Short communicationIdentifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions

Authors Organisations
  • Richard Kipling(Author)
  • A. Bannink(Author)
    Wageningen University and Research Centre
  • D. J. Bartley(Author)
    Moredun Research Institute
  • I. Blanco-Penedo(Author)
    Swedish University of Agricultural Sciences (SLU)
  • P. Faverdin(Author)
  • A. I. Graux(Author)
  • N. J. Hutchings(Author)
    Aarhus University
  • I. Kyriazakis(Author)
    Veterinary Sciences Division
  • M. Macleod(Author)
    Scotland's Rural College
  • S. Østergaard(Author)
    Aarhus University
  • T. P. Robinson(Author)
    Food and Agriculture Organization of the United Nations
  • A. Vitali(Author)
    University of Tuscia
  • B. Vosough Ahmadi(Author)
    Food and Agriculture Organization of the United Nations
  • Özkan(Author)
    Wageningen University and Research Centre
    Norwegian University of Life Sciences
Type Article
Original languageEnglish
Article number100023
Issue number1
Early online date17 Dec 2020
Publication statusPublished - 01 Jan 2021
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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.


  • Agricultural modelling, Climate change, Dairy production, Greenhouse gas emissions, Livestock health