Forecasting Helicoverpa armigera (Lepidoptera: Noctuidae) larval phenology in pigeonpea and chickpea crops using growing degree days

Abstract

Gram pod borer, Helicoverpa armigera is a serious insect pest of pigeonpea and chickpea crops, responsible for huge economic losses. Timely forecasting and subsequent sensible management practices of H. armigera would save the crops from economic damage. In the present study, H. armigera larval incidence data was recorded from sixteen pigeonpea and chickpea growing locations (Maharashtra, India) for three seasons (2015, 2016 and 2017). Observed accumulated GDD (from 40 SMW to 7 SMW) revealed, H. armigera completed one generation in 29 days to develop 4 generations across the locations and seasons. After accumulating 86GDD (40 SMW) and 62 GDD (43 SMW), larval ‘biofix’ (initial incidence of larvae) was started in pigeonpea and chickpea, respectively. Logistic regression model estimated accumulated GDD required by H. armigera larvae to reach ETL in pigeonpea (629 GDD) and chickpea (378 GDD), which was same as observed accumulated GDD. Statistical criteria viz., Adjusted r2, AIC and BIC projected logistic regression model as a better performer in most cases. The geographically unique models developed based on biofix and accumulated GDD in this study can be used for timely advisories and sustainable management of H. armigera in pigeonpea and chickpea crops after field validation.

Keywords

• AIC, Boltzmann, BIC, biofix, logistic, regression