This paper presents a novel approach to mulberry crop protection. Linear and multiple regression models were fitted to predict and forecast the extent of infestation by leaf roller (Diaphania pulverulentalis H.) on mulberry under the prevailing weather conditions. A systematic survey for periodical recording of Percentage of Pest Incidence (PPI), pest population and crop loss caused by the pest was done for two consecutive years (September 2002 to August 2004), in 68 mulberry plots of Tumkur district (Karnataka). All the three parameters were maximum in winter season. The recorded data served as the basis for regression studies. Optimum regression model indicated the strong influence of maximum temperature, minimum temperature and relative humidity on PPI (R sup(2)= 91.1%) and pest population (R sup(2)= 73.6%). The linear regression implied a strong positive association (R sup(2)= 81%) between pest population and PPI. Both PPI and population density had significant negative correlation with maximum temperature (r= -0.69 and r= -0.89 respectively). Leaf yield loss had significant positive correlation with PPI (r=0.90) and pest population (r=0.89). The forecast model can be used to predict the initiation and red alert1 season of the pest attack. This serves as a scale for the farmers to adopt effective crop protection measures at appropriate time.
This paper presents a novel approach to mulberry crop protection. Linear and multiple regression models were fitted to predict and forecast the extent of infestation by leaf roller (Diaphania pulverulentalis H.) on mulberry under the prevailing weather conditions. A systematic survey for periodical recording of Percentage of Pest Incidence (PPI), pest population and crop loss caused by the pest was done for two consecutive years (September 2002 to August 2004), in 68 mulberry plots of Tumkur district (Karnataka). All the three parameters were maximum in winter season. The recorded data served as the basis for regression studies. Optimum regression model indicated the strong influence of maximum temperature, minimum temperature and relative humidity on PPI (R sup(2)= 91.1%) and pest population (R sup(2)= 73.6%). The linear regression implied a strong positive association (R sup(2)= 81%) between pest population and PPI. Both PPI and population density had significant negative correlation with maximum temperature (r= -0.69 and r= -0.89 respectively). Leaf yield loss had significant positive correlation with PPI (r=0.90) and pest population (r=0.89). The forecast model can be used to predict the initiation and red alert1 season of the pest attack. This serves as a scale for the farmers to adopt effective crop protection measures at appropriate time.
