Md S. Islam

1.4k total citations
52 papers, 888 citations indexed

About

Md S. Islam is a scholar working on Plant Science, Biomedical Engineering and Surgery. According to data from OpenAlex, Md S. Islam has authored 52 papers receiving a total of 888 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Plant Science, 15 papers in Biomedical Engineering and 10 papers in Surgery. Recurrent topics in Md S. Islam's work include Sugarcane Cultivation and Processing (34 papers), Rice Cultivation and Yield Improvement (22 papers) and Biofuel production and bioconversion (15 papers). Md S. Islam is often cited by papers focused on Sugarcane Cultivation and Processing (34 papers), Rice Cultivation and Yield Improvement (22 papers) and Biofuel production and bioconversion (15 papers). Md S. Islam collaborates with scholars based in United States, China and Japan. Md S. Islam's co-authors include David D. Fang, Gregory N. Thyssen, Sushma Sood, Johnie N. Jenkins, Jianping Wang, Jack C. Comstock, Xiping Yang, Christopher D. Delhom, Yanhong Dong and Linghe Zeng and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Molecular Sciences and Frontiers in Plant Science.

In The Last Decade

Md S. Islam

48 papers receiving 868 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Md S. Islam United States 17 816 127 125 115 90 52 888
Chen Ru-kai China 17 789 1.0× 248 2.0× 95 0.8× 34 0.3× 19 0.2× 97 898
B. J. Croft Australia 16 674 0.8× 142 1.1× 111 0.9× 19 0.2× 17 0.2× 68 710
A. Selvi India 16 764 0.9× 345 2.7× 262 2.1× 79 0.7× 20 0.2× 35 828
Gabriel Rodrigues Alves Margarido Brazil 17 767 0.9× 166 1.3× 88 0.7× 323 2.8× 103 1.1× 41 948
Florence Paulet France 12 704 0.9× 370 2.9× 173 1.4× 66 0.6× 40 0.4× 25 745
Zuhu Deng China 16 550 0.7× 177 1.4× 113 0.9× 61 0.5× 18 0.2× 54 614
Luciana Rossini Pinto Brazil 19 1.0k 1.3× 449 3.5× 312 2.5× 151 1.3× 30 0.3× 61 1.1k
Jérôme Pauquet France 9 634 0.8× 144 1.1× 68 0.5× 103 0.9× 12 0.1× 12 685
K. M. Oliveira Brazil 13 763 0.9× 346 2.7× 242 1.9× 153 1.3× 40 0.4× 14 818
Rodrigo Gazaffi Brazil 13 506 0.6× 131 1.0× 83 0.7× 116 1.0× 24 0.3× 35 592

Countries citing papers authored by Md S. Islam

Since Specialization
Citations

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

Fields of papers citing papers by Md S. Islam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Md S. Islam. 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 Md S. Islam. The network helps show where Md S. Islam may publish in the future.

Co-authorship network of co-authors of Md S. Islam

This figure shows the co-authorship network connecting the top 25 collaborators of Md S. Islam. A scholar is included among the top collaborators of Md S. Islam 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 Md S. Islam. Md S. Islam 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.
Sood, Sushma, et al.. (2025). ‘CP 16‐1883’ a new sugarcane cultivar released for Florida organic soils. Journal of Plant Registrations. 19(1).
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Sood, Sushma, Miguel Baltazar, R. Wayne Davidson, et al.. (2024). Registration of ‘CP 15‐2516’ sugarcane for organic soils in Florida. Journal of Plant Registrations. 18(2). 329–340. 1 indexed citations
5.
Islam, Md S., et al.. (2024). Registration of ‘CP 15‐1407’ sugarcane for muck soils. Journal of Plant Registrations. 18(2). 341–351. 2 indexed citations
6.
Sood, Sushma, Miguel Baltazar, R. Wayne Davidson, et al.. (2023). Registration of ‘CP 14‐1377’ sugarcane for organic soils in Florida. Journal of Plant Registrations. 17(1). 114–124. 1 indexed citations
7.
Islam, Md S., et al.. (2023). A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane. Frontiers in Plant Science. 14. 1205999–1205999. 11 indexed citations
8.
Islam, Md S., et al.. (2021). Identification of quantitative trait loci (QTL) controlling fibre content of sugarcane (Saccharum hybrids spp.). Plant Breeding. 140(2). 360–366. 4 indexed citations
9.
Zhao, Duli, R. Wayne Davidson, Vanessa S. Gordon, et al.. (2020). Registration of ‘CP 11‐2248’ sugarcane for Florida organic soils. Journal of Plant Registrations. 14(3). 318–327. 4 indexed citations
10.
You, Qian, Xiping Yang, Ze Peng, et al.. (2019). Development of an Axiom Sugarcane100K SNP array for genetic map construction and QTL identification. Theoretical and Applied Genetics. 132(10). 2829–2845. 46 indexed citations
11.
Islam, Md S., David D. Fang, Johnie N. Jenkins, et al.. (2019). Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton. Molecular Genetics and Genomics. 295(1). 67–79. 31 indexed citations
12.
Wubben, Martin J., Gregory N. Thyssen, Franklin E. Callahan, et al.. (2019). A novel variant of Gh_D02G0276 is required for root-knot nematode resistance on chromosome 14 (D02) in Upland cotton. Theoretical and Applied Genetics. 132(5). 1425–1434. 15 indexed citations
13.
Davidson, R. Wayne, Eduardo Osorio-Hernández, Vanessa S. Gordon, et al.. (2019). Registration of ‘CP 08‐1968’ Sugarcane. Journal of Plant Registrations. 13(2). 178–186. 4 indexed citations
14.
Thyssen, Gregory N., Johnie N. Jenkins, Jack C. McCarty, et al.. (2018). Whole genome sequencing of a MAGIC population identified genomic loci and candidate genes for major fiber quality traits in upland cotton (Gossypium hirsutum L.). Theoretical and Applied Genetics. 132(4). 989–999. 57 indexed citations
15.
Yang, Xiping, et al.. (2018). Identifying Quantitative Trait Loci (QTLs) and Developing Diagnostic Markers Linked to Orange Rust Resistance in Sugarcane (Saccharum spp.). Frontiers in Plant Science. 9. 350–350. 56 indexed citations
16.
Bechere, Efrem, David D. Fang, Hirut Kebede, et al.. (2017). Quantitative trait loci analysis for net ginning energy requirements in upland cotton (Gossypium hirsutum L.). Euphytica. 213(7). 3 indexed citations
17.
Islam, Md S., David D. Fang, Gregory N. Thyssen, et al.. (2016). Comparative fiber property and transcriptome analyses reveal key genes potentially related to high fiber strength in cotton (Gossypium hirsutum L.) line MD52ne. BMC Plant Biology. 16(1). 36–36. 50 indexed citations
18.
Islam, Md S., Linghe Zeng, Gregory N. Thyssen, et al.. (2016). Mapping by sequencing in cotton (Gossypium hirsutum) line MD52ne identified candidate genes for fiber strength and its related quality attributes. Theoretical and Applied Genetics. 129(6). 1071–1086. 47 indexed citations
19.
Islam, Md S., Gregory N. Thyssen, Johnie N. Jenkins, et al.. (2016). A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genomics. 17(1). 903–903. 91 indexed citations
20.
Petersen, Stine, Jeanette Lyerly, Anne L. McKendry, et al.. (2016). Validation of Fusarium Head Blight Resistance QTL in US Winter Wheat. Crop Science. 57(1). 1–12. 155 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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