关键词: 稻纵卷叶螟, 气象因子, 卡尔曼滤波算法, 候发生量预报模型, 准确率

Abstract: In this paper, the pentad systematic investigation data of C. Medinalis at the four representative plant protection stations of four main rice-growing regions (including the rice-growing region of the south China, the rice-growing region of the southwestern China, the rice-growing region between the Nanling mountains and the Yantze River valley and the rice-growing region between the Yantze River valley and the Huaihe River valley) in China was collected from 1994 to 2014, the key meteorological factors influencing on C. Medinalis’ occurrence amount were screened out and Kalman filter algorithm was used to establish the short-term forecasting models of C. Medinalis’ pentad occurrence amount at the four plant protection stations, including Quanzhou in the Guangxi Zhuang Autonomous Region, Xiushan in Chongqing city, Xiangyin in Hunan province and Zhangjiagang in Jiangsu province in the immigration and damage period of C. Medinalis respectively. Based on the back substitution fittings and forecasting tests of the model, the errors and stability and accuracy rates of the Kalman model were calculated. The results showed as follows: (1) for four stations, the occurrence amount of C. medinalis in the present pentad was significantly and positively correlated (P<0.01) with the C. medinalis’s moth amounts of the preceding pentad and the preceding two pentads in the field respectively. There were significantly positive correlations (P<0.01) between the occurrence amounts of C. medinalis in the present pentad and the minimum air temperature, mean air temperature and maximum air temperature in the preceding pentad. But the pentad occurrence amount was significantly and negatively correlated with the surface pressure in the preceding pentad. (2) The back substitution fitting calculations from the Kalman model on the occurrence amount of C. Medinalis from 1994 to 2011 and the trial forecast tests from 2012 to 2014 showed that the comprehensive mean error (ME) of the occurrence amounts by the Kalman model was -88.63, the mean absolute error (MAE) was 217.72, the comprehensive root mean square error (RMSE) was 605.04, the comprehensive mean accuracies (MA) was 84.33%, and the fitting rate was 83.33%. The Kalman model’s forecasting results were basically consistent with measured values, which indicated that the model could be applied to the prediction of occurrence amount of C. medinalis .

Key words: Cnaphalocrocis medinalis Guenee, Meteorologic elements, Kalman Filter Algorithm, Pentad forecasting model of occurrence amount, Accuracy rate