Uncertainty of global warming potential for milk production on a New Zealand farm and implications for decision making

Abstract

As a food exporting nation, New Zealand recognises that the Global Warming Potential (GWP) impact of agriculture has become important to food customers. Food production policy and industry analysts make GWP decisions based on greenhouse gas inventory and life cycle assessment (LCA) results. For decision making, the level of confidence associated with information is important. However, treatment of uncertainty has been problematic in LCA, especially in agricultural systems. In this paper, the GWP of 1 kg of milk was used as a case study to test the feasibility of quantifying uncertainties by Monte Carlo simulation in an LCA applied to an agriculture product. The study also contributes to the development of good practice and has implications for the incorporation of uncertainties into decision making. We distinguished between three sources of variation. First, there is variability amongst basic units such as dairy cattle, soils and farm characteristics which may be quantified by the standard deviation (SD). Second, there is uncertainty about true population means, which is typically provided by a sample and can be measured by the standard error of the mean (SEM). Third, choices, such as the time horizon for computing GWP, can strongly affect the LCA outcomes. The first two sources were analysed by compiling input variable statistics and undertaking Monte Carlo numerical simulations. The third source of variation was quantified by sensitivity analysis. Up to the farm-gate stage, the mean GWP of 1 kg of milk (computed over 100 years) was 0.96 kg CO2-eq. The associated SD was 38% of the mean when using the SD of input variables (and called “variability”) and 7% when using the SEM (and called “uncertainty”). The GWP was most sensitive to uncertainty of pasture dry matter intake by grazing cattle. The second and third key input variables were the cattle excreta nitrous oxide emission factor and the enteric fermentation methane emission factor, respectively. Changing the GWP from a 100-year computation to one of 20 years corresponded to GWP increasing by 92%, while for a change from 100 to 500 years GWP declined by 54%. Data compilation for the uncertainty analyses was challenging because the measurements available were made over smaller time and space scales than ideal, so observations had to be generalised and data gaps filled by expert judgement. Uncertainty analysis using the SEM of input variables was considered most adapted in LCA, so it is recommended as best practice. Identification of the key parameters responsible for uncertainty in the LCA revealed knowledge gaps where research should be directed, such as for methane digestion and nitrous oxide emissions from N excreted by ruminants. Moreover, richer information for those key parameters could be used to build a typology of more meaningful simulations instead of a single, virtual average for analysing environmental impacts of the agricultural system. The use of Monte Carlo simulations for uncertainty and sensitivity analyses of LCA estimates for an agricultural product was feasible and recommendations were made. Developing a typology of realistic simulations based on the key parameters identified in the sensitivity analysis could provide decision makers with more information. Furthermore, in comparative LCA studies, a probabilistic framework provides further information including the statistical significance of differences between technological options. This would represent considerable progress for the decision-making process. We recommend that uncertainty information such as SEM, when available, be part of inventory data for agriculture systems in public reports and databases including assessment of its statistical meaning and consistency.