Project Details:

Molecular Quantification of Soybean Cyst Nematodes in Soil in North Dakota

Parent Project: This is the first year of this project.
Checkoff Organization:North Dakota Soybean Council
Categories:Insects and pests, Crop management systems, Breeding & genetics
Organization Project Code:QSSB
Project Year:2018
Lead Principal Investigator:Guiping Yan (North Dakota State University)
Co-Principal Investigators:

Contributing Organizations

Funding Institutions

Information and Results

Comprehensive project details are posted online for three-years only, and final reports indefinitely. For more information on this project please contact this state soybean organization.

Click a section heading to display its contents.

Final Project Results

Updated July 5, 2018:

View uploaded report Word file


JUNE, 2018

Principal Investigator:
Dr. Guiping Yan, Dept. Plant Pathology, NDSU

The soybean cyst nematode (SCN) continues to be a major threat to soybean production in North Dakota (ND). Other nematodes including sugar beet cyst nematode (SBCN), clover cyst nematode and cereal cyst nematode may occur in ND fields. These nematodes are traditionally differentiated based on morphology. However, distinction between SCN and these nematodes using the traditional method is not only difficult and time-consuming but also requires expertise in nematode taxonomy. The primary goal of this project was to develop a molecular identification and quantification tool for SCN alternative to the traditional method. The specific objectives were to design real-time PCR (qPCR) primers to detect SCN in soil and discriminate it from SBCN and other species, and to develop a qPCR assay to quantify SCN from DNA extracts of field soils.

In this project, we designed qPCR primers (SCNF/SCNR) which showed high specificity to SCN. The specificity of the primers was evaluated using seven isolates of SCN and 31 other nematode species. Varying numbers of SCN eggs or juveniles (0, 1, 4, 16, 64, 256) were inoculated into 0.25 g sterilized soil from which soil DNA was extracted. A standard curve relating threshold cycle and log values of nematode number was generated. The assay was validated by quantifying different SCN numbers artificially added to a sterilized soil.

The validated assay was used to estimate SCN numbers in 34 field soil samples from ND naturally infested with the nematode at varying levels. For each soil sample, 400 g of soil was collected and divided in half for molecular quantification, and traditional SCN extraction and microscopic enumeration. We also designed another primer pair (CLE2F/CLE2R) specific to both SCN and SBCN but are able to separate them simultaneously. Finally, we found that different soil textural classes may have effects on quantification efficiency as soils with more clay content may inhibit qPCR amplification.

The developed molecular assay provides a platform to detect and quantify SCN specifically and directly from DNA extracts of field soils obviating the time-consuming steps of nematode extraction, microscopic identification and counting. The qPCR assay is highly specific to SCN and will improve SCN detection efficiency in soybean fields in ND and help prevent false positive or negative detection results for soil samples submitted by growers. Further, this assay provides a distinction method between SCN and other closely related cyst nematodes for effective SCN management using crop rotation.

Project Years