Trends in Manure Sample Data


Most manure book values used today from the MidWest Plan Service (MWPS) and American Society of Agricultural and Biological Engineers (ASABE) were derived from manure samples prior to 2003. To update these manure test values, the University of Minnesota in partnership with the Minnesota Supercomputing Institute, is working to build a dynamic manure test database called ManureDB. During this database construction, the University of Minnesota collected manure data spanning the last decade from five labs across the country. Trends, similarities, and challenges arose when comparing these samples. Having current manure test numbers will assist in more accurate nutrient management planning, manure storage design, manure land application, and serve agricultural modeling purposes.

What Did We Do?

We recruited five laboratories for this preliminary study who shared some of their manure sample data between 2012-2021, which represented over 100,000 manure samples. We looked at what species, manure types (liquid/solid), labels, and units we had to work with between the datasets to make them comparable. Once all the samples were converted into either pounds of nutrient/ton for solid manure or pounds of nutrient/1000 gallons for liquid manure, we took the medians of total nitrogen, ammonium-nitrogen (NH4-N), phosphate (P2O5), and potassium oxide (K2O) analyses from those samples and compared them to the MWPS and ASABE manure nutrient values.

What Have We Learned?

There is no standardization of laboratory submission forms for manure samples. The majority of samples have minimal descriptions beyond species of animal and little is known about storage types. With that said, we can still detect some general NPK trends for the beef, dairy, swine, poultry manure collected from the five laboratories in the last decade, compared to the published book values. For liquid manure, the K2O levels generally increased in both the swine and poultry liquid manure samples. For the solid swine manure and solid beef manure, total N, P2O5, and K2O levels all increased compared to the published book values. The solid dairy manure increased in P2O5 and K2O levels, and the solid poultry manure increased in total N and K2O. See Figure 1 for the general trends in liquid and solid manure for swine, dairy, beef, and poultry.

Table 1. Manure sample trends 2012-2021 compared to MWPS/ASABE manure book values. (+) = trending higher, (o) = no change/conflicting samples, (-) = trending lower

Liquid Total N NH4N P2O5 K2O
Swine o o +
Dairy o o
Beef o o o o
Poultry o + +
Solid Total N NH4N P2O5 K2O
Swine + o + +
Dairy o o + +
Beef + + +
Poultry + o o +

Future Plans

The initial data gives us a framework to standardize fields for the future incoming samples (location, manure type, agitation, species, bedding, storage type, and analytical method) along with creating a unit conversion mechanism for data uploads. We plan to recruit more laboratories to participate in the ManureDB project and acquire more sample datasets. We will compare and analyze this data as it becomes available, especially more detailed data for each species. We will be designing ManureDB with statistical and data visualization features for future public use.


Nancy L. Bohl Bormann, Graduate Research Assistant, University of Minnesota

Corresponding author email address

Additional authors

Melissa L. Wilson, Assistant Professor, University of Minnesota

Erin L. Cortus, Associate Professor and Extension Engineer, 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 website: (coming soon!)

Twitter: @ManureProf, @nlbb

Lab websites:


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, the University of Minnesota College of Food, Agricultural and Natural Resource Sciences, and the Minnesota Supercomputing Institute.


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