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
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.
Building Environment and Air Quality – Presented by Al Heber
Development of Draft Emission Estimating Methodologies for AFOs: Process Overview – Presented by Ian Rumsy
National Air Emissions Monitoring Study Status Update – Presented by Bebhinn Do
Purpose
The National Air Emissions Monitoring Study, or NAEMS, was conducted from 2007 – 2010 to gather data to develop scientifically credible methodologies for estimating emissions from animal feeding operations (AFOs). It followed from a 2002 report by the National Academy of Sciences that recommended the development of the emission models. NAEMS was funded by the AFO industry as part of a 2005 voluntary air compliance agreement with the U.S. Environmental Protection Agency (EPA). The goals of the air compliance agreement were to reduce air pollution, monitor AFO emissions, promote a national consensus on emissions estimating methodologies, and ensure compliance with requirements of the Clean Air Act and notification provisions of the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA), and the Emergency Planning and Community Right-to-Know Act (EPCRA). Thus, the design of the study was based both on principles set forth by the National Academy of Sciences and on the needs of EPA and the AFO industry to satisfy the compliance agreement.
What Did We Do
NAEMS monitored barns and lagoons at 25 AFOs in 10 states for approximately 2 years each to measure emissions of ammonia, hydrogen sulfide, particulate matter, and volatile organic compounds. University researchers conducted this monitoring with EPA oversight. The types of AFOs monitored included swine, broiler chickens, egg-laying operations, and dairies. Participating AFOs made their operations available for monitoring for two years and cooperated with the researchers, industry experts, and EPA during the study.
In 2012, EPA used information gathered in NAEMS, along with information provided as part of a 2011 Call for Information, to develop draft emission models for some of the AFO sectors that were monitored. The EPA Science Advisory Board (SAB) conducted a peer review of these original draft emission models and made suggestions for improving the models. Since 2017, EPA began applying the SAB suggestions and developing new draft emission models for each AFO sector. The models estimate farm-scale emissions using information that producers already record or is easy to get (like weather data). The models are not “process-based.” However, the approach aims to estimate emissions from sources based on statistical relationships between air emissions and the meteorological and housing parameters collected that are known to affect processes that generate emissions. The development of process-based models remains a long-term goal of the agency, as we acknowledge process-based models improve the accuracy of emission estimates for the livestock and poultry sectors.
What Have We Learned
During the workshop, panelists will discuss in more detail the lessons learned at various stages of the NAEMS project and how those lessons could inform future work.
Future Plans
The EPA team continues to develop draft emission models using the NAEMS data. It is anticipated that the AFO emission models will be finalized after incorporating input from a stakeholder review period.
Authors
Presenting Authors
Albert J. Heber, Professor Emeritus, Agricultural and Biological Engineering
Ian C. Rumsey, Physical Scientist, Office of Research & Development, U.S. Environmental Protection Agency
Bebhinn Do, Physical Scientist, U.S. Environmental Protection Agency
Corresponding Author
Bebhinn Do, Physical Scientist, U.S. Environmental Protection Agency do.bebhinn@epa.gov
U.S. Environmental Protection Agency – Office of Research & Development Emission Estimating Methodology development team: Maliha Nash, John Walker, Yijia Dietrich, Carry Croghan
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.
The day-to-day loss of animals is a fact of life all cattle producers must face and prepare for. Unfortunately, most published data of animal mortalities are for one-time, catastrophic die offs – where all the cattle on a farm must be exterminated because of disease outbreaks or natural disasters. Routine mortalities on cattle farms do not happen all at once, and mortality rates vary greatly between different life stages of animals and types of production systems.
An expert panel was convened by the Agricultural Working Group of the Chesapeake Bay Program to determine annual mortality, nitrogen and phosphorus masses produced by cow-calf, dairy and cattle on feed (feedlot) operations in the watershed. This paper concentrates on the annual mortality masses estimations determined by the panel. Cattle and Dairymen can use these values to plan for disposal of routine losses.
What Did We Do?
The panel looked, at depth, into existing production systems, and combined morality rates at different life stages, the size of animals at time of death, and the carcass composition varying with age to determine mortality and nutrient masses produced by typical cattle farms in the watershed.
The panel chose a 50-cow cow-calf operation as a model system, where cattle are on pasture 95% of the time. Under ideal conditions, each cow will yield one calf per year to be sold by year’s end. Some female calves will be retained to replace culled cows from the herd, maintaining the same general herd size. It was assumed there was no death loss of mother cows in the herd. We used USDA-APHIS (2010) data of average annual death loss of immature cattle combined with the average weight of cattle at different life-stages to determine weight of mortalities produced each year.
A total confinement beef feedlot was used to model mortalities for cattle on feed. Cattle were assumed to grow linearly with cattle placed in the feedlot at 400 to 600 pounds, and leaving at 1,000 to 1,200 pounds with an average time on lot of 120 days. Midwestern data (Vogel et al, 2015) was used to estimate annual deathrates per feedlot space at 30-day increments since placement in the feedlot.
A 100-cow milking herd was used as a reference for dairy systems. The reference farm contained 50 female calves and 50 heifers in development. Heifers are bred at 15 months and give birth around 24 months (2 years) of age. Male calves are exported from the farm as soon as possible for development as lower grade beef cattle. The reference dairy had heifers and dry cows on pasture, with the active milking herd in free-stall barns or alternative confinement for a 300-day lactation. USDA-APHIS (2016) data of average annual death loss of all types of dairy cattle was combined with the average weight of cattle at different life-stages to determine weight of mortalities produced each year.
What Have We Learned?
Figure 1 shows the estimated total weight of mortalities produced by a 50 cow, cow-calf herd each year broken down by age of animal dying. As can be seen in Figure 1, the greatest weight of mortalities occurred before calves were weaned – assuming no death of mother cows. The values in Figure 1 represent 1.52 calves born dead, 1.92 calves dying before weaning, and 0.87 head dying after weaning. This means a farmer should prepare for the loss of 2 newborn calves, 2 un-weaned calves, and one weaned steer/heifer per 50 mother cows each year. Dividing the total weight of mortalities by 50 head gives an average per cow annual mortality of 32 pounds per year.
Figure 1. Estimated Total Annual Weight of Mortalities Produced by a 50 Cow, Cow-Calf Herd.
Figure 2 shows the estimated total weight of mortalities produced by a 100-head-space feedlot. The greatest source of mortalities is steers and heifers weighing close to 700 pounds (31 to 60 days after arrival on the feedlot. Dividing the total weight of mortalities by 100 gives an average annual mortality weight of 18 pounds per head-space per year. The feedlot owner should prepare for approximately 3 animals dying each year per 100 head-space.
Figure 2. Estimated Total Annual Weight of Mortalities Produced by a 100 head-space feedlot.
Figure 3 shows the estimated total weight of mortalities produced by a 100-cow dairy. Dividing the total weight of mortalities by 100 head gives an average annual mortality weight of 90 pounds per milking cow. The greatest source of mortalities is mature cows. Dairies should prepare for as much as 6 mature cows, 3 pre-weaned calves and heifers, and 1 weaned heifer dying each year per 100 mature cows.
Figure 3. Estimated Total Annual Weight of Mortalities Produced by a 100 milking head dairy.
Future Plans
Cattle producers can use the values estimated by this project to determine resources needed to prepare for mortalities. If burial is the preferred option, the space required to bury mortalities for the expected life of the operation; for composting, the area, and weight of carbon source required to compost; and for incineration, an incinerator capable of handling the largest animal housed on the farm.
Authors
Douglas W. Hamilton, Ph.D. P.E., Extension Waste Management Specialist, Oklahoma State University
Corresponding author email address
dhamilt@okstate.edu
Additional authors
Thomas M. Bass, Livestock Environment Associate Specialist, Montana State University; Amanda Gumbert, PhD., Water Quality Extension Specialist, University of Kentucky; Ernest Hovingh, DVM, PhD., Research Professor Extension Veterinarian, Pennsylvania State University; Mark Hutchinson, Extension Educator, University of Maine; Teng Teeh Lim, PhD, P.E., Extension Professor, University of Missouri; Sandra Means, P.E., USDA NRCS, Environmental Engineer, East National Technology Support Center (Retired); George “Bud” Malone, Malone Poultry Consulting; Jeremy Hanson, WQGIT Coordinator – STAC Research Associate, Chesapeake Research Consortium – Chesapeake Bay Program
Additional Information
Hamilton, D., Bass, T.M., Gumbert, A., Hovingh, E., Hutchinson, M., Lim, T.-T., Means, S., and G. Malone. (2021). Estimates of nutrient loads from animal mortalities and reductions associated with mortality disposal methods and Best Management Practices (BMPs) in the Chesapeake Bay Watershed (DRAFT). Edited by J. Hanson, A. Gumbert & D. Hamilton. Annapolis, MD: USEPA Chesapeake Bay Program.
USDA-APHIS (2010). Mortality of Calves and Cattle on U.S. Beef Cow-calf Operations: Info Sheet, 2010. Fort Collins, CO: USDA-APHIS.
USDA-APHIS. (2016). Dairy 2014: Health and Management Practices on US Dairy Operations, 2014. Report, 3, 62-77. Fort Collins, CO: USDA-APHIS,.
Vogel, G. J., Bokenkroger, C. D., Rutten-Ramos, S. C., & Bargen, J. L. (2015). A retrospective evaluation of animal mortality in US feedlots: rate, timing, and cause of death. Bov. Pract, 49(2), 113-123.
Acknowledgements
Funding for this project was provided by the US-EPA Chesapeake Bay Program through Virginia Polytechnic and State University EPA Grant No. CB96326201
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.
Sound management of manure is essential to optimize its benefits for soil health and crop production, and to minimize costs and environmental risks. Along with changes in farm scale and practices, modern farms are increasingly looking to process or treat manure to address problem areas and to take advantage of market opportunities on their operations. A variety of manure treatment technologies are available and new technologies continue to be developed for managing nutrients, solids, energy, water, and other components of manure. But, while these new treatment technologies hold potential to improve the environmental, economic, and social sustainability of livestock and poultry production, questions remain regarding producer adoption of treatment systems on their operations. To improve our understanding of decision-making processes employed when producers evaluate and adopt manure treatment technologies, the authors conducted a survey aimed at dairy and swine producers in the Midwest.
What did we do?
Two surveys were developed, one tailored to dairy producers and one for swine producers. All operation sizes and production systems were included. The surveys were administered using Qualtrics, an online survey platform. Questions asked covered manure-related practices in animal facilities, manure handling, and land application. Additional questions asked producers to prioritize their needs for manure treatment, factors influencing technology selection, current technologies being utilized, and principal barriers for adoption. Respondents were asked to select up to three critical outcomes for their farms’ manure treatment technologies, the most influential factors (or technology characteristics) for manure treatment adoption, and the main barriers for technology adoption. The authors collaborated with Nebraska Extension and with state producer associations to reach swine and dairy producers in Nebraska and other Midwest states, with the survey first launched in the fall of 2021. Magazine articles, radio programs, listservs, and social media were used to promote the surveys.
Responses were analyzed using descriptive methods. Eighteen respondents provided information to characterize seven swine farms and ten dairy operations. Swine respondents had farms in Nebraska (7), Iowa (2), and Ohio (1). For dairy, 7 of the farms were in Nebraska and 1 was in Minnesota. Swine farm systems were divided between the ones that had farrowing (farrow-to-finish and farrow-to-wean systems) and the ones without it (grow-to-finish and wean-to-finish systems) (Table 1). Respondents were asked to provide insights for their farms’ primary manure management systems. A dairy operation’s primary manure management system was defined as the one receiving manure from the lactating cows. For swine, the primary manure management system received manure from the gestation sows or the finishing herd. For both swine and dairy, secondary systems were defined as utilizing separate storage and handling facilities.
Table 1. Herd size information of dairy and swine farms represented in the survey responses.
Species and herd type
Number of farms
Herd size – average
Herd size – range
Dairy – lactating cow herd
8
933
30 to 2,150
Swine (farrowing) – sow herd
4
2,762
250 to 7,500
Swine (finishing) – finisher herd
5*
23,600
1,200 to 70,000
Note: *One finishing farm did not share its herd size information.
What have we learned?
The dairy and swine farms demonstrated differences in manure treatment needs and consequently adopted different treatment technologies (Figures 1 and 2).
Figure 1. Farm characterization and manure management of ten swine farms. FTF = farrow-to-finish PSOP = partially slotted open pens PP = pull-plugs FTW = farrow-to-wean ISWPSF = individual stalls w/partial slotted floor DP = deep pits GF-F = grow-finish or finishing ASFB = all slotted-floor building FL = flushing WTF = wean-to-finish CH = chemicals AE = aeration LA = lagoons AD = anaerobic digestion CO = compostingFigure 2. Farm characterization and manure management of eight dairy farms. CS = corn stalks Sd = sedimentation DD = direct drying Mch = mechanical TL = treatment lagoon Co = composting Stt = sand settling lane or basin AE = aeration NS = no separation AD = anaerobic digestion
The most-used technologies in the primary manure management system for each industry were: mechanical separation, sand settling lanes, and sedimentation basins for dairy farms; and addition of chemicals, treatment lagoons, and composting for swine operations (Figure 3).
Figure 3. Manure treatment technologies being used in primary manure management systems.
Allowing water to be reused and exporting nutrients were the primary desired outcomes of implementing manure treatment technologies for dairy and swine farms, respectively (Figure 4). Accordingly, 6 of 7 dairy farms were recycling water in their operations, while only 1 out of 10 was doing so on the swine side.
Figure 4. Primary desired outcomes of the implementation of manure treatment technologies in swine and dairy farms.
Diverse factors influenced the selection of the implemented technologies in both livestock operations. Low management demand, low maintenance, “performs best functionally” (best performance achieving the desired goals of manure treatment), and low initial cost are among the most-mentioned factors (Figure 5).
Figure 5. Factors that most influenced the selection of implemented manure treatment technologies.
Swine and dairy farmers identified initial cost, operational cost, and return on investment as the primary barriers to future technology adoption (Figure 6). Management demand was another important barrier among swine producers.
Figure 6. Barriers of highest concern when upgrading manure management systems on farms, especially through the adoption of manure treatment technology.
None of the survey respondents used membranes, electrochemical precipitation, or gasification technologies, demonstrating that cutting-edge manure treatment technologies are being more slowly adopted by regional livestock producers. The high cost and potential high management demand of these technologies could be barriers for their adoption.
Future plans
Our research work has moved into qualitative exploration. Focus groups will be held with swine and dairy producers, where they will discuss and share their manure treatment needs and desired outcomes from new treatment options. These activities will be organized online and will allow producers to share their manure management perspectives for the present and future. The results of our surveys and focus groups are being used to inform a decision-support tool being developed as part of the Management of Nutrients for Reuse (MaNuRe) project. Our findings will also be used to help develop extension programs that meet the needs of producers for manure management in Nebraska and neighboring states.
Authors
Juan Carlos Ramos Tanchez, Graduate Research Assistant, University of Nebraska-Lincoln.
Corresponding author email address
jramostanchez2@huskers.unl.edu
Additional authors
Richard Stowell, Professor of Biological Systems Engineering, University of Nebraska-Lincoln.
Amy Schmidt, Associate Professor of Biological Systems Engineering, University of Nebraska-Lincoln.
Acknowledgements
Funding for this effort came from the USDA NIFA AFRI Water for Food Production Systems program, grant #2018-68011-28691. The authors would like to express gratitude to Dr. Teng Lim and Timothy Canter (University of Missouri), Mara Zelt, and Lindsey Witt-Swanson (University of Nebraska-Lincoln) for their relevant support to this study. We would also like to thank the staff at the Nebraska Pork Producers Association and the Nebraska State Dairy Association for their collaboration on our research.
The “manureshed” refers to the land base needed to assimilate the nutrients produced by a livestock operation without presenting a danger to water, land, and air resources. Trends toward large dairies in many regions of the US, often with high density of livestock relative to the amount of land available for nutrient application, have increased in recent decades. Consequently, import of feed and forage often leads to nutrient surpluses and the need to transport manure off farm for land application. Our purpose was to evaluate the status of dairy manuresheds across the US to highlight challenges and opportunities to improve nutrient balances and facilitate manure nutrient redistribution when needed.
What Did We Do
Our group produced case-studies of manureshed management from four major dairy producing states across the US. We reviewed the predominate structure of dairies in those states and analyzed the primary challenges that must be addressed to safely assimilate nutrients. We focus on reviewing the extent of off-farm redistribution of manure that is needed in each of those states, limitations to redistribution, and approaches that can be built upon to facilitate redistribution. In the Minnesota case-study, where nutrient management data is publicly available for Confined Feeding Operations, GIS software was used to estimate manure transport distances for varying cropping systems and dairy cattle breeds. For Idaho, New Mexico, and Pennsylvania, whole-farm modelling is referenced to understand nitrogen (N) and phosphorous (P) balances on a range of dairies.
What Have We Learned
Soil P assimilation capacity was the predominate factor constraining manureshed land requirements in three of the four states studies. However, nitrate leaching potential was the largest constraint in New Mexico, where dairy forages were largely grown on irrigated lands near rivers. GIS analysis in Minnesota estimated that an average travel distance of 4.1 km for manure transport was required for dairy with 1000 or more cows. The Minnesota case-study also revealed smaller manuresheds were required, per unit of energy-corrected milk, for Jersey cattle compared to the larger Holsteins. Modelled nutrient budgets for Idaho indicated a greater need for off-farm transport, suggesting that expanded application of dairy manures on alternative crops (such as potatoes, sugar beets, and barley) should be considered. In New Mexico, large dairies and limited cropland has caused extensive import of feed from other states and Mexico, with informal nutrient brokering networks developing. In Pennsylvania, dairy producing counties are largely overall sinks for nutrients, but historic heavy manure applications on fields near dairy barns often necessitates greater redistribution of manure nutrients within individual dairies or transfer to local crop farms.
Multiple approaches for improving nutrient balances and distribution of manure were identified in the case studies. Continuing advances in dairy nutrition and cattle genetics are helping to improve nutrient balances and reduce quantities of N and P excreted. When nutrient surpluses necessitate off-farm transport, informal networks for connecting dairies with surplus nutrients with crop farms that have nutrient assimilation capacity, described in New Mexico, provide a basis for development of similar networks elsewhere. Manure processing developments also provide possibilities for more economical transport or reuse of manure nutrients from farms with liquid handling.
Future Plans
The current work provides an overview of the current status of manureshed management in dairy regions. Continuing work is needed to refine nutrient balances for individual farms and to continue to develop tools that assist farmers in understanding nutrient balances and manureshed requirements on their farms. Involvement of social scientists and economists is needed to further develop networks for manure redistribution. Our work also points to the need for greater federal and state cost sharing and more technical support from government, universities, and farm organizations to facilitate more intensive evaluation of manureshed requirements and transport of manure when needed.
Authors
Curtis Dell, Soil Scientist, USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, PA Curtis.Dell@usda.gov
Additional Authors
John Baker, USDA-ARS, St. Paul, MN
Sheri Spiegal, USDA-ARS, Las Cruces, NM
Sarah Porter, Environmental Working Group, Minneapolis, MN
April Leytem, USDA-ARS, Kimberly, ID
Colton Flynn, USDA-ARS, Temple, TX
Alan Rotz, USDA-ARS, University Park, PA
David Bjornberg, USDA-ARS, Kimberly, ID
Ray Bryant, University Park, PA
Robert Hagevoort, New Mexico State Univ., Clovis, NM
Jeb Williamson, New Mexico State Univ., Las Cruces, NM
Amalia Slaughter, USDA-ARS, Las Cruces, NM
Peter Kleinman, USDA-ARS, Fort Collins, CO
Additional Information
C.J. Dell et al., 2022. Challenges and opportunities for manure management across US Dairy systems: Case Studies from four regions. Journal of Environmental Quality. (In press in pending special edition on manureshed management).
Acknowledgements
USDA Agricultural Research Service and the Dairy Agroecosystems Workgroup (DAWG, USDA-ARS)
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.
Conceptualizing, modeling, and controlling carbon flows at the farm scale can improve efficiency in production, reduce costs, and promote beneficial products and byproducts of agricultural processes through best management practices. On dairy farms, opportunities exist for farmers to control factors affecting greenhouse gas (GHG) emissions and diversions from production and operations. Complex programs to model the effects of different carbon management strategies on net emissions are very useful to farmers, but lack visualization of flows through a user interface to show effects of different management choices in real time.
Previously, a collaborative research team at Penn State University and the University of Wisconsin-Madison has developed a Virtual Dairy Farm website to share information about how dairy farms incorporate best management practices and other on-farm production choices to reduce environmental impacts. The website is organized in two model farm configurations, a 150-cow and a 1500-cow modern dairy farm. Website users can find information on different components of the farm by exploring locations on the farm. Links to information about farm operations are structured in multiple levels such that information is understandable to the general public but also supported by technical factsheets for agriculture professionals.
What did we do?
Building on the strengths of the Penn State Virtual Dairy Farm interactive website, the Team has developed a concept for an “Adventure Dairy” package for users to explore how management choices affect total on-farm GHG emissions for a model Pennsylvania dairy farm (Figure 1). Five main categories serve as management portals superimposed on the Virtual Dairy interface, including Cow Life Cycle, Manure Management, Crop Production, Energy Use on Farm, and Feed on Farm.
Figure 1. Example landing page for the proposed Adventure Dairy website. Different regions to explore to access the GHG emissions calculator are highlighted in different colors.
For each category, users can select from multiple options to see how these decisions increase or decrease emissions. Along the bottom of the webpage, a calculator displays the net carbon balance for the model system and change emissions estimates as users choose feed composition, land use strategies, and other important components (Figure 2). Under each category, users can make choices about different management practices that affect on-farm carbon cycling. For example, different choices for feed additives change total net GHG emissions, and, in turn, can affect total manure production. A change in management and operational choices, such as storage, is visually communicated through interactions and on the interface (Figure 3). These management portals can be seamlessly integrated with the Virtual Dairy Farm as an addition to the right sidebar. The click-through factsheets currently a part of the interface can be preserved through new informational “fast facts” overlays with accompanying infographics and charts. Pathways to optimizing carbon flows to ensure maximum production and minimum environmental impact will be featured as “demo” examples for users.
Figure 2. Example Adventure Dairy user interface for manure management. User choices to increase and decrease total GHG emissions are included in the right sidebar. Along the bottom of the page, a ribbon shows the total emissions from each category.Figure 3. A different user choice for manure storage type on the Adventure Dairy interface. Total GHG emissions decrease by 14% because of the switch from an uncovered to a covered anaerobic lagoon because of reduced volatilization of methane.
What have we learned?
This model offers a novel platform for more interactive software programs and websites for on-farm modeling of carbon emissions and will inform future farm management visualizations and data analysis program interfaces. The Team envisions the Adventure Dairy platform as an important tool for Extension specialists to share information with dairy professionals about managing carbon flows on-farm. Simultaneously, consumers increasingly seek information on the environmental impacts of agriculture. This interactive website is a valuable educational and technical tool for a variety of audiences.
Uniquely, a multidisciplinary team of agriculture and engineering graduate students from multiple institutions are leading this project, as facilitated by faculty. This Cohort Challenge model allows for graduate students to engage with complex food-energy-water nexus problems at the level of faculty investigators in a virtual educational resource center. Future INFEWS-ER teams and “wicked problems” challenge projects will continue to develop this model of learning and producing novel research products.
Future plans
The Cohort Challenge Team is entering a peer/faculty review process of the simplified carbon model for the Virtual Dairy Farm website. The user interface for the Adventure Dairy calculator is not currently a part of the Penn State Virtual Dairy Farm. The Team will be working with software developers to integrate our model in the existing interface. Additional components under consideration for inclusion in the Adventure Dairy calculator include cost comparisons for different best management practices, an expanded crop production best management calculator, and incorporation of
Authors
Student Team: Margaret Carolan,1 Joseph Burke,2 Kirby Krogstad,3 Joslyn Mendez,4 Anna Naranjo,4 and Breanna Roque4
Project Leads: Deanne Meyer,4 Richard Koelsch,3 Eileen Fabian,5 and Rebecca Larson6
Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA crln@uiowa.edu
Texas A&M University
University of Nebraska-Lincoln
University of California-Davis
Penn State University
University of Wisconsin-Madison
Acknowledgements
Funding for the INFEWS-ER was provided by the National Science Foundation #1639340. Additional support was provided by the National Institute for Food and Agriculture’s Sustainable Dairy CAP and the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.
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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.
USDA-NRCS has nearly fifteen years of Conservation Innovation Grant project experience, and several of these projects have provided a means to learn more about various techniques for addressing air emissions from animal agriculture. The overall goal of the Conservation Innovation Grant program is to provide an avenue for the on-farm demonstration of tools and technologies that have shown promise in a research setting and to further determine the parameters that may enable these promising tools and technologies to be implemented on-farm through USDA-NRCS conservation programs.
What Did We Do?
Several queries for both National Competition and State Competition projects in the USDA-NRCS Conservation Innovation Grant Project Search Tool (https://www.nrcs.usda.gov/wps/portal/nrcs/ciglanding/national/programs/financial/cig/cigsearch/) were conducted using the General Text Search feature for keywords such as “air”, “ammonia”, “animal”, “beef”, “carbon”, “dairy”, “digester”, “digestion”, “livestock”, “manure”, “poultry”, and “swine” in order to try and capture all of the animal air quality-related Conservation Innovation Grant projects. This approach obviously identified many projects that might be related to one or more of the search words, but were not directly related to animal air quality. Further manual review of the identified projects was conducted to identify those that specifically had some association with animal air quality.
What Have We Learned?
Out of nearly 1,300 total Conservation Innovation Grant projects, just under 50 were identified as having a direct relevance to animal air quality in some way. These projects represent a USDA-NRCS investment of just under $20 million. Because each project required at least a 50% match by the grantee, the USDA-NRCS Conservation Innovation Grant program has represented a total investment of approximately $40 million over the past 15 years in demonstrating tools and technologies for addressing air emissions from animal agriculture.
The technologies that have been attempted to be demonstrated in the animal air quality-related Conservation Innovation Grant projects have included various feed management strategies, approaches for reducing emissions from animal pens and housing, and an approach to mortality management. However, the vast majority of animal air quality-related Conservation Innovation Grant projects have focused on air emissions from manure management – primarily looking at anaerobic digestion technologies – and land application of manure. Two projects also developed and enhanced an online tool for assessing livestock and poultry operations for opportunities to address various air emissions.
Future Plans
The 2018 Farm Bill re-authorized the Conservation Innovation Grant Program through 2023 at $25 million per year and allows for on-farm conservation innovation trials. It is anticipated that additional air quality projects will be funded under the current Farm Bill authorization.
Authors
Greg Zwicke, Air Quality Engineer, USDA-NRCS National Air Quality and Atmospheric Change Technology Development Team
greg.zwicke@ftc.usda.gov
Additional Information
More information about the USDA-NRCS Conservation Innovation Grants program is available on the Conservation Innovation Grants website (https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/programs/financial/cig/), including application information and materials, resources for grantees, success stories, and a project search tool.
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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.
Consumer interest and concern is growing in regards to sustainability of livestock production systems. Demand for reduced carbon emissions within agricultural systems has been growing along with increasing demand for food. Baseline fossil fuel consumption within agricultural systems, including dairy production, is scarce. Therefore, there is a need to discern where and how fossil energy is being used within dairy production systems. Determining baseline energy use is the first step in investigating the demand for a reduced carbon footprint within dairy production systems. The objective of this study was to measure total electricity use and determine specific areas of high energy consumption in commercial dairy barns located in the Upper Midwest of the United States.
What did we do?
Four commercial dairy barns representative of typical Midwest dairy farms and located in west central Minnesota were evaluated in the study. The dairy farms were: 1) a 9,500 cow cross-ventilated barn with a rotary milking parlor (Farm A), 2) a 300 cow naturally-ventilated barn with stirring fans for air movement and 6 automatic milking systems (Farm B), 3) a 200 cow naturally-ventilated barn with stirring fans for air movement and a parabone milking parlor (Farm C), and 4) a 400 cow naturally-ventilated barn with stirring fans for air movement and a parallel milking parlor (Farm D).
Electricity use was monitored from July 2018 to December 2018 with a goal of collecting two years of total energy usage. Two-hundred ninety-two electric loads across the four farms were monitored on the farm side of the electric utility meter to evaluate areas of highest energy usage (Figure 1). Some of the monitored electric loads included freestall barn fans, water heaters, compressors, chillers, manure pumps, and pressure washers. The electric loads were monitored by data loggers (eGauge, Boulder, CO) and electric current sensors at the circuit panels. Electrical use data (kWh) of each load were collected and analyzed on a monthly basis. In addition, monthly inventory of cows on farm, cows milked per day, and milk production was recorded. Bulk tank production records (milk, fat percentage, protein percentage, and somatic cell count) were also recorded.
Figure 1. Data loggers with electric current sensors installed on farm circuit panel boxes.
What have we learned?
Based on preliminary results, fans were the largest electrical load across all four dairy farms. Fan usage during the summer ranged from 36 to 59% of the total electricity measured (Figure 2). Regular maintenance, proper control settings, design, sizing, location, selecting energy efficient fans and motors, and other factors all could influence the efficiency of these ventilation/cooling systems. Farms B, C, and D had greater electricity usage across all months for milk cooling (compressors and chillers) than Farm A. This is likely due to the fact that Farm A does not utilize bulk tanks to store milk, but instead, milk is directly loaded onto bulk milk trucks. Lighting use ranged from 7 to 21% of the total electricity use measured across the four farms, which suggests there is potential to reduce energy usage by upgrading to more efficient lighting systems such as LEDs. For heating, energy usage includes water heating, heating units in the milking parlor or work rooms, waterer heating elements, and generator engine block heaters. Average monthly heating use ranged from 5% of electricity used in Farm A to 32% of electricity used in Farm C.
Figure 2. The average monthly electrical use measured by data loggers and the percent used by each electrical load category. The average monthly total electricity in kWh is displayed at the top of each bar.
Future plans
Based on the preliminary analysis, clean energy alternatives and energy-optimized farms will be modeled as clean energy alternatives for Minnesota dairy facilities. An economic analysis will also be conducted on the clean energy alternatives and farms. Potential on-site renewable electric generation may supply some or the entire electric load allowing the buildings to approach net-zero (producing as much energy as is used).
The results of this study provide recent energy usage for farm energy benchmarks, agricultural energy policy, economic evaluations, and further research into dairy farm energy studies. The data will also be useful to producers who are searching for areas for reduced energy usage in their own production systems. Improving the efficiency of electrical components in dairy operations could provide opportunities to improve the carbon footprint of dairy production systems.
Authors
Kirsten Sharpe, Animal Science Graduate Research Assistant, West Central Research and Outreach Center (WCROC), Morris, MN, sharp200@umn.edu
Bradley J. Heins, Associate Professor, Dairy Management, WCROC, Morris, MN
Eric Buchanan, Renewable Energy Scientist, WCROC, Morris, MN
Michael Cotter, Renewable Energy Researcher, WCROC, Morris, MN
Michael Reese, Director of Renewable Energy, WCROC, Morris, MN
Additional information
The West Central Research and Outreach Center (WCROC) has developed a Dairy Energy Efficiency Decision Tool to help provide producers a way to estimate possible energy and costs savings from equipment efficiency upgrades. The tool can be used to evaluate areas of a dairy farm that may provide the best return on investment for energy usage. Furthermore, a guidebook has been developed for Optimizing Energy Systems for Midwest Dairy Production. This guidebook provides additional information about energy usage issues as well as a decision tool. More information may be found at https://wcroc.cfans.umn.edu/energy-dairy
Acknowledgements
The funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR).
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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.
To meet the nutritional needs of a growing population, dairy producers must increase milk production while minimizing farm environmental impacts. As we look to the future, management practices must also be adapted to maintain production under projected climate change. To plan for the future, better information is needed on practices that can reduce emissions from the farm and adapt to changes in the climate while maintaining or improving production and profitability.
What did we do?
We conducted a comprehensive assessment of the effects of climate change on both the productivity and environmental performance of farms as influenced by strategies to reduce emissions and adapt to the changing climate. Production systems were evaluated using three representative northeastern dairy farms: a 1500-cow farm in New York, a 150-cow farm in Wisconsin and a 50-cow farm in southern Pennsylvania. A cradle-to-farm gate life cycle assessment was conducted using farm-scale process-based modeling and climate projections for high and low emission scenarios. Environmental considerations included the carbon footprint of the milk produced and reactive N and P losses from the farms.
What we have learned?
We found that the environmental impact of the three representative dairy farms generally increased in the near future (2050) climate if no mitigation measures were taken. Overall, feed production was maintained as decreases in corn grain yield were compensated by increases in forage yields. Adaptation of the cropping system through changes in planting and harvest dates and corn variety led to a smaller reduction in corn grain yield, but the detrimental effects of climate change could not be fully negated. Considering the increased forage yield, total feed production increased except for the most severe projected climate change. Adoption of farm-specific beneficial management practices substantially reduced the greenhouse gas emissions and nutrient losses of the farms in current climate conditions and stabilized the environmental impact in future climate conditions, while maintaining feed and milk production (See Figure 1 for example results).
Figure 1. Carbon footprint, reactive nitrogen footprint and P loss in recent (2000) and future (2050) climate conditions (RCP4.5 and RCP8.5) for a 1500-cow farm in New York with baseline and Best Management Practice (BMP) scenarios, with and without crop system adaptions in 2050. Error bars represent the standard deviation of IFSM simulations for 3 climate scenarios per RCP. Unadapt = not adapted cropping system. Adapt = adapted cropping system.
The take-home message is that with appropriate management changes, our dairy farms can become more sustainable under current climate and better prepared to adapt to future climate variability.
Future plans
A more comprehensive life cycle assessment is being done by linking the output of the farm model with life cycle assessment software. The process level simulation of the farm provides inventory information for an inclusive life cycle assessment with multiple environmental considerations. This integrated software will provide a more complete sustainability assessment of the potential benefits of alternative management strategies for both now and the future.
Authors
Karin Veltman, University of Michigan; C. Alan Rotz, USDA-ARS; Larry Chase, Cornell University; Joyce Cooper, Washington State University; Chris Forest, Penn State University; Pete Ingraham, Applied GeoSolutions; R. César Izaurralde, University of Maryland; Curtis D. Jones, University of Maryland; Robert Nicholas, Penn State University; Matt Ruark, University of Wisconsin; William Salas, Applied GeoSolutions; Greg Thoma, University of Arkansas; Olivier Jolliet, University of Michigan.
Additional information
Information on the Integrated Farm System Model is available in the reference manual:
Information on the analysis of Best Management Practices on northeastern dairy farms is available in:
Veltman, K., C. A. Rotz, L. Chase, J. Cooper, P. Ingraham, R. C. Izaurralde, C. D. Jones, R. Gaillard, R. A. Larsson, M. Ruark, W. Salas, G. Thoma, and O. Jolliet. 2017. A quantitative assessment of beneficial management practices to reduce carbon and reactive nitrogen footprints and phosphorus losses of dairy farms in the Great Lakes region of the United States. Agric. Systems 166:10-25.
Acknowledgements
This work was supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2013-68002-20525. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.
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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.
Manage Cookie Consent
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional
Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
