Evaluate the population development of green leafhopper as a tungro vector in the endemic area from long-term data

Abstract

Abstract The epidemiological aspect including monitoring is a critical component of implementing an early warning system. The tungro epidemic in rice is influenced by the population dynamics of the green leafhopper vector. Likewise, rainfall and biotic factors affect the abundance of green leafhopper vectors. Long-term data are essential to explain that mechanism, so population predictions on a seasonal scale can be obtained accurately. This paper analyses weekly data on catching green leafhoppers and other arthropods in a continuous cropping system from 2013-2019. We utilize the climatic data gathered to assess the impact of climatic conditions on population dynamics. To improve the additive and homoscedasticity of the time series, we transform the data. The data are fitted in a linear model. The results showed that the green leafhopper population affected the incidence of tungro in the field. From January to December, the population dynamics of the green leafhopper can be characterized. It can assist in monthly population forecasting for the following years. Exogenous and endogenous variables, however, still need to be studied more comprehensively to identify any other predictive components that should be incorporated.