J. B., et al.. (2010). A survey of the castor oil content, seed weight and seed-coat colour on the United States Department of Agriculture germplasm collection. Plant Genetic Resources. 8(3). 229–231. 18 indexed citations
13.
Pinnow, David, et al.. (2009). Plant Resistance to TSWV and Seed Accumulation of Resveratrol within Peanut Germplasm and its Wild Relatives in the US Collection. Plant Pathology Journal. 8(2). 53–61. 2 indexed citations
14.
Barkley, Noelle A., et al.. (2009). First Report of Tomato spotted wilt virus Infecting African Clover (Trifolium tembense) in Georgia. Plant Disease. 93(2). 202–202. 1 indexed citations
15.
Jarret, Robert L., et al.. (2008). The occurrence and control of pepper mild mottle virus (PMMoV) in the USDA/ARS Capsicum germplasm collection. 20 indexed citations
16.
Pinnow, David, et al.. (2007). Preliminary Screening of Peanut Germplasm in the US Collection for TSWV Resistance and High Flavonoid Content. Plant Pathology Journal. 6(3). 219–226. 6 indexed citations
17.
Gillaspie, A. G., Roy N. Pittman, David Pinnow, & B. G. Cassidy. (2000). Sensitive Method for Testing Peanut Seed Lots for Peanut stripe and Peanut mottle viruses by Immunocapture-Reverse Transcription-Polymerase Chain Reaction. Plant Disease. 84(5). 559–561. 14 indexed citations
18.
Gillaspie, A. G., H. R. Pappu, R. K. Jain, et al.. (1998). Characteristics of a Latent Potyvirus Seedborne in Guar and of Guar Green-Sterile Virus. Plant Disease. 82(7). 765–770. 4 indexed citations
19.
Gillaspie, A. G., M. S. Hopkins, David Pinnow, & R. Jordan. (1998). Characteristics of a Potyvirus of the Bean Yellow Mosaic Virus Subgroup in Sesbania speciosa Germ Plasm. Plant Disease. 82(7). 807–810. 3 indexed citations
20.
Pinnow, David, et al.. (1990). A practical method for the detection of peanut stripe virus in peanut seed.. Europe PMC (PubMed Central). 12–12. 5 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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