The North Central Soybean Research Program, a collaboration of 12 state soybean associations, invests soybean check-off funds to improve yields and profitability via university research and extension. Visit Site

View the current 2018 NCSRP-funded research projects and progress reports.


Tue - April 3, 2018
Variety selection is the most important management practice for producers with chlorosis-prone soils. No soybean variety is immune to chlorosis, but large differences occur between the most tolerant and most susceptible varieties.

With checkoff funding provided by the NCSRP, a group of soybean geneticists and plant breeders from four states have identified the genomic regions in soybean associated with IDC tolerance. With this new understanding, they have been able to develop new, highly accurate genetic markers that breeders are now using to select for IDC-tolerant varieties.  MORE
Thu - March 15, 2018
After a 20-year hiatus, the SCN Coalition is back encouraging soybean farmers to “Take the test. Beat the pest.” Like the predecessor, the new SCN Coalition is a public/checkoff/private partnership formed to help the agricultural industry speak with one voice about soybean cyst nematode management.  MORE
Thu - March 8, 2018
The effects of cropping system diversification — encompassing both crop rotations and organic soil amendments — on the incidence of sudden death syndrome (SDS) and soybean yield under field conditions was assessed in a 6-year study.

The diversification of the soybean-corn rotation with oat, and clover or alfalfa, in conjunction with the use of composted manure amendments, greatly suppressed SDS development and protected soybean yield. This study provides the strongest evidence to date that diversified cropping systems offer another approach for SDS management.   MORE
Mon - February 26, 2018
Newer statistical methods were used to develop a model that predicts the probability of apothecia of S. sclerotiorum being present in soybean fields during the R1-R3 flowering period. The model uses site-specific, remotely-accessible weather data, and other variables such as crop development stage and canopy closure. In the 2016 and 2017 growing seasons, we found that the model predicted the presence of apothecia with 80% accuracy.

A smart-phone application is currently being developed that utilizes this model for farmers’ use.  MORE