Javare Gowda , V. Maheswarappa , L. Krishna Naik

Abstract

Teak defoliator (Hyblaea puera Cramer) is the most devastating insect pest on Teak which causes heavy defoliation in its larval stage. The outbreak of this moth is noticed during June to September. A light trap was installed in Teak plantation to study the effect of various weather parameters. The path analysis of five associated weather parameters indicated that, on the outbreak of moth rainfall had positive direct effect on emergence of male and female moth during 2003-04 and 2004-05 (0.7079 r= 0.7920; 0.7308 r= 0.7904 and 0.4608 r= 0.4342; 0.5555 r= 0.5018, respectively). Indirectly relative humidity in the evening through rainfall influences the outbreak of male moth (0.1947 r= 0.4549) and through maximum temperature it influences emergence of female moth (0.2110 r= 0.4175) during 2003-04. Similarly, relative humidity in the morning through relative humidity in the evening influences the outbreak of male and female moth (0.8190 r= 0.1402; 0.7988 r= 0.1688, respectively) during 2004-05.

Keywords

Teak Defoliator, Hyblaea puera Cramer, Weather Parameters, Path Analysis,
Uttara Kannada District, Karnataka

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V. Maheswarappa , Parasurama Janagiri , S. T. Naik

Abstract

Pink disease (Corticium salmonicolor) of Acacia hybrid was noticed in 5 years old plantations of Hosanagar Division which were raised by Mysore Paper Mill , Bhadravati , Karnataka. An assessment of the disease severity was calculated using 0-3 scale in three plantations of Hosanagar Division. The observed Disease Severity Index (DSI) values were fitted to autoregression method for calculating the disease severity in relation to time. Thus the autoregression model is of the form Y_t+1 = 0.761 Y_t with autocorrelation coefficient R= 0.976. Similarly , the disease severity was predicted using logistic method and the model is of the form Yt =1001 l+e ^5.59+0.16t with coefficient of determination r^∗=0.945 and estimated DSI Y= 5.59+ 0.16x. Since the autocorrelation coefficient value in autoregression method is high (0.967) which is robust method and hence the logistic method is best suited for predicting the disease severity as coefficient of determination r^∗=0.945.

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