Study On Regions Of Prevention And Control Division And Forecast Of The Main Camellia Oleifera Pests And Diseases

Camellia oleifera pests and diseases being as a kind of biological disaster occurring frequently are an important influence constrainting factor for the production and the economic value of C. oleifera. Hunan province has the biggest Camellia area and Camellia yield in China. At the same time, Camellia pests and diseases are very common for Hunan province. In order to reduce the loss because of the damage of Camellia pests and diseases and realize the regionalization and scientific prevention of Camellia pests and diseases, and then to take measures to local conditions. Through the stepwise regression, the major climatic factors affecting occurred area of main Camellia pests and diseases were screened out, then the fuzzy clustering analysis and statistics analysis were used to divide the prevention and control division and dangerous classes of the main Camellia pests and diseases in Hunan. Finally, forecast models of climatic factors and occurred area of main Camellia pests and diseases were established by multiple linear regression. The forecasting accuracy of models was tested. The main conclusions were as follows:(1) Research on the relationship between major climatic factors and occurred area of main Camellia pests and diseases. Based on the analysis of climatic factors and occurred area of main Camellia pests and diseases, it was shown that, the average relative humidity of March and April, the number of rainy days of March and April, the effective accumulative temperature with the mount≥10℃, the average relative humidity of May and June, the average temperature of July and Autumn, the average relative humidity of July and Autumn, the frost season were the relative important climatic factors affecting occurred area of main Camellia pests and diseases.(2) Research on the the prevention and control division and dangerous classes of the main Camellia pests and diseases.25 counties in Hunan province were classified as 3 regions including normal, occasional and none plague areas and 3 dangerous classes including class I, class II, class III. Colletotrichum gloeosporioides: normal plague areas have 6 class I and 1 class II; occasional plague areas have 2 class I,6 class II and 1 class III, none plague areas have 4 class II and 4 class III. Agaricodochium camelliae:normal plague areas have 1 class I and 4 class II; occasional plague areas have 2 class II and 4 class III, none plague areas have 14 class III. Neocapnodium sp:normal plague areas have 4 class II and 4 class III; occasional plague areas have 7 class II and 4 class III, none plague areas have 7 class III. Biston marginata:there is no normal plague area and just has occasional and none plague areas; occasional plague areas have 2 class I and 5 class II, none plague areas have 18 class III. Curcrulio chinensis:Changnin is the only normal plague area and belong to classⅠ; occasional plague areas have 4 class II and 2 class III, none plague areas have 18 classⅢ.(3) Research on the application and forecast models of occurred area of main Camellia pests and diseases. The forecasting model of Colletotrichum gloeosporioides was Y=-2161.033-235.9642,-117.70322-41.62623+5.831Z4-3.013 Z5+307.47926+63.523Z7+2.66528+20.02529, the average forecasting accuracy of model was 93.27%, The forecasting model of Agaricodochium camelliae was Y=-2161.033-235.964Z1-117.703Z2-41.626Z3+5.831Z4-3.01325+307.47926+63.523 Z7+2.66528+20.02529, the average forecasting accuracy of model was 89.82%, The forecasting model of Neocapnodium sp. was Y=-2161.033-235.964Z1-117.703Z2-41.62623+5.831Z4-3.01325+307.47926+63.523Z7+2.66528+20.02529, the average forecasting accuracy of model was 90.67%, The forecasting model of Biston marginata was Y=-2161.033-235.964Z1-117.70322-41.62623+5.831Z4-3.01325+ 307.479Z6+63.523Z7+2.66528+20.025Z9, the average forecasting accuracy of model was 95.6%, The forecasting model of Curcrulio chinensis was Y=-2161.033-235.964Z1-117.703Z2-41.62623+5.831Z4-3.013Z5+307.479Z6+63.523Z7+2.66528+ 20.025Z9, the average forecasting accuracy of model was 94.8%.