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

GROUP 1, SESSION A: ANIMAL PRODUCTION SYSTEMS 8 th Int. Conference on LCA in the
648
Agri-Food Sector, 1-4 Oct 2012
Poster session A - Tuesday, 2 October, 16.20-16.50
Group 1, Session A : Animal Production Systems
1. Coupling LCA and GIS for the assessment of greenhouse gas
emissions from global livestock production
Alessandra Falcucci 1,* , Giuseppe Tempio 1 , Michael Macleod 1 , Carolyn Opio 1 , Theun Vellinga 2 , Pierre
Gerber 1
1 Food and Agriculture Organization of the United Nations, 2 Wageningen Livestock Research, The Netherlands,
Corresponding author. E-mail: alessandra.falcucci@fao.org
Usually life cycle assessments (LCA) are produced outside of any spatially explicit context even though the
integration with a Geographical Information Systems (GIS) would provide the necessary tools to fully implement
a spatially explicit LCA. The few examples available provide only a partial integration among the
two, with the GIS used only for specific aspects of the LCA (e.g. land use). We provide an example of a
complete integration between LCA and GIS with the general aim of assessing GHG emissions from livestock
at the global level. In particular, using a process based approach, we estimated GHG emissions from different
compartments, namely: feed production (including cultivation, induced land use change, manufacture of
fertiliser and processing and transport), manure management, , enteric fermentation, energy use (embedded
and direct), and post-farm emissions to the point of retail. The entire LCA was implemented in GIS using as
inputs spatially explicit layers available at the global level from different sources, and representing the different
variables included in the model (e.g. climate, agro-ecological zones, etc.). The approach that we propose
has many advantages. It allows for: (1) a global analysis that still maintains a reasonable spatial resolution
compared to more traditional national and/or regional analyses; (2) the inclusion of the many spatially
explicit variables developed in the last few years by a wealth of international research centres, and; (3) a
better integration of variables that are naturally highly variable in time and space (e.g. temperatures, yields,
etc.) and that represent the main drivers of important GHG emission sources (such as feed production, manure
management). In addition, using a spatially explicit database it is possible to combine, aggregate and/or
extract the data depending on the particular question at hand, considering different spatial scales and different
administrative regions (or other spatial aggregations). Outputs from the model can also be represented as
GHG emissions maps, with a details that is by far greater than a simple country or regional result. Future
developments of our approach are possible refining the number of variables and processes to be considered
(e.g. including better estimates of transportation distances), improving the resolution and the accuracy of the
data considered, and investigating livestock related impacts on nutrient balances, water consumption and
biodiversity.