Abstract
Liquid manure (slurry) from livestock releases methane (CH4) that contributes significantly to global warming. Existing models for slurry CH4 production—used for mitigation and inventories—include effects of organic matter loading, temperature, and retention time but cannot predict important effects of management, or adequately capture essential temperature-driven dynamics. Here we present a new model that includes multiple methanogenic groups whose relative abundance shifts in response to changes in temperature or other environmental conditions. By default, the temperature responses of five groups correspond to those of four methanogenic species and one uncultured methanogen, although any number of groups could be defined. We argue that this simple mechanistic approach is able to describe both short- and long-term responses to temperature where other existing approaches fall short. The model is available in the open-source R package ABM (https://github.com/sashahafner/ABM) as a single flexible function that can include effects of slurry management (e.g., removal frequency and treatment methods) and changes in environmental conditions over time. Model simulations suggest that the reduction of CH4 emission by frequent emptying of slurry pits is due to washout of active methanogens. Application of the model to represent a full-scale slurry storage tank showed it can reproduce important trends, including a delayed response to temperature changes. However, the magnitude of predicted emission is uncertain, primarily as a result of sensitivity to the hydrolysis rate constant, due to a wide range in reported values. Results indicated that with additional work—particularly on the magnitude of hydrolysis rate—the model could be a tool for estimation of CH4 emissions for inventories.
Highlights
Methane (CH4) emissions from livestock production make a significant contribution to global warming, and manure management on farms contributes about 6.5% of global anthropogenic CH4 emissions [1, 2]
Current emissions estimates in national inventories are based on guidelines from the IPCC [3], which offer a simple “Tier 1” approach with default emission factors for livestock categories and average annual temperature, and a more detailed “Tier 2” approach considering effects of organic matter loading, retention time, and temperature, i.e., properties that vary with farming practices and location
An empirical model that accounted for daily temperature and volatile solids (VS) degradation still failed to capture the observed dynamics of CH4 emissions, and it was concluded that the description of methanogenic activity under variable slurry storage conditions was inadequate [5]
Summary
Methane (CH4) emissions from livestock production make a significant contribution to global warming, and manure management on farms contributes about 6.5% of global anthropogenic CH4 emissions [1, 2]. Tier 2 estimates are currently based on a modification of the model presented by Mangino et al [4], in which the fraction of VS converted to CH4 within each month is calculated from a van ’t Hoff-Arrhenius equation with an empirical estimate of activation energy and a reference point corresponding to 100% degradable VS conversion at 30 or 35 ̊C This provides a more site-specific estimate of CH4 emissions than fixed emission factors, the method has been found to poorly describe both temporal dynamics and total CH4 emissions in farm- and pilot-scale experiments [5,6,7]. An empirical model that accounted for daily temperature and VS degradation still failed to capture the observed dynamics of CH4 emissions, and it was concluded that the description of methanogenic activity under variable slurry storage conditions was inadequate [5]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
