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A Bibliometric Analysis and Visualisation of Research Trends in Corn Smut
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| Author:
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KESHAV JINDAL
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| Abstract:
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Corn smut is a fungal infection caused by the fungus “Ustilago maydis”.This fungus can survive in the soil for long period. Corn smut is a plant disease that affects corn yields. This plant disease is severe in hot weather. The common symptoms are the appearance of whitish galls on plants and it’s the main cause for transmission of this disease to other plants. This bibliometric analysis had been conducted to understand the active authors, organizations, journals, and countries involved in the research domain of “corn smut”. All published articles related to “corn smut” from “Scopus”, were analyzed using the VOS viewer to develop analysis tables and visualization maps. This article had set the objective to consolidate the literature regarding the corn smut and also to find out the trends related to the same. Fungal Genetics And Biology is the most active journal with the highest number of publications and Molecular Microbiology is the journal with the highest number of co-authorship links. Plant Cell is the journal with the highest citation. Annual Review Of Phytopathology is the most productive journal in this research domain with the highest average citations. The most active country is the USA and Germany is the most productive country with the highest citations and the highest average citations per publication. Department Of Plant Pathology, University Of Georgia is the most active organization with the highest publications and citations. Environmental and Life Sciences Graduate Program, Trent University is a highly productive organization in this research domain, with the highest co-authorship links and v is the organization with the highest average citation. The most active authors are Gold S.E. and Doehlemann G. with the highest number of publications; citations and co-authorship links respectively. Bottin A. is the most productive author with the highest citation rate.
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| Keyword:
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Corn smut, Bibliometric analysis, VOS viewer, Plant disease
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.SP3.016
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| Download:
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Request For Article
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