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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Short communication: Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions
Animal, Volume 15, No. 1, Article 100023, Year 2021
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Description
Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding. © 2020 The Authors
Authors & Co-Authors
Bannink, André D.
Netherlands, Wageningen
Wageningen University & Research
Bartley, David Jon
United Kingdom, Midlothian
Moredun Research Institute
Blanco-Penedo, Isabel
Sweden, Uppsala
Sveriges Lantbruksuniversitet
Faverdin, Philippe
France, Paris
Inrae
Graux, Anne Isabelle
France, Paris
Inrae
Hutchings, Nicholas John
Denmark, Aarhus
Aarhus Universitet
Kyriazakis, Ilias
United Kingdom, Belfast
Queen's University Belfast
MacLeod, Michael J.
United Kingdom, Edinburgh
Scotland’s Rural College Sruc
Robinson, Timothy P.
Italy, Rome
Food and Agriculture Organization of the United Nations
Statistics
Citations: 6
Authors: 9
Affiliations: 11
Identifiers
Doi:
10.1016/j.animal.2020.100023
ISSN:
17517311
Study Design
Cross Sectional Study
Study Approach
Quantitative