This webinar highlights ManureDB, a database of manure samples informing “book values”. Having current manure test numbers will assist in more accurate nutrient management planning, manure storage design, manure land application, and serve agricultural modeling purposes. This presentation was originally broadcast on June 17, 2022. Continue reading “Manure nutrient trends and creating dynamic “book values” through ManureDB”
Trends in Manure Sample Data
Purpose
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 | NH4–N | P2O5 | K2O |
| Swine | o | o | – | + |
| Dairy | – | o | – | o |
| Beef | o | o | o | o |
| Poultry | o | + | – | + |
| Solid | Total N | NH4–N | 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.
Authors
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: http://manuredb.umn.edu/ (coming soon!)
Twitter: @ManureProf, @nlbb
Lab websites:
https://wilsonlab.cfans.umn.edu/
https://bbe.umn.edu/people/erin-cortus
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, the University of Minnesota College of Food, Agricultural and Natural Resource Sciences, and the Minnesota Supercomputing Institute.
The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.
Dynamic manure “book values” through the U.S. National Manure Database
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.
The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.
Environmental Footprint, Cost, and Nutrient Database of the US Animal Feed Ingredients
Why Look at Feed Ingredients and Environmental Footprint?
The US Pig Production Environmental Calculator (PPEC) was built upon cradle-to-farm-gate life-cycle assessment (LCA) of pork production combined with the US National Resource Council (NRC 2012) swine nutrient requirements models (NRC 2012), farm operation inputs, and animal feed database. The purpose of the US Animal Feed Database is to compile environmental, economic, and nutrient content of animal feed ingredients in a single location and integrate it into a PPEC economic model of swine operations. (Click on image at right to view a handout of the poster).
What did we do?
We collected data from different sources including NRC (2012) feed nutrient characteristics, Feedstuffs (2014) for feed prices, US agricultural and product LCA models built in SimaPro 7.3.3 (PRé Consultants 2011) and LCA databases (Swiss Centre for Life Cycle Inventories 2010; EarthShift 2011; Blonk Consultants 2014) for environmental footprints. Table 1 shows a list of top US pig feed ingredients.
What have we learned?
Feed ingredients with highest costs are additives (e.g. paylean) and amino acids. Milk by-products have the largest climate change impact, water and land use.
Future Plans
The information from this database will be used as a starting point for identifying potential mitigation options in pig diet formulation. The database will be updated as new information becomes available.
Authors
Jasmina Burek, Research Associate, University of Arkansas jburek@uark.edu
Greg Thoma, Jennie Popp, Charles Maxwell, Rick Ulrich
Additional information
National Pork Board (2015) Carbon Footprint of Pork Production Calculator – Pork Checkoff.
Pesti G, Thomson E, Bakalli R, et al. (2004) Windows User-Friendly Feed Formulation (WUFFF DA) Version1.02.
PRé Consultants (2014) SimaPro 8.3. 4555022.
Pig Production Environmental Calculator
Life-Cycle Assessment Modeling for the Pork Industry
Acknowledgements
This research is part of the program “Climate Change Mitigation and Adaptation in Agriculture,” and is supported by Agriculture and Food Research Initiative Competitive Grant no. 2011-68002-30208 from the USDA National Institute of Food and Agriculture.
The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2015. Title of presentation. Waste to Worth: Spreading Science and Solutions. Seattle, WA. March 31-April 3, 2015. URL of this page. Accessed on: today’s date.