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

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...

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Autores principales:	Frederik R Dalby, Sasha D Hafner, Søren O Petersen, Andrew Vanderzaag, Jemaneh Habtewold, Kari Dunfield, Martin H Chantigny, Sven G Sommer
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b56ff89b302c4002bfe6288c85a1699a
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spelling	oai:doaj.org-article:b56ff89b302c4002bfe6288c85a1699a2021-12-02T20:10:49ZA mechanistic model of methane emission from animal slurry with a focus on microbial groups.1932-620310.1371/journal.pone.0252881https://doaj.org/article/b56ff89b302c4002bfe6288c85a1699a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252881https://doaj.org/toc/1932-6203Liquid 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.Frederik R DalbySasha D HafnerSøren O PetersenAndrew VanderzaagJemaneh HabtewoldKari DunfieldMartin H ChantignySven G SommerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252881 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Frederik R Dalby Sasha D Hafner Søren O Petersen Andrew Vanderzaag Jemaneh Habtewold Kari Dunfield Martin H Chantigny Sven G Sommer A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
description	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.
format	article
author	Frederik R Dalby Sasha D Hafner Søren O Petersen Andrew Vanderzaag Jemaneh Habtewold Kari Dunfield Martin H Chantigny Sven G Sommer
author_facet	Frederik R Dalby Sasha D Hafner Søren O Petersen Andrew Vanderzaag Jemaneh Habtewold Kari Dunfield Martin H Chantigny Sven G Sommer
author_sort	Frederik R Dalby
title	A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
title_short	A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
title_full	A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
title_fullStr	A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
title_full_unstemmed	A mechanistic model of methane emission from animal slurry with a focus on microbial groups.
title_sort	mechanistic model of methane emission from animal slurry with a focus on microbial groups.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b56ff89b302c4002bfe6288c85a1699a
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A mechanistic model of methane emission from animal slurry with a focus on microbial groups.

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