The soybean cyst nematode is a major pest for soybean production. Other cyst nematodes such as sugar beet cyst nematode, clover cyst nematode and cereal cyst nematode are known to or may be present in fields in North Dakota. These nematodes are very similar to SCN in morphology. Distinction between SCN and these nematodes is difficult and time consuming based on morphology using traditional microscopic methods. Molecular techniques offer new possibilities for distinguishing cyst nematode species. The goals of this research are;
1) To differentiate SCN from sugar beet cyst nematode that is known to occur in ND and other closely related cyst nematodes that may be present in ND.
2) To detect SCN sensitively and directly from DNA extracts of field soils with low SCN densities.
The project we propose here will lead to an effective detection system that is capable of identifying SCN from fields co-infested with sugar beet cyst nematode and improving SCN detection efficiency in soybean fields infested with low level of SCN. This detection system will speed SCN test results to growers and prevent false positive detection from the traditional SCN counting method. Sensitive and accurate detection of SCN is essential for recommending effective measures to control SCN.