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

GROUP 4, SESSION B: CROP PRODUCTION SYSTEMS 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
103. The role of spatial modelling using GIS in the development of life
cycle inventory for Australian agriculture
Jonathan Hercule 1 , Sandra Eady 2,* , Tim Grant 3 , Russell Lyons 4
1 AgroParisTech, Paris, France, 2 CSIRO, Locked Bag 1, Armidale NSW 2350, Australia, 3 Lifecycle Strategies,
Swanson Street, Melbourne VIC 3000, Australia, 4 CSIRO, 306 Carmody Road, St Lucia QLD 4067,
Australia, * Corresponding author. E-mail: sandra.eady@csiro.au
In Life Cycle Assessment, spatial data has been used in a range of applications: to enable site or region specific
impact assessment, to track the flow of pollutants through the environment, for biodiversity assessments
of land use, and for developing new impact categories such as desertification. However, there has been
limited application of spatial data to generate underlying life cycle inventory. This paper examines how spatial
data can assist in building a national inventory for agriculture, where a consistent approach across the
nation is required, and detailed data collection at all sites or regions is not feasible.
One of the first tasks in establishing national life cycle inventory for agriculture is to define the major production
systems that represent the country’s production. In some instances, this can be approached using GIS
layers to describe land systems within which the production of an agricultural commodity is likely to be relatively
consistent. This approach has been applied to Australian wheat and wool production systems, with
combinations of GIS layers for soil type, rainfall and land use being used to identify relatively homogeneous
regions for production. The goal of national inventory is to present data with a structure that allows both
regional differences in production systems to be identified as well as inventory in a format that is appropriate
for the next user in the supply chain. GIS layers can be used to provide data to make this inventory spatially
explicit.
Emissions to the environment are often dependant on the geo-location of the agricultural production system;
this includes emissions such as pesticides, nutrient discharge to waterways and indirect N2O emissions from
fertiliser and animal waste, where regional differences in rainfall, temperatures and soils can be major determinants
of flows to the environment. By geo-locating agricultural production these flows can be estimated in
a consistent manner across the nation by using appropriate GIS layers.
Inputs from the techno-sphere for a number of important agricultural operations are influenced by factors
related to the geo-location of the production system. As part of the AusAgLCI project we have been investigating
means of using GIS data layers to standardise and simplify the choice of inventory for agricultural
production, so that important factors determining variation in inputs are accounted for without the need for
detailed individual research by the LCA practitioner. As long as the geo-location of the production system is
known, standard data can be accessed to give appropriate inventory for that region. These include inputs
such as pumping energy required for irrigation (largely determined by the height water needs to be pumped)
and fuel inputs for cultivation (largely determined by clay content of the soil). With GIS data now at the
scale of 5km 2 or less, it becomes feasible to use this resource to accurately represent the local conditions for
agricultural production.
There are a number of ways in which GIS data can be used to enhance the development of LCI, in a manner
that assists with consistency for national databases, allows a level of automated updating, and improves the
accuracy of data inputs for production systems. The challenge is in turning these opportunities into reality,
with the provision of easy to use interfaces between GIS data and LCI, a step that is only just commencing
for enabling the use of GIS data.
809