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

PARALLEL SESSION 6A: TOOLS AND DATABASES 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
484
LCI-dataset gap bridging strategies in the program Agri-BALYSE
Peter Koch 1,* , Gérard Gaillard 1 , Thibault Salou 2,3 Anne Paillier 4
1
Agroscope Reckenholz-Tänikon Research Station ART, CH-8046 Zürich, Switzerland
2
INRA, UMR1069, Soil Agro and hydroSystem, F-35000 Rennes, France
3
Agrocampus Ouest, Soil Agro and hydroSystem, F-35000 Rennes, France
4
ADEME, F-49004, France
*
Corresponding author. E-mail: peter.koch@art.admin.ch
ABSTRACT
The situation is well known and each LCA-practitioner has to deal with it: Once the system model is designed and the unit processes
as well as their in- and outputs are identified, they should be assigned to the appropriate life cycle inventory (LCI) in an existing
database (e.g. ecoinvent). The question is only: what is appropriate? Is it appropriate to assign a “6-row self propelled tanker harvester
with a 20 tons tank” used to harvest sugar beets in France e.g. to the existing ecoinvent LCI-dataset “harvesting, by complete
harvester, beets, CH” knowing that this process uses a 1-row harvesting machine? The number of agricultural activities and inputs
used in “real world” agricultural practice applied in France exceeds the number of available and accurate LCI-datasets by far (see
table 1). While this issue may be of minor concern for a one-product LCA study, it will become a very important question when
creating a multi-product LCI-database with the aim of comparison: How to deal with these “upstream-dataset gaps”? How to ensure
comparable quality of the resulting LCA’s? Recently, Milà i Canals et al., (2011) suggested four different strategies to bridge data
gaps (scaled, direct and averaged proxies as well as extrapolated data). In the framework of its program, Agri-BALYSE adopted this
approach to state a clear strategy to face upstream-dataset gaps.
The program Agri-BALYSE is an initiative launched by the French authorities in order to develop a public LCI-database of agricultural
products in France (including a small panel of imported tropical products) by the end of 2012. The program is managed by a
consortium consisting of fourteen partners (ADEME, INRA, ART, CIRAD and ARVALIS, CETIOM, UNIP, IFV, ITB, CTIFL,
ASTREDHOR, IFIP, ITAVI, Institut d’Elevage). As data collection is not performed centrally, the fourteen partners have developed
several tools that ensure the comparability and consistency of the data: A data collection tool, an accompanying data collection guide
as well as a framework of data processing tools in order to calculate the LCI data (see figure 1). An important step in this phase is the
assignment of the raw data to the existing LCI-datasets.
According to the ILCD Handbook (2010), methodological consistency is a “shall-criterion” when selecting secondary data sets.
Hence, the program Agri-BALYSE abstained from using datasets from several LCI-databases. Focussing on a single LCI-database,
the gap-bridging-strategies proposed by Milà i Canals et al., (2011) are a suitable resort. Agri-BALYSE defined for each category of
agricultural input category a specific strategy to treat inputs with missing LCI-datasets (see table 1). For fertilisers, Agri-BALYSE
uses the average proxy approach by creating a proxy-LCI dataset reflecting an “average French fertiliser” based on the fertiliser
consumption 2005-2009 (differentiated by N-, P- and K-fertilisers), whereas for active ingredients direct proxies are applied on the
basis of their chemical structure. Agricultural machines as well as processes are extrapolated by adopting the available data sets with
their main activity parameters (life time and weight for machines; energy consumption and working time for processes).
Data collection tool providing raw data
Assessing inputs to LCI production dataset
Dataset available
Meta-Data
Dataset not available
supply ... full new inventory
extrapolation
averaged proxy
direct proxy
Adding support processes
i.e. transports from point of sale to consumer, based on average
transport models by input category
Adding data quality information
Based on information about data sources
Calculating direct emissions based on models
Apply existing models
e.g. N 2O IPCC 2006 Tier 2
Adopt existing models
e.g. Erosion RUSLE
Figure 1. Assessing inputs to existing LCI-datasets is an important step when converting raw data to LCI-data.
The figure shows all parts of the data processing phase in the framework of the program Agri-BALYSE: LCIdataset
assessing as well as adding of support process, quality information, meta-data and direct emissions.
Inventory data