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An LCA researcher's wish list-data and emission models needed to improve LCA studies of animal production.
RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Sveriges tekniska forskningsinstitut, SIK – Institutet för livsmedel och bioteknik.
RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Sveriges tekniska forskningsinstitut, SIK – Institutet för livsmedel och bioteknik.
RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Sveriges tekniska forskningsinstitut, SIK – Institutet för livsmedel och bioteknik.
2013 (English)In: Animal, ISSN 1751-7311, E-ISSN 1751-732X, Vol. 7, no Suppl 2, p. 212-219Article in journal (Refereed) Published
Abstract [en]

The last decade has seen an increase in environmental systems analysis of livestock production, resulting in a significant number of studies with a holistic approach often based on life-cycle assessment (LCA) methodology. The growing public interest in global warming has added to this development; guidelines for carbon footprint (CF) accounting have been developed, including for greenhouse gas (GHG) accounting of animal products. Here we give an overview of methods for estimating GHG emissions, with emphasis on nitrous oxide, methane and carbon from land use change, presently used in LCA/CF studies of animal products. We discuss where methods and data availability for GHGs and nitrogen (N) compounds most urgently need to be improved in order to produce more accurate environmental assessments of livestock production. We conclude that the top priority is to improve models for N fluxes and emissions from soils and to implement soil carbon change models in LCA/CF studies of animal products. We also point at the need for more farm data and studies measuring emissions from soils, manure and livestock in developing countries.

Place, publisher, year, edition, pages
2013. Vol. 7, no Suppl 2, p. 212-219
Keywords [en]
Food Engineering
Keywords [sv]
Livsmedelsteknik
National Category
Food Science
Identifiers
URN: urn:nbn:se:ri:diva-8346DOI: 10.1017/S1751731113000785PubMedID: 23739464Scopus ID: 2-s2.0-84887750129OAI: oai:DiVA.org:ri-8346DiVA, id: diva2:966217
Available from: 2016-09-08 Created: 2016-09-08 Last updated: 2020-12-01Bibliographically approved

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