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Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops
Analysis of agricultural production with life cycle based methodologies is data demanding. To build comprehensive life cycle inventories, secondary datasets are commonly used when primary data are not available. However, different inventory data and modelling approaches are used to populate secondar...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750820/ https://www.ncbi.nlm.nih.gov/pubmed/29358847 http://dx.doi.org/10.1016/j.jclepro.2017.03.179 |
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author | Corrado, Sara Castellani, Valentina Zampori, Luca Sala, Serenella |
author_facet | Corrado, Sara Castellani, Valentina Zampori, Luca Sala, Serenella |
author_sort | Corrado, Sara |
collection | PubMed |
description | Analysis of agricultural production with life cycle based methodologies is data demanding. To build comprehensive life cycle inventories, secondary datasets are commonly used when primary data are not available. However, different inventory data and modelling approaches are used to populate secondary datasets, leading to different results. The present study analyses the features of twelve secondary datasets to support datasets selection and proper interpretation of results. We assess twelve datasets for arable crop production in France, as modelled in three databases often used in the LCA field (Agri-footprint, ecoinvent and AGRIBALYSE). First, we compared system boundaries and general assumptions. Second, we focused on foreground systems comparing, inventory data, data sources and modelling approaches. Third, we performed a contribution analysis of impact assessment results to identify modelling choices that contribute most to differences in the results. Nine relevant elements were identified and assessed: definition of system boundaries and modelling of agricultural practices, characteristics of inventory data, agricultural operations, fertiliser application and fate, plant protection products application and fate, heavy metals inputs to the agricultural system and fate, irrigation assumptions, land use and transformation. The datasets differ greatly with respect to these elements. Hence, recommendations are drawn from the datasets comparison, supporting the selection of the datasets coherently with the goal and scope of a study and interpretation of results. |
format | Online Article Text |
id | pubmed-5750820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57508202018-01-20 Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops Corrado, Sara Castellani, Valentina Zampori, Luca Sala, Serenella J Clean Prod Article Analysis of agricultural production with life cycle based methodologies is data demanding. To build comprehensive life cycle inventories, secondary datasets are commonly used when primary data are not available. However, different inventory data and modelling approaches are used to populate secondary datasets, leading to different results. The present study analyses the features of twelve secondary datasets to support datasets selection and proper interpretation of results. We assess twelve datasets for arable crop production in France, as modelled in three databases often used in the LCA field (Agri-footprint, ecoinvent and AGRIBALYSE). First, we compared system boundaries and general assumptions. Second, we focused on foreground systems comparing, inventory data, data sources and modelling approaches. Third, we performed a contribution analysis of impact assessment results to identify modelling choices that contribute most to differences in the results. Nine relevant elements were identified and assessed: definition of system boundaries and modelling of agricultural practices, characteristics of inventory data, agricultural operations, fertiliser application and fate, plant protection products application and fate, heavy metals inputs to the agricultural system and fate, irrigation assumptions, land use and transformation. The datasets differ greatly with respect to these elements. Hence, recommendations are drawn from the datasets comparison, supporting the selection of the datasets coherently with the goal and scope of a study and interpretation of results. Elsevier Science 2018-01-20 /pmc/articles/PMC5750820/ /pubmed/29358847 http://dx.doi.org/10.1016/j.jclepro.2017.03.179 Text en © 2017 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Corrado, Sara Castellani, Valentina Zampori, Luca Sala, Serenella Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops |
title | Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops |
title_full | Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops |
title_fullStr | Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops |
title_full_unstemmed | Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops |
title_short | Systematic analysis of secondary life cycle inventories when modelling agricultural production: A case study for arable crops |
title_sort | systematic analysis of secondary life cycle inventories when modelling agricultural production: a case study for arable crops |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750820/ https://www.ncbi.nlm.nih.gov/pubmed/29358847 http://dx.doi.org/10.1016/j.jclepro.2017.03.179 |
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