The article is organised as follows: first, system boundaries and underlying assumptions adopted in secondary datasets for arable crops within three databases are reviewed. Furthermore, the results may inform dataset developers about the need for potential improvements to, for example, modelling approaches and underlying assumptions on which datasets were built. Results of the present study could help LCA practitioners to choose secondary datasets which are consistent with the goal and scope of their study and interpret results properly. We identified and analysed elements in datasets which may influence LCA results the most, as well as strengths and weaknesses of the modelling approaches adopted. It aims to understand similarities and differences in datasets of arable crop cultivation and the extent to which the differences may affect LCIA results.
(1998) with some adaptations for the agricultural context. Hence, the present study analyses secondary datasets of arable crop production, based on the approach adopted by Peereboom et al. To our knowledge, however, systematic analysis of secondary datasets modelling arable crops has not been performed to date. Garraín et al., 2015, Grabowski et al., 2015), (ii) estimating influence of datasets quality on life cycle impact assessment (LCIA) results ( Peereboom et al., 1998), (iii) developing approaches based on a descriptive and statistical analysis to assess reliability of secondary data used in LCA ( Teixeira, 2015), and (iv) adopting meta-analysis to estimate average values of environmental impacts (e.g. Several authors have already analysed secondary data from different points of view: (i) developing criteria for assessing data quality (e.g. LCA practitioners are, therefore, recommended to choose datasets carefully according to the goal and scope of their studies ( Fazio et al., 2015). Indeed, different modelling assumptions in datasets aiming to represent the same product system can lead to different results, affecting the reliability of the LCA study ( Williams et al., 2009). Peereboom et al., 1998, found out a variation of impact results from 10% to 100% when different datasets were used in a case study on PVC). The choice of the secondary datasets to be used is considered one of the challenges for a robust LCA study ( Notarnicola et al., 2017) and can influence the results of the LCA study (e.g. This approach helps to streamline estimation of the product's environmental profile ( Teixeira, 2015), reducing the resources required to collect data and allowing a LCA to be performed when the necessary life cycle inventory data are not available from primary sources of data. wheat) are often not collected directly, relying instead on “secondary data” ( Williams et al., 2009). a food product), data on agricultural stages of basic ingredients (e.g. However, when the subject of the study is a manufactured product (e.g. Life Cycle Assessment (LCA) is a reference methodology for supply-chain impact assessment ( ISO, 2006). The aim is to identify drivers of environmental impacts associated with food production and possible improvements thereof. 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.Īssessment of environmental profiles of the food supply chain is increasingly needed in the context of sustainable production and consumption initiatives. The datasets differ greatly with respect to these elements.
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. Third, we performed a contribution analysis of impact assessment results to identify modelling choices that contribute most to differences in the results. Second, we focused on foreground systems comparing, inventory data, data sources and modelling approaches. First, we compared system boundaries and general assumptions. 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). The present study analyses the features of twelve secondary datasets to support datasets selection and proper interpretation of results. However, different inventory data and modelling approaches are used to populate secondary datasets, leading to different results. To build comprehensive life cycle inventories, secondary datasets are commonly used when primary data are not available. Analysis of agricultural production with life cycle based methodologies is data demanding.
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