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A review of whole farm-system analysis in evaluating greenhouse-gas mitigation strategies from livestock production systems

R. Rawnsley,R. Dynes,K. Christie,M. Harrison,Natalie A. Doran-Browne,R. Vibart,R. Eckard

Published 2016 in Animal Production Science

ABSTRACT

Recognition is increasingly given to the need of improving agricultural production and efficiency to meet growing global food demand, while minimising environmental impacts. Livestock forms an important component of global food production and is a significant contributor to anthropogenic greenhouse-gas (GHG) emissions. As such, livestock production systems (LPS) are coming under increasing pressure to lower their emissions. In developed countries, LPS have been gradually reducing their emissions per unit of product (emissions intensity; EI) over time through improvements in production efficiency. However, the global challenge of reducing net emissions (NE) from livestock requires that the rate of decline in EI surpasses the productivity increases required to satisfy global food demand. Mechanistic and dynamic whole farm-system models can be used to estimate farm-gate GHG emissions and to quantify the likely changes in farm NE, EI, farm productivity and farm profitability as a result of applying various mitigation strategies. Such models are also used to understand the complex interactions at the farm-system level and to account for how component mitigation strategies perform within the complexity of these interactions, which is often overlooked when GHG mitigation research is performed only at the component level. The results of such analyses can be used in extension activities and to encourage adoption, increase awareness and in assisting policy makers. The present paper reviews how whole farm-system modelling has been used to assess GHG mitigation strategies, and the importance of understanding metrics and allocation approaches when assessing GHG emissions from LPS.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Animal Production Science

  • Publication date

    2016-09-01

  • Fields of study

    Agricultural and Food Sciences, Environmental Science

  • Identifiers
    DOI 10.1071/AN15632
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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