Tag: greenhouse gas

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Distillers grains impact on feedlot pen surface material

Purpose

Distillers grains (DGs) have been heavily researched as a diet additive for cattle since the early 2000s. Research has considered the nutritional value, optimization, and even how it impacts odors and greenhouse gases emitted from the surface of the pens that house cattle fed these diets. However, no work has been conducted to determine if there are changes in pen surface material properties after exposure to manure from diets containing DGs. Recent conversations with producers highlighted changes in pen surface characteristics such as significant loss in material and inability to maintain mounds in the pen. after DGs were fed for prolonged periods. Research has shown that manure from distillers diets contain excess proteins which we hypothesized could cause interruptions in soil particle interactions thus leading to a loss in integrity of the pen surface. The purpose of this work was to investigate if excess excreted protein in urine was the cause of changes in the properties of pen surface material.

What Did We Do?

This work was comprised of a large-scale study at a feedlot and a lab-scale study. In the feedlot study, cattle were fed either control (no DGs), wet DGs (40%) or dry DGs (40%) for 180 days. Once cattle were finished and removed from their pens, pen surface material (PSM) was collected from 4 general locations within each pen: behind the apron, on top of the mound, the side of the mound and the bottom of the pen. Samples from each pen with the same treatment were pooled into one single composite to represent each of the treatments. Samples were divided into two sets and analyzed by a commercial laboratory as either soil or manure. Soil analysis included pH, soluble salts, organic matter, nitrate nitrogen, potassium, sulfate, zinc, copper, calcium, sum of cations, % saturation of calcium and magnesium, and Mehlich-III phosphorus. Manure analysis included organic nitrogen, ammonium nitrogen, nitrate, phosphorus, potassium, sulfur, calcium, magnesium, sodium, zinc, iron, manganese, copper, boron, soluble salts pH, and moisture

For the lab-scale study, PSM was collected from a feedlot that does not feed DGs. Material was dried, ground, and sieved. Synthetic urine was added daily to bottles containing 300 g of PSM for 3 weeks to simulate prolonged addition of urine to feedlot pen surface. Samples were then shaken for 30 minutes and left at room temperature unsealed overnight. Synthetic urine contained either 0, 8, 16, or 32% additional protein. At the end of the study, samples were dried and sent to a commercial lab to be tested as soil in which the same properties listed above were again reported.

What Have We Learned?

In the feedlot study, differences (p < 0.05) in soluble salts were observed between all three treatments. Differences (p<0.05) were observed between the control and  DGs diets for soluble salts, organic matter, potassium, sulfate, magnesium saturation, Mehlich P, pH, ammonium nitrogen, organic N, total N, phosphate, total phosphorus, and sulfur.

For the lab-scale study, properties in which differences (p<0.05) were measured between the control and treatments include: nitrate N, cation exchange capacity, magnesium, sodium, zinc, calcium saturation and magnesium saturation. Analysis which resulted in differences (p < 0.05) between control and all three added protein treatments include Mehlich P, potassium, calcium, and copper. No significant differences were determined between the control and the treatments for zeta potential and conductivity. Results of the feedlot study compared to the lab scale study suggest that changes in PSM are not solely caused by excess soluble protein excretion.

Future Plans

The lab scale study will be used to determine if fiber has any contribution to the observed changes in PSM properties. The results of this study will help us determine how best to manage feedlot pens when varying forms and concentrations of DGs are fed to the cattle. It may also provide insight into potential pen surface amendments that may be used to mitigate the negative effects of feeding DGs to cattle.

Authors

Corresponding author

Bobbi Stromer, Research Chemist, US Meat Animal Research Center, Bobbi.stromer@usda.gov

Additional authors

Mindy Spiehs, Research Nutritionist, US Meat Animal Research Center

Bryan Woodbury, Research Engineer, US Meat Animal Research Center

Additional Information

USDA is an equal opportunity provider and employer

Acknowledgements

The authors wish to thank Victor Gaunt for assistance with data collection.

 

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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Ammonia and greenhouse gas emissions when chicken litter is added to beef pen surface material

Purpose

One of the big challenges in animal agricultural waste management is reduction of greenhouse gas (GHG) emissions. Pen surface material (PSM) from beef feedlots has been characterized for its GHG emission profile and research has now shifted to focus on emission-reducing treatments for pen surfaces. Chicken litter (CL) has a nutrient and microbial profile unique from beef manure which was hypothesized to cause a change in GHG emissions.  This study was conducted to determine if the addition of CL to beef PSM would reduce methane (CH4), carbon dioxide (CO2), ammonia (NH3), and nitrous oxide (NO2) emissions.

What Did We Do?

A lab scale study was conducted in which 24 stainless steel pans (12.75 x 20.75 x 2.5 in, L x W x H) were filled with PSM (3000 g, control) that had been collected from USMARC feedlot in August. Twelve pans of PSM had chicken litter (20% wt/wt) added to the top of the pan and gently raked into the PSM. All pans had 1000 g of water added. All samples were kept in an environmentally controlled chamber at 25 C for 18 days and watered after each measurement to keep sample moisture consistent. Sample pH and loss in water were recorded throughout the experiment. Flux measurements of CH4, CO2, N2O and NH3 were measured on days 0, 1, 3, 6, 8, 10, 13, 15, and 18 using Thermo Scientific gas analyzers. Data was analyzed for statistical differences in emissions as a function of time (days), treatment (control vs chicken litter), and time*treatment. At the conclusion of emission measurements, samples were pooled and sent to a commercial lab for nutrient analysis.

What Have We Learned?

All measured gases showed significant changes over the time of the experiment (p < 0.05). Significant differences between treatments (p < 0.05) were recorded for N2O with a higher emission recorded for PSM+CL.  Significant treatment* day interactions were observed for CH4, NH3, and N2O (p < 0.05). Methane and NH3 emissions peaked on day 1 and steadily decreased over the 18 days; N2O emissions steadily rose from day 0 to day 8 and then steadily decreased through day 18. Nutrient analysis determined PSM with chicken litter contained significantly higher levels of organic N, ammonium N, and total nitrogen. There was no significant difference of N2O in control vs treated samples. Chicken litter treated samples showed higher levels of P2O5, K2O, sulfur, calcium, magnesium, sodium, zinc, copper, boron, soluble salts, and organic matter. From this work, we conclude that addition of chicken litter to PSM did not favorably alter emissions of greenhouse gasses. Mixing the manures may be beneficial for land application to cropland or for composting.

Future Plans

Future research will evaluate different sources of composted CL, the emission profile of CL, and consideration of how mixtures of PSM and CL impact nutrient retention and composting.

Authors

Presenting & corresponding author

Bobbi Stromer, Research Chemist, US Meat Animal Research Center, Bobbi.stromer@usda.gov

Additional authors

Mindy Spiehs, Research Nutritionist, US Meat Animal Research Center

Bryan Woodbury, Research Engineer, US Meat Animal Research Center

Additional Information

USDA is an equal opportunity provider and employer

Acknowledgements

The authors wish to thank Victor Gaunt for assistance with data collection

 

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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Optimizing Manure Application Timing for Methane Reduction and Economic Gains through Carbon Credits

Purpose

Methane emissions from manure storages significantly contribute to the livestock industry’s carbon footprint. While various manure management strategies are used to reduce greenhouse gas (GHG) emissions on farms, such as anaerobic digestion and composting, many of these strategies are cost-prohibitive for small-to-medium-sized farms. Strategic manure application timing to limit GHG emissions is a practical, scalable option to reduce methane production in manure storages.

Carbon credits are financial incentives for farmers who adopt practices that reduce greenhouse gas emissions, such as cover crops or methane emissions abatement. These credits can then be sold to companies seeking to offset their emissions. This study evaluates the impact of manure application timing on methane emissions from storages and explores how carbon credits could act as an incentive for farms to employ climate-smart manure management practices. By comparing different manure application strategies (fall, spring, in-season sidedress, and split applications), we assess the methane reductions and improved economics of optimized timing.

What Did We Do?

Methane emissions were estimated using data from a lab-based study conducted by Andersen et al. (2015), who measured methane emissions from deep-pit swine manure at various temperatures. From this data, we created a model incorporating manure production rates and ambient temperature dynamics to predict daily methane emissions from a 4800-head slurry storage and 4800-head deep-pit swine production facility.

Seven application scenarios were compared: fall (November 1), spring (April 15), sidedress (June 1), fall-spring, fall-sidedress, spring-sidedress, and fall-spring-sidedress split applications. Total methane emissions were calculated for each scenario, allowing us to determine the GHG emissions abated by shifting from a fall application to an alternate strategy. An economic assessment was conducted using a $30/metric ton carbon dioxide equivalent (MT CO2e) carbon credit valuation to determine the financial implications of these methane mitigation strategies.

What Have We Learned?

For our swine slurry store model, methane emissions were highest in the single fall application scenario due to the full storage attained during peak summer temperatures, with annual emissions totaling nearly 0.5 MT CO2e/pig-space (Figure 1). Shifting application to spring or sidedress reduced emissions by approximately 50%. Split applications showed a further reduction in emissions by maintaining lower storage volumes throughout the year.

Figure 1: Estimated methane emissions in metric tons of carbon dioxide equivalent (MT CO2e) from slurry storage for fall, spring, sidedress, fall-spring split (F-S), fall-sidedress split (F-SD), spring-sidedress split (S-SD), and fall-spring-sidedress split (F-S-SD) applications.
Figure 1: Estimated methane emissions in metric tons of carbon dioxide equivalent (MT CO2e) from slurry storage for fall, spring, sidedress, fall-spring split (F-S), fall-sidedress split (F-SD), spring-sidedress split (S-SD), and fall-spring-sidedress split (F-S-SD) applications.

From an economic perspective, carbon credits significantly enhanced the financial viability of the new application strategies. Carbon credits from abated emissions are projected to bring a maximum of $10/pig-space, or about $74/acre, to the farm annually in the F-S-SD scenario (Table 1). The improved manure application timing can also benefit crop yield, making a spring or sidedress manure application even more economically favorable.

Table 1: Projected carbon credit income for a 4800-head wean to finish swine farm with a slurry storage for fall, spring, sidedress, fall-spring split (F-S), fall-sidedress split (F-SD), spring-sidedress split (S-SD), and fall-spring-sidedress split (F-S-SD) applications.

Fall Spring Sidedress F-S F-SD S-SD F-S-SD
Carbon Credit Income

($/acre)

 $           –  $    33.63  $    33.71  $    41.95  $    45.82  $    45.69  $    52.06
Carbon Credit Income

($/pig-space)

 $           –  $       6.50  $       6.51  $       8.10  $       8.85  $       8.83  $    10.06

Future Plans

Further research should be conducted to refine the temperature aspect of the model. In the slurry store model, we assume that the manure temperature equals the 10-day average temperature. A study to verify the true manure temperature throughout the year would improve the confidence level of the current model. For deep pit barns, we use measured temperature data from 58 barns over 13 months, but manure temperatures were collected from the manure pump out access port and may not represent average manure temperatures in the barn. Future models to assess differences between deep pit and slurry store emissions will highlight the optimal manure management strategies for limiting GHG emissions.

Using specialized high-clearance irrigation equipment, like the 360 RAIN from 360 Yield Center, could enhance the feasibility of more frequent manure applications, reducing methane emissions while maintaining crop nitrogen availability. Additionally, developing standardized carbon credit protocols for manure management could create opportunities for more producers to monetize methane reduction efforts, further incentivizing climate-smart manure application strategies.

Authors

Presenting author

Jacob R. Willsea, Graduate Research Assistant, Iowa State University Department of Agricultural and Biosystems Engineering

Corresponding author

Daniel S. Andersen, Associate Professor, Iowa State University Department of Agricultural and Biosystems Engineering, dsa@isatate.edu

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. https://doi.org/10.1016/j.jenvman.2015.05.003

Talkin’ Crap Podcast Episode:

https://talkincrappodcast.buzzsprout.com/2163071/episodes/16472267-timing-is-everything-reducing-methane-emissions-with-manure-management

Andersen Lab Poster Repository:

https://iastate.box.com/s/3kkzdzcjlk9qcfrgbv6mj9x7vdk1v0fp

Acknowledgements

USDA-NRCS

Brent Renner

360 Yield Center

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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Laboratory estimation of methane emission rates from Midwest dairy manure samples representing common manure types and storage conditions

Purpose

Methane (CH4) emissions from manure storage are a substantial contributor to the cradle-to-farmgate climate footprint for many dairy farms, especially for farms storing manure as liquid or slurry (Rotz et al., 2021). Dairy systems handle, treat, and store manure in various ways. In combination with environmental conditions, these differences in manure-related structures and processes potentially cause substantial farm-to-farm variability in CH4 production and intensity. However, few methods are available to estimate CH4 emissions specific to a manure storage or farm system.

To enable estimation of CH4 emission rate per unit of manure (methane emission rate, MER), research by Andersen et al. (2015) tested a laboratory assay on swine manure from deep pits. These authors showed that MER was related to manure chemical composition and varied across the year, with the highest values recorded in late fall. Our research aimed to build on Andersen et al. (2015) by testing dairy rather than swine manure to 1) compare MER across a variety of manure types, storage types, and typical storage durations, 2) examine seasonal differences in MER, and 3) quantify farm-to-farm and storage-to-storage variation in MER. Ultimately, we expected to illustrate how the MER laboratory assay could be used in estimating farm-specific CH4 emission rates from dairy manure storages.

What Did We Do?

We partnered with 27 dairies in the U.S. Upper Midwest with liquid and slurry manure storages. At approximately 2–4-month intervals throughout 2024, we collected composite samples (n = 208) representing various manure types, typical storage durations, and storage types. Most samples were whole manure (n = 165, 79%) or liquid separated manure (n = 34, 16%), with remaining samples representing flush water and digestate. Samples represented areas where manure was stored for short durations (≤1 mo.; n = 120, 58%) and long durations (>1 mo.; n = 88, 42%). Most long-term storage was unroofed, and most short-term storage was roofed. Samples represented transfer pits (n = 84, 40%), unroofed basins or pits (n = 67, 32%), and below-building pits (n = 30, 14%), among other storage types. Samples were distributed evenly across seasons for most farms, except that fewer samples were collected during winter due to outdoor storages freezing over.

For the MER assay, we incubated 75.06 ± 0.02 g (mean ± standard error) of manure at 72°F in triplicate 100 mL serum bottles for 2.99 ± 0.01 days. Then, we measured gas displacement with a syringe and headspace CH4 concentration with gas chromatography (Agilent 490 Micro GC, Agilent Technologies, Inc., Santa Clara, CA). We calculated MER as the average CH4 emission (mL) at 72°F per liter of manure per day. To examine differences due to manure type, typical storage duration, storage type, and season, we fit linear mixed models to log-transformed MER, then back-transformed model-implied means and standard errors. Additionally, we examined variance components attributable to individual storages and farms in relation to the residual variance. Storage-to-storage differences explained a small amount of total variance, so the random effect of storage was removed. Significance was declared at p<0.05.

What Have We Learned?

Across samples, the MER was highly variable and right-skewed (mean = 37, median = 21, standard deviation = 45 mL CH4 L-1 d-1; Figure 1), with a small fraction of extremely high values (maximum = 236 mL CH4 L-1 d-1). In contrast with our expectations, we found no effect of manure type, typical storage duration, and storage type on MER. Season influenced MER (F [3, 183.4] = 11.3, p < 0.001), with Fall samples exhibiting a larger MER compared with other seasons (Table 1). Larger MERs in Fall samples were driven by greater gas volume and CH4 concentrations in headspace; model-implied means of both variables nearly doubled in Fall compared with other seasons. Considering that all samples were incubated at the same temperature during the MER assay, greater MER during Fall may indicate that these samples had more abundant and active methanogen populations. Additionally, differences in chemical and physical properties of manure may have enhanced substrate availability for methanogenesis in Fall samples relative to other seasons.

Table 1. Results of a laboratory assay to estimate methane emission rate from dairy manure samples (n = 208) by incubating at 72°F in serum bottles for 3 days.
Model-Implied Mean (Confidence Interval)
Variable Spring Summer Fall Winter
Volume displacement, mL 14 (3, 25) 16 (4, 27) 26 (14, 37) 13 (0, 26)
Headspace methane, % 5 (3, 10) 8 (5, 16) 14 (8, 26) 6 (3, 12)
Methane emission rate,
mL CH4 L-1 d-1		 13 (7, 25) 22 (11, 43) 41 (21, 79) 15 (7, 33)

 

Although our results illustrated that the mean MER was generally similar across categories of manure types, storage durations, and storage types, we found that between-farm differences accounted for 18% of the total variance in MER. In other words, samples from the same farm were correlated on average 0.18. This suggests that there are farm-to-farm differences in MER that were not explained by the predictors we considered as fixed effects.

Figure 1. Methane emission rates of samples (n = 208 points) showing the median and first and third quartiles (box) with whiskers 1.5 times the interquartile range.
Figure 1. Methane emission rates of samples (n = 208 points) showing the median and first and third quartiles (box) with whiskers 1.5 times the interquartile range.

Future Plans

In future work on this project, we plan to explore if between-farm differences in MER can be explained by other farm meta-data such as bedding type, manure removal frequency, storage volume, and surface area of manure. Additionally, we will explore relationships between manure chemical composition (total solids, volatile solids, total nitrogen) and MER. Similar to Andersen et al. (2015), we are examining the temperature sensitivity of methanogenesis in different sample types. In subsequent work, we may consider relating MER to other chemical constituents in manure samples related to substrate availability (e.g., fiber fractions) or fermentation end-products (e.g., volatile fatty acids).

Authors

Presenting author

MaryGrace Erickson, Postdoctoral Associate, University of Minnesota

Corresponding author

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

Additional author

Noelle Cielito Soriano, Ph.D. Candidate, 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. https://doi.org/10.1016/j.jenvman.2015.05.003

Rotz, A., Stout, R., Leytem, A., Feyereisen, G., Waldrip, H., Thoma, G., Holly, M., Bjorneberg, D., Baker, J., Vadas, P., & Kleinman, P. (2021). Environmental assessment of United States dairy farms. Journal of Cleaner Production, 315, 128153. https://doi.org/10.1016/j.jclepro.2021.128153

Acknowledgements

We thank the farms who participated in this research for providing samples and data. Additionally, we are grateful to Kevin Bourgeault, Seth Heitman, Sabrina Mueller, and Jacob Olson for contributing to sampling and laboratory analysis. This research is supported by 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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Greenhouse gas impacts resulting from co-digestion of dairy manure with community substrates

Purpose

The US Dairy industry established a voluntary environmental stewardship goal to achieve greenhouse gas (GHG) neutrality by 2050 among farmers and processors collectively. Manure management and enteric emissions combined account for approximately 70% of the GHG footprint of the US dairy industry, with nearly equal contributions from each (Thoma, 2013). There are multiple manure management systems used by dairy farmers in the Northeast and Upper Midwest that substantially impact GHG emissions. Quantification of GHG emissions for different manure management systems is necessary to compare options and strategies that can be applied to reduce GHG, especially methane, to move toward sustainability and reach the targets set by industry and governments.

Methane is the primary GHG emitted from the long-term storage of dairy manure, a water quality best management practice employed by many dairy farms today. Landfills are also a significant source of methane emission primarily due to degradation of organic waste, notably pre- and post-consumer food wastes (community substrates). Methane is a highly potent GHG that impacts warming by 25 – 28 times as much as carbon dioxide (CO2) on a 100-year global warming potential (GWP) time scale (US EPA). However, because methane has a lifespan in the atmosphere of around 12 years, it has been accounted for on a 20-year GWP scale (84 times the impact of CO2) by the State of New York (Climate Leadership and Community Protection Act). Manure management systems that substantially reduce methane, such as the co-digestion of manure with food waste, can achieve significant reductions of the GHG emissions associated with milk production.

What Did We Do?

The GHG emissions resulting from the anaerobic co-digestion of raw dairy manure and community substrate (i.e., food processing waste mixture diverted from landfilling) in an equal mass of each (total mass basis) were calculated as part of a larger study comparing eight different manure management systems. The community substrate was modeled as 50% ice cream and 50% dog food by mass. Methane and nitrous oxide emissions were calculated with equations that use the mass flow of volatile solids (VS) and nitrogen through the co-digestion manure management system that included digestate solid-liquid separation using a screw press and the long-term storage of separated liquid. Carbon dioxide and methane associated with system energy use and energy production as pipeline-quality renewable natural gas (RNG), as well as landfill organics diversion were also calculated. The parasitic energy use (heat and electricity) of the digester and related manure management and biogas upgrading equipment was supplied on an average annual load basis by a portion of the biogas produced. The total net GHGs were summed using a CO2-equivalent (CO2e) methodology (both GWP100 and GWP20 were computed) and normalized on a per lactating cow per year basis. A sensitivity analysis of eleven variables was conducted to quantify the impact of each on the net GHG result.

What Have We Learned?

The co-digestion system net annual GHG impact was calculated to be −16 metric tons (MT) CO2e cow-1 (GWP100) and −43 MT CO2e cow-1 (GWP20). For the co-digestion mixture analyzed (50% liquid dairy manure, 25% ice cream, and 25% dog food), the anaerobic digester biogas production was 4 times greater than the biogas production for manure alone (on a per lactating cow basis). This significant energy production potential contributed an offset of 3.9 MT CO2 cow-1 year-1, assuming the net RNG after supplying the system’s parasitic energy usage displaced the CO2 emissions from combusting approximately 380 gallons of diesel. In comparison, a methane leakage (or loss) of 2% from the digester to RNG system was equivalent to 18% of the energy offset at GWP100 (0.7 MT CO2e cow-1 year-1) and 62% at GWP20 (2.4 MT CO2e cow-1 year-1). Despite the greater contribution of methane leakage at GWP20 on a CO2e basis, the methane offset from landfilling the community substrate also substantially increased, resulting in just a 5 – 6% increase in the net annual GHG (remaining net negative) when methane leakage was varied from 1 to 3% under both GWP time scales. The methane leakage amount was also the most sensitive variable studied for the co-digestion system and the relatively low impact on total net GHG indicates the effectiveness of this type of manure management system as a tool to reach net GHG neutrality.

Future Plans

A next step in the assessment of co-digestion of dairy manure and food waste diverted from landfills is to continue improvement of our Cornell Dairy Digester Simulation Tool that predicts biogas production from a variety of food wastes combined in different quantities with dairy manure. This tool will also allow for the economic feasibility analysis of different co-digestion system sizes and substrate mixtures, inclusive of tipping fee variation and energy generation options (electricity and RNG) and associated values. This work will help farmers assess the feasibility of implementing or participating in a co-digestion system for manure management.

In future work contingent on funding, we plan to conduct comprehensive field measurements of methane emissions from the long-term storage of raw manure, separated manure liquid, and digested effluent. The equations that calculate methane are gross and depend on volatile solid content and degradability of the stored material, as well as temperature and retention time. Verification of these equations and inputs will give more confidence in utilizing bottom-up calculations of GHGs from manure management practices.

Authors

Lauren Ray, Extension Support Specialist III, Cornell PRO-DAIRY Dairy Environmental Systems Program

Corresponding author email address

LER25@cornell.edu

Additional authors

Curt A. Gooch, Sustainable Dairy Product Owner, Land O’Lakes – Truterra; Peter E. Wright, Extension Associate, Cornell PRO-DAIRY Dairy Environmental Systems Program

Additional Information

More information on related work can be found on the Cornell University PRO-DAIRY website under Environmental Systems: https://cals.cornell.edu/pro-dairy/our-expertise/environmental-systems.

Thoma, G., J. Popp, D. Shonnard, D. Nutter, M. Matlock, R. Ulrich, W. Kellogg, D. S. Kim, Z. Neiderman, N. Kemper, F. Adom, and C. East. (2013). Regional analysis of greenhouse gas emissions from USA dairy farms: A cradle to farm-gate assessment of the American dairy industry circa 2008. Int. Dairy J. 31:S29–S40. https://doi.org/10.1016/j.idairyj.2012.09.010.

US EPA, https://www.epa.gov/ghgemissions/understanding-global-warming-potentials. Accessed 2/24/2022.

Climate Leadership and Community Protection Act. 2020. New York State Senate Bill S6599.

Acknowledgements

The Coalition for Renewable Natural Gas and the New York State Department of Agriculture and Markets provided a portion of the financial resources to support the development of this work.

 

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.

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Existing Data on Long Term Manure Storages, Opportunities to Assist Decision Makers

Long-term manure storages on dairy farms are temporary containment structures for byproducts of milk production. Manure, milkhouse wash, bedding, leachate, and runoff are stored until they can be utilized as fertilizer, bedding, irrigation, or energy. The practice of long-term storage creates stakeholders who collect data in their interactions with storages. This presents an opportunity to support data driven  decision making on best use and operation of storages.

What Did We Do?

Prevalent stakeholders who collected data on storages were identified and the information they collected was examined. Data that could assist in depicting storage infrastructure was retained. Data not collected but of value to decision makers was noted. From this a combined data set was proposed that could depict the size, state, and impact of storage infrastructure. The feasibility of such a combined data set and opportunities from it were considered.

What Have We Learned?

General volume, general configuration, and year installed are most often collected by stakeholders while detailed configuration and detailed waste type are rarely collected. Cost is not collected. (Table 1) Stakeholders do not collect data on operations of all sizes. Most data is collected on large and medium operations while data is rarely collected on small operations. Stakeholders use their own definitions and classification structures.

Table 1 Combined data to be collected to assist decision makers
Data Specificity Currently collected by
Location County State, NRCS, CNMP
City STATE, CNMP
Address STATE, CNMP
Lat, Long NONE
Storage Volume Total STATE, NRCS, CNMP
Operational STATE, CNMP
Geometric Dimensions STATE, CNMP
Above/Below Ground STATE, NRCS, CNMP
Year Built Year Built STATE, NRCS, CNMP
Year Inspected STATE, CNMP
Year Recertified STATE, CNMP
Year Upgraded STATE, CNMP
Configuration Liner (Dug,Clay,Plastic,Concrete,Steel) STATE, NRCS, CNMP
Certification(313,PE,ACI318,ACI350) STATE, NRCS, CNMP
Cover(none, rain, gas) STATE, NRCS, CNMP
Waste Volume Produced STATE, CNMP
Type(manure,washwater,leachate,runoff) STATE, CNMP
Manure Type(liquid, stack, pack, liquid sand, liquid recycled) CNMP
Advanced Treatment CNMP
Costs Total NONE
Per Component NONE
Operational NONE
*STATE-State of Michigan

*NRCS-United States Department of Agriculture Natural Resources Conservation Service
Table 2 First level characterization
Parameter
Number
Location
Age
Total Stored Capacity
Precipitation Stored Capacity
Waste Stored Capacity
Produced Waste Volume
Produced Waste Type
Produced Manure Volume
Produced Manure Type
Liner Type
Cover Type
Certification Type

A first level characterization of storage infrastructure is proposed from Table 1, Table 2. Items in the first level characterization depict the location and condition of the storage infrastructure. Each of these items may be represented over a specific geographic area, such as state, watershed, or county. In a yearly inventory each of these items may be represented over time.  

Table 3 Second level characterization
Parameter
Length of Storage Estimate
Proximity to Sensitive Area Estimate
Storage Density
Seepage Estimate
Emissions Estimate

Using Table 2 a second level characterization is proposed, Table 3. Items in the second level characterization estimate the capacity and impact of the state’s storage infrastructure. Supplementary information to estimate certain parameters is required.  Each of these items may be represented over time and specific geographic area. Cost to implement and operate storage infrastructure are the third characterization, Table 4. Each of these items may be represented over time and specific geographic area.

Table 4 Cost characterization
Parameter
Cost Estimate
Implement, Per Volume
Per Configuration
Operate, Per Volume
Per Configuration

Combining and characterizing data from different stakeholders can provide a data-driven representation of storage infrastructure. Condition, capability, and impact of the storage infrastructure can be represented over time and geographic area. Monitoring, evaluating actions, forecasting issues, and targeting priority areas1 is made feasible.  Example opportunities are as follows.

Long-term storage is desirable to enable storage of manure during winter months. Combined data can provide feedback on average days of storage in the state or watershed. The cost to achieve target days of storage may be estimated and the days of storage may be tracked over time as a result of funding efforts.

New York State released $50 million for water quality funding, which assisted in the implementation of new storages. In the implementation of these storages opportunity exits to collect cost data to inform future funding levels, quantify the increase in long-term storage provided as a result of the funding, and forecast when these storages are projected to reach the end of their lifecycle2.   

As interest in cover and flare storages increase to offset livestock emissions combined data sets can assist in evaluating feasibility of such a proposal3 4 5. Potential emissions to be captured and cost to implement can be estimated.  

Obstacles to collecting and combining data are cost, insufficiency, and misuse. As specificity in the data to be collected increases so does the cost to collect, combine, and maintain. Additionally, stakeholders have existing data collection infrastructure that must be modified at cost to allow combination. If the combined data set is not sufficiently populated by stakeholders is will depict an inaccurate representation of storage infrastructure. Finally, the risk of misuse and conflict amongst decision makers is present. Stakeholders may purposely or inadvertently use the inventory to reach erroneous conclusions.  

Future Plans

Obstacles to implementation are not insignificant. Detailed analysis is required to determine the exact data to be collected, definitions to be agreed upon, and extent of coverage such that maximum benefit will be derived for decision makers.

Full benefit of storage data is increased by additional data sets such as state-wide livestock numbers, precipitation and temperature distributions, surface water locations, ground water levels, populations center locations, well locations, shallow bedrock locations, karst locations, complaint locations, and operator violations locations. The feasibility of obtaining these data sets should be determined.

The implementation and use of storages has additional stakeholders outside of those identified here. Additional stakeholders should be identified that can enhance or derive value from a combined data set on long term storages, such as manure applicators, handling and advanced treatment industry, extension services, zoning officials, professional engineers, environmental groups, and contractors.

Authors

Corresponding author

Michael Krcmarik, P.E., Area Engineer, United States Department of Agriculture Natural Resources Conservation Service, Flint, Michigan

Michael.Krcmarik@usda.gov

Other authors

Sue Reamer, Environmental Engineer, United States Department of Agriculture Natural Resources   Conservation Service, East Lansing, Michigan

Additional Information

    1. “Conservation Effects Assessment Project (CEAP).” Ceap-Nrcs.opendata.arcgis.com, ceap-nrcs.opendata.arcgis.com/.
    2. $50 Million in Water Quality Funding Available for NY Livestock Farms.” Manure Manager, 27 Sept. 2017, www.manuremanager.com/state/$50-million-in-water-quality-funding-available-for-ny-livestock-farms-30286.
    3. Wright, Peter, and Curt Gooch. “ASABE Annual International Meeting.” Estimating the Economic Value of the Greenhouse Gas Reductions Associated with Dairy Manure Anaerobic Digestions Systems Located in New York State Treating Dairy Manure, July 16-19 2017.
    4. Wightman, J. L., and P. B. Woodbury. 2016. New York Dairy Manure Management Greenhouse Gas Emissions and Mitigation Costs (1992–2022). J. Environ. Qual. 45:266-275. doi:10.2134/jeq2014.06.0269
    5. Barnes, Greg. “Smithfield Announces Plans to Cover Hog Lagoons, Produce Renewable Energy.” North Carolina Health News, 28 Oct. 2018, www.northcarolinahealthnews.org/2018/10/29/smithfield-announces-plans-to-cover-hog-lagoons-produce-renewable-energy/.
    6. Michigan Agriculture Environmental Assurance Program. MAEAP Guidance Document For Comprehensive Nutrient Management Plans. 2015,www.maeap.org/uploads/files/Livestock/MAEAP_CNMP_Guidance_document_April_20_2015.pdf.

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.

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Feed Manipulation, Manure Treatment and Sustainable Poultry Production

This study examined the effects of different treatments of poultry faecal matter on potential greenhouse gas emission and its field application and also evaluated dietary manipulation of protein on the physico-chemical quality of broiler faeces and response of these qualities to 1.5% alum (Aluminium sulphate) treatment during storage.

Poultry litters were randomly assigned to four treatments: salt solution, alum, air exclusion and the control (untreated). Chicks were allotted to corn-soy diets for 42d. The diets were 22 and 20% CP with methionine + lysine content balance and, 22 and 20% CP diets with 110% NRC recommendation of methionine and lysine.

Alum treated faeces had higher (p<0.05) nitrogen retention than other treatments. Treated faecal samples retained more moisture (p < 0.05) than control. The pH tended to be acidic in treated samples (alum, 6.03, p<0.05) and alkaline in the control (7.37, p<0.05). Mean faecal temperature was lower for alum treated faecal samples (28.58oC, p<0.05) and highest for air-tight (29.4oC, p<0.05). Nitrogen depletion rate was significant lower (p<0.05) in alum treated faecal samples. Post-storage, samples treated with alum increased substantially (≥ 46.51%) in total microbial count, while total viable count was lower (p>0.05; 2.83×106 cfu/ml) in air-tight treatment. Maize seeds planted on alum, air-excluded and control litter soils had average germination percentage range of 65–75%, 54–75% and 74-75%, respectively. In Sorghum plots, GP was 99%, and 89%, respectively for alum and air-tight treated soil 2WAP. Average maize height 21DAP was 48 cm and 23 cm for alum and air-tight treatment, respectively. Salt treated faecal samples did not support germination. Faecal pH of broiler fed low protein diets was acidic (4.76-4.80) while treatment with alum (1.5%) led to further reduction in pH (4.78 to 4.58) faecal nitrogen and organic matter compared with control faeces in a 7 days storage. Faecal minerals were generally lower. In conclusion, feeding low level of dietary protein with or without methionine and lysine supplementation in excess of requirement is a suitable mitigation for nitrogen emission and mineral excretion in broiler production. Alum treated poultry litter will mitigate further nitrogen loss in storage because it lowered nitrogen depletion rate, pH, weight, temperature and supports potential agronomic field application index.

On-farm Demonstration of the application of these results to assist farmers to produce poultry sustainably.

Further reading

https://scholar.google.com/citations?hl=en&user=NZGTKC8AAAAJ#d=gs_md_cita-d&u=%2Fcitations%3Fview_op%3Dview_citation%26hl%3Den%26user%3DNZGTKC8AAAAJ%26citation_for_view%3DNZGTKC8AAAAJ%3AW7OEmFMy1HYC%26tzom%3D-60  

*BOLU, Steven Abiodun, ADERIBIGBE, Simeon Adedeji  OLAWALE, Simon, Malomo, G. A., Olutade, S.G and Suleiman, Z.G. Department of Animal Production, University of Ilorin, Ilorin, Kwara State, Nigeria.
*Corresponding Author: Department of Animal Production, University of Ilorin, Ilorin, Kwara State, Nigeria.
Email: bolusao2002@yahoo.co.uk Phone: +234 8060240049

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.

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Lifecycle greenhouse gas (GHG) analysis of an Anaerobic Co-digestion Facility Processing Dairy Manure and Industrial Food Waste in NY State

While the theoretical benefits of anaerobic digestion have been documented, few studies have utilized data from commercial-scale digesters to quantify impacts.  Previous studies have analyzed a range of empirical studies to constuct emission factors for a generic European AD plant processing source separated municipal solid waste.  However, most U.S. studies have applied reporting protocols and have been based upon theoretical assumptions.  Furthermore, GHG analyses of U.S. co-digestion facilities are limited to one scenario in protocol based analysis of community digester options. 

Purpose          

We are not aware of any peer-reviewed studies of US anaerobic co-digestion. Several case studies have presented calculations of impacts using GHG reporting protocols, however significant portions of the lifecycle have been neglected such as the feedstock reference case emissions, digestate storage emissions and fertilizer displacement impacts. Furthermore, they have often been modeled using general theoretical assumptions such as number of cows rather than empirical data on feedstock volume and characteristics and digester operation.

What did we do? 

A lifecycle GHG analysis was performed based upon data reported on a farm-based anaerobic co-digestion system in New York State, resulting in an 71% reduction in GHG impact relative to conventional treatment of manure and food waste.

The objective of this study was to provide a comprehensive analysis of GHG emissions based upon a NYS digester that co-digests manure and industrial-sourced food waste. Empirical data on feedstock (t-km transport, avoided disposal, TS, VS, TKN), digester operation (m3CH4, KWh, exhaust emissions) and effluent properties (TS,VS,TKN) were combined with regional parameters (i.e., climate, soil type and management practices) to represent a state-of-the-art, anaerobic co-digestion facility in NYS. This data was combined with information collected through interviews in order to model a reference case, representing the business-as-usual food waste disposal and manure management practices en lieu of the anaerobic co-digestion system.

What have we learned? 

Displacement of grid electricity provided the largest benefit followed by avoidance of food waste landfill emissions and reduced impacts associated with storage of digestate vs. undigested manure. Nominal land application N2O emissions were offset by inorganic fertilizer displacement and carbon sequestration in both cases. The higher volume of digestate increased net land application emissions as did increased transportation distance to the fields and lower carbon sequestration. Digestate is a by-product of the co-digestion process and its treatment must be considered in an LCA. Modeling of land application impacts are highly uncertain and can be significant.

The largest source of direct emissions was CH4 emissions. N2O emissions were larger in the land application phase than during storage. Direct fossil fuel emissions had a minor impact. Emissions were offset by displacement of grid electricity and fossil based fertilizers along with carbon sequestration.

Future Plans    

More empirical research is needed to measure emissions and to provide emission factors that incorporate key variables and characteristics affecting emissions. A whole system, dynamic approach is necessary to incorporate complex interdependencies between stages of farm and manure management.

Authors

Jennifer L. Pronto, Research Assistant, Cornell University jlp67@cornell.edu

Ebner, Jackie      jhe5003@rit.edu              Rochester Institute of Technology

Rodrigo A. Labatut, Matthew J. Rankin, Curt A. Gooch, Anahita A. Williamson, Thomas A. Trabold

Additional information               

www.manuremanagement.cornell.edu

Figure 1: Contributional analysis of GHG impacts for the reference and anaerobic co-digestion cases.

Figure 1: Contributional analysis of GHG impacts for the reference and anaerobic co-digestion cases.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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.

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Reducing or Mitigating Greenhouse Gas Emissions In Animal Agriculture

Animal agriculture has dramatically increased its production efficiency over time, as it continues to produce more products with fewer resources. Although its overall carbon footprint is relatively small compared to other sectors of the economy such as energy and transportation, it is often called upon to defend its impact on the environment. Recent commitments made by livestock and poultry industry groups to reduce greenhouse gas emissions shows that animal agriculture is willing to do its part as good stewards of shared natural resources and to protect the environment.

Factsheet: Mitigation of Greenhouse Gas Emissions in Animal Agriculture (look below the fact sheet and title for a “download” link)

Measures to mitigate or reduce greenhouse gas emissions must be weighed on a farm by farm basis, as types of animal production among species and geographic locations are extremely diverse. There is no magic bullet or one size fits all solution to reduce greenhouse gas emissions among animal agriculture.

There are four main approaches to mitigation greenhouse gas emissions in livestock and poultry systems.

(1) Production efficiency – producing more output of meat, milk and eggs per unit input (water, feed, fertilizer, etc.)

(2) Manure management – applying manure collection, storage, and disposal practices that not only reduce greenhouse gas emissions, but at the same time address water and air quality concerns.

(3) Energy efficiency – as we continue the trend toward more controlled environments within animal production, there is a growing need to be more energy efficient in our lighting, heating and cooling systems.

(4) Carbon capture (also called carbon sequestration) – capturing and storing carbon in the soil by maintaining cover crops, or by planting trees or other perennial vegetation increases organic matter content and also retains carbon that would have otherwise been released as carbon dioxide into the atmosphere.

All Species

  • Increase conception and pregnancy rate
  • Improve animal health
  • Reduce animal stress
  • Lower mortality (death) rates
  • Use feed analysis/precision feeding – match dietary requirements and nutritional needs
  • Practice genetic selection for increased production efficiency and/or reduced maintenance energy requirements

Beef Cattle

  • Increase weight gain through concentrates, improved pastures and dietary supplements
  • Increase digestibility of feed/forage
  • Encourage earlier weaning
  • Use proper stocking rates & rotational grazing
  • Move to low input production
  • Breed for better heat tolerance and pest resistance

Dairy Cattle

  • Increase milk production per head
  • Encourage earlier weaning
  • Improve energy efficiency of exhaust fans, lighting, generators, and incinerators
  • Improve cow comfort through improved cooling systems and bedding material

Swine

Also see a related project on pork production and environmental footprint.

  • Reduce crude protein content in diet and supplement with amino acids
  • Switch from dry feed to wet/dry feeders
  • Improve bedding materials
  • Improve energy efficiency of exhaust fans, lighting, and generators

Poultry

  • Use insulated curtains in houses without walls
  • Insulate walls in houses with walls
  • Install circulatory fans to prevent temperature stratification inside barns
  • Improve energy efficiency of exhaust fans, lighting, generators, and incinerators

Manure Management Strategies

  •  Anaerobic digestion captures methane (a greenhouse gas) and destroys it or utilizes it for energy generation.
  • Composting manure – can reduce greenhouse gases by avoiding methane production that would be seen if the feedstock was landfilled or stored in an open air anaerobic system (such as a lagoon)  [1]
  • Covered manure storage – can capture methane and either destroy it (flare) or utilize it for energy generation
  • Frequent removal of manure from confined facilities
  • Separating manure liquids from solid

Educator Materials

If you would like to use the video, slides, or factsheet for educational programs, please visit the curriculum page for download links for this and other climate change topics.

Recommended Reading on Reducing Emissions from Animal Production

All Livestock Species

Greenhouse Gas Mitigation Opportunities for Livestock Management in the United States (Duke University Nicholas Institute, 2012)
Mitigation of Greenhouse Gas Emissions in Livestock Production (FAO, 2013)
Livestock’s Long Shadow, FAO report

Beef Cattle

Dietary Mitigation of Enteric Methane from Cattle (Beauchemin, K. A. et al., 2009)

Dairy Cattle

DMI Sustainability Website
Sustainability in Practice-A Collection of Success Stories from the Dairy Industry
Greenhouse Gas Emissions from the Dairy Sector, FAO report

Swine

Swine Carbon Footprint Facts
Evaluating the Environmental Footprint of Pork Production

Poultry

Carbon Footprint of Poultry Production Farms (C. Dunkley Webcast)
Global Warming: How Does it Relate to Poultry (C. Dunkley 2011, Factsheet)

Acknowledgements

Author: David Schmidt, University of Minnesota schmi071@umn.edu

This page was developed as part of a project “Animal Agriculture and Climate Change” an extension facilitation project to increase capacity for ag professionals. It was funded by USDA-NIFA under award # 2011-67003-30206.

References

[1] http://faculty.washington.edu/slb/docs/slb_JEQ_08.pdf

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