Weather based forecasting models for prediction of leafhopper population Idioscopus nitidulus Walker; (Hemiptera: Cicadellidae) in mango orchard

Abstract

Predicting the population of Idioscopus nitidulus well in advance can lead to the successful IPM program where a timely intervention and proper management of pest can be scheduled. The present study aimed at determining the effect of abiotic variables on population buildup of leafhoppers in an organic mango orchard to develop weather forecast models for hoppers. Correlation matrix between I. nitidulus and weather parameters was worked out, followed by regression to obtain a comprehensive weather forecast model for the pest. Significant (p= 0.05) correlations were observed in trends of hopper population and between maximum temperature (positive) and relative humidity-I (negative). The simple linear regression explained the highest variability R2= 0.77 and R2= 0.42 with maximum temperature and relative humidity-I respectively. Multiple regression analysis with both maximum temperature and relative humidity-I as independent variables could explain the variability up to 70%. Thus, simple linear regression model derived for maximum temperature had the strongest relationship for the population build-up of hoppers. The best single predictor, maximum temperature is proposed as a reasonable precision indicator suitable for forecasting the changes in population of hoppers that can be used in management decisions.