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Modeling of greenhouse gas emission from livestock

Authors :
Sanjo eJose
Veerasamy eSejian
Bagath eMadiyajagan
Athira P Ratnakaran
Angela M Lees
Yaqoub eAl-Hosni
Megan eSullivan
Raghavendra eBhatta
John eGaughan
Source :
Frontiers in Environmental Science, Vol 4 (2016)
Publication Year :
2016
Publisher :
Frontiers Media S.A., 2016.

Abstract

The effects of climate change on humans and other living ecosystems is an area of on-going research. The ruminant livestock sector is considered to be one of the most significant contributors to the existing greenhouse gas (GHG) pool. However the there are opportunities to combat climate change by reducing the emission of GHGs from ruminants. Methane (CH4) and nitrous oxide (N2O) are emitted by ruminants via anaerobic digestion of organic matter in the rumen and manure, and by denitrification and nitrification processes which occur in manure. The quantification of these emissions by experimental methods is difficult and takes considerable time for analysis of the implications of the outputs from empirical studies, and for adaptation and mitigation strategies to be developed. To overcome these problems computer simulation models offer substantial scope for predicting GHG emissions. These models often include all farm activities while accurately predicting the GHG emissions including both direct as well as indirect sources. The models are fast and efficient in predicting emissions and provide valuable information on implementing the appropriate GHG mitigation strategies on farms. Further, these models help in testing the efficacy of various mitigation strategies that are employed to reduce GHG emissions. These models can be used to determine future adaptation and mitigation strategies, to reduce GHG emissions thereby combating livestock induced climate change.

Details

Language :
English
ISSN :
2296665X
Volume :
4
Database :
Directory of Open Access Journals
Journal :
Frontiers in Environmental Science
Publication Type :
Academic Journal
Accession number :
edsdoj.556ec38fbc394fefa4050b3a467264db
Document Type :
article
Full Text :
https://doi.org/10.3389/fenvs.2016.00027