R. M. Sundaram

4.9k total citations
151 papers, 2.7k citations indexed

About

R. M. Sundaram is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, R. M. Sundaram has authored 151 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 136 papers in Plant Science, 41 papers in Genetics and 18 papers in Molecular Biology. Recurrent topics in R. M. Sundaram's work include Rice Cultivation and Yield Improvement (64 papers), Genetic Mapping and Diversity in Plants and Animals (41 papers) and Plant-Microbe Interactions and Immunity (37 papers). R. M. Sundaram is often cited by papers focused on Rice Cultivation and Yield Improvement (64 papers), Genetic Mapping and Diversity in Plants and Animals (41 papers) and Plant-Microbe Interactions and Immunity (37 papers). R. M. Sundaram collaborates with scholars based in India, Philippines and Croatia. R. M. Sundaram's co-authors include S. M. Balachandran, N. Shobha Rani, G. S. Laha, M. Sheshu Madhav, Ramesh V. Sonti, C. N. Neeraja, B. C. Viraktamath, Sunil Biradar, Gajjala Ashok Reddy and K. Sakthivel and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

R. M. Sundaram

134 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R. M. Sundaram India 28 2.4k 666 584 149 119 151 2.7k
Ramil Mauleon Philippines 28 3.1k 1.3× 1.2k 1.8× 1.0k 1.7× 116 0.8× 68 0.6× 70 3.5k
Apichart Vanavichit Thailand 28 1.8k 0.7× 543 0.8× 460 0.8× 102 0.7× 178 1.5× 100 2.1k
Nai‐Qian Dong China 14 1.8k 0.8× 525 0.8× 1.1k 1.8× 77 0.5× 57 0.5× 17 2.3k
Stefano Pavan Italy 27 2.1k 0.9× 323 0.5× 618 1.1× 199 1.3× 184 1.5× 65 2.6k
Haiying Zhang China 28 2.1k 0.9× 858 1.3× 1.2k 2.1× 107 0.7× 51 0.4× 91 2.6k
Hiroyuki Fukuoka Japan 33 2.8k 1.2× 538 0.8× 1.5k 2.5× 151 1.0× 68 0.6× 82 3.3k
Kishor Gaikwad India 28 2.4k 1.0× 582 0.9× 945 1.6× 138 0.9× 63 0.5× 130 2.8k
Concetta Lotti Italy 26 1.5k 0.6× 349 0.5× 377 0.6× 108 0.7× 144 1.2× 56 1.8k
Ainong Shi United States 28 2.4k 1.0× 315 0.5× 320 0.5× 146 1.0× 73 0.6× 157 2.6k
Abdul Rahim Harun Malaysia 22 2.0k 0.8× 505 0.8× 611 1.0× 92 0.6× 42 0.4× 85 2.2k

Countries citing papers authored by R. M. Sundaram

Since Specialization
Citations

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

Fields of papers citing papers by R. M. Sundaram

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R. M. Sundaram

This figure shows the co-authorship network connecting the top 25 collaborators of R. M. Sundaram. A scholar is included among the top collaborators of R. M. Sundaram 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 R. M. Sundaram. R. M. Sundaram 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.
Salman, Mehmet Coşkun, Haritha Bollinedi, Anjali Anand, et al.. (2025). In vitro glycemic profiling of rice: A dual-index approach using predictive glycemic index and inherent glycemic potential. Journal of Food Composition and Analysis. 140. 107229–107229.
2.
Barbadikar, Kalyani M., Satendra K. Mangrauthia, D. Subrahmanyam, et al.. (2025). Integrated omics analysis of rice gene expression profiles and microRNAs identifies crucial target genes for nitrogen use efficiency. Plant Physiology Reports. 30(2). 379–399. 1 indexed citations
3.
Vijayakumar, S, et al.. (2025). Perennial rice – An alternative to the ‘one-sow, one-harvest’ rice production: Benefits, challenges, and future prospects. SHILAP Revista de lepidopterología. 3(2). 100137–100137. 1 indexed citations
4.
Srivastava, Akanksha, Amber Gupta, Sujit Kumar Bishi, et al.. (2025). Tolerance of Oryza sativa to low phosphate is associated with adaptive changes in root architecture and metabolic exudates. Plant Science. 353. 112415–112415. 1 indexed citations
5.
Neeraja, C. N., et al.. (2025). Mapping Genomic Regions for Grain Protein Content and Quality Traits in Milled Rice (Oryza sativa L.). Plants. 14(6). 905–905. 2 indexed citations
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Kumar, Aman, et al.. (2025). Comparative metabolites analysis of resistant, susceptible and wild rice species in response to bacterial blight disease. BMC Plant Biology. 25(1). 178–178. 2 indexed citations
9.
Solanki, Manish, V. Prakasam, C. N. Neeraja, et al.. (2024). Identification and expression analysis of long non-coding RNAs of rice induced during interaction with Rhizoctonia solani. Physiological and Molecular Plant Pathology. 134. 102389–102389. 1 indexed citations
10.
Pullakhandam, Raghu, et al.. (2024). RP-HPLC-RID analysis of InsP6 to InsP3 in Indian cereals, legumes, and their products: A comparative evaluation of PRP-1 Vs C18 column. Journal of Chromatography B. 1245. 124271–124271. 2 indexed citations
11.
Shah, Priya S., Kalyani M. Barbadikar, Tejas C. Bosamia, et al.. (2023). Long non-coding RNA-mediated epigenetic response for abiotic stress tolerance in plants. Plant Physiology and Biochemistry. 206. 108165–108165. 8 indexed citations
12.
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Rathod, Santosha, et al.. (2023). Characterization of heterogeneity in popular rice landrace through field and molecular evaluation. Field Crops Research. 304. 109181–109181. 2 indexed citations
14.
Bandumula, Nirmala, et al.. (2022). An Economic Evaluation of Improved Rice Production Technology in Telangana State, India. Agriculture. 12(9). 1387–1387. 7 indexed citations
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Sundaram, R. M., et al.. (2022). Deciphering combining ability for yield and its components under saline environment in rice (Oryza sativa L.). Electronic Journal of Plant Breeding. 13(2). 690–696. 1 indexed citations
17.
Mangrauthia, Satendra K., Sridev Mohapatra, Channakeshavaiah Chikkaputtaiah, et al.. (2021). Genetic, Epigenetic, Genomic and Microbial Approaches to Enhance Salt Tolerance of Plants: A Comprehensive Review. Biology. 10(12). 1255–1255. 24 indexed citations
18.
Mallikarjuna, G. B., R. M. Sundaram, G. S. Laha, et al.. (2018). Expression Profile of Defense Genes in Rice Lines Pyramided with Resistance Genes Against Bacterial Blight, Fungal Blast and Insect Gall Midge. Rice. 11(1). 40–40. 14 indexed citations
19.
Senguttuvel, P., G. Padmavathi, R. M. Sundaram, et al.. (2016). Identification and quantification of salinity tolerance through salt stress indices and variability studies in rice (Oryza sativa L.). SABRAO Journal of Breeding and Genetics. 48(2). 172–179. 4 indexed citations
20.
Prasad, M. S., M. Sheshu Madhav, G. S. Laha, et al.. (2012). Introgression of Blast Resistance Gene Pi-kh into Elite indica Rice Variety Improved Samba Mahsuri. Indian journal of plant protection. 40(1). 52–56. 4 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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