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Linking Soil Health Assessment to Edge-of-Field Water Quality in the Great Lakes Basin

Overview for Potential Research Collaborators

Molly Meyers, Researcher

University of Wisconsin – Green Bay

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Project overview & team

We’re linking soil health indicators, on-farm management, and edge-of-field water quality on agricultural fields across the Great Lakes Basin.
Multi-institution team: UW–Green Bay, Purdue University, USGS, NRCS, UW–Madison.
Funded by the Great Lakes Restoration Initiative through NRCS.
This presentation provides an overview of the project and how others can use the dataset in collaborative research.

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Why soil health and water quality matter in the Great Lakes

Agricultural nonpoint runoff is a major source of nutrient and sediment loading to the Great Lakes.
Soil health practices (e.g., cover crops, reduced tillage, diversified rotations) are expected to improve soil function and reduce nutrient and sediment losses to downstream waters.
Few datasets track soil health indicators, management, and edge-of-field water quality together over multiple years on working farms.

Source: The Nature Conservancy (TNC)

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Study sites and management practices

Agricultural fields on working farms across the U.S. Great Lakes Basin.
Edge-of-field monitoring has been ongoing since 2016 at multiple sites.
Fields represent common regional systems (e.g., corn–soybean and dairy rotations).
Management includes a range of soil health practices (cover crops, reduced tillage, diversified rotations, manure/fertilizer strategies).

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What’s in the dataset (soils, water, management)

Soils: repeated sampling by depth (e.g., 0–5, 5–15 cm) for chemical, physical, and biological soil health indicators (e.g., SOC, water-extractable C, N, and P, respiration, bulk density, aggregate stability).
Water quality: edge-of-field surface runoff and tile flow (where present) with event-based measurements of nutrients (e.g., TP, ortho-P, TN, NO₃-N) and suspended sediment.
Management: field-level records of crops, tillage, manure and fertilizer applications, and cover crops for each monitored field and year.
Context data: site characteristics (e.g., slope, catchment size) and precipitation data to support cross-site comparisons and modeling.

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How these data can be used

Using process-based models (e.g., APEX, SWAT, other field-scale models) to simulate runoff, nutrient loss, and soil C/N dynamics against observed soils and edge-of-field data.
Applying additional statistical and machine learning approaches to link soil health indicators, management, and edge-of-field losses.
Comparing responses across sites, soil types, and management systems to identify where practices are most effective.
Building on ongoing work to test and refine soil health indicators as predictors of water quality outcomes.

One unique opportunity is on the modeling side: using process-based models such as APEX, SWAT, or other field-scale models to simulate runoff, nutrient losses, and soil carbon and nitrogen dynamics, and to compare those outputs with our observed soils and edge-of-field data.

On the analysis side, we have already begun applying statistical approaches to link soil health indicators, management, and edge-of-field losses, and to evaluate how well different indicators predict water quality outcomes. There is still substantial potential to expand this work, including with more advanced statistical and machine learning methods.

The multi-site design also supports comparisons across soil types, climates, and management systems, which can help identify where particular practices are most effective and under what conditions they perform best.

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Collaboration and data access

Cleaned datasets and metadata for the first several years are available, including soils, edge-of-field water quality, management, and site/context variables.
These data are archived and shared via USGS ScienceBase; additional years and variables that are not yet published may be available through collaboration prior to public release.
We are interested in collaborations on modeling, advanced statistical analyses, indicator development, and cross-site syntheses using these data.
If you are interested in using the dataset or discussing potential collaborations, please contact Molly Meyers (meyersm@uwgb.edu).

Soil data: Meyers, M., Loken, L.C., Fermanich, K.J., Dornbush, M., Gray, M.B., Turco, R., and Komiskey, M.J., 2021,Soil physical, chemical, and biological data from edge-of-field agricultural water quality monitoring sites in Great Lakes States: U.S. Geological Survey data release, https://doi.org/10.5066/P99ETDL5.

Water data: Komiskey, M.J., Stuntebeck, T.D., Loken, L.C., Hood, K.A., Danz, M.E., Rachol, C.M., Toussant, C.A., Dobrowolski, E.G., Kowalczk, A.J., Ennis, R.P., Snarski, S.A., Hardebeck, M.J., Mevis, I.J., and Carvin, R.B., 2021,Nutrient and sediment concentrations, loads, yields, and rainfall characteristics at USGS surface and subsurface-tile edge-of-field agricultural monitoring sites in Great Lakes States (ver. 3.0, November 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P9LO8O70.

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Thank you!

Please reach out if you see a fit between your work and this project.
Contact: Molly Meyers at meyersm@uwgb.edu