Help Us Beta Test the New COMMONS Compost Planner Tool
April 2, 2026

As Point Blue Conservation Science explores climate resilient management for working landscapes, we are excited to share a new partnership focused on grassland compost application. The collaborative project entitled COMMONS, for Changing Organic Matter Management Of National Soils, has produced a spatial planning tool to help guide compost application to maximize soil health and carbon storage benefits, called the COMMONS Compost Planner. The online Compost Planner (commons-planner.com) is now launching as a pilot project in Marin and Sonoma Counties .
In early 2025, our Point Blue Conservation Science team joined a decades-long effort building on foundational modeling work from Dr. Christopher Potter of Casa Systems 2100, LLC and tool development from Kevin Brown and the Axios Software team. Today, Point Blue manages the project, provides scientific support, and hosts the Planner.
The project began as local Marin County Land Steward John Wick sought ways to improve grassland bird habitat and vegetation structure. After inviting UC Berkeley researchers to quantify compost impacts, the team found that a single-time compost application boosted plant productivity through improved soil health (1). With visibly more native grass and more carbon locked away in the soil via increased photosynthesis each year, researchers quantified that compost benefits also can include climate mitigation potential (2). Research is now ongoing to quantify ecosystem benefits throughout the state of California to expand on modeled climate mitigation potential (3).
How the COMMONS Compost Planner Works
The COMMONS Compost Planner fits into the California regulatory and legislative landscape to encourage compost application on grasslands. Designed primarily with regional land planners in mind who manage up to 500,000 acres, the tool assists in planning by demonstrating where compost is appropriate and most effective to maximize its benefits including carbon accrual and soil water holding capacity. Dr. Potter designed the tool to use remote sensing spatial layers to determine appropriate lands for compost application (4), currently applying four primary filters:
- Land cover type to identify suitable grasslands,
- Soil Productivity Index to find soils with ‘room for improvement’, (slightly degraded, with chemistry leading to higher potential for carbon accrual),
- Biomass level to determine where plant productivity would most benefit from compost, and
- Baseline soil organic carbon map to evaluate the potential benefits from initial conditions.
We look forward to further scientific validation of the Planner estimates first in Marin and Sonoma Counties and will be gathering feedback to expand the filter options and geographies.

We invite you to test the Marin and Sonoma County beta version of our tool at commons-planner.com and provide feedback here. The tool functions best if you follow the entire process from start to finish, then clear your browsing history before starting a new set of parcels. Please forward this blog post to others in your network to co-create a planning tool that works best for our community and our ecosystems.
Questions or longer feedback? Contact us at commons-planner@pointblue.org.
References
[1] Ryals, R., Hartman, M. D., Parton, W. J., DeLonge, M. S., & Silver, W. L. (2016). Grassland compost amendments increase plant production without changing plant communities. Ecosphere, 7(3), e01270. https://doi.org/10.1002/ecs2.1270.
[2] Ryals, R., & Silver, W. L. (2013). Effects of organic matter amendments on net primary productivity and greenhouse gas emissions in annual grasslands. Ecological Applications, 23(1), 46–59. https://doi.org/10.1890/12-0620.
[3] Mayer, A., & Silver, W. L. (2022). The climate change mitigation potential of annual grasslands under future climates. Ecological Applications, e2705. https://doi.org/10.1002/eap.2705.
[4] Potter, C., & Wick, J. (2025). Planning Studies for Compost Application to California Rangelands Using Landsat Satellite Imagery, Carbon Modeling, and Machine Learning. Natural Resources. https://doi.org/10.4236/nr.2025.162002.