Jyothi Badri

793 total citations
28 papers, 290 citations indexed

About

Jyothi Badri is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Jyothi Badri has authored 28 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Plant Science, 14 papers in Genetics and 2 papers in Molecular Biology. Recurrent topics in Jyothi Badri's work include Rice Cultivation and Yield Improvement (22 papers), Genetic Mapping and Diversity in Plants and Animals (14 papers) and GABA and Rice Research (10 papers). Jyothi Badri is often cited by papers focused on Rice Cultivation and Yield Improvement (22 papers), Genetic Mapping and Diversity in Plants and Animals (14 papers) and GABA and Rice Research (10 papers). Jyothi Badri collaborates with scholars based in India, Philippines and Mexico. Jyothi Badri's co-authors include G. S. Laha, Divya Balakrishnan, R. M. Sundaram, Arvind Kumar, Malathi Surapaneni, V. Ravindra Babu, D. Subrahmanyam, Sarla Neelamraju, G. Padmavathi and Sukumar Mesapogu and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Jyothi Badri

26 papers receiving 282 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jyothi Badri India 9 266 104 18 11 9 28 290
Vishnu Varthini Nachimuthu India 7 333 1.3× 184 1.8× 29 1.6× 9 0.8× 4 0.4× 11 354
S. Muniswamy India 8 385 1.4× 81 0.8× 54 3.0× 15 1.4× 9 1.0× 38 408
Malathi Surapaneni India 10 355 1.3× 197 1.9× 38 2.1× 10 0.9× 6 0.7× 17 374
Claudia Bedoya United States 5 208 0.8× 157 1.5× 28 1.6× 9 0.8× 3 0.3× 5 247
Paulo Izquierdo United States 8 333 1.3× 58 0.6× 22 1.2× 15 1.4× 2 0.2× 15 348
T. S. Raveendran India 8 287 1.1× 33 0.3× 35 1.9× 4 0.4× 6 0.7× 38 297
C. M. Dußle Germany 9 328 1.2× 112 1.1× 85 4.7× 7 0.6× 20 2.2× 9 347
Olufisayo Kolade Ivory Coast 6 195 0.7× 50 0.5× 47 2.6× 6 0.5× 2 0.2× 11 209
Honghai Yan China 7 157 0.6× 81 0.8× 46 2.6× 6 0.5× 3 0.3× 17 178
Meera Kumari Kar India 10 199 0.7× 49 0.5× 32 1.8× 4 0.4× 8 0.9× 18 214

Countries citing papers authored by Jyothi Badri

Since Specialization
Citations

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

Fields of papers citing papers by Jyothi Badri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jyothi Badri. 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 Jyothi Badri. The network helps show where Jyothi Badri may publish in the future.

Co-authorship network of co-authors of Jyothi Badri

This figure shows the co-authorship network connecting the top 25 collaborators of Jyothi Badri. A scholar is included among the top collaborators of Jyothi Badri 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 Jyothi Badri. Jyothi Badri 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.
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
2.
Badri, Jyothi, et al.. (2024). Mapping Quantitative Trait Loci (QTLs) for Reproductive Stage Salinity Tolerance in Rice. SHILAP Revista de lepidopterología. 4(4). 684–700. 3 indexed citations
3.
4.
Badri, Jyothi, et al.. (2024). Genome-wide association studies for a comprehensive understanding of the genetic architecture of culm strength and yield traits in rice. Frontiers in Plant Science. 14. 1298083–1298083. 3 indexed citations
5.
Balakrishnan, Divya, S. K. Hajira, Puvvada Perraju, et al.. (2023). Molecular and Morphological Characterization of Introgression Lines with Resistance to Bacterial Leaf Blight and Blast in Rice. Plants. 12(16). 3012–3012. 2 indexed citations
6.
Barbadikar, Kalyani M., et al.. (2023). Assessment of Multiple Tolerance Indices to Identify Rice Lines Suitable for the Aerobic System of Cultivation. International Journal of Plant & Soil Science. 35(11). 16–28.
7.
Badri, Jyothi, et al.. (2023). Conventional and new breeding approaches to enhance grain yield in rice. ORYZA- An International Journal on Rice. 60(0). 1–20. 1 indexed citations
8.
Barbadikar, Kalyani M., et al.. (2023). G×E analysis to Identify the Stable High-yielding Rice Lines among a Set of Selected Germplasm Panel. International Journal of Environment and Climate Change. 13(7). 10–23. 1 indexed citations
9.
Sunanda, Tuladhar, Divya Balakrishnan, Pardeep Kumar, et al.. (2023). Low phosphorus tolerance in selected advanced introgression lines derived from wild accession of Oryza nivara. Current Advances in Agricultural Sciences(An International Journal). 15(1). 26–32. 1 indexed citations
10.
11.
Singh, Uma Maheshwar, Shamshad Alam, Challa Venkateshwarlu, et al.. (2021). Marker-assisted forward and backcross breeding for improvement of elite Indian rice variety Naveen for multiple biotic and abiotic stress tolerance. PLoS ONE. 16(9). e0256721–e0256721. 23 indexed citations
12.
Balakrishnan, Divya, et al.. (2020). Genetic analysis of dormancy and shattering traits in the backcross inbred lines derived from Oryza sativa cv. Swarna / O. nivara Ac. CR100008. ORYZA- An International Journal on Rice. 57(1). 1–13. 2 indexed citations
13.
Vemireddy, Lakshminarayana R., Vijaya S.R. Kola, Eswarayya Ramireddy, et al.. (2019). Uncovering of natural allelic variants of key yield contributing genes by targeted resequencing in rice (Oryza sativa L.). Scientific Reports. 9(1). 8192–8192. 6 indexed citations
14.
Sandhu, Nitika, Shalabh Dixit, B. P. Mallikarjuna Swamy, et al.. (2019). Marker Assisted Breeding to Develop Multiple Stress Tolerant Varieties for Flood and Drought Prone Areas. Rice. 12(1). 8–8. 43 indexed citations
15.
Dey, Susmita, et al.. (2019). Current Status of Rice Breeding for Sheath Blight Resistance. International Journal of Current Microbiology and Applied Sciences. 8(2). 163–175. 4 indexed citations
16.
Arra, Yugander, R. M. Sundaram, Kuldeep Singh, et al.. (2018). Incorporation of the novel bacterial blight resistance gene Xa38 into the genetic background of elite rice variety Improved Samba Mahsuri. PLoS ONE. 13(5). e0198260–e0198260. 35 indexed citations
17.
Balakrishnan, Divya, D. Subrahmanyam, Jyothi Badri, et al.. (2016). Genotype × Environment Interactions of Yield Traits in Backcross Introgression Lines Derived from Oryza sativa cv. Swarna/Oryza nivara. Frontiers in Plant Science. 7. 1530–1530. 70 indexed citations
18.
Dey, Susmita, Jyothi Badri, V. Prakasam, et al.. (2016). Identification and agro-morphological characterization of rice genotypes resistant to sheath blight. Australasian Plant Pathology. 45(2). 145–153. 25 indexed citations
19.
Johnson, T. Sudhakar, et al.. (2013). Genetic Improvement of Biofuel Plants: Recent Progress and Patents. PubMed. 7(1). 2–12. 4 indexed citations
20.
Rashmi, Havalli Bommegowda, et al.. (2011). Research and Development Perspectives of Transgenic Cotton: Evidence from Patent Landscape Studies. 8 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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