An Integrated Bio meteorological Forecasting Method for the Occurrence Level of Locust Around the Bohai Sea of China

Locust is an important loss causing pest around the Bohai Sea of China, and its occurrence degree is close related with weather and climate conditions. In order to study the relationship objectively, four typical locust areas around the Bohai Sea are chosen as experimental areas, which are coast, reservoir, depression shallow lake and water logging areas, respectively. Based on the observed climate and locust data from 1980 to 2008, Spearman order correlation method is used to analyze the meteorological factors affecting the occurrence degree of locust. The results show that meteorological factors have accumulated effects on locust. The temperature in July and August has a significant effect on summer locust of the next year in four locust areas, and higher temperature is more favorable for locust growth and reproduction. October is the key time for the egg life of autumn locust in three areas (reservoir, depression shallow lake and water logging areas), and sufficient rainfall is favorable for the development of locust egg, increasing the occurrence degree of summer locust in the next year. In addition, air temperature in winter and spring and rainfall affect the extent of summer locust in four areas before locust eggs hatch and come out of soil. The historical modeled accuracy are 81%—93% and 78%—89% by the Euclidean distance model modified with the weights of meteorological factors and biological model based on locust bio characteristics for predicting the locust extent, respectively. The extended forecast result of last two years is fairly accurate, i.e., one level difference for one area of the former model and one level difference for two areas of the latter model, and correct in all other areas. Then a comprehensive model is established by integrating the meteorological and biological models to forecast the locust occurrence extent and its historical modeled accuracy are 85%—96%. There is only one level difference in one area for the two years extended forecast, showing that the accuracy of integrated model is better than the single models.