Diana M. Danforth

510 total citations
25 papers, 372 citations indexed

About

Diana M. Danforth is a scholar working on Plant Science, General Agricultural and Biological Sciences and Economics and Econometrics. According to data from OpenAlex, Diana M. Danforth has authored 25 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Plant Science, 7 papers in General Agricultural and Biological Sciences and 6 papers in Economics and Econometrics. Recurrent topics in Diana M. Danforth's work include Genetically Modified Organisms Research (6 papers), Agricultural Economics and Policy (5 papers) and Research in Cotton Cultivation (3 papers). Diana M. Danforth is often cited by papers focused on Genetically Modified Organisms Research (6 papers), Agricultural Economics and Policy (5 papers) and Research in Cotton Cultivation (3 papers). Diana M. Danforth collaborates with scholars based in United States, South Korea and Norway. Diana M. Danforth's co-authors include Rodolfo M. Nayga, Bruce L. Dixon, Lawton Lanier Nalley, Michael R. Thomsen, James M. Smartt, Pedro A. Alviola, Stephen Devadoss, Jeff Luckstead, Ellen J. Van Loo and Wim Verbeke and has published in prestigious journals such as PLoS ONE, Physica A Statistical Mechanics and its Applications and Food Control.

In The Last Decade

Diana M. Danforth

21 papers receiving 341 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Diana M. Danforth United States	10	188	87	79	78	31	25	372
Michael S. Jones United States	9	138 0.7×	70 0.8×	51 0.6×	19 0.2×	12 0.4×	23	331
P. Ramasundaram India	10	89 0.5×	151 1.7×	39 0.5×	22 0.3×	17 0.5×	32	349
Benjamin M. Onyango United States	15	477 2.5×	102 1.2×	73 0.9×	104 1.3×	51 1.6×	47	745
Fengying Nie China	10	71 0.4×	92 1.1×	49 0.6×	22 0.3×	9 0.3×	41	369
Joseph F. Guenthner United States	12	287 1.5×	65 0.7×	57 0.7×	13 0.2×	27 0.9×	40	487
Shuoli Zhao United States	9	102 0.5×	54 0.6×	15 0.2×	28 0.4×	83 2.7×	31	288
Nirmal Singh India	7	167 0.9×	16 0.2×	56 0.7×	41 0.5×	8 0.3×	22	450
Bert Morrow United States	5	197 1.0×	100 1.1×	32 0.4×	34 0.4×	15 0.5×	6	323
John J. VanSickle United States	11	147 0.8×	99 1.1×	11 0.1×	20 0.3×	30 1.0×	46	378
Guido Ruivenkamp Netherlands	12	119 0.6×	21 0.2×	44 0.6×	18 0.2×	9 0.3×	55	333

Countries citing papers authored by Diana M. Danforth

Since Specialization

Citations

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

Fields of papers citing papers by Diana M. Danforth

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diana M. Danforth. 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 Diana M. Danforth. The network helps show where Diana M. Danforth may publish in the future.

Co-authorship network of co-authors of Diana M. Danforth

This figure shows the co-authorship network connecting the top 25 collaborators of Diana M. Danforth. A scholar is included among the top collaborators of Diana M. Danforth 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 Diana M. Danforth. Diana M. Danforth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Tsiboe, Francis, et al.. (2017). Ghanaian Consumers’ Attitudes toward Cisgenic Rice: Are all Genetically Modified Rice the Same?. Ghana Journal of Development Studies. 14(1). 1–1. 3 indexed citations

Luckstead, Jeff, Stephen Devadoss, & Diana M. Danforth. (2017). The size distributions of all Indian cities. Physica A Statistical Mechanics and its Applications. 474. 237–249. 24 indexed citations

Nalley, Lawton Lanier, Bruce L. Dixon, Diana M. Danforth, et al.. (2015). Revisiting GMOs: Are There Differences in European Consumers’ Acceptance and Valuation for Cisgenically vs Transgenically Bred Rice?. PLoS ONE. 10(5). e0126060–e0126060. 81 indexed citations

Dixon, Bruce L., et al.. (2015). Global rice trade competitiveness: a shift‐share analysis. Agricultural Economics. 46(5). 667–676. 17 indexed citations

Shew, Aaron M., Lawton Lanier Nalley, Diana M. Danforth, et al.. (2015). Are all GMOs the same? Consumer acceptance of cisgenic rice in India. Plant Biotechnology Journal. 14(1). 4–7. 23 indexed citations

Shew, Aaron M., et al.. (2015). Are all GMO’s the same? Consumer acceptance of cisgenic rice in India. AgEcon Search (University of Minnesota, USA). 3 indexed citations

Devadoss, Stephen, Jeff Luckstead, Diana M. Danforth, & Sherzod B. Akhundjanov. (2015). The power law distribution for lower tail cities in India. Physica A Statistical Mechanics and its Applications. 442. 193–196. 23 indexed citations

Nalley, Lawton Lanier, et al.. (2013). A scan level cotton carbon life cycle assessment: has bio-tech reduced the carbon emissions from cotton production in the USA?. The journal of cotton science/Journal of cotton science. 17(2). 80–88. 3 indexed citations

Alviola, Pedro A., Rodolfo M. Nayga, Michael R. Thomsen, Diana M. Danforth, & James M. Smartt. (2013). The effect of fast-food restaurants on childhood obesity: A school level analysis. Economics & Human Biology. 12. 110–119. 73 indexed citations

10.

Dixon, Bruce L., et al.. (2011). Competing risks models of Farm Service Agency seven‐year direct operating loans. Agricultural Finance Review. 71(1). 5–24. 7 indexed citations

11.

Wailes, Eric J., et al.. (2011). DISTRIBUTIONAL IMPACTS OF CAPPING ELIGIBILITY FOR COMMODITY PROGRAM PAYMENTS. AgEcon Search (University of Minnesota, USA). 26(4). 1–5.

12.

Popp, Jennie, et al.. (2010). Comparison of Factors Influencing Salaries of Agricultural Economics Professionals in Academic and Federal Employment. RePEc: Research Papers in Economics. 1 indexed citations

13.

Dixon, Bruce L., et al.. (2010). Determinants of FSA Direct Loan Borrowers' Financial Improvement and Loan Servicing Actions. Journal of agribusiness. 28(2). 131–149.

14.

Dixon, Bruce L., et al.. (2007). FSA Direct Farm Loan Program Graduation Rates and Reasons for Exiting. Journal of Agricultural and Applied Economics. 39(3). 471–487. 2 indexed citations

15.

Ahrendsen, Bruce L., et al.. (2005). Farm Service Agency Direct Farm Loan Program Effectiveness Study. Kagoshima Daigaku Kogakubu Kenkyu Hokoku.

16.

Danforth, Diana M., et al.. (2004). Consequences of square shed following pre-flower infestations of tarnished plant bug (Lygus linneolaris palisot de beauvois) in Arkansas cotton. 2 indexed citations

17.

Tugwell, N. P., et al.. (2000). Monitoring cotton plant growth and response using COTMAN to evaluate effects of chemical control of cotton aphid.. 1217–1220. 1 indexed citations

18.

Tugwell, N. P., et al.. (2000). A square abscission-node growth balance ratio for early-season decisions about cotton plant grow, square shed, plant growth regulators and utility of COTMAN.. 695–696.

19.

Tugwell, N. P., et al.. (2000). COTMAN in cotton research.. 48–54. 3 indexed citations

20.

Mi, Sha, et al.. (1998). Plant-Based Economic Injury Level for Assessing Economic Thresholds in Early-Season Cotton. 11 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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