Development of weather based prediction model for leaf roller population of Sesame in Bundelkhand zone of Madhya Pradesh

Abstract

Field experiment was conducted during twelve consecutive Kharif seasons from 1995 to 2006 at Zonal Agricultural Research Station, Tikamgarh to find out the impact of weather parameters on the incidence and activity of Antigastra catalaunalis (Dupnocbel) in Sesame cv JT-7. The analysis revealed that the pest activity started to buildup from 30^th standard meteorological week and remained up to 40^th standard meteorological weeks (SMW). Larval population has been correlated with weather data and correlation coefficient, regression equations were worked out for development of weather based prediction model. Significant positive correlation with maximum and mean temperature (maximum, minimum) and negative relationships with rainfall was observed. Best fitted polynomial models were developed using the whole season data which explained 60 to 90 per cent variability due to weather parameters. The multiple regression technique has been used for developing predictive model using larval population and weather data not only for the corresponding week but also for preceding weeks. The prediction model for leaf roller explained 88% variability of the pest population. The model predicted peak larval population was in good agreement with observed peak larval population.