LCA Food 2012 in Saint Malo, France! - Manifestations et colloques ...

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PARALLEL SESSION 2C: QUANTIFICATION AND REDUCTION OF UNCERTAINTY 8 th Int. Conference on LCA in the Agri-Food Sector, 1-4 Oct 2012 Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Lean, J., Lowe, D.C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., Van Dorland, R., 2007. Changes in atmospheric constituents and in radiative forcing. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories. (2006 Guidelines) http://www.ipccnggip.iges.or.jp/public/2006gl/index.html Katajajuuri, J.M., 2008. Experiences and improvement possibilities — LCA case study of broiler chicken production. Proc. 6th Int. Conf. Life Cycle Assessment in the Agri-Food Sector. Zurich, Switzerland. Leinonen, I., Williams, A.G., Wiseman, J., Guy, J., Kyriazakis, I., 2012a. Predicting the environmental impacts of chicken systems in the UK through a Life Cycle Assessment: broiler production systems. Poult. Sci. 91, 8-25. Leinonen, I., Williams, A.G., Wiseman, J., Guy, J., Kyriazakis, I., 2012b. Predicting the environmental impacts of chicken systems in the UK through a Life Cycle Assessment: egg production systems. Poult. Sci. 91, 26-40. Mollenhorst, H., Berentsen, P.B.M., De Boer I.J.M., 2006. On-farm quantification of sustainability indicators: an application to egg production systems. Br. Poult. Sci. 47, 405-417. Pelletier, N., 2008. Environmental performance in the US broiler poultry sector: Life cycle energy use and greenhouse gas, ozone depleting, acidifying and eutrophying emissions. Agric. Syst. 98, 67-73. Sandars, D.L., Audsley, E., Canete, C., Cumby, T.R., Scotford, I.M., Williams, A.G., 2003. Environmental benefits of livestock manure management practices and technology by Life Cycle Assessment. Biosyst. Eng. 84:267-281. Wellock, I.J., Emmans, G.C., Kyriazakis, I., 2003. Modelling the effects of thermal environment and dietary composition on pig performance: model logic and concepts. Anim. Sci. 77, 255-266. Williams, A.G., Audsley, E., Sandars, D.L., 2006. Determining the environmental burdens and resource use in the production of agricultural and horticultural commodities. Main Report. Defra Research Project IS0205. http://preview.tinyurl.com/Cranfield- IS0205 Williams, A.G., Audsley, E., Sandars, D.L., 2010. Environmental burdens of producing bread wheat, oilseed rape and potatoes in England and Wales using simulation and system modelling. The Int. J. Life Cycle Assess. 15, 855-868. Wiltshire, J., Tucker, G., Williams, A.G., Foster, C., Wynn, S., Thorn, R., Chadwick, D., 2009. Supplementary Technical Report to “Scenario building to test and inform the development of a BSI method for assessing GHG emissions from food”. Final report to Defra on research project FO0404, London, UK. Accessed Oct. 18, 2011. http://preview.tinyurl.com/Fin-Rpt-FO0404 203
PARALLEL SESSION 2C: QUANTIFICATION AND REDUCTION OF UNCERTAINTY 8 th Int. Conference on LCA in the Agri-Food Sector, 1-4 Oct 2012 204 Influence of scenario uncertainty in agricultural inputs on LCA results for agricultural production systems Kiyotada Hayashi 1,* , Naoki Makino 2 , Koichi Shobatake 2 , Shingo Hokazono 1 1 National Agriculture and Food Research Organisation 2 TCO2 Co. Ltd. Corresponding author. E-mail: hayashi@affrc.go.jp ABSTRACT Practical applications of life cycle assessment (LCA) to agricultural production systems necessitate articulating uncertainties caused by scenario indeterminacy, because practitioners do not have sufficient knowledge about agricultural input production processes. However, current understanding about scenario uncertainties is still limited on account of insufficient knowledge. Here, we propose a method to quantify scenario uncertainty in agricultural inputs and to assess the uncertainty in comparative LCA of agricultural production systems. We formulate mathematical expressions about uncertainty intervals due to scenario indeterminacy and derive uncertainty intervals for conventional, environmentally friendly, and organic rice production systems in Japan. Scenario uncertainty in chemical fertiliser production is analysed as an example. The results indicate that uncertainty intervals are useful in understanding the stability of results. The methodology proposed in this study can be further developed as a technique to deal with uncertainty and instability in LCA of agricultural production systems. Keywords: scenario uncertainty, agricultural inputs, adaptation, comparative LCA, inventory analysis 1. Introduction Uncertainty analysis using the pedigree matrix for data quality together with Monte Carlo simulations is a common practice in life cycle assessment (LCA), and several LCA software products provide simulation functions. Because of certain special characteristics of agriculture, uncertainties in parameters such as crop yield and direct field emissions are integrated into the LCA for agriculture (Basset-Mens et al., 2006). In addition, uncertainties attributed to a wide variety of management practices and uncertainties in the relationship between management practices and environmental impacts have been estimated using statistical resampling (nonparametric bootstrapping) (Hayashi, 2011). Although these studies mainly clarified uncertainties in models and parameters used to conduct LCA, practical applications of LCA to agricultural production systems necessitate articulating uncertainties caused by scenario indeterminacy, because practitioners do not have sufficient knowledge of the details in agricultural input production processes (background processes of agricultural production). For example, farmers in general do not know what kind of technologies are used for making chemical fertilisers and where fertiliser factories are located. In other words, decision makers or analysts face decision problems under insufficient knowledge. However, current understanding about scenario uncertainties is still limited in LCA of agricultural production systems as a result of insufficient knowledge. Therefore, this study establishes a method to quantify scenario uncertainty in agricultural inputs and assesses the influence of this uncertainty on a comparative LCA of agricultural production systems. 2. Methods 2.1. Classification of uncertainties We classify uncertainty using a tri-partition for the uncertainty typology, namely parameter, model, and scenario uncertainty. Parameter uncertainty reflects our incomplete knowledge about the true value of a parameter (Huijbregts et al., 2003) and is related to inventory data and characterisation and weighting factors. Common sources of parameter uncertainty are imprecise measurements, incomplete or outdated measurements, and no measurements (lack of data) (Huijbregts, 1998). Model uncertainty concerns assumptions and simplifications that lead to uncertainty about the validity of the model’s predictions for a real world situation (Huijbregts et al., 2003). An important example of model uncertainty is the loss of spatial and temporal characteristics in inventory analysis. Scenario uncertainty was originally termed ‘uncertainty due to choices’ (Huijbregts, 1998) and ‘decision rule uncertainty’ (Hertwich et al., 2000), because it refers to uncertainty caused by normative choices on functional units and system boundaries in goal and scope definition, allocation in inventory analysis, and the number of impact categories and definitions in impact assessment (Huijbregts et al., 2003). We extend the scope of scenario construction to scenario indeterminacy, which we encounter in practical situations.
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Welcome ii Soyez les bienvenus à L
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General Information 8 th Internatio
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Table of Contents for Sessions TUES
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