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Studies on the Seasonal Incidence of Coconut Eriophyid Mite Aceria guerreronis Keifer (Acari:Eriophyidae)


Affiliations
1 Dept. of Entomology, Tamil Nadu Agricultural University, Coimbatore (T.N.), India
2 Dept. of Entomology, Centre for Plant Molecular Biology & Biotechnology, T.N.A.U., Coimbatore (T.N.), India
     

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The seasonal incidence of coconut eriophyid mite, A. guerreronis was studied during November 2000 to December 2001 in coconut plantations at Kadavasal, Chidambaram, Tamil Nadu. The incidence of mite was found throughout the year. But the peak incidence was observed during dry climate April, May and June and started declining during wet climate July. The correlation between temperature, rainfall and mite population revealed that the population density was positively correlated with temperature and negatively correlated with rainfall. The prediction model has been developed for the given set of parameters.

Keywords

Eriophyid Mite, Seasonal Incidence, Prediction Model.
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  • Studies on the Seasonal Incidence of Coconut Eriophyid Mite Aceria guerreronis Keifer (Acari:Eriophyidae)

Abstract Views: 287  |  PDF Views: 0

Authors

K. Balaji
Dept. of Entomology, Tamil Nadu Agricultural University, Coimbatore (T.N.), India
A. Thanga Hemavathy
Dept. of Entomology, Centre for Plant Molecular Biology & Biotechnology, T.N.A.U., Coimbatore (T.N.), India

Abstract


The seasonal incidence of coconut eriophyid mite, A. guerreronis was studied during November 2000 to December 2001 in coconut plantations at Kadavasal, Chidambaram, Tamil Nadu. The incidence of mite was found throughout the year. But the peak incidence was observed during dry climate April, May and June and started declining during wet climate July. The correlation between temperature, rainfall and mite population revealed that the population density was positively correlated with temperature and negatively correlated with rainfall. The prediction model has been developed for the given set of parameters.

Keywords


Eriophyid Mite, Seasonal Incidence, Prediction Model.