Tag: methane emissions

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Measured methane emission rates relative to the chemical composition of dairy manure samples

Purpose

Methane emissions from liquid manure storage systems contribute a significant portion of methane emissions from the US agricultural sector (EPA, 2024). There is a need for more farm-level methane emission values to guide decision-making activities within the dairy industry and by government agencies. Cost, time, and labor constraints are challenges related to on-farm methane emission measurements. There is a need for simpler emission measurement methods.

The purpose of this work was to investigate relationships between methane emission rates (MER) in a laboratory assay and commonly measured characteristics including total solids (TS), volatile solids (VS), ash, and total Kjeldahl nitrogen (TKN). The relationships were also examined in conjunction with storage type, season, manure type, and storage duration.

What Did We Do?

We collected dairy manure samples from manure storages at 27 farms in Minnesota and Wisconsin at 2 to 4-month intervals throughout 2024. These samples represented various storage types, storage durations, and manure temperatures. To date, a majority of these samples have been processed for TS, VS, Ash, and TKN using standard methods for manure chemical analyses (American Water Works Association, 2017; Wilson et al., 2022). Additionally, MER were estimated in triplicate with a 3-day in vitro assay (Andersen et al., 2015). Relationships between MER and these manure chemical constituents were examined using Spearman correlation analysis and bivariate plots across all manure samples and with respect to other manure management characteristics. These include storage type, season, manure type, and storage duration. Manure chemical constituents were treated as numerical data whereas storage characteristics were treated as categorical data in the analysis. It is important to note that many samples may only have a partial set of manure analyses completed at this point. This resulted in varying counts of available samples used in the statistical analyses below (Table 1-3). Summary statistics (mean, median, range) are also presented for the different manure chemical constituents.

What Have We Learned?

Table 1 shows summary statistics of dairy manure samples processed for this work (wet basis). There was a wide range of concentrations observed for each manure chemical constituent; however, average values were comparable to American Society of Agricultural and Biological Engineers (ASABE) manure characteristics values (ASABE, 2019).

Table 1: Summary statistics of dairy manure chemical characteristics (% wet basis) (n = 148 for TKN, n = 155 for other manure constituents)

Mean Median Min Max
TS 5.92% 4.98% 0.53% 17.89%
VS 4.39% 3.50% 0.27% 16.60%
Ash 1.53% 1.21% 0.26% 11.12%
TKN 0.31% 0.29% 0.08% 0.83%

Generally, overall correlations (Table 2) and correlations within categories (Table 3) were not strong, however there were some exceptions. These exceptions were observed in TS and VS relationships with MER for flush water and long-term storage duration (Table 3). Here, both positive and negative correlations that were at least moderately strong (rs ≥ |0.5|) were observed. Since methane emissions are a product of organic matter degradation, positive correlations between VS and MER were expected, but not always reflected in the results. Other trends in relationships between other manure constituents and MER are not well understood. However, manure management factors may also influence other microbial activity with respect to TS, Ash, and TKN content, which may have indirect effects on MER that cannot be discerned from a correlative relationship.

Additionally, given the wide range in the concentrations of all manure constituents and MER, it may be difficult to distinguish these relationships when comparing across the aggregate values. Instances where the strongest correlations were observed (Table 3) describe samples from a single farm, which suggests conducting a similar analysis within individual farms to better understand these relationships.

Table 2: Overall Spearman correlation values (rs) between manure constituents and MER

Total solids Volatile solids Ash Total Kjeldahl Nitrogen
MER 0.060 0.052 0.137 -0.046

 

Table 3: Spearman correlation values (rs) between manure constituents and MER by manure storage type, manure type, storage duration, and season

TS vs MER VS  vs MER Ash vs MER TKN vs MER
Manure storage type Transfer pit (n = 169) 0.150 0.124 0.271 0.075
Underfloor pit (n = 34) -0.103 -0.121 -0.125 -0.153
Manure type Raw manure (including bedding (n=36) 0.270 0.267 0.312 0.369
Raw manure (including bedding + others) (n = 140) 0.042 0.047 0.094 -0.093
Liquid separated manure (n =24) -0.213 -0.297 -0.025 -0.156
Flush water (n = 9) 0.800 0.800 0.883 0.833
Storage duration Short term (< 1 month) (n = 176) 0.082 0.090 0.117 -0.020
Long term (> 1 month) (n =6) -0.771 -0.771 -0.143 -0.200
Point sample (not a storage) (n= 29) 0.029 -0.078 0.350 -0.136
Season Winter (n= 23) 0.013 0.081 -0.011 0.132
Spring (n= 57) 0.406 0.399 0.411 0.238
Summer (n = 46) -0.268 -0.291 -0.165 -0.436
Fall (n =66 -0.188 -0.198 -0.023 -0.011

 Future Plans

We plan to conduct a stepwise regression analysis to better understand the significant independent variables (manure constituents) that influence MER. Correlations between manure constituents and MER using measurements from samples within individual farms will also be conducted.

Authors

Presenting author

Noelle Soriano, PhD candidate, University of Minnesota

Corresponding author

Erin Cortus, Associate Professor and Extension Engineer, University of Minnesota, Ecortus@umn.edu

Additional author

MaryGrace Erickson, Postdoctoral associate, University of Minnesota

Additional Information

Andersen, D. S., Van Weelden, M. B., Trabue, S. L., & Pepple, L. M. (2015). Lab-assay for estimating methane emissions from deep-pit swine manure storages. Journal of Environmental Management, 159, 18-26.

American Water Works Association. (2017). Standard Methods for the Examination of Water and Wastewater. American Water Works Association.

ASABE. (2019). Manure Production and Characteristics (ASAE D384.2). ASABE.

EPA. (2024). Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2022 (No. EPA 430-R-24-004). U.S. Environmental Protection Agency. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-andsinks-1990-2022

Wilson, M., Brimmer, R., Floren, J., Gunderson, L., Hicks, K., Hoerner, T., Lessl, J., Meinen, R. J., Miller, R. O., Mowrer, J., Porter, J., Spargo, J. T., Thayer, B., & Vocasek, F. (2022). Recommended Methods Manure Analysis (M. Wilson & S. Cortus, Eds.; 2nd ed.). University of Minnesota Libraries Publishing.

Acknowledgements

We are grateful to the farms that participated in this research for providing samples and for sharing their observations with us. We are also grateful to Kevin Bourgeault, Seth Heitman, Sabrina Mueller, and Jacob Olson for contributing to sampling and laboratory analysis.

This research is supported by through USDA NIFA Award 2023-68008-39859, and the Minnesota Rapid Agricultural Response Fund.

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. 2025. Title of presentation. Waste to Worth. Boise, ID. April 7-11, 2025. URL of this page. Accessed on: today’s date. 

 

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A mass balance approach to estimate methane and ammonia emissions from non-ruminant livestock barns

Purpose

Producers are under pressure to demonstrate and document environmental sustainability. Responding to these pressures requires measurements to demonstrate greenhouse gas (GHG) emissions and/or changes over time. Stored manure emissions are a critical piece of livestock agriculture’s contribution to GHG production. Manure sample‐based estimates show promise for estimating methane (CH4) production rates from stored manure but deserve more extensive testing and comparison to farm‐level measurements. Understanding the causes for variability offer opportunity for more realistic and farm‐specific GHG emissions. Improved GHG measurements or estimates will more accurately predict current GHG emission levels, identify mitigation techniques, and focus resources where they are needed. This project offers an innovative approach to improvement of air quality and strengthens engagement by the livestock sector in sustainability discussions.

Although CH4 and ammonia (NH3) emissions from non-ruminant livestock production systems are primarily released from stored manure, current emission inventories (models) do not account for all production and management systems. The purpose of this project was to track flows of nitrogen, volatile solids (VS), and ash into and out of several commercial livestock barns to estimate CH4 and NH3 emissions. Using a mass balance approach, volatile components like nitrogen and volatile solids are supported through simultaneous balances with ash (fixed solids). These mass-balance based estimates can be compared to national inventory emission estimates and serve as sustainability metrics, regulatory reporting, and management decisions.

What Did We Do?

In the initial step of this project, experimental data for VS, the precursor to methane, are compared to fixed estimates in methane emission estimation tools, like the EPA State Greenhouse Gas Inventory Tool (US EPA, 2017).

The litter from a commercial turkey finishing barn housing between 13,000 and 18,000 birds was sampled weekly for one month, with one additional sampling day one month later. VS concentrations were analyzed for each sample and used to estimate total VS production per year assuming six 15,000 bird flocks (Soriano et al., 2022). A range of VS percentage values for deep-pit cattle facilities were taken from Cortus et al. (2021) and converted to total VS production per year. A range of VS concentrations for deep-pit swine manure storage were taken from Andersen et al. (2015) and used to find total VS production per year of that system as well. Next, total VS productions per year were estimated for the same three systems using the State Greenhouse Gas Inventory Tool.

What Have We Learned?

Table 1 summarizes all calculated total VS values and CH4 estimates per year for both the estimation tool and the experimental data. For each of the three systems, the state inventory estimated total VS value falls within the ranges calculated with experimental data, however, the estimates cannot account for the variabilities found within each system. As seen in the experimental total VS values, there can be a large range of VS production due to differences within specific operations of each system. Total VS relates directly to CH4 emissions, so accurate estimates are important for determining greenhouse gas emission potential of a specific operation.

Table 1. All calculated total VS values and CH4 emissions estimates for each of the three systems.
Total VS production (kg/yr) Emissions*
State Inventory Experimental Values m3CH4
Feedlot Steer (500 head) 334,990 260,758 – 1,002,675 1,262**
Grower-Finisher Swine (1,200 head 160,408 107,514  – 216,669 19,050
Turkey (15,000 head) 314,594 206,838 – 359,245 1,699
*Emissions estimates found through the State Greenhouse Gas Inventory Tool
**Feedlot steer emission estimate assumes an open feedlot manure management system

Future Plans

Next steps for this study will include manure sampling at additional commercial turkey barns, deep-pit grower-finisher swine barns, and dairy cattle systems. Similar mass balances will be performed to determine total VS and nitrogen content to calculate CH4 and NH3 emissions from each system. These calculated values will again be compared to outputs of emission estimating tools.

Authors

Anna Warmka, Undergraduate Student, University of Minnesota – Twin Cities, Department of Bioproducts and Biosystems Engineering

Corresponding author email address

warmk011@umn.edu

Additional authors

Erin Cortus, Associate Professor, University of Minnesota – Twin Cities, Department of Bioproducts and Biosystems Engineering

Noelle Soriano, MS Student, University of Minnesota – Twin Cities, Department of Bioproducts and Biosystems Engineering

Melissa Wilson, Assistant Professor, University of Minnesota – Twin Cities, Department of Soil, Water, and Climate

Bo Hu, Professor, University of Minnesota – Twin Cities, Department of Bioproducts and Biosystems Engineering

Additional Information

Andersen, D.S., M.B. Van Weelden, S.L. Trabue, and L.M. Pepple. “Lab-Assay for Estimating Methane Emissions from Deep-Pit Swine Manure Storages.” Journal of Environmental Management 159 (August 2015): 18–26. https://doi.org/10.1016/j.jenvman.2015.05.003.

Cortus, E.L., B.P. Hetchler, M.J. Spiehs, and W.C. Rusche. “Environmental Conditions and Gas Concentrations in Deep-Pit Finishing Cattle Facilities: A Descriptive Study.” Transactions of the ASABE 64, no. 1 (2021): 31–48. https://doi.org/10.13031/trans.14040.

US EPA, OAR. “State Inventory and Projection Tool.” Data and Tools, June 30, 2017. https://www.epa.gov/statelocalenergy/state-inventory-and-projection-tool.

Soriano, N.C., A.M. Warmka, E.L. Cortus, M.L. Wilson, B. Hu, K.A. Janni. “A mass balance approach to estimate ammonia and methane emissions from a commercial turkey barn.” unpublished (2022).

Acknowledgements

This research was supported by the Rapid Agricultural Response Fund. We also express appreciation to farmer cooperators who allowed us to collect data on their farms and shared their observations with us.

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Livestock Methane Emissions Estimated and Mapped at a County-level Scale for the Contiguous United States


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Purpose         

This analysis of methane emissions used a “bottom-up” approach based on animal inventories, feed dry matter intake, and emission factors to estimate county-level enteric (cattle) and manure (cattle, swine, and poultry) methane emissions for the contiguous United States.

What did we do? 

Methane emissions from enteric and manure sources were estimated on a county-level and placed on a map for the lower 48 states of the US. Enteric emissions were estimated as the product of animal population, feed dry matter intake (DMI), and emissions per unit of DMI. Manure emission estimates were calculated using published US EPA protocols and factors. National Agricultural Statistic Services (NASS) data was utilized to provide animal populations. Cattle values were estimated for every county in the 48 contiguous states of the United States. Swine and poultry estimates were conducted on a county basis for states with the highest populations of each species and on a state-level for less populated states. Estimates were placed on county-level maps to help visual identification of methane emission ‘hot spots’. Estimates from this project were compared with those published by the EPA, and to the European Environmental Agency’s Emission Database for Global Atmospheric Research (EDGAR).

What have we learned? 

Overall, the bottom-up approach used in this analysis yielded total livestock methane emissions (8,888 Gg/yr) that are comparable to current USEPA estimates (9,117 Gg/yr) and to estimates from the global gridded
EDGAR inventory (8,657 Gg/yr), used previously in a number of top-down studies. However, the
spatial distribution of emissions developed in this analysis differed significantly from that of
EDGAR.

Methane emissions from manure sources vary widely and research on this subject is needed. US EPA maximum methane generation potential estimation values are based on research published from 1976 to 1984, and may not accurately reflect modern rations and management standards. While some current research provides methane emission data, a literature review was unable to provide emission generation estimators that could replace EPA values across species, animal categories within species, and variations in manure handling practices.

Future Plans    

This work provides tabular data as well as a visual distribution map of methane emission estimates from enteric (cattle) and manure (cattle, swine, poultry) sources. Future improvement of products from this project is possible with improved manure methane emission data and refinements of factors used within the calculations of the project.

Corresponding author, title, and affiliation        

Robert Meinen, Senior Extension Associate, Penn State University Department of Animal Science

Corresponding author email    

rjm134@psu.edu

Other authors   

Alexander Hristov (Principal Investigator), Professor of Dairy Nutrition, Penn State University Department of Animal Science Michael Harper, Graduate Assistant, Penn State University Department of Animal Science Richard Day, Associate Professor of Soil

Additional information                

None.

Acknowledgements       

Funding for this project was provided by ExxonMobil Research and Engineering.

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. 2017. Title of presentation. Waste to Worth: Spreading Science and Solutions. Cary, NC. April 18-21, 2017. URL of this page. Accessed on: today’s date.

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Methane Mitigation Strategies for Dairy Herds


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Purpose 

The U.S. dairy industry has committed to lowering the carbon footprint of milk production by 25% by 2020. A key factor in meeting this goal is reducing enteric greenhouse gas (GHG) emissions which represent about 51% of the carbon footprint of a gallon of milk. Methane (CH4) is the primary GHG emitted by dairy cows. Total methane emissions represented 10.6% of the total U.S. GHG emissions in 2014. Enteric CH4 emissions were 22.5% of the total methane emissions. Methane emissions from dairy cattle were 5.7% of total U.S. methane emissions or 0.6% of all U.S. GHG emissions. The purpose of this project was to examine nutrition and management options to lower methane emissions from dairy cattle.

What did we do?

This project utilized a number of approaches. One was to develop a base ration using the Cornell Net Carbohydrate and Protein System (CNCPS) model to evaluate the impact of level of dry matter intake and milk production on methane emissions. A second approach was to compile a database of commercial herd rations from 199 dairy farms. This database was used to examine relationships between the feeding program and CH4 emissions. A third component was to utilize published review papers to estimate potential on-farm CH4 reductions based on research data.

What have we learned? 

A base ration developed in the CNCPS model was evaluated at milk production levels ranging from 40 to 120 pounds of milk. As milk production increased, CH4 emissions increased from 373 to 509 grams/cow/day. This is primarily due to increasing levels of dry matter intake as milk production increases. However, the CH4 emissions per pound of milk decreased from 9.32 to 4.24 g as milk production increased. The 199 commercial herd database had an average input milk of 83.7 pounds per day with a range from 50 to 128 pounds. Daily dry matter intake (DMI) averaged 51.4 pounds with a range of 35.2 to 69.8. Simple correlations were run between CH4 emissions and ration components. Dry matter intake had a positive (0.795) correlation with CH4 emissions (g/day). However, the correlation between DMI and CH4/pound of milk was -0.65. These results agree with published research on the relationship of DMI and CH4 emissions. Starch intake also had a positive correlation (0.328) while percent ration starch was negatively correlated (-0.27) with CH4 emissions. There was also a positive correlation (0.79) between the pounds of NDF intake and CH4 emissions.

A review paper indicated that the maximum potential reduction in CH4 emissions by altering rations was 15% (Knapp et. al., 2014). Projected reductions from genetic selection, rumen modifiers and other herd management practices were 18, 5 and 18% in this same paper. The reduction by combining all approaches was estimated to be 30%. A second review paper listed mitigation strategies as low, medium or high (Hristov et. al. 2013). Potential reductions for the low group was <10% while the medium group was 10-30%. The high group had >30% potential to lower CH4 emissions. Ionophores, grazing management and feed processing were in the low group. Improving forage quality, feeding additional grain and precision feeding were in the low to medium group. Rumen inhibitors were listed in the low to high group. No items were listed only in the high group. These results provide guidance in terms of items to concentrate on at the farm level to reduce methane emissions.

Future Plans 

The number of commercial herds in the database will be expanded to increase the types of rations represented and the simple correlations run. In addition, a multiple regression approach will be used to better understand the relationships of ration components and CH4 emissions. Whole herd data will be obtained and examined to determine the proportion of the total herd CH4 emissions contributed by the various animal groups. The CNCPS program will also be used on rations at constant DMI to better understand the impact of specific ration components on CH4 emissions. These results of these will permit a more defined and targeted approach to adjusting rations to decrease CH4 emissions.

Corresponding author, title, and affiliation        

Dr. Larry E. Chase, Professor Emeritus, Dept. of Animal Science, Cornell University

Corresponding author email     

lec7@cornell.edu

Additional information               

Hristov A.N., J. Oh, J.L. Firkins, J. Dijkstra, E. Kebreab, G. Waghorn, H.P.S. Makkar, A.T. Adesogan, W. Yang, C. Lee, P.J. Gerber, B. Henderson and J.M. Tricarico. 2013. Mitigation of methane and nitrous oxide emissions from animal operations: I. Review of enteric methane mitigation options. J. Anim. Sci. 91:5045-5069.

Knapp J.R., G.L. Laur, P.A. Vadas, W.P. Weiss an d J.M. Tricarico. 2014. Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:3231-3261.

Acknowledgements       

This material is based upon work that is 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 author and do not necessarily reflect the view of the U.S. Department of 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. 2017. Title of presentation. Waste to Worth: Spreading Science and Solutions. Cary, NC. April 18-21, 2017. URL of this page. Accessed on: today’s date.

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