8 September 2017

Great Lakes Restoration Initiative Ag Programs Under Review

Posted by John Freeland

Edge of field pump station lifts and discharges subsurface drain tile water to roadside ditch in Monroe County, Michigan, 2017 (author)..

The Great Lakes Commission (GLC) has received a two-year $750,000 grant to evaluate the effectiveness of money spent on farm conservation programs by the Great Lakes Restoration Initiative (GLRI). In addition to the GLC, Ohio State University and Michigan State University will play a role in the evaluation process.

Earlier this year, the Trump Administration proposed to end funding for the Great Lakes Restoration Initiative and National Sea Grant programs. After hearing strong bipartisan support, Congress has plans to continue funding both for 2018.

According to the GLC press release, since 2010, GLRI has paid out $100 million to farmers as incentives to implement agricultural conservation programs. Are the programs achieving the desired water quality goals?

It will be interesting to see what criteria are used to evaluate the GLRI conservation efforts. For an example, will the the GLC take a value engineering approach? As summarized here:

“Value is the ratio of function to cost. Therefore, lowering cost while maintaining function increases value. Value engineering is a focused, systematic approach used to analyze a system, service or facility to identify the best way to manage essential functions while lowering cost.”

Whatever the approach, the GLC assessment will likely hinge on the quality and availability of data that apply to performance criteria. For example, states and provinces of the Great Lakes region have rules in place for water users to report their water usage each year. The GLC publishes annual reports on usage, including compliance, according to categories of use. In their latest report, Table 1 summarizes reporting compliance rates by jurisdiction. Michigan has the lowest compliance (75%) for both the self-supply livestock and self-supply irrigation categories. Data gaps like these are likely to create uncertainty in the evaluation process.