Abstract

                                Studies were conducted to develop weather based forewarning model for the major pests of high altitude rice viz., green leaf hopper (GLH), brown plant hopper (BPH), leaf folder, yellow stem borer and ear head bug. Three field trials were conducted at Hybrid Rice Evaluation Centre, Tamil Nadu Agricultural University, Gudalur, The Nilgiris, Tamil Nadu during Kharif 2010 to 2012. Results revealed that the population of GLH and BPH started from first and second week of September, respectively and leaf folder and yellow stem borer damage started from third week of September and continued till harvest (December) of the crop. Ear head bug population was noticed during first week of October coinciding flowering of the crop with a peak during December first week and persised up to the harvest. The results of correlation and multiple regression analysis revealed that the population of GLH, BPH, ear head bug, leaf folder and stem borer damage had exerted a significant positive association with maximum temperature and significant negative association with morning relative humidity and rainfall. However, the influence of minimum temperature and evening relative humidity was not significant and 80, 81, 92, 67 and 76 per cent of variation in the population of GLH, BPH, ear head bug, leaf folder damage and stem borer damage, respectively was influenced by weather parameters. The multiple regression equation fitted with weather parameters to predict the GLH population is Y = -2.28 + 0.416 X1 - 0.043 X3 – 0.018 X5, BPH population is Y = 62.73 + 0.28 X1 – 0.67 X3 – 0.010 X5, leaf folder damage is Y = 248.23 – 1.22 X2 – 2.35 X3 + 0.04 X5, yellow stem borer damage is Y = 29.09 + 0.32 X1 -0.36 X3 – 0.002 X5, ear headbug population is Y = 26.64 – 0.253 X2 – 0.234 X3 – 0.005 X5. where, X1 - maximum temperature (oC), X2 - minimum temperature (oC), X3 - morining relative humidity (%), X4 - Evening relative humidity (%) and, X5 - rainfall (mm).

Key words : Rainfed rice, Seasonal incidence, Weather parameters, Forewarning model, Rice pests