Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Changes in the antioxidant enzyme efficacy in two high yielding genotypes of mulberry (Morus alba L.) under NaCl salinity
This map shows the geographic impact of C. Sudhakar'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 C. Sudhakar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Sudhakar more than expected).
This network shows the impact of papers produced by C. Sudhakar. 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 C. Sudhakar. The network helps show where C. Sudhakar may publish in the future.
Co-authorship network of co-authors of C. Sudhakar
This figure shows the co-authorship network connecting the top 25 collaborators of C. Sudhakar.
A scholar is included among the top collaborators of C. Sudhakar 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 C. Sudhakar. C. Sudhakar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kiranmai, K., et al.. (2018). Multigenic Groundnut Transgenics: An Advantage Over TraditionalSingle Gene Traits in Conferring Abiotic Stress Tolerance: A Review. 7(2). 113–120.2 indexed citations
8.
Sudhakar, C., et al.. (2017). Reduction of Wild boar (Sus scrofa L.) damage in Maize (Zea mays L.) by using Castor (Ricinus communis L.) as barrier. Journal of Entomology and Zoology Studies. 5(6). 426–428.3 indexed citations
9.
Ranganayakulu, Gogineni, C. Sudhakar, & Palakolanu Sudhakar Reddy. (2015). EFFECT OF WATER STRESS ON PROLINE METABOLISM AND LEAF RELATIVE WATER CONTENT IN TWO HIGH YIELDING GENOTYPES OF GROUNDNUT (Arachis hypogaea L.) WITH CONTRASTING DROUGHT TOLERANCE. Journal of Experimental Biology and Agricultural Sciences. 3(1). 97–103.4 indexed citations
10.
Sudhakar, C., et al.. (2015). Traditional management methods used to minimize wild boar (Sus scrofa) damage in different agricultural crops at Telangana state, India. International Journal of Multidisciplinary Research and Development. 2(2). 32–36.12 indexed citations
11.
Sudhakar, S., et al.. (2015). Inhibitory Effect of Prosopis Juliflora on Plant and Human Pathogens. International Journal of Advanced Science and Engineering. 1(3). 11761–11766.1 indexed citations
12.
Sudhakar, C., et al.. (2014). Development of intron-flanking EST-specific primers from drought responsive expressed sequence tags (ESTs) in safflower.. SABRAO Journal of Breeding and Genetics. 46(1). 56–66.1 indexed citations
Kiranmai, K., et al.. (2013). Role of Plant Fatty acid Elongase (3 keto acyl-CoA Synthase) gene in Cuticular Wax Biosynthesis. 2(4). 35–42.10 indexed citations
Reddy, P. S., C. Sudhakar, & K. Veeranjaneyulu. (1990). Water stress induced changes in enzymes of nitrogen metabolism in horsegram, Macrotyloma uniflorum (Lam), seedlings.. Indian Journal of Experimental Biology. 28(3). 273–276.6 indexed citations
20.
Reddy, P. S., et al.. (1990). Photosynthetic CO2 assimilation and carbohydrate status during water stress in cowpea.. Indian Journal of Experimental Biology. 28(4). 346–348.2 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.