techniques was available for a wide range of Australian and New Zealand sites (Newnham et al. 2010). Basic details of grass type and species, height, fuel condition, and growth stage were recorded. At Majura in the Australian Capital Territory (ACT), database records indicated that the native perennial, spear grass (Austrostipa bigeniculata), was the dominant species along with other less
abundant species including the native perennial, common wheat grass (Elymus scaber), and introduced species such as an annual, vulpia (Vulpia muralis) and a perennial, tall fescue. At another site in the ACT, Tidbinbilla, the perennial native, weeping grass (Microlaena stipoides) was recorded, along with introduced
perennials, tall fescue and phalaris, and an introduced annual, vulpia (Vulpia ciliata). No other indication of the relative proportion of each species was recorded. In June 2008, the Majura and Tidbinbilla field sites were inspected to collect details that could be used as starting inputs for DST simulation. Soil colour and texture were assessed to a depth of around 10 cm and dominant pasture
species identified. At the time of this visit, the Majura site was heavily grazed by kangaroos, and not actively managed for livestock production, and the Tidbinbilla site had a pasture comprised of phalaris, native perennial grasses and broad-leaf weeds, with Angus cattle grazing at low intensity. The SGS Pasture Model was used to simulate historical curing estimates as it was the only model to include parameters for the native perennial species present at those sites at the time of inspection.
The Majura site was described as Landscape Unit Mu4 in the Bushfire CRC database, and the Northcote classification Gn2.15 from the soil atlas in
GrassGro™ (Bureau of Rural Sciences (after CSIRO) 1991) best described the red clay-loam soil observed at the site. Full details of the soil water module input variables are shown in Table 3.11.
The Tidbinbilla site was described as Landscape Unit Pb8 in the Bushfire CRC database, and the Northcote classification Dy3.41 best described the yellow- grey duplex soil observed at the site. Soil water module input variables are shown in Table 3.12.
Table 3.11. SGS Pasture Model soil water module inputs for Majura, ACT were derived from Principal Profile Form Gn2.15 from the Soil Atlas (Bureau of Rural Sciences (after CSIRO) 1991) in GrassGro™ (GG).
Soil Layer Surface A horizon / topsoil B1 horizon /subsoil B2 horizon /subsoil DST input SGS GG SGS SGS GG SGS Depth 2 cm 150 mm 15 cm 100 cm 1000 mm 100 cm Field capacity 27 %vol 0.27 m3/m3 27 %vol 29 %vol 0.29 m3/m3 29 %vol Wilting point 13 %vol 0.13 m3/m3 13 %vol 17 %vol 0.17 m3/m3 17 %vol Bulk density 1.4 g/cm3 1.4 mg/m3 1.4 g/cm3 1.5 g/cm3 1.5 mg/m3 1.5 g/cm3 Saturated conductivity 240 cm/day 100 mm/hr 240 cm/day 240 cm/day 100 mm/hr 240 cm/day Saturated water content
48 %vol n/a 48 %vol 48 %vol n/a 48 %vol
Air dry water content
10 %vol n/a 10 %vol 13 %vol n/a 13 %vol
Table 3.12. SGS Pasture Model soil water module inputs for Tidbinbilla, ACT, were derived from Principal Profile Form Dy3.41 from the Soil Atlas (Bureau of Rural Sciences (after CSIRO) 1991) in GrassGro™ (GG).
Soil Layer Surface A horizon / topsoil B1 horizon /subsoil B2 horizon /subsoil DST input SGS GG SGS SGS GG SGS Depth 2 cm 300mm 30 cm 100 cm 1200 mm 120 cm Field capacity 24 %vol 0.24 m3/m3
24 %vol 31 %vol 0.31 m3/m3 31 %vol Wilting
point
14 %vol 0.13 m3/m3
14 %vol 22 %vol 0.22 m3/m3 22 %vol Bulk
density
1.6 g/cm3 1.6 mg/m3 1.6 g/cm3 1.7 g/cm3 1.7 mg/m3 1.7 g/cm3 Saturated
conductivity
72 cm/day 30mm/hr 72 cm/day 7.2 cm/day 3mm/hr 7.2 cm/day Saturated
water content
48 %vol n/a 48 %vol 48 %vol n/a 48 %vol
Air dry water content
13 %vol n/a 13 %vol 13 %vol n/a 13 %vol
Little information on livestock and management practices was available because these aspects were outside the scope of the original Bushfire CRC research. As a result, some starting inputs required to simulate these grazing systems had to be estimated. The main variables for the two simulations are described in Table 3.13.
Table 3.13. Weather, soil, pasture and animal variables used in the SGS Pasture Model to simulate the Majura and Tidbinbilla, ACT sites.
Location Majura Tidbinbilla
Simulation length 1/1/1960-31/12/2010
SILO weather location Canberra Airport Tidbinbilla Northcote Soil classification Gn2.15 Dy3.41
Grass type Native
Composition and
proportion of the pasture at the start of simulation
Native C3 perennial (40%), tall fescue (20%), annual ryegrass (40%)
Native C3 perennial (33%), tall fescue (33%), phalaris (33%)
Paddock size (ha) 100
Livestock Wethers in lieu of kangaroos Wethers in lieu of cattle
Stocking rate (DSE) 10 5
Body weight (kg) 50 Minimum weight allowed (kg)
30
Supplementary feeding Forage - in response to ME requirements, to replicate ability of kangaroos to access feed out of this paddock
Concentrate - in response to ME requirements
Livestock rotation Set stocked Shearing date September 1st Greasy fleece weight (kg) 5.5
In the absence of actual stocking rate data regarding the kangaroos at Majura, and cattle at Tidbinbilla, grazing pressure was simulated using sheep. The
simulations were designed to contrast the pasture production of the two sites, rather than be representative of all facets of the whole system. Therefore, the Majura site had the better soil, was heavily grazed and still dominated by native pasture species, whereas the Tidbinbilla site had a poorer soil, and lower carrying capacity. Where specific inputs for the simulations were not known, either default values supplied by the DST, or estimates based on observations from the field visits, were used. The pasture growth module within the SGS Pasture Model was initialised with 2.5 t/ha dry matter (DM) at Majura and 3 t/ha (DM) at Tidbinbilla, allocated between the species as indicated in Table 3.13. Initial soil nutrient status was based on default values in the SGS Pasture Model and no fertiliser or
irrigation was added. In some instances, substitutions were made. The only annual grass species available in the SGS Pasture Model was annual ryegrass and this was substituted for the annual vulpia in the Majura simulation.
Evaluation of the simulation outputs was based on the relative distribution of grass species over simulation runs of 50 years, to ensure that the native species remained dominant. The length of the runs lessened the influence of the starting values on the pasture outputs by 2005 which coincided with the curing
measurements available in the Bushfire CRC database. Pasture composition was checked over the length of the run to ensure that each species remained present in the simulation in correct relative amounts, and that the pasture availability
responded appropriately to the effects of droughts and good seasons. No more specific information was available on the sites with which to validate the simulations more robustly.
3.2.3 Statistical analyses
Curing values were calculated from outputs or extracted from outputs from the different DST (as detailed in Chapter 1, section 1.6.2.5). DST-generated curing percentages were plotted against curing assessments from the Levy Rod technique (Levy and Madden 1933; Anderson et al. 2011) and destructive sampling either collected at the South East field sites in South Australia, or recorded in the Bushfire CRC database. As field measurements were fewer than those generated by simulation, DST-generated values of curing were averaged over the 2.5 week interval surrounding the date of field measurements in South Australia, to produce a similar number of samples for comparison. Distance to field sites and limited resources constrained attempts to collect destructive samples, particularly when inclement weather conditions compromised the value of these samples. Destructive sampling was unable to be conducted frequently enough in South Australia to warrant further analysis and the Levy Rod method
distributions of the curing estimates generated by the DST and Levy Rod curing estimates collected in the field, non-parametric Friedman tests were used to test for differences in curing estimates over time, using PROC FREQ (SAS Institute Inc. 2002-3).