Key Technology of Cotton Pest Ecological Control and Environment Health in Arid Regions

　　Cooperative Organization: Entomology Institute, Commonwealth Scientific & Industrial Research Organization (CSIRO)

　　Project Period: 2008~2011

　　Main progress results:

　　1. Establishment of a short-term forecast model of the number of cotton aphids in China by introducing and absorbing the technology of forecasting pests from Australia to provide theoretical foundation of forecasting tiny insects and the function relation between intrinsic growth rate of cotton aphids and temperature based on life table of cotton aphids. Establishment of a dynamic forecasting model of aphid population in case of J-shape growth in a short period which forecasts aphid population 2-8 days in advance with an accuracy of 90%. Software system of the model has been developed, promoted and applied in over 80 county-level units of Xinjiang.

　　　2. High temperature in mid-summer is the key factor to control cotton aphid population growth, and critical temperature is above 35 degrees centigrade

　　3. It shows that crop diversity (the proportion of cotton) is in inverse proportion to cotton bollworm population in arid region at landscape scale. Cotton bollworm population reduces significantly with the increase of cotton acreage, which provides scientific basis for naturally attracting cotton bollworms and resistance management of transgenic cotton (shelter).

　　　4. On resistance of Xinjiang cotton bollworms to transgenic cotton: the Ministry of Agriculture forbids planting transgenic cotton in Xinjiang, but it is difficult to be realized . It is determined that resistance of cotton bollworm rises 40 to 80 times by using technology and material provided by Australia. It rises fastest in South Xinjiang and basically has no change in cotton areas of North Xinjiang, which serves as a benchmark for monitoring resistance of cotton bollworm in the region.

　　 Major technique breakthrough

　　Forecasting technique of cotton aphid and cotton bollworm

　　Automatic monitoring of cotton bollworm and digital early warning