应用马尔科夫链模型对草地螟发生程度的预测

草地螟Loxostege stictialis L.是我国北方农牧业生产上一种重要迁飞性、暴发性害虫,一旦暴发会给当地农牧生产造成严重危害.根据康保县1977-2008年1代草地螟幼虫发生程度的时间序列资料,应用马尔科夫链的转移概率预测法,构建了1～3阶转移概率矩阵,组建模型对该县2009-2011年1代草地螟发生程度进行了预测,结果与大田实际发生情况完全一致,准确率100％.对1980-2011年的历史资料进行回检,历史符合率89.9％,该方法可对草地螟进行长期预报,为草地螟长期预报提供了一种准确有效的方法,对草地螟发生程度的长期预报具有重要指导意义.

Predicting beet webworm occurrence with the Markov chain model

The beet webworm Loxostege stictialis （ L. ） is one of the main migratory pests in North China. Outbreak populations of this pest can seriously damage host crops. On the basis of data on the first generation of beet webworm larva collected from 1977 to 2008, we constructed a matrix of migration probability by applying the Markov chain model and then used this to predict the population of the first generation in 2009 - 2011. By using the transition probability method of Markov chain theory, a transition probability matrix of 1 to 3 steps was constructed based on time series data on the abundance of the pests＇ first generation from 1979 to 1999. This was used to predict the beet webworm abundance in Kangbao city from 2009 to 2011. All predictions were 100% accurate and the results show that the predictive accuracy of the data collected from 1980 to 2011was 89.9%. The predicted population completely conformed to that actually observed in the field.