TY - JOUR
T1 - Assessing greenhouse gas emissions of milk production
T2 - which parameters are essential?
AU - Wolf, Patricia
AU - Groen, Evelyne A.
AU - Berg, Werner
AU - Prochnow, Annette
AU - Bokkers, E.A.M.
AU - Heijungs, Reinout
AU - de Boer, Imke J.M.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Correlation
KW - Dairy
KW - Life cycle assessment
KW - Monte Carlo simulation
KW - Sensitivity analysis
U2 - 10.1007/s11367-016-1165-y
DO - 10.1007/s11367-016-1165-y
M3 - Article
AN - SCOPUS:84980025244
VL - 22
SP - 441
EP - 455
JO - The International Journal of Life Cycle Assessment
JF - The International Journal of Life Cycle Assessment
SN - 0948-3349
IS - 3
ER -