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Research Article Open access CC BY 4.0

Epidemiology of Necrosis Virus Disease of Cotton Caused by Tobacco Streak Virus

G. Basavaraja, M. R. Govindappa, Aswathanarayana, D. S, Palaiah, P, J. M. Nidagundi, Manjunatha, N, Anandkumar, V

International Journal of Environment and Climate Change · pp. 170–179 · Published 6 Nov 2025

10.9734/ijecc/2025/v15i115105

Abstract

Cotton (Gossypium spp. L.), a vital natural fibre and cash crop, significantly contributes to India’s economy but it is increasingly threatened by tobacco streak virus (TSV)-induced necrosis, a major emerging viral disease in southern India. The present study on the Epidemiology of necrosis virus disease of cotton caused by tobacco streak virus (TSV) was conducted during the Kharif season of 2024-25 at the Agricultural Research Station, Hagari, Ballari, Karnataka, using the susceptible cotton hybrid RCH 659. The study aimed to determine the influence of weather parameters and thrips population on the development of cotton necrosis disease. The onset of disease was observed during the 32nd standard meteorological week (SMW) (6-12 August) with 6.00 per cent incidence and reached its peak of 54.00 per cent during the 38th SMW (17-23 September), coinciding with favourable weather conditions and high thrips activity. Correlation analysis revealed a highly significant positive relationship between disease incidence, sunshine hours (0.551) and thrips population (0.889). Maximum temperature showed a positive but non-significant correlation (0.256), while minimum temperature, rainfall and relative humidity (morning and evening) exhibited weak, non-significant relationships. The multiple regression analysis (0.860) confirmed that thrips population had the greatest positive influence on disease incidence, followed by morning relative humidity and rainfall. These results indicate that thrips abundance, together with conducive weather conditions during August and September, played a decisive role in the spread and intensity of cotton necrosis disease. The findings contribute to a better understanding of the disease epidemiology and can assist in forecasting and developing effective management strategies.

Cotton necrosis disease correlation disease incidence epidemiology tobacco streak virus thrips weather parameters

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