Glen L. Ritchie

2.3k total citations
95 papers, 1.6k citations indexed

About

Glen L. Ritchie is a scholar working on Plant Science, Soil Science and Ecology. According to data from OpenAlex, Glen L. Ritchie has authored 95 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Plant Science, 30 papers in Soil Science and 18 papers in Ecology. Recurrent topics in Glen L. Ritchie's work include Research in Cotton Cultivation (46 papers), Irrigation Practices and Water Management (24 papers) and Remote Sensing in Agriculture (18 papers). Glen L. Ritchie is often cited by papers focused on Research in Cotton Cultivation (46 papers), Irrigation Practices and Water Management (24 papers) and Remote Sensing in Agriculture (18 papers). Glen L. Ritchie collaborates with scholars based in United States, Brazil and China. Glen L. Ritchie's co-authors include Craig W. Bednarz, Katie L. Lewis, Sukhbir Singh, James P. Bordovsky, Jared R. Whitaker, Sanjit K. Deb, Nithya Rajan, John Burke, Christopher D. Rock and Amandeep Mittal and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Glen L. Ritchie

85 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Glen L. Ritchie United States	25	1.3k	583	249	237	201	95	1.6k
Clinton C. Shock United States	25	1.2k 0.9×	969 1.7×	171 0.7×	398 1.7×	199 1.0×	112	2.1k
Morio Iijima Japan	26	1.4k 1.1×	554 1.0×	156 0.6×	165 0.7×	425 2.1×	110	1.9k
Robert B. Hutmacher United States	27	1.7k 1.3×	855 1.5×	295 1.2×	336 1.4×	254 1.3×	76	2.5k
S. V. Ngachan India	21	797 0.6×	671 1.2×	187 0.8×	75 0.3×	258 1.3×	95	1.5k
Larry M. York United States	20	2.0k 1.5×	478 0.8×	214 0.9×	168 0.7×	478 2.4×	47	2.4k
Peter Lootens Belgium	24	1.1k 0.8×	391 0.7×	630 2.5×	234 1.0×	169 0.8×	87	1.9k
Tobias Wojciechowski Germany	16	1.9k 1.4×	330 0.6×	127 0.5×	166 0.7×	316 1.6×	28	2.2k
J. R. B. Farias Brazil	27	1.6k 1.2×	329 0.6×	314 1.3×	130 0.5×	214 1.1×	84	1.9k
Anzhen Qin China	21	776 0.6×	447 0.8×	235 0.9×	171 0.7×	492 2.4×	54	1.2k
Antonio Montagnoli Italy	21	782 0.6×	388 0.7×	89 0.4×	340 1.4×	82 0.4×	76	1.3k

Countries citing papers authored by Glen L. Ritchie

Since Specialization

Citations

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

Fields of papers citing papers by Glen L. Ritchie

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Glen L. Ritchie. 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 Glen L. Ritchie. The network helps show where Glen L. Ritchie may publish in the future.

Co-authorship network of co-authors of Glen L. Ritchie

This figure shows the co-authorship network connecting the top 25 collaborators of Glen L. Ritchie. A scholar is included among the top collaborators of Glen L. Ritchie 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 Glen L. Ritchie. Glen L. Ritchie 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

Impa, Somayanda M., et al.. (2025). Assessment of biomass and nutritive value of warm season annual forages, including prussic acid free sorghum. Plant Physiology Reports. 30(4). 749–761.

Guo, Wenxuan, et al.. (2025). Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems. Current Plant Biology. 43. 100519–100519.

Adedeji, Oluwatola, et al.. (2025). Simulating crop yields and water productivity for three cotton-based cropping systems in the Texas High Plains. SHILAP Revista de lepidopterología. 4(2). 83–96. 1 indexed citations

Pabuayon, Isaiah Catalino M., et al.. (2025). Dynamics of source-sink relationships in crops under marginal environments. Current Plant Biology. 44. 100563–100563.

Kafle, Arjun, Sukhbir Singh, Sanjit K. Deb, Catherine Simpson, & Glen L. Ritchie. (2025). Physiology, growth, and yield of sweet corn as affected by growth stage‐based irrigation management and biochar application. Crop Science. 65(2).

Gu, Haibin, et al.. (2024). Water Stress Assessment of Cotton Cultivars Using Unmanned Aerial System Images. Remote Sensing. 16(14). 2609–2609. 5 indexed citations

Lewis, Katie, et al.. (2023). Soil Potassium Effects on Cotton Lint Yield and Fiber Quality on the Texas High Plains. The journal of cotton science/Journal of cotton science. 27(1). 12–27. 1 indexed citations

Ritchie, Glen L., et al.. (2023). CottonSense: A high-throughput field phenotyping system for cotton fruit segmentation and enumeration on edge devices. Computers and Electronics in Agriculture. 216. 108531–108531. 11 indexed citations

Dotray, Peter A., et al.. (2023). Effects of 2,4-D choline on fruiting in sensitive cotton. Weed Technology. 37(4). 352–360. 1 indexed citations

10.

Bordovsky, James P., et al.. (2023). Fruiting patterns impact carbon accumulation dynamics in cotton. Field Crops Research. 295. 108892–108892. 7 indexed citations

11.

Saini, Dinesh Kumar, Somayanda M. Impa, Gunvant Patil, et al.. (2023). High day and night temperatures impact on cotton yield and quality—current status and future research direction. Journal of Cotton Research. 6(1). 21 indexed citations

12.

Dotray, Peter A., et al.. (2020). Dicamba effects on fruiting in sensitive cotton. Weed Technology. 35(1). 43–48. 7 indexed citations

13.

Ulloa, Mauricio, et al.. (2018). Irrigation’s effect and applied selection on the fiber quality of Ethyl MethaneSulfonate (EMS) treated upland cotton (Gossypium hirsutum L.). Journal of Cotton Research. 1(1). 6 indexed citations

14.

Ritchie, Glen L.. (2014). Revisiting the Maturity Paradigm in Cotton. 1 indexed citations

15.

Mittal, Amandeep, Srinivas S. L. Gampala, Glen L. Ritchie, et al.. (2014). Related to ABA‐Insensitive3(ABI3)/Viviparous1 and AtABI5 transcription factor coexpression in cotton enhances drought stress adaptation. Plant Biotechnology Journal. 12(5). 578–589. 91 indexed citations

16.

Ritchie, Glen L., Jared R. Whitaker, & Guy D. Collins. (2011). Effect of sample size on cotton plant mapping analysis and results.. The journal of cotton science/Journal of cotton science. 15(3). 224–232. 12 indexed citations

17.

Ritchie, Glen L., Jared R. Whitaker, Craig W. Bednarz, & James E. Hook. (2009). Subsurface Drip and Overhead Irrigation: A Comparison of Plant Boll Distribution in Upland Cotton. Agronomy Journal. 101(6). 1336–1344. 49 indexed citations

18.

Frantz, Jonathan M., et al.. (2005). (347) Identifying Common Reflectance Properties in Diverse Ornamental Species to Nondestructively Determine Nitrogen Status. HortScience. 40(4). 1060C–1060. 1 indexed citations

19.

Frantz, Jonathan M., et al.. (2004). Exploring the Limits of Crop Productivity: Beyond the Limits of Tipburn in Lettuce. Journal of the American Society for Horticultural Science. 129(3). 331–338. 9 indexed citations

20.

Wright, Dennis L., Glen L. Ritchie, V. Philip Rasmussen, R. Douglas Ramsey, & Doran J. Baker. (2003). Managing Protein in Spring Wheat with Aerial and Satellite Imagery. 2(3). 21. 1 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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