Influence of emission-factor uncertainty and farm-characteristic variability in LCA estimates of environmental impacts of French dairy farms

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

“…Let us consider that random variables X and Y have values of x = (1, 2, 3) and y = (8,12,16). The covariance of X and Y is 4, and its correlation coefficient is 1.…”

“…Most of the literature that deals with quantifying GHG emissions in the dairy sector has used the MCS method for uncertainty analysis [7][8][9]. These studies have assumed that all input variables follow parametric distributions, including lognormal and/or normal distributions.…”

“…Although the MCS method is the method of choice for quantifying uncertainty in the dairy sector, its use for uncertainty analysis has limitations. It requires defining the probability distribution of each input variable, which might be more difficult if empirical information is unavailable [8]. Basset-Mens et al [7] highlighted the difficulties in defining probability distribution for input variables in the life cycle assessment (LCA) studies.…”

Benchmarking the environmental performance of dairy farming systems

“…Most studies that benchmark the environmental performance of dairy farming systems, however, do not consider the impact of data uncertainty Powell, 2010). A few studies examined the impacts of epistemic uncertainty of nutrient flows on quantification of nutrient use efficiency and the impacts of uncertainties of emission factors and natural variability of farm data on LCA results for assessing environmental performance of milk production Chen et al, 2014). These studies, however, did not examine the impact of epistemic uncertainty on benchmarking results.…”

“…Most studies that assess the environmental performance of dairy farming systems do not cope with data uncertainty Powell, 2010). A few studies did incorporate data uncertainty, but they did not examine the impact of epistemic uncertainty on benchmarking results Chen et al, 2014;. In Chapter 4, we benchmarked the N use efficiency of farms and found that ranking of farms is not possible when the epistemic uncertainty of parameters is large and differences in N use efficiency are small.…”