Cause Analysis Of Epidemic And Forecasting Methods On Maize Dwarf Mosaic

Maize dwarf mosaic (MDM) is one of the most important corn diseases in the world. Maize dwarf mosaic was very severe in China in 1990s and caused epidemics in some provinces such as ShanXi, GanSu, ShanDong, HeBei. And it reduced a lot of yield. Based on historical materials and our investigations, epidemic causes of MDM in China were analyzed using failure analysis method. Some biological properties of this disease were researched in laboratory and field. Maize dwarf mosaic was predicted using growth model forecasting method, Markov chains forecasting method and regression model forecasting method. And yield loss of MDM was assessed. The main results were as follows:1. In 1990s, the main factor of maize dwarf mosaic epidemics in China was variety resistance. In addition, favorable meteorologic conditions and aphid quantity were main causes of maize dwarf mosaic epidemics.2. Using seedling symptom method, 26277 maize seedlings were investigated from 338 maize treatments. There were 53 treatments carrying virus and its ratio is 15.68%. The rates of seeds carrying SCMV-MDB were between 1.04%-20.00% with an average rate 2.64%. Detection of SCMV-MDB in single com seed was made using dot-ELISA and detection system was built. Using dot-ELISA and the methods of hybridization, genetic research result validated the previous research result that pollen of maize could not transmit the pathogen of MDM.3. Based on historical materials and our investigation data, forecasting system of maize dwarf mosaic was built primarily. Growth model forecasting method could be used to make short-term and metaphase forecasting of MDM for spring corn after growing to certain phase and to make short-term and metaphase forecasting of MDM for summer corn. Regression forecasting model could be used to make metaphase forecasting of MDM. Markov chains forecasting method could make probability prediction of MDM and could be a reference to long-term developing trend forecasting of this disease.4. Monopoint forecasting models were built primarily not only between maize yield components and disease grades, but also between loss rate of maize yield components and disease grades. Yield components of same inbred line showed various differences between different disease grades. Furthermore, yield components also showed great differences between inbred lines in same disease grade.