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        Assessing the environmental impact of ruminant production systems

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        Author(s)
        Takahashi, Taro
        A. McAuliffe, Graham
        Lee, Michael R. F.
        Language
        English
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        Abstract
        One of the most common methods to evaluate environmental footprints of farming systems is life cycle assessment (LCA). Although LCA itself is suitable for and adopted by a wide range of industries far beyond agriculture, what separates agriculture, and in particular pasture-based ruminant production systems, is the high degree of uncertainties associated with physical, chemical and biological processes that underpin production. Through a review of recent literature and a quantitative case study, this chapter explores how the practical trade-off between feasibility and scientific rigour should be addressed in the field of ruminant production systems. Finally, the chapter provides detailed guidance on where to look for further information in the field.
        URI
        https://library.oapen.org/handle/20.500.12657/61472
        Keywords
        Life cycle assessment (LCA); Ruminant production systems; Pasture-based agriculture
        DOI
        10.19103/AS.2018.0044.14
        ISBN
        9781838798758, 9781838798758
        Publisher
        Burleigh Dodds Science Publishing
        Publisher website
        https://bdspublishing.com/
        Publication date and place
        Cambridge, 2019
        Grantor
        • Rothamsted Research - [...]
        Imprint
        Burleigh Dodds Science Publishing
        Series
        Burleigh Dodds Series in Agricultural Science,
        Classification
        Agronomy and crop production
        Sustainable agriculture
        Animal husbandry
        Pages
        18
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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