Seasonal time series forewarning model for population dynamics of mango hopper (Hemiptera: Cicadellidae) in humid agro-climatic conditions

Abstract

Mango hopper (Hemiptera: Cicadellidae) is serious and widespread monophagous pests of mango, Mangifera indica L. in tropical and sub-tropical region of India. The present investigation was carried out for weekly data interval of 20 consecutive years (1998–2017) to understand the population dynamics of mango hoppers and developed good fit time series prediction model for better management of hoppers in humid agro-climatic conditions. The relationship between weather parameters and mango hopper population showed that maximum temperature and relative humidity had significant effect on mango hopper population dynamics. Time series seasonal autoregressive integrated moving average (SARIMA) model was fitted from several plausible SARIMA models for forecasting the mango hoppers population. A best-fit SARIMA (1, 0, 2) × (1, 1, 1)52 model within tolerable errors with fitted comparative performance parameters in terms of root mean square error (RMSE), MSE, mean absolute error (MAE) and MA percentage error (MAPE) parameters were observed. Forecasting model develop in this study will predict mango hopper well in advance which can be used for timely better management of hoppers in mango agro-ecosystem.