Abstract:

The beet armyworm, Spodoptera exigua (Hübner), is a major agricultural pest in China, which frequently breaks out in many parts of China in the last 20 years. To explore the rule of the population dynamics and to establish the forecasting mode of population trends of S. exigua in quantity, the time series analysis and stepwise regression analysis methods were used for studying the law of wide-area (greater range) atmospheric temperature and rainfall influence on outbreak frequency of S. exigua in China. The results indicated that the wide-area temperature and rainfall had a complex effect on the long-term trends and fluctuations of S. exigua outbreak frequency. During 1979-2008, the frequency of the beet armyworm outbreaks in China showed an upward trend wave, and its average annual growth rate of the outbreak index was 0.076, while the widearea temperature of China (taking the 27 city-level observation points as a statistics example) in the years 1990-2008 increased at the average annual incremental rate of 0.039℃, showing a rising frequency of the beet armyworm outbreak with wide-area temperature rising trend. Ten factors, which had a significant effect (P<0.05 or 0.01) on the regression forecasting model and could be used to forecast quantitatively a wide-area outbreak trend index of S. exigua (the forecast accuracy of the simulation model was more than 99%), were screened out from 52 factors (the temperature and rainfall of the monthly and annual average of January to December of the current year and last year). The author believes that this close relationship between the wide-area temperature, rainfall factors and the beet armyworm outbreak trend index is not a mere coincidence, but a necessary consequence of environments (temperature and rainfall) acting on creatures (beet armyworm).

Key words: Spodoptera exigua, forecast model, wide-area temperature, wide-area rainfall, time series analysis, stepwise-regression analysis, trend forecasting