Regional Optimization of Roadside Turfgrass Seed Mixtures Phase 3: Assessing Long-Term Performance and Creating a Web-Based Decision Tool

Principal Investigator(s):

Eric Watkins, Professor, Horticultural Science


Project summary:

Current Minnesota Department of Transportation (MnDOT) specifications for roadside turfgrasses suggest statewide planting of mixtures grouped into five broad categories such as low-maintenance turf and high-maintenance turf. In Phase 1 of this project, researchers identified turfgrass cultivars that performed well when subjected to the stresses of heat, ice cover, and salt. Then, in Phase 2, the research team used the results from the first phase to develop a series of mixtures and monocultures that were seeded and assessed at 14 roadside sites across Minnesota. Also, using construction input quantity and cost data from MnDOT, researchers developed a budget tool that can predict the total cost for roadside projects so that decision makers can make informed decisions about roadside turfgrass establishment projects. The new prediction tool requires only about 15 inputs from the user, and the average prediction accuracy of these models is 95.4 percent. Field results from Phase 2 showed that increasing the number of species in a mixture improved turf performance and reduced weed invasion. The research team also determined that MnDOT should consider the creation of three separate turfgrass roadside mixture recommendations based on location and soil quality. In Phase 3, researchers will take advantage of the existing field sites to collect longer-term data across Minnesota to strengthen our findings from Phase 2 and ensure the best possible roadside turfgrass mixture recommendations for public stakeholders. Finally, the budget tool developed in Phase 3 will be augmented with new data, including field trial results, and developed into a web-based tool easily accessible by users.

Project details:

  • Project number: 2023007
  • Start date: 08/2022
  • Project status: Active
  • Research area: Environment and Energy
  • Topics: Data and modeling, Environment