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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
UNCERTAINTIES IN LCA

Abstract

Purpose

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.

Methods

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.

Conclusions

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.

Keywords

Correlation Dairy Life cycle assessment Monte Carlo simulation Sensitivity analysis 

Notes

Acknowledgments

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)

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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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