Assessing greenhouse gas emissions of milk production: which parameters are essential?

  • Patricia Wolf
  • Evelyne A. Groen
  • Werner Berg
  • Annette Prochnow
  • Eddie A. M. Bokkers
  • Reinout Heijungs
  • Imke J. M. de Boer



Life cycle assessment (LCA) studies of food products, such as dairy, require many input parameters that are affected by variability and uncertainty. Moreover, correlations may be present between input parameters, e.g. between feed intake and milk yield. The purpose of this study was to identify which input parameters are essential to assess the greenhouse gas (GHG) emissions of milk production, while accounting for correlations between input parameters, and using a systematic approach.


Three diets corresponding to three grazing systems (zero-, restricted and unrestricted grazing) were selected, which were defined to aim for a milk yield of 10,000 kg energy corrected milk (ECM) cow−1 year−1. First, a local sensitivity analysis was used to identify which parameters influence GHG emissions most. Second, a global sensitivity analysis was used to identify which parameters are most important to the output variance. The global analysis included correlations between feed intake and milk yield and between N fertilizer rates and crop yields. The local and global sensitivity analyses were combined to determine which parameters are essential. Finally, we analysed the effect of changing the most important correlation coefficient (between feed intake and milk yield) on the output variance and global sensitivity analysis.

Results and discussion

The total GHG emissions for 1 kg ECM ranged from 1.08 to 1.12 kg CO2 e, depending on the grazing system. The local sensitivity analysis identified milk yield, feed intake, and the CH4 emission factor of enteric fermentation of the cows as most influential parameters in the LCA model. The global sensitivity analysis identified the CH4 emission factor of enteric fermentation, milk yield, feed intake and the direct N2O emission factor of crop cultivation as most important parameters. For both grazing systems, N2O emission factor for grazing also turned out to be important. In addition, the correlation coefficient between feed intake and milk yield turned out to be important. The systematic approach resulted in more parameters than previously found.


By combining a local and a global sensitivity analysis, parameters were determined which are essential to assess GHG emissions of milk production. These parameters are the CH4 emission factor of enteric fermentation, milk yield, feed intake, the direct N2O emission factor of crop cultivation and the N2O emission factor for grazing. Future research should focus on reducing uncertainty and improving data quality of these essential parameters.


Correlation Dairy Life cycle assessment Monte Carlo simulation Sensitivity analysis 



Funding of E.A. Groen was provided by the Seventh Framework Programme (FP7) EU project, grant agreement no. 286141. Funding of P. Wolf was provided by the Senate Competition Committee (SAW) within the Joint Initiative for Research and Innovation of the Leibniz Association (Grant Number SAW-2013-ATB-4)

Supplementary material

11367_2016_1165_MOESM1_ESM.docx (48 kb)
ESM 1 (DOCX 48 kb)


  1. Basset-Mens C, Kelliher FM, Ledgard S, Cox N (2009) Uncertainty of global warming potential for milk production on a New Zealand farm and implications for decision making. Int J Life Cycle Assess 14:630–638CrossRefGoogle Scholar
  2. Chen XB, Corson MS (2014) Influence of emission-factor uncertainty and farm-characteristic variability in LCA estimates of environmental impacts of French dairy farms. J Clean Prod 81:150–157CrossRefGoogle Scholar
  3. Crosson P, Shalloo L, O’Brien D, Lanigan GJ, Foley PA, Boland TM, Kenny DA (2011) A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems. Anim Feed Sci Technol 166–67:29–45CrossRefGoogle Scholar
  4. de Koning A, Schowanek D, Dewaele J, Weisbrod A, Guinée J (2009) Uncertainties in a carbon footprint model for detergents; quantifying the confidence in a comparative result. Int J Life Cycle Assess 15:79–89CrossRefGoogle Scholar
  5. de Vries M, de Boer IJM (2010) Comparing environmental impacts for livestock products: a review of life cycle assessments. Livest Sci 128:1–11CrossRefGoogle Scholar
  6. EC (2009) The Council of the European Union: Council Regulation (EC) No 72/2009 of 19 January 2009 on modifications to the Common Agricultural Policy by amending Regulations (EC) No 247/2006, (EC) No 320/2006, (EC) No 1405/2006, (EC) No 1234/2007, (EC) No 3/2008 and (EC) No 479/2008 and repealing Regulations (EEC) No 1883/78, (EEC) No 1254/89, (EEC) No 2247/89, (EEC) No 2055/93, (EC) No 1868/94, (EC) No 2596/97, (EC) No 1182/2005 and (EC) No 315/2007. BrusselsGoogle Scholar
  7. FAO (2015) Food and Agriculture Organization of the United Nations (Statistics Division). Economic and Social Development Department. Accessed 06-12-2015 2015
  8. Flysjo A, Henriksson M, Cederberg C, Ledgard S, Englund JE (2011) The impact of various parameters on the carbon footprint of milk production in New Zealand and Sweden. Agric Syst 104:459–469CrossRefGoogle Scholar
  9. Garcia-Launay F, van der Werf HMG, Nguyen TTH, Le Tutour L, Dourmad JY (2014) Evaluation of the environmental implications of the incorporation of feed-use amino acids in pig. Production using life cycle assessment. Livest Sci 161:158–175CrossRefGoogle Scholar
  10. Geisler G, Hellweg S, Hungerbühler K (2005) Uncertainty analysis in life cycle assessment (LCA): case study on plant - protection products and implications for decision making. Int J Life Cycle Assess 10(3):184–192Google Scholar
  11. Gerber PJ et al. (2013) Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), RomeGoogle Scholar
  12. Gibbons JM, Ramsden SJ, Blake A (2006) Modelling uncertainty in greenhouse gas emissions from UK agriculture at the farm level. Agric Ecosyst Environ 112:347–355CrossRefGoogle Scholar
  13. Groen, EA (2016) An uncertain climate: the value of uncertainty and sensitivity analysis in environmental impact assessment of food. Ph.D. thesis, Wageningen University. doi: 10.18174/375497
  14. Groen EA, van Zanten HHE, Heijungs R, Bokkers EAM, de Boer IJM (2016) Sensitivity analysis of greenhouse gas emissions from a pork production chain. J Clean Prod 129:202–211CrossRefGoogle Scholar
  15. Gruber L et al. (2005) Vorhersage der Futteraufnahme von Milchkühen - Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs und der Schweiz. In: VDLUFA (ed) Qualitätssicherung in landwirtschaftlichen Produktionssystemen, Rostock, Germany, 2004. Verband Deutscher Landwirtschaftlicher Untersuchungs- und Forschungsanstalten e.V., SpeyerGoogle Scholar
  16. Haenel H-D et al. (2014) Calculations of gaseous and particulate emissions from German agriculture 1990-2012–report on methods and data (RMD) submission 2014 vol 17. Johann Heinrich von Thünen-Institut, Braunschweig. doi: 10.3220/REP_17_2014 Google Scholar
  17. Heijungs R (1994) A generic method for the identification of options for cleaner products. Ecol Econ 10:69–81CrossRefGoogle Scholar
  18. Heijungs R (1996) Identification of key issues for further investigation in improving the reliability of life-cycle assessments. J Clean Prod 4:159–166CrossRefGoogle Scholar
  19. Heijungs R (2010) Sensitivity coefficients for matrix-based LCA. Int J Life Cycle Assess 15:511–520CrossRefGoogle Scholar
  20. Heijungs R, Suh S (2002) The computational structure of life cycle assessment. Kluwer Academic, Dordrecht. doi: 10.1007/978-94-015-9900-9 CrossRefGoogle Scholar
  21. Heijungs R, Suh S, Kleijn R (2005) Numerical approaches to life cycle interpretation–the case of the Ecoinvent’96 database. Int J Life Cycle Assess 10:103–112CrossRefGoogle Scholar
  22. Henriksson M, Flysjo A, Cederberg C, Swensson C (2011) Variation in carbon footprint of milk due to management differences between Swedish dairy farms. Animal 5:1474–1484CrossRefGoogle Scholar
  23. IPCC (1996) Chapter 4: agriculture. International Panel on Climate ChangeGoogle Scholar
  24. IPCC (2006a) Chapter 10: emissions from livestock and manure management. International Panel on Climate ChangeGoogle Scholar
  25. IPCC (2006b) Chapter 11: N2O emissions from managed soils, and CO2 emissions from lime and urea application. International Panel on Climate ChangeGoogle Scholar
  26. IPCC (2007) 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, New YorkGoogle Scholar
  27. ISO (2006a) ISO 14040: 2006 Environmental management—life cycle assessment—principles and framework. International Organization for StandardizationGoogle Scholar
  28. ISO (2006b) ISO 14044: 2006 Environmental management—Life cycle assessment—requirements and guidelines. International Organization for StandardizationGoogle Scholar
  29. Jung J, von der Assen N, Bardow A (2014) Sensitivity coefficient-based uncertainty analysis for multi-functionality in LCA. Int J Life Cycle Assess 19:661–676CrossRefGoogle Scholar
  30. Kirchgeßner M, Roth FX, Schwarz FJ, Stangel GI (2011) Tierernährung; Leitfaden für Studium, Beratung und Praxis vol 13. DLG-Verlags-GmbH, Frankfurt am Main, GermanyGoogle Scholar
  31. Krauß M, Kraatz S, Drastig K, Prochnow A (2015) The influence of dairy management strategies on water productivity of milk production. Agr Water Manage 147:175–186CrossRefGoogle Scholar
  32. Leistungs-Kostenrechnung Pflanzenbau (2015) Kuratorium für Technik und Bauwesen in der Landwirtschaft e.V. KTBL.;jsessionid=0DF7CF2AFBEAB7E4B7D165EABB344A72. Accessed 27-05-2015
  33. LEL (2014) Kalkulationsdaten Futterbau 3.8; Grünland / Ackerfutter / Pflanzliche Substrate für Biogas; Deckungsbeiträge / Vollkosten. Landesanstalt für Entwicklung der Landwirtschaft und der ländlichen Räume Schwäbisch Gmünd (LEL), Schwäbisch Gmünd, GermanyGoogle Scholar
  34. Lovett DK, Shalloo L, Dillon P, O’Mara FP (2008) Greenhouse gas emissions from pastoral based dairying systems: the effect of uncertainty and management change under two contrasting production systems. Livest Sci 116:260–274CrossRefGoogle Scholar
  35. Mutel CL, de Baan L, Hellweg S (2013) Two-step sensitivity testing of parametrized and regionalized life cycle assessments: methodology and case study. Environ Sci Technol 47:5660–5667. doi: 10.1021/es3050949 CrossRefGoogle Scholar
  36. Myhre G et al. (2013) Anthropogenic and Natural Radiative Forcing, . Cambridge, New York. doi: 10.1017/CBO9781107415324
  37. Ross SA, Chagunda MGG, Topp CFE, Ennos R (2014) Effect of cattle genotype and feeding regime on greenhouse gas emissions intensity in high producing dairy cows. Livest Sci 170:158–171CrossRefGoogle Scholar
  38. Saltelli A et al. (2008) Global sensitivity analysis: the primer. Wiley, New York. doi: 10.1002/9780470725184 Google Scholar
  39. Spiekers H, Potthast V (2004) Erfolgreiche Milchviehfütterung vol 4. DLG, Frankfurt am MainGoogle Scholar
  40. UBA (2014) Federal Environment Agency: National Inventory Report, Germany–2014–submission under the United Nations Framework Convention on Climate Change and the Kyoto Protocol 2014–National Inventory Report for the German Greenhouse Gas Inventory 1990–2012 Umweltbundesamt (Federal Environmental Agency), Dessau, GermanyGoogle Scholar
  41. Veerkamp RF, Oldenbroek JK, Van Der Gaast HJ, Van Der Werf JHJ (2000) Genetic correlation between days until start of luteal activity and milk yield, energy balance, and live weights. J Dairy Sci 83:577–583CrossRefGoogle Scholar
  42. Walker WE, Harremoës P, Rotmans J, van der Sluijs JP, van Asselt MBA, Janssen P, Krayer von Krauss MP (2003) Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integr Assess 4:5–17CrossRefGoogle Scholar
  43. Wei W, Larrey-Lassalle P, Faure T, Dumoulin N, Roux P, Mathias JD (2015) How to conduct a proper sensitivity analysis in life cycle assessment: taking into account correlations within LCI data and interactions within the LCA calculation model. Environ Sci Technol 49:377–385CrossRefGoogle Scholar
  44. Weiß J, Pabst W, Granz S (2011) Tierproduktion vol 14. Enke, Stuttgart. doi: 10.1055/b-002-8312 Google Scholar
  45. Xu CG, Gertner GZ (2008) Uncertainty and sensitivity analysis for models with correlated parameters. Reliab Eng Syst Saf 93:1563–1573CrossRefGoogle Scholar
  46. Yan MJ, Humphreys J, Holden NM (2011) An evaluation of life cycle assessment of European milk production. J Environ Manag 92:372–379CrossRefGoogle Scholar
  47. Zehetmeier M, Baudracco J, Hoffmann H, Heißenhuber A (2012) Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach. Animal 6:154–166CrossRefGoogle Scholar
  48. Zehetmeier M, Gandorfer M, Hoffmann H, Muller UK, de Boer IJM, Heißenhuber A (2014) The impact of uncertainties on predicted greenhouse gas emissions of dairy cow production systems. J Clean Prod 73:116–124CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Patricia Wolf
    • 1
  • Evelyne A. Groen
    • 2
  • Werner Berg
    • 1
  • Annette Prochnow
    • 1
    • 3
  • Eddie A. M. Bokkers
    • 2
  • Reinout Heijungs
    • 4
    • 5
  • Imke J. M. de Boer
    • 2
  1. 1.Leibniz Institute for Agricultural Engineering Potsdam-BornimPotsdamGermany
  2. 2.Animal Production Systems GroupWageningen UniversityWageningenthe Netherlands
  3. 3.Faculty of Life Sciences, Chair Utilization Strategies for BioresourcesHumboldt-Universität zu BerlinBerlinGermany
  4. 4.Department of Econometrics and Operations ResearchVU University AmsterdamAmsterdamthe Netherlands
  5. 5.Institute of Environmental SciencesLeiden UniversityLeidenthe Netherlands

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