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  • 标题:Comparison of production-phase environmental impact metrics derived at the farm- and national-scale for United States agricultural commodities
  • 本地全文:下载
  • 作者:Christine Costello ; Xiaobo Xue ; Robert W Howarth
  • 期刊名称:Environmental Research Letters
  • 印刷版ISSN:1748-9326
  • 电子版ISSN:1748-9326
  • 出版年度:2015
  • 卷号:10
  • 期号:11
  • 页码:114004
  • DOI:10.1088/1748-9326/10/11/114004
  • 语种:English
  • 出版社:IOP Publishing Ltd
  • 摘要:Agricultural production is critical for human survival and simultaneously contributes to ecosystem degradation. There is a need for transparent, rapid methods for evaluating the environmental impacts of agricultural production at the system-level in order to develop sustainable food supplies. We have developed a method for estimating the greenhouse gas (GHG), land use and reactive nitrogen inputs associated with the agricultural production phase of major crop and livestock commodities produced in the United States (US). Materials flow analysis (MFA) and life cycle assessment (LCA) techniques were applied to national inventory datasets. The net anthropogenic nitrogen inputs (NANI) toolbox served as the primary accounting tool for LCA and MFA. NANI was updated to create links between nitrogen fertilizer and nitrogen fixation associated with feed crops and animal food commodities. Results for the functional units kilogram (kg) of product and kg of protein for 2002 data fall within ranges of published LCA results from farm-scale studies across most metrics. Exceptions include eutrophication potential for milk and GHGs for chicken and eggs, these exceptions arise due to differing methods and boundary assumptions; suggestions for increasing agreement are identified. Land use for livestock commodities are generally higher than reported by other LCA studies due to the inclusion of all land identified as pasture or grazing land in the US in this study and given that most of the estimates from other LCAs were completed in Europe where land is less abundant. The method provides a view of the entire US agricultural system and could be applied to any year using publically available data. Additionally, utilizing a top-down approach reduces data collection and processing time making it possible to develop environmental inventory metrics rapidly for system-level decision-making.
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