Purpose
Most manure composition data summaries available in the U.S. are outdated because genetics, feed rations, manure handling, and housing practices have evolved over the past two decades. This means that the community that uses this manure data, such as farmers developing manure management plans, engineers designing manure storages, state and federal regulators establishing best management practices for manure land application, or researchers modeling nutrient cycling and gas emissions, is using outdated information. Thousands of manure samples, however, are analyzed every year by university and commercial labs across the country and could provide an up-to-date source of information. Until recently, there has been no mechanism for combining and summarizing this valuable data in a way that makes the results accessible to the broader community of users. Together with the Minnesota Supercomputing Institute (MSI) and the Minnesota Department of Agriculture (MDA) – who runs the only manure analysis proficiency program in the United States – researchers at the University of Minnesota are developing a national database for manure test results. The database, or ManureDB, will meet FAIR principles (Findable, Accessible, Interoperable, and Reusable) to ensure the data is shared and used by a wide audience.
What Did We Do?
The project team brought together a stakeholder group involved with manure management, regulation, lab analysis, and research to help us develop standards and best practices for data management. The stakeholder team helped inform the creation of several deliverables to date, including a schema and framework for the database, as well as a data use agreement template. The MSI is currently working on the development of the public-facing website that will interface with the database as well as a data cleaning tool to help standardize the data as it is uploaded.
What Have We Learned?
The stakeholder group identified that data privacy is a top priority. Customer data (i.e., name and address) will be removed, though state and zip codes will remain with the data (full zip codes will not be shared publicly). We also found that there is a stark difference between what data the full stakeholder team would like to see (i.e., manure data for livestock facilities by county or watershed code for different livestock species and manure storage types) versus what commercial laboratories collect (i.e. livestock species and sometimes the address of the livestock facility, but more often the address of the person requesting the tests). Standardizing manure submission forms in the future will potentially help ensure that information collected for each sample is consistent. Future educational efforts for those advising farmers on manure testing will be needed to ensure the forms are filled out accurately instead of being left blank.
Future Plans
This project is ongoing. We are in the process of working with our current participating labs to sign data use agreements and then to clean and upload data. New labs will be recruited throughout the project period. A public-facing dashboard will be created to search through aggregate data. We are working with our stakeholder groups to design websites for other potential use cases, including a site to download cleaned data for research purposes and potentially a site for labs to be able to benchmark their samples against labs from within and outside of their regions.
Authors
Melissa L. Wilson, Assistant Professor and Extension Specialist, University of Minnesota
Corresponding author email address
mlw@umn.edu
Additional authors
Erin L. Cortus, Associate Professor and Extension Engineer, University of Minnesota
Nancy L. Bohl Bormann, Graduate Research Assistant, University of Minnesota
Kevin Janni, Extension Engineer, University of Minnesota
Larry Gunderson, Pesticide & Fertilizer Management, Minnesota Department of Agriculture
Tom Prather, Senior Software Developer, University of Minnesota
Kevin Silverstein, Scientific Lead RIS Informatics Analyst, University of Minnesota
Additional Information
Manuredb.umn.edu (coming soon)
Acknowledgements
This work is supported by the AFRI Foundational and Applied Science Program [grant no. 2020-67021-32465] from the USDA National Institute of Food and Agriculture. We’d also like to thank our stakeholders for their time commitment.
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