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Research Process – Southern Livestock Adaptation 2030

Research Process

There were two key components to the project:

  1. Producer locations – researchers, extension experts and producers worked side by side at 46 regional locations across southern Australia. Using sophisticated livestock production modelling tools, Global Circulation Models, local weather data and  producers’ own production and financial data they firstly modelled and validated what had been experienced in the past (1970-1999 and 2000-2009) and then looked ahead to consider the impacts in 2030 and beyond. Various adaptations were also modelled to see what worked and what didn’t.

The period 1970-1999 was considered the ‘base period’ as it as seen as a relatively average weather period (with yearly / seasonal fluctuations). The period 2000-2009 was selected as it was a period of extended drought across much of Southern Australia. Modelled data from these two periods was ‘validated’ by producers at each regional location. Producers needed to be assured that the models could reflect what they had experienced in the recent past before they would be confident about projections going forward.

The project process was:

    • Select a town (location) and enterprise (type of farm) data.
    • Model the livestock enterprise for 1970 to 1999 to establish base data.
    • Set a limit on ground cover so that the models maintain a balance between production and the environment (for example, ground cover limits were set at 70% of years had a minimum ground cover of 70% so as to not destroy pastures and subject the landscape to erosion – as farmers do now). This establishes the stocking rate.
    • Run the models for 2000 to 2009 as a recent reference point, but one which had fairly unique climatic conditions (very dry).
    • Test with producers whether the 1970-1999 and 2000/09 data looks right. If 9it does, proceed. If not go back and re-evaluate
    • Model future climate scenarios for 2030. This involves running exactly the same production system except change the daily weather data to 2030 and increase CO2 levels to 444ppm.
    • ModelRun this for the 4 selected Global Circulation Models (GCM’)
    • Use same ground cover rules to establish the new stocking rates for each GCM.
    • Look at the impact on production and profitability in 2030. Then apply adaptations (management changes) as suggested by farmers
    • Re-run the models
  1. Research centres – modellers / researchers at the University of Melbourne, the Tasmanian Institute of Agricultural Research and CSIRO undertook a series of modelling studies on climate change impacts, adaptation and mitigation strategies for southern Australian livestock industries.

They used a range of biophysical models (DairyMod, SGS Pasture model, and the GRAZPLAN simulation models of pasture and livestock production, such as Grassgro) along with the latest downscaled climate change projections from Ozclim to develop future climate scenarios (for more information on downscaled climate change projections, click here). The biophysical models were used to predict the impact of changed climate conditions on plant and animal productivity. Impacts, adaptations and mitigation options to climate change were assessed based on regional differences in:

    • Climate (rainfall and temperature changes);
    • Soil types (site specific characteristics);
    • Pasture species (legumes, temperate and sub-tropical grasses); and
    • Grazing system (beef, sheep and dairy).

They looked at questions such as:                                                               

    • how pastures at different locations will respond to increased temperature over a range of rainfall scenarios;
    • general trends in climate, pasture and animal production and profit across southern Australia in 2030, 2050 and 2070;
    • impacts of shorter growing seasons;
    • whether managing for climate variability will also help manage for climate change;
    • farm greenhouse gas (GHG) emissions intensity across various livestock enterprises;
    • Potential strategies to reduce GHG emissions intensity on dairy farms