Sushanta Kumar Dash

499 total citations
33 papers, 323 citations indexed

About

Sushanta Kumar Dash is a scholar working on Plant Science, Genetics and Ecology. According to data from OpenAlex, Sushanta Kumar Dash has authored 33 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Plant Science, 16 papers in Genetics and 4 papers in Ecology. Recurrent topics in Sushanta Kumar Dash's work include Rice Cultivation and Yield Improvement (22 papers), Genetic Mapping and Diversity in Plants and Animals (16 papers) and GABA and Rice Research (12 papers). Sushanta Kumar Dash is often cited by papers focused on Rice Cultivation and Yield Improvement (22 papers), Genetic Mapping and Diversity in Plants and Animals (16 papers) and GABA and Rice Research (12 papers). Sushanta Kumar Dash collaborates with scholars based in India, South Korea and United Kingdom. Sushanta Kumar Dash's co-authors include Lambodar Behera, Binay Panda, Birendra Prasad Shaw, Ravindra Donde, Ekamber Kariali, Pravat Kumar Mohapatra, Padmini Swain, Jitendra Kumar, Khirod Kumar Sahoo and Gayatri Gouda and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Sushanta Kumar Dash

31 papers receiving 315 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sushanta Kumar Dash India 11 277 89 37 22 20 33 323
Carlos Maldonado Chile 12 240 0.9× 167 1.9× 51 1.4× 20 0.9× 19 0.9× 33 363
Balpreet K. Dhatt United States 11 295 1.1× 96 1.1× 65 1.8× 27 1.2× 16 0.8× 16 326
Konstantinos N. Blazakis Greece 6 248 0.9× 73 0.8× 53 1.4× 23 1.0× 54 2.7× 8 292
Chandrapal Vishwakarma India 7 249 0.9× 33 0.4× 30 0.8× 17 0.8× 38 1.9× 13 289
Cécile Brabant Switzerland 7 243 0.9× 58 0.7× 45 1.2× 16 0.7× 6 0.3× 10 270
Minsu Kim South Korea 10 240 0.9× 34 0.4× 37 1.0× 10 0.5× 32 1.6× 21 283
Benedict C. Oyiga Germany 10 415 1.5× 112 1.3× 42 1.1× 9 0.4× 12 0.6× 16 453
Rosario Jimenez Philippines 6 339 1.2× 55 0.6× 41 1.1× 8 0.4× 36 1.8× 7 392
P. Senguttuvel India 12 267 1.0× 85 1.0× 25 0.7× 7 0.3× 5 0.3× 50 301
Afolabi Agbona Nigeria 10 191 0.7× 48 0.5× 7 0.2× 14 0.6× 17 0.8× 19 244

Countries citing papers authored by Sushanta Kumar Dash

Since Specialization
Citations

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

Fields of papers citing papers by Sushanta Kumar Dash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sushanta Kumar Dash. 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 Sushanta Kumar Dash. The network helps show where Sushanta Kumar Dash may publish in the future.

Co-authorship network of co-authors of Sushanta Kumar Dash

This figure shows the co-authorship network connecting the top 25 collaborators of Sushanta Kumar Dash. A scholar is included among the top collaborators of Sushanta Kumar Dash 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 Sushanta Kumar Dash. Sushanta Kumar Dash 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.
Dash, Sushanta Kumar, et al.. (2025). Predicting the Severity of Pulmonary Disease from Respiratory Sounds using ML Algorithms. 1750–1755. 1 indexed citations
2.
Barik, Saumya Ranjan, Elssa Pandit, Lambodar Behera, et al.. (2025). Transfer of deeper rooting and phosphorus uptake QTL into the popular rice variety ‘maudamani’ via marker-assisted backcross breeding. Scientific Reports. 15(1). 25418–25418. 1 indexed citations
3.
Anilkumar, C., Asit Kumar Pradhan, R. Beena, et al.. (2024). Genome‐Wide Association Study Revealed the Genetics of Seed Vigour Traits in Rice (Oryza sativa L.). Plant Breeding. 144(1). 122–133. 1 indexed citations
4.
Dash, Sushanta Kumar, et al.. (2023). Exploring the physiological efficiencies of promising rice (Oryza sativa) accessions for increasing grain yield. SHILAP Revista de lepidopterología. 93(11). 1180–1185.
5.
Mohapatra, S., Saumya Ranjan Barik, Prasanta K. Dash, et al.. (2023). Molecular Breeding for Incorporation of Submergence Tolerance and Durable Bacterial Blight Resistance into the Popular Rice Variety ‘Ranidhan’. Biomolecules. 13(2). 198–198. 9 indexed citations
6.
Raju, Dhandapani, Sudhir Kumar, Chandrapal Vishwakarma, et al.. (2023). Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice. Agriculture. 13(4). 852–852. 16 indexed citations
7.
Samal, Kailash Chandra, et al.. (2023). Exploring the physiological basis of yield enhancement in New Generation Rice (NGR): a comparative assessment with non-NGR rice genotypes. Plant Physiology Reports. 28(4). 543–555. 2 indexed citations
8.
9.
Chakraborti, Mridul, Tapan Kumar Mondal, Soham Ray, et al.. (2021). The core set of sequence-tagged microsatellite sites markers between halophytic wild rice Oryza coarctata and Oryza sativa complex. Euphytica. 217(4). 1 indexed citations
10.
Panigrahy, Madhusmita, Ekamber Kariali, Sushanta Kumar Dash, et al.. (2021). MicroRNAs modulate ethylene induced retrograde signal for rice endosperm starch biosynthesis by default expression of transcriptome. Scientific Reports. 11(1). 5573–5573. 11 indexed citations
11.
Anandan, A., et al.. (2021). Aerobic dry direct-seeded rice A sustainable approach in rice cultivation. Indian Farming. 71(4). 1 indexed citations
12.
13.
Gouda, Gayatri, Manoj Gupta, Ravindra Donde, et al.. (2020). Characterization of haplotypes and single nucleotide polymorphisms associated with Gn1a for high grain number formation in rice plant. Genomics. 112(3). 2647–2657. 11 indexed citations
14.
Donde, Ravindra, S. Mohapatra, Somnath Roy, et al.. (2020). Identification of QTLs for high grain yield and component traits in new plant types of rice. PLoS ONE. 15(7). e0227785–e0227785. 22 indexed citations
15.
16.
Donde, Ravindra, et al.. (2018). Study of genetic diversity and effectiveness of traits for direct selection under drought as well as non-stress condition for Rainfed upland rice. Journal of Pharmacognosy and Phytochemistry. 7(3). 2060–2067. 2 indexed citations
17.
Das, Kaushik, Binay Panda, Birendra Prasad Shaw, et al.. (2018). Grain density and its impact on grain filling characteristic of rice: mechanistic testing of the concept in genetically related cultivars. Scientific Reports. 8(1). 4149–4149. 16 indexed citations
18.
Dash, Sushanta Kumar, et al.. (2017). Effect of drought on morpho-physiological, yield and yield traits of chromosome segment substitution lines (CSSLs) derived from wild species of rice.. ORYZA- An International Journal on Rice. 54(1). 65–72. 3 indexed citations
19.
Das, Bappa, Rabi Narayan Sahoo, Gopal Krishna, et al.. (2017). Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 192. 41–51. 51 indexed citations
20.
Behera, Lambodar, S. Mohanty, Sharat Kumar Pradhan, et al.. (2013). Assessment of genetic diversity of rainfed lowland rice genotypes using microsatellite markers. Indian Journal of Genetics and Plant Breeding (The). 73(2). 142–142. 6 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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