"7275","10","archive","128",,,"disk0/00/00/72/75","2013-12-05 04:50:25","2013-12-05 04:50:25","2013-12-05 04:50:25","conference_item",,,"show","Library-ICRISAT@CGIAR.ORG",,,"","","","","","","","","","",,,,"Farquharson","R","","","","","","","Ramilan","T","","","","",,,,,"","",,,,,"","","Melbourne School of Land & Environment and Future Farm Industries Cooperative Research Centre(Melbourne)","Australia","Modelling to evaluate agricultural adaptation to climate
change in southern Australia","pub","s2.8","","","public",,"paper","farming systems, climate change, economic, adaptation, linear programming",,,"An important issue for Australian agriculture is the capacity to adapt to predicted climate change.
The International Panel on Climate Change (IPCC) (2009) refers to adaptation as ‘adjustment in natural and
human systems in response to actual or expected climate stimuli or their effects, which moderates harm or
exploits beneficial opportunities’. Conceptualizing and evaluating adaptation options in agricultural
industries should be conducted at the farming systems level (Rickards et al. (2012), Hayman et al. (2012))
because it is at this level that management decisions are made and financial as well as natural resource
impacts will be felt. In particular whole-farm analysis can represent purposeful, goal-seeking systems (Dillon
1976) to assess farmers’ profitability and the system’s sensitivity to risks such as climate variability and
change. The potential of Australian dryland agricultural systems to adapt to climate change with perennial
plants was assessed by Farquharson et al. (2013). Perennial plants have deeper rooting systems with
improved access to soil moisture, making them better suited to warmer and drier climates. Climate data were
generated using Global Circulation Models (GCMs) downscaled to specific locations and corrected for bias
(Liu and Zuo 2012). The climate data were used to estimate growth and yield of grain crops, pastures, and an
energy-tree crop using process models such as APSIM (McCown et al. (1996)) and GrassGro (Moore et al.
(1997). Plant yield and production estimates and economic data (prices of inputs and commodities) were used
in bio-economic models (MIDAS (Kingwell and Pannell 1987) and IMAGINE (Abadi and Cooper 2004)) to
identify the most profitable land use and the cash flow of options available to growers.","2013","published",,,,,,,"1-7",,"20th International Congress on Modelling and Simulation (MODSIM2013)","Adelaide, Australia","1–6 December 2013","conference",,,,,,"TRUE",,,,,,,,"","http://www.mssanz.org.au/modsim2013/index.html","http://scholar.google.co.in/scholar?as_q=%22Modelling+to+evaluate+agricultural+adaptation+to+climate+change+in+southern+Australia%22&as_epq=&as_oq=&as_eq=&as_occt=title&as_sauthors=&as_publication=&as_ylo=&as_yhi=&btnG=&hl=en&as_sdt=0%2C5","pub",,"","",,,,,,"",,,,,,,"",,,,,"",,,,,"","",,,,,"","",,,,,
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