A mechanistic model of methane emission from animal slurry with a focus on microbial groups

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.

Simple Management Changes Drastically Reduce Pig House Methane Emission in Combined Experimental and Modeling Study

“…The sensitivity analysis results are shown in the Supporting Information, Figure S3. From previous performance tests, the q max,opt fit was best at 40% of the reference values and therefore the parameter selection for optimization was based on the model sensitivity analysis with q max,opt = 40% of reference values (equivalent to q max,opt of 0.6, 1.44, and 2.24 g COD‑S g COD‑B –1 d –1 for methanogen groups m0, m1, and m2, respectively) and α opt = 0.02 d –1 . Model sensitivity analysis suggested that methanogenesis (mainly controlled by q max,opt ) will be rate-limiting for methane production, and q max,opt , α opt , K Scoef , and C Xi,in were chosen as optimization parameters.…”

“…The ABM predicts organic matter transformation to methane and carbon dioxide by simulating (i) initial disintegration, hydrolysis, and fermentation of degradable volatile solids (VSd) to VFA through a single first-order reaction and (ii) methanogenesis using Monod kinetics for describing VFA conversion by active methanogens, resulting in the production of methane and carbon dioxide. The ABM explicitly simulates development of a methanogen community and by default includes five methanogen populations, 1 which are active in different temperature ranges. These default settings were initially chosen based on fitting to methane productivity at varying temperatures as reported by Elsgaard et al 25 Here, the numbers of methanogen groups were reduced to three (m0, m1, and m2) to decrease computation time and complexity during parameter estimation.…”

“…Enrichment of VSd in the residual slurry remaining after slurry removal (from pig houses as well as from outside storages) was implemented in a similar fashion as for methanogens, which has previously been described. 1 Washing of the pig sections between batches of pigs was simulated by the initial removal of slurry, leaving only the residual mass, which was then diluted with water (70 kg pig −1 ), and finally removal of diluted slurry to the slurry level before washing (but after the initial removal of slurry). This simulation would have the net effect of reducing the amount of VSd and methanogens present in the pit before the next batch of pigs enters the section.…”

“…28 Degradable VS was calculated as 70% of VS, 29,30 and a factor of 1.54 gCOD gVSd −1 was used for conversion to COD. 1 The ratio between q max,opt for the three methanogen populations (m0:m1:m2) was fixed at 1:2.4:3.73 during optimization. Optimization was performed with a quasi-Newton method 31 using the optim() function in base R (stats package, v4.2.1) (R core team, 2022) and specifying method argument as "L-BFGS-B" with parameter boundaries.…”

“…In such systems, the slurry environment is anaerobic, and anaerobic microbes transform organic matter to methane and carbon dioxide. 1,2 The biological processes driving organic matter transformation depend strongly on temperature. 3−5 Therefore, strategic removal of slurry from pig houses with a temperature around 20 °C to cold outside storages or anaerobic digesters can reduce net methane emission from the whole management chain.…”