Population forecasting model of Nilaparvata lugens and Sogatella furcifera (Homoptera: Delphacidae) based on Markov chain theory.

ABSTRACT

Nilaparvata lugens (Stål) and Sogatella furcifera (Horváth) are the two most important long-distance migratory insect pests that cause great yield losses to rice in China. Accurate long-term population forecast is needed to implement effective management strategies for these two rice pests. In this paper, a transition probability matrix of 5-yr steps of Markov chain theory was constructed based on 31-yr light-trapping data of the two pests from 1977 to 2007 in Jiangkou County, Guizhou, China. The weight of each step for the transition probability matrix was calculated according to its prediction accuracy. Insect occurrence levels in the sixth year were predicted based on the occurrences of the previous 5 yr. Nonparametric Wilcoxon paired sample tests showed that there were no significant differences between the actual and predicted occurrences for both N. lugens and S. furcifera. In addition, the models accurately forecasted field occurrence in 2008 in Jinangkou County for both species. The results showed that the Markov models developed in this study offer an effective method for long-term population forecasting of N. lugens and S. furcifera and thus provide plant protection agencies and organizations with valuable information in implementing appropriate management strategies for these two devastating rice pests in Jiangkou and neighboring areas.