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
2. Discussion
Limited data availability remains a generic problem in LCIs, mainly due to the great resources needed to
collect primary data. Where datasets are available, however, they often describe limited geographical areas
where practices in e.g. agriculture differ depending upon micro-climate and soil characteristics. The inconsistencies
in the inventories presented above may therefore be the result of six alternative soybean processes
rather any nationwide averages. In search for such more general processes, the methodology presented here
enables secondary foreground data to be critically evaluated. When averaging unit process data into, for example,
country-wide averages, different economic and environmental flows may also be used complementary
to produce better characterised processes. This may be values for water use reported in some studies,
while inventories of pesticide use only are available in other studies. Moreover, the proposed approach enables
unit process data to move beyond point values and minimizes the use of unrepresentative parameters. It
also helps to identify areas of great uncertainty and in the process allows for inherent variability to be calculated.
With many incentives to include quantified uncertainties into LCA results, an initial step will be to define
basic uncertainty parameters in the LCI phase, allowing for different kinds of uncertainty tests/methods to be
applied (e.g. not only Monte Carlo analysis). Including most sources of uncertainty in these parameters is
crucial, as they will determine the application and outcome of any later implemented test/methodology. With
a wide range of sources and types of uncertainties, we herein highlight the distinction between inherent standard
deviations and representativeness. While the latter easily can be estimated, the former are not always
available for unit processes. Even when available, inherent uncertainties are often neglected, as in the above
example of soybean yield averages over several years. When calculated using the approach proposed above,
inherent standard deviations often exceed those of representativeness. For example, the geometric standard
deviation amongst the reported uses of potassium in the studies discussed above, amounts to 1.43 compared
to an estimated geometric standard deviation of 1.1-1.27 for representativeness. The inherent geometric standard
deviations of nitrogen showed even greater divergence, but these are somewhat distorted by the reported
null values. Temporal inherent variability is also relevant for food production systems, where the
yield average over five years, reported by FAO, exhibit a geometric standard deviation of 1.11. This is
largely the result of a close link between food production systems and stochastic natural events. Agriculture
practices also exhibit large model uncertainties when calculating field emissions, which add further inherent
uncertainty to this field of LCA research.
Interconnected (co-variation) parameters (e.g. the amount of fertiliser used and resulting N2O emissions)
were here not accounted for, resulting in an overestimation of the uncertainty. In the meantime, inherent
uncertainties are not always considered in the background database, thereby underestimating the degree of
overall uncertainties. Neither were the temporal correlations embedded in the database representative of present
time, as they are benchmarked at the time of release of the database. With some NUSAP categories being
quantifiable (e.g. less than 3 years), others remain open for interpretation which may also result in interpretation
errors.
5. Conclusion
Inventory data remains a major source of uncertainty for LCA results, where LCIs describing the same
production system often experience large differences at the unit process data level. Many of the values used
are also cross-references which sometimes date back over ten years. Scrutiny is therefore needed when developing
process datasets, as is better reporting of the origins of data, standard deviations around means and
identification of known sources of uncertainty. Using the standardised decision tree for data sourcing and
weighting means amongst studies can therefore result in more representative and rigid results.
Quantitative uncertainties need to be included in LCAs in order to statistically validate conclusions and
strengthen the credibility of LCA results. The herein proposed approach presents one way for entailing uncertainty
parameters that complement each other (inherent and representativeness), rather than replacing each
other. A standardised vocabulary is also needed for further uncertainty discussions within the field of LCA.
Here we have e.g. shown that a notable distinction is needed between inherent uncertainty and variability,
and representativeness. Food production systems are especially sensitive to inherent deviations, especially as
yields which are subject to annual fluctuations often represent the functional unit.
6. References
Cederberg, C., 1998. Life Cycle Assessment of Milk Production – a Comparison of Conventional and Organic Farming. SIK Report
643. Gothenburg.
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