An historical analysis of the changes in pasture production and growing season in three dairy regions of south east Australia - Richard Rawnsley
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An historical analysis of the changes in pasture production and growing season in three dairy regions of south east Australia - Richard Rawnsley

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An historical analysis of the changes in pasture production and growing season in three dairy regions of south east Australia - Richard Rawnsley Presentation Transcript

  • 1. An historical analysis of the changes in pasture production and growing season in three dairy regions of South East Australia Richard Rawnsley1, Brendan Cullen2, Karen Christie1 and Richard Eckard21Tasmanian Institute of Agricultural Research, University of Tasmania, Burnie, Tasmania 7320, Australia 2Melbourne School of Land and Environment, University of Melbourne, Victoria 3010, Australia
  • 2. Biophysical Modelling approach• SE Vic, SW Vic and Tas dairy regions are predominantly pasture based.• Changes in the “wetness”, “dryness” and “length of growing season” will be key drivers for adaptation.• Why ? – Strong influence on key management decision such as: • Stocking rate and calving date • Wintering off and implementation of infrastructure such as feedpads, herd homes etc. • Nitrogen usage and conservation practices • Drying off times and herd culling • Planting of forage crops, irrigation start up, scheduling and requirements
  • 3. Biophysical Modelling approach • What have we done? – Defined growing season • 14 day average growth rate > “break even” point. – Eg. At 2.0 cows/ha and 15 kg DMI/day = 30 kg DM/ha.day. – Defined wetness • Soil moisture > field capacity – Defined dryness • Readily Available Water (0.5PAW) removed – Modelled with biophysical pasture simulation model DairyMod (Johnson et al. 2008).Johnson IR, Chapman DF, Snow VO, Eckard RJ, Parsons AJ, Lambert MG, Cullen BR (2008)DairyMod and EcoMod: Biophysical pastoral simulation models for Australia and New Zealand.Australian Journal of Experimental Agriculture 48, 621–631.
  • 4. Are the models accurate ? Figure 1. Measured and modelled monthly mean daily net herbage accumulation rates (kg DM/ha.day), including measured variability (grey shaded). Adapted from Cullen et al. 2008.Cullen BR, Eckard RJ, Callow MN, Johnson IR, Chapman DF, Rawnsley RP, Garcia SC, White T, Snow VO (2008)Simulating pasture growth rates in Australian and New Zealand grazing systems. Australian Journal of AgriculturalResearch 59, 761-768.
  • 5. Historical Analysis of SE Aus2008 a 2008 b 2008 c2004 2004 20042000 2000 20001996 1996 19961992 1992 19921988 1988 19881984 1984 19841980 1980 19801976 1976 19761972 1972 19721968 1968 19681964 1964 19641960 1960 1960 05-Nov 26-Nov 05-Nov 26-Nov 05-Nov 26-Nov 18-Feb 11-Mar 01-Apr 22-Apr 18-Feb 11-Mar 01-Apr 22-Apr 18-Feb 11-Mar 01-Apr 22-Apr 02-Jul 23-Jul 13-Aug 03-Sep 24-Sep 07-Jan 28-Jan 02-Jul 23-Jul 13-Aug 03-Sep 24-Sep 07-Jan 28-Jan 02-Jul 23-Jul 13-Aug 03-Sep 24-Sep 07-Jan 28-Jan 15-Oct 15-Oct 15-Oct 17-Dec 17-Dec 17-Dec Figure 2 The simulated commencement date and duration of the growing period, for years 1960/61 to 2008/09 at Elliott (a), Ellinbank (b) and Terang (c).
  • 6. Historical Analysis of SE Aus a b c 100 100 100 80 80 80Percentile Percentile Percentile 60 60 60 40 40 40 20 20 20 0 0 0 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 Figure 3 The simulated number of days in years, expressed as yearly percentiles that the 14 day mean pasture growth rate > 30 kg DM/ha , for years 1960/61 to 2008/09 at Elliott (a), Ellinbank (b) and Terang (c)
  • 7. Historical Analysis of SE Aus 4 3 2 1 Z value 0 -1 -2 -3 -4 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Figure 4 The Standardised Precipitation Index (SPI) for cumulative 12 month precipitation (1900-2010) for Ellinbank
  • 8. Historical Analysis of SE Aus Z value rain Z value production 4 Precipitation 3 Pasture production 2 4 1 Z value 3 0 2 -1 -2 1Z value -3 0 -4 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 -1 -2 Figure 5 The Standardised Precipitation Index (SPI) for cumulative 12 month precipitation (1900-2010) and the corresponding Z value for simulated -3 annual pasture production for Ellinbank -4 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
  • 9. Historical Analysis of SE Aus 3 April to September 2 1 Z value 0 -1 -2 -3 1900 1920 1940 1960 1980 2000 3 October to March 2 1 Z value 0 -1 -2 -3 1900 1920 1940 1960 1980 2000 Figure 6 The 6 month Standardised Precipitation Index (SPI) for April to September (RED) and October to March (BLUE) for Ellinbank
  • 10. Historical Analysis of SE Aus 24000 24000 R2 = 0.00 R2 = 0.49 22000 22000Annual Pasture Yield (kg DM/ha) Annual Pasture Yield (kg DM/ha) 20000 20000 18000 18000 16000 16000 14000 14000 12000 12000 10000 10000 8000 8000 6000 6000 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 6 month SPI (September to April) 6 month SPI (October to March) Figure 7 The regression between simulated annual pasture yield (kg DM/ha.year) against the 6 month Standardised Precipitation Index (SPI) for April to September (RED) and October to March (BLUE) for Ellinbank
  • 11. Summary • In recent years the number of days that feed supply > feed demand has declined at all three sites • There is sufficient variation in historical records to examine adaptation options • There is an urgent need for whole of farm system analysis to accurately simulate production, profitability and risk • There are potentially three levels of adaptation that need be explored – Adapting within the current feed base – Modifying the feed base by adopting different forage options – Adapting to a new farming system
  • 12. AcknowledgmentsThis project is supported by funding from Dairy Australia, Meatand Livestock Australia and the Australian GovernmentDepartment of Agriculture, Fisheries and Forestry under itsAustralia’s Farming Future Climate Change Research Program