LCA Food 2012 in Saint Malo, France! - Manifestations et colloques ...

PARALLEL SESSION 2C: QUANTIFICATION AND REDUCTION OF UNCERTAINTY 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
sentative inventories and inherent uncertainty ranges for secondary foreground data from different sources,
we here present a methodology for selecting and weighting inventory values, and in the meantime produce
estimates for inherent uncertainty parameters. The methodology will be practically exemplified by Brazilian
soybean production, as soybeans often constitute more than 30% of aquaculture feeds.
Materials and methods
As the example here is assumed to rely upon the ecoinvent v2.2 database for background data, the aim is to
be consistent with choices made therein. Therefore, in parallel with ecoinvent v2.2, the NUSAP approach
described by Weidema and Wesnaes (1996) was adopted, categorising the origins of representativeness into
reliability, completeness, temporal correlation, geographical correlation and further technical correlation.
The additional category of sample size and the assigned uncertainty factors suggested by Frischknecht et al.,
(2007) were also implemented.
As an initial step, a decision tree was developed for foreground data, with a general distinction between primary
and secondary data (Erreur ! Source du renvoi introuvable.1). The decision tree guides the practitioner
towards recommended approaches when sourcing process data, assuming a default log-normal distribution
of datasets. Log-normal distributions are favoured as to avoid negative values, better represent large
variances and to be consistent with the ecoinvent v2.2 database. Primary data are, moreover, prioritised as
they are assumed to be up-to-date, highly relevant, and provide a higher level of detail. Secondary data are
previously published data describing the process in focus, where the final selection of values should be inline
with the goal and scope definition of the specific study at stake. Where relevant multiple secondary data
sources exist, a weighted mean approach is recommended. Each outcome in the decision tree defines a recommended
type of mean, standard deviation and distribution, or alternative approaches in certain cases.
Figure 1. Decision tree for sourcing unit process data for foreground processes.
Weighted means between secondary foreground parameters were calculated using Weighted means
( ) can be calculated from the aggregated means ( ) of samples (n), with the NUSAP derived geometric
standard deviation used as the weighting factor ( ). The inherent variability can, in turn, be calculated
amongst the different parameters where several sources exist. Missing values are excluded from the calculations
and null values are calculated using 10% or the smallest value, due to the limitations of the logarithmical
scale. To generate the final overall uncertainty parameter, the inherent geometric standard deviation
needs to be summed with the most representative NUSAP indicator for each inventory flow (Frischknecht et
al., 2007).
194