Production of a map of greenhouse gas emissions and energy use from Australian agriculture

Abstract

The agriculture and forestry sector accounts for approximately 24% of total greenhouse gas (GHG) emissions in Australia. Over the years researchers have produced new knowledge about agricultural GHG emissions and energy use patterns and opportunities to decrease them, albeit in small-scale studies or large-scale ones with coarse resolution. Linking the multiple, diverse and rich datasets around agricultural production in Australia into one dataset that allows for an estimate of GHG emissions and energy use related to agriculture at a national scale and high resolution has not been done before. The approach we describe here is based upon a link between operational data sourced from gross margin (GM) handbooks and life cycle assessment (LCA) process data. We have collected and processed these datasets to produce a comprehensive database of typical agricultural operations covering 72 commodities grown in 42 regions across Australia. We have also created a system that estimates the GHG emission and energy use patterns of the aforementioned commodities using the best available LCA process data. To capture GHG emissions of non-domestically produced fertiliser, we queried the United Nations Commodity Trade Statistics Database (COMTRADE) to analyse the fertiliser and pesticide import patterns for Australia between 2000-2010. This analysis determined the average country energy-mix for fertiliser and pesticide manufacturing and allowed linking the associated GHG emissions to Australian agricultural production. Finally we spatialised agricultural operational data, emissions and energy use at the national scale using the latest Australian Land Use Map (2005/06). Our findings suggest that in 2005/06 greenhouse gas emissions related to Australian agricultural production equate to a total of 95.8 Mt CO-e using 75.7 GWh of energy. According to our results 29.4% of these emissions come from sources that were categorised as non-agricultural (e.g. industrial processes or energy use) in the Australian National Greenhouse Gas Inventory (NGGI) 2006. We find that the provision of transparently modeled GHG emissions and having them linked to a spatially explicit component helps identifying new opportunities for emission reduction and facilitates an assessment of their effects. For example, our findings suggest production of ethanol from corn stover and sugarcane bagasse could have avoided 4.37 Mt CO-e emissions (4.56% of total) without affecting food production.