2.2 Vegetation monitoring and assessment

The major organisations currently undertaking research and development in this area include the CSIRO Earth Observation Centre, in collaboration with CSIRO Land and Water Resources, the Environmental Resources Information Network (ERIN) within the Commonwealth Department of the Environment, the Queensland Department of Natural Resources, and the West Australian Departments of Agriculture (WADA) and Land Administration (DOLA).

2.2.1 Remote sensing

Vegetation cover is important as an indicator of available fodder and to protect the soil resource from erosion. Cover can be estimated using remote sensing, field measurements, pasture and crop models and farm survey data.

Remote sensing has been used to assist in assessing the severity of drought across Australia, current satellite imagery being compared with that in previous years. It has also helped in determining the spatial extent of exceptional droughts (McVicar and Jupp 1998a). Another valuable use is in aiding the validation of temporal (including temporal/spatial) agronomic models, as are being incorporated into the National Drought Alert System known as Aussie GRASS (Australian Grassland and Rangeland Assessment by Spatial Simulation) (Brook and Carter 1994).

The most commonly used measure of vegetative condition has been the Normalised Difference Vegetation Index (NDVI) based on the reflection of red and near infrared (NIR) light. Chlorophyll pigments in leaves absorb red light, and changes in leaf structure can influence NIR reflectance. Foliage presence, as measured for example through the leaf area index (LAI), is generally related to vegetative condition. Thermal data, transformed to the Normalised Difference Temperature Index (NDTI), are also of value in assessing vegetative cover and drought monitoring (Bierwirth and McVicar 1998)

The Advanced Very High Radiometric Resolution (AVHRR) sensor on the polar orbiting NOAA (U.S. National Oceanographic and Atmospheric Administration) satellites are probably the main tool for vegetation monitoring. Other platforms include the polar-orbiting U.S. Landsat and French SPOT satellites and the Japanese Geostationary Meteorological (GMS) satellite located above the equator at 140ºE. The NOAA and Landsat satellites orbit at about 700 km altitude, whereas the GMS satellite is at an altitude of some 36,000 km, providing views of cloud cover across Australasia.

As a service to the agricultural and environmental sectors, the CSIRO Division of Marine Research produce a composite NDVI image of the whole of Australia every two weeks using data obtained from the Australian Centre for Remote Sensing (ACRES). A two-week compositing period is used in order to minimise the cloud cover in the data. The composite images have a resolution of 1 km. They are made available to customers about 10 days after the end of the two-week period. Historical data are also available going back to 1991.

 Bureau of Meteorology

AVHRR data are recorded and archived daily within the Bureau of Meteorology Research Centre (BMRC). A compositing pathway has been established using these data. To highlight changes in the monthly maximum value composite NDVI between sequential months, the Maximum Value Composite Differential (MVCD) has been developed (Tuddenham et al. 1994, Tuddenham and Le Marshall, 1996). The MVCD is based on the difference between two images recorded at say two-monthly intervals, with a log stretch to enhance the subtle difference in the NDVI signal that has occurred over that time. The changes may involve either a browning or greening of the vegetation cover during the two month period. This can be useful to identify whether a season is atypical in terms of the timing of either seedling emergence or herbage drying off.

CSIRO Earth Observation Centre, Canberra

The CSIRO Earth Observation Centre (EOC) is the science program within the CSIRO Office of Space Science and Applications (COSSA). Dr David Jupp is the Head of COSSA and the Science Leader of the EOC.

The EOC brings a level of collaborative and coordinated underpinning for generic Earth Observation science in CSIRO. Supported research activities are driven by a wide range of applications in Australia and internationally and are being actively undertaken by a distributed group of about 50 scientists spread through CSIRO and its collaborators.

The EOC focuses on four primary areas: applications support, measurement models, data systems and sensor systems.

EOC Tasks are the basic management units, and can take the form of:

• Working Groups - explorations of issues and Earth Observation science problems by groups from the EOC Divisions and other contributing groups;
• Science Projects - teams of scientists from the EOC Divisions and other groups coming together to resolve issues. Science Projects are outcome-oriented and collaborative; and
• Implementation Teams - Working Groups and/or Science Projects can lead to specific outcomes which may need implementing (eg as software, manuals, official standards) or technology transfer.

CSIRO's Access to the Research Aircraft Facilities program is a part of the EOC.

For more information, visit the EOC Web Site: http://www.eoc.csiro.au/

Use of thermal data in drought monitoring:

 In Australia, daytime thermal data are used to monitor regional environmental conditions relevant to the Drought Exceptional Circumstances (DEC) decision making process. McVicar et al. (1992) and Jupp et al. (1998) jointly developed the Normalised Difference Temperature Index (NDTI), to remove seasonal trends from the analysis of daytime land surface temperatures derived from the AVHRR sensor. The NDTI has the form:

<image here>

where:

T<image here>

is a modelled surface temperature if there is an infinite surface resistance, that is, ET is zero;
T_s is the surface temperature observed from the AVHRR sensor; and
T₀ is a modelled surface temperature if there is zero surface resistance; hence ET equals ET_p.

T<image here>

and T₀ can be thought of as the physically-limited upper and lower temperatures respectively, for given meteorological conditions and surface resistances. They define an envelope within which meaningful AVHRR surface temperatures must fall. If T_s is close to the T₀ value it is an indication of conditions being 'wet'. Whereas if T_s is close to the T<image here>

value, dryness is indicated.

T<image here>

and T₀ are calculated through the inversion of a resistance energy balance model. The parameters which are required at the time of satellite overpass are meteorological and vegetation related parameters. Meteorological data which are required includes air temperature, solar radiation, relative humidity (or some other measure of vapor pressure) and wind speed. However, many meteorological stations only record daily air temperature extremes and rainfall. McVicar and Jupp (1998) have tested and extended strategies to determine air temperature, solar radiation and relative humidity at the time of the satellite overpass. Wind speed can be obtained from daily wind run data, if available, or long-term climate surfaces.

Vegetation parameters, mainly Leaf Area Index (LAI) (m² leaf per m² ground), are obtained from reflective data. For four dates in 1995, in cereal cropping and pasture environments in Victoria, relationships were developed between 1 m² in situ LAI measurements and the planetary-corrected albedo LANDSAT TM simple ratio (McVicar et al. 1996b). These relationships were then used to scale the TM simple ratio to provide estimates of LAI at a 30 m² cell size for the entire TM scene centred on the township of Elmore, approximately 45 kms north west of Bendigo. These data were then related to AVHRR simple ratio with a resampled cell size of 1km² (McVicar et al. 1996a). Hence 1m² measurements of LAI were scaled to 1km² estimates of LAI by using TM data as the intermediate scalar. For wooded sites within the Murray-Darling Basin, 30 m² field sites were established and LAI measured, which was subsequently related to AVHRR vegetation indices (McVicar et al. 1996d). This enables AVHRR reflective data to be scaled to estimates of LAI for cropping and pastures (McVicar et al. 1996a) and wooded vegetation (McVicar et al. 1996d). Hence the NDTI is calculated at the points, which are sometimes separated by distances of 500 km, where meteorological data are recorded to support the calculation. AVHRR-derived NDVI and T_s are used as covariates to interpolate the NDTI away from the ground meteorological stations using a spline interpolation algorithm called ANU_SPLIN (Hutchinson 1995). This results in NDTI images. This has been done for 10 years of AVHRR data focusing on the Murray-Darling Basin, Southeast Australia.

The thermal data used in the NDTI calculations are affected by a few environmental parameters. The controlling parameter of the NDTI is the partitioning of the available energy into the latent and sensible heat fluxes; this partitioning is determined by the available moisture to be transferred to the atmosphere via ET. The amount of energy partitioned into the sensible heat flux is one determinant of the observed surface temperature. Consequently the NDTI is more sensitive to changes in resource availability than the NDVI, which integrates the response of the environment to the resource. The NDTI has a greater ability to map the availability of water. This provides a measure of stress when plants are not yet responding to a reduction in chlorophyll content, thereby reducing the NDVI. More important is the ability of the NDTI to map moisture availability that will be influenced by rainfall which falls between meteorological stations. The NDVI will not be able to map these events with the same temporal resolution due to the time lag between rainfall and plant response.

The aim of producing the NDTI is to allow insight into the regional water balance. This is achieved by ET being common to both water balance and energy balance model formulations. In water balance models ET is defined in terms of volume of water, usually measured as millilitres per day. In energy balance models ET is defined in terms of energy, measured in watts per unit area. Independent of any remote sensing input a water balance model, based on the analysis of meteorological data, can be calculated. Outputs from water balance models include estimates of soil moisture and the moisture availability. The water balance derived moisture availability can be used to determine the amount of net available energy (AE) at the Earth's surface utilised by the latent heat flux. The remaining AE is partitioned toward the sensible heat fluxes. The sensible heat flux can then be physically inverted to provide a modelled surface temperature based on the water balance moisture availability, denoted, T _{s WB}. This can be compared with the AVHRR-derived surface temperature, denoted T_{s AVHRR}.

The residual between the two parameters T_{s AVHRR} and T _{s WB} is minimised using a global optimisation technique called simulated annealing, which alters some water balance operating characteristics (McVicar et al. 1996c). This allows daytime thermal observations to be linked to the water balance model by bringing the two temperatures into agreement over the 10 years of data. The residual, expressed as

<image here>

, is minimised.

Department of Land Administration, W.A.

There are two groups producing a continental Normalised Difference Vegetation Index. CSIRO in Hobart is using data from the Australian Centre for Remote Sensing (ACRES), Alice Springs, and the Western Australian Department of Land Administration (DOLA) is using data from Perth, Darwin and Melbourne. Given that the long-term commitment of ACRES to supplying such data may be in doubt, then the DOLA approach could be more robust. Furthermore, DOLA was able to use NOAA-9 to obtain continental data during the very severe drought of 1994 when NOAA data were unavailable via ACRES. There are currently two operational satellites, NOAA-12 in the morning and evening, and NOAA-14 in the afternoon and midnight. There are also two other earlier NOAA satellites still orbiting in standby mode.

DOLA undertook a project for several years to monitor vegetation condition using time series analysis of the NDVI obtained from the AVHRR sensor (Smith 1994). This has been applied to the extensive rangelands of WA (Cridland et al. 1994). Stocking density and when to muster are important issues, exacerbated by the size of individual paddocks. Pastoralists only want to muster livestock once a year. Having an indication of the available feed can assist in the decision of when to muster.

Cridland et al. (1994) analysed the four years of NDVI data, by plotting the NDVI signal as a time series. The height, in NDVI units, from a varying baseline to the maximum peak within the growing season, is calculated. This green 'flush' is the response of the landscape to rainfall. The baseline was varied to take account of the influence of perennial cover on the NDVI signal. The baseline is defined as the minimum from the previous year.

The vegetation response or 'flush' recorded as the maximum for a particular year is then considered relative to the absolute maximum 'flush' within the four (or more) years of data. As well as indicating where and when grazing condition is poor, both images may be used to highlight opportunities to increase stocking densities due to an increase in available feed. This can help place individual years within an historical context.

There is a need to establish a drought monitoring system based on NOAA data, using a consistent baseline, time series and agreed products. There needs to be a client in the Federal government. There also needs to be a dual system using both water balance modelling and remotely sensed data. Both techniques throw up anomalous data, so there is a need for cross-checking.

Probably the ideal system is where CSIRO produce the software and a consistent long-term NDVI dataset free of orbital and sensor drift, with atmospheric correction and BRDF (Bi-directional Reflectance Distribution Function). BoM is the major collector and source of NOAA data, but DOLA and CSIRO Hobart are the major users, and the major source of processed NOAA data, including the NDVI. There is a need to coordinate the activities of these groups. ERIN should become a receiver of the DOLA NDVI information.

DOLA is also engaged in monitoring bushfire activity in northern Australia, at a continental scale on behalf of ERIN, and at a finer resolution for the Bushfire Services of W.A. and the Bushfire Council of the N.T. Fire without follow-up rain can be ecologically devastating, so fire control and hotspot monitoring are very relevant to managing for climate variability, this being particularly important in the savanna country of Northern Australia.

Landsat Thematic Mapper (TM) and occasionally Spot data are used for monitoring land surface and cover, and productivity or yield. Information on the area of crop sown and cereal yield forecasts is provided on request to the Australian Wheat Board (commercial-in-confidence).

Contacts and institutions

Dr Richard Smith, Remote Sensing Services, DOLA, P.O. Box 471, Wembley W.A. 6014; Ph: (08) 9340 9330; Fax: (08) 9383 7142; richard_smith@notes.dola.wa.gov.au ; http://www.rss.dola.wa.gov.au/

Environmental Resources Information Network

The Environmental Resources Information Network (ERIN) has, over a number of years, used a number of techniques to analyse changes in AVHRR derived NDVI images. Recently all AVHRR data held by ERIN have been recalibrated using the method proposed by Roderick et al. (1996). There are a number of analytical tools that have been used to interpret the NDVI data.

Firstly mapping the divergence of NDVI relative to the long term mean has been done at 2-monthly intervals. Having such a fine temporal resolution is important for putting data into an historical context as it allows changes due to vegetation phenology, inherent seasonal changes in solar radiation and air temperature to be normalised. This is similar to the MVCI proposed above, and is important for determining the divergence from "normal" conditions for the particular month rather than using yearly extremes. Secondly, the analytical approach of the 'flush' which has been applied to WA and follows the idea of determining the flush of NDVI at a pixel level on an annual basis and will be applied to the entire Australian continent.

Queensland Department of Natural Resources

 Satellite-based information on vegetative cover is an important layer within a GIS devoted to monitoring seasonal changes in vegetation, land clearing and the extent and severity of drought (Brook and Carter 1994; Carter et al. 1996). Considerable emphasis has been devoted to field validation of NDVI data and model output with respect to pasture biomass and tree cover (Wood et al. 1996).

1) Monthly NOAA NDVI satellite data are presented as decile (relative) greenness maps in the same manner as rainfall is often reported [ref. Ken Day's Drought Report Product**]

2) A new NOAA receiver has been installed for fire mapping. CAPS software will be used for post-acquisition processing. Maps of fire scars will be used to 'reset' grass biomass in spatial models and to investigate fire frequency in grazed lands. Combining data on area burnt with model biomass and nitrogen content will allow calculation of greenhouse gas emissions.

3) Calibration and validation of spatial models: NDVI and thermal data provide a high resolution (spatial and temporal) data set that can be matched to a synthetic NDVI produced by biological models. For project QPI20, NDVI data were compared to the model's synthetic NDVI signal in order to independently validate the model, both spatially and temporally. In the current Aussie GRASS project, the NDVI imagery is being used to spatially fine tune some of the pasture growth parameters. NDVI data are also being used with a genetic algorithm to investigate optimisation of model parameters such as transpiration use efficiency (Carter et al., in press).

4) Tree and land use and soil attribute mapping:

Long-term mean NDVI data have been used to map tree density and cropping areas on a national basis. Tree density is an input to spatial models and cropping areas are used to modify stock densities (Carter et al. 1996). Long-term mean NDVI and thermal data have been used as an input into soil organic carbon mapping in Queensland (McKeon et al. 1998).

5) Research is in progress to translate mean NDVI and air temperature data into tree biomass data for Australia.

6) In Queensland the State Landuse and Trees Study (SLATS) is mapping tree density, tree clearing rates and some land use with Landsat TM imagery for the entire State. Images for 1988, 1991, 1995 and 1997 have been acquired and are being processed. In the future some historic data will be acquired to map historic clearing and regrowth.

Data from this project will upgrade existing NOAA-based tree maps used in spatial models. The data are also being investigated for mapping land degradation.

Agriculture Western Australia (AWA)

NOAA imagery from DOLA is being used to monitor vegetation greenness in Western Australia, following on from the earlier work of Smith (1994) and Cridland et al. (1994). AWA is using Intergraph which uses a more generic grid analysis (GRID) package than used by Cridland. Monitoring of the rangelands is continuing, thus providing another layer of data to enhance and validate Aussie GRASS in Western Australia (G. Beeston, personal communication). This is an integral part of the rangelands component of the recently established National Land and Water Audit. Alec Holm of AWA is primarily responsible for this work; he is currently undertaking PhD studies at the University of Western Australia. GRASP is being tested further north by Ian Watson at the Northam Office of AWA, in collaboration with Greg McKeon and Wayne Hall of QDPI/QDNR.

Remote sensing information is now incorporated into the Pastoral Lease inspection reports that are undertaken every 5 years in Western Australia. Agribusiness (e.g. Heytesbury Pastoral Company) is now also interested in pasture monitoring, including the use of the Aussie GRASS model for estimating spatial and temporal changes in vegetation cover.

New South Wales Agriculture

NSW Agriculture uses NOAA satellite data to produce "vegetation greenness" maps of NSW, on a monthly basis. The maps are based on the application of an NDVI ratio across the data. The data have the NDVI ratio calculated prior to NSW Agriculture receiving it from the CSIRO Division of Marine Research. Data used are taken from a series of up to 14 days, to make a composite. The NDVI index has been routinely derived and archived from NOAA satellite data since 1986, and serves as a good basis for monitoring vegetation trends over this period. The index is a valuable agricultural tool for monitoring green vegetation extent and vigour. NSW Agriculture developed a simple change image that better highlights areas of increasing or decreasing vegetation vigour.

Both vegetation greenness and vegetation greenness change maps are produced monthly by the Resource Information Unit, NSW Agriculture. End users include NSW Agriculture field staff, Stock and Station Agents, Bee Keepers, Bush fire Brigades, Grain buyers, Soil Conservationists and other Government Departments.

The maps are available via the Internet at www.agric.nsw.gov.au/climate/green and www.agric.nsw.gov.au/climate/change. Hard copy maps are available for either the whole State or for particular areas or time periods if required. Additional vector overlays are available to assist with location of features.

Specific research projects currently (or recently) being undertaken in this area include

• Drought monitoring of the Australian continent by satellite - Dr Richard Smith, Department of Land Administration, Western Australia (LWRRDC and RIRDC)
• Use by managers in rangeland environments of near real-time satellite measures of seasonal vegetation response - Dr S. Cridland, ERIN
• Developing a sustainable satellite fire monitoring program for rural northern Australia - Mr Jeremy Russell-Smith, Bushfires Council of the Northern Territory
• Development of a national drought alert strategic information system - Mr John Carter, QDNR

References

1. Bierwirth, P.N. and McVicar, T.R. (1998). Rapid monitoring and assessment of drought in Papua New Guinea using satellite imagery. Consultancy Report to United Nations Development Program, Port Moresby, Papua New Guinea, pp. 60.

2. Brook, K.D. and Carter, J.B. (1994). Integrating satellite data and pasture growth models to produce feed deficit and land condition alerts. Agricultural Systems & Information Technology 6(2), 38-40, 54-56.

3. Bryceson, K.P., Brook, K.D. and White, D.H. (1993). Integration of spatial data and temporal models to improve drought preparedness, monitoring and management. In Proceedings of the international conference on "Applications of advanced information technologies for the management of natural resources", Spokane, Washington, June 17-19, 1993, pp. 158-166.

4. Carter, J.O., Hall, W.B., Brook, K.D., McKeon, G.M., Day, K.A. and Paull, C.J. (in press). Aussie GRASS: Australian Grassland and Rangeland Assessment by Spatial Simulation. In Applications of seasonal climate forecasting in agricultural and natural ecosystems - the Australian experience, edited by G. Hammer, N. Nicholls and C. Mitchell, Kluwer Academic Publishers.

5. Carter, J., Flood, N., Danaher, T., Hugman, P., Young, R., Duncalfe, F., Barber, D., Flavel, R., Beeston, G., Mlodawski, G., Hart, D., Green, D., Richards, R., Dudgeon, G., Dance, R., Brock, D. and Petty, D. (1996). Development of a National Drought Alert Strategic Information System. Volume 3. Development of data rasters for model inputs. Final report on QPI20 to Land and Water Resources Research and Development Corporation.

6. Cridland, S.W., Burnside, D.G. and Smith, R.C.G. (1994). Use by managers in rangeland environments of near real-time satellite measurements of seasonal vegetation response. In Mapping resources, monitoring the environment and managing the future. Proceedings of the 7^th Australasian Remote Sensing Conference, vol. 2, 1-4 March 1994, Melbourne.

7. Danaher, T., Carter, J.O., Brook, K.D. and Dudgeon, G. (1992). Broadscale Vegetation Mapping Using NOAA AVHRR Imagery. In Proceedings of the Sixth Australasian Remote Sensing Conference, 2-6 November 1992, Wellington, New Zealand, 3, pp. 128-137.

8. Hutchinson, M.F. (1995). ANUSPLIN VERSION 3.2, http://cres.anu.edu.au/software/anusplin.html

9. Jupp, D.L.B., Tian, G., McVicar, T.R., Qin, Y. and Fuqin, L. (1998). Soil Moisture and Drought Monitoring Using Remote Sensing I: Theoretical Background and Methods. CSIRO Earth Observation Centre, Canberra, 96 pp..

10. King, E.A. (1998). AVHRR activities at the EOC in Canberra. In Proceedings of the Land AVHRR Workshop (edited by T.R. McVicar), 9^th Australasian Remote Sensing Photogrammetry Conference, 24^th July 1998, Sydney, pp. 59-65.

11. McKeon, G.M., Carter, J.O., Day, K.A., Hall, W.B. and Howden, S.M. (1998). Evaluation of the impact of climate change on northern Australian grazing industries. Final report for the Rural Industries Research and Development Corporation (DAQ139A), 287 pp.

12. McVicar, T.R. and Jupp, D.L.B. (in press). One time of day interpolation of meteorological data from daily data as inputs to remote sensing based estimates of energy balance components. Agriculture and Forest Meteorology.

13. McVicar, T.R., Jupp, D.L.B. and Williams, N.A. (1996a). Relating AVHRR vegetation indices to LANDSAT TM leaf area index estimates. CSIRO, Division of Water Resources, Canberra, ACT, 33 pp.

14. McVicar, T.R., Jupp, D.L.B., Reece, P.H. and Williams, N.A. (1996b). Relating LANDSAT TM vegetation indices to in situ leaf area index measurements. CSIRO, Division of Water Resources, Canberra, ACT, 80 pp.

15. McVicar, T.R., Jupp, D.L.B., Yang, X. and Tian, G. (1992). Linking Regional Water Balance Models with Remote Sensing. In Proceedings of the 13th Asian Conference on Remote Sensing, Ulaanbaatar, Mongolia, pp. B.6.1-B.6.6.

16. McVicar, T.R., Jupp, D.L.B., Billings, S.D., Tian, G. and Qin, Y. (1996c). Monitoring Drought using AVHRR. In Proceedings of the 8th Australasian Remote Sensing Conference, March 25-29 Canberra, pp. 254 - 261.

17. McVicar, T.R., Walker, J., Jupp, D.L.B., Pierce, L.L., Byrne, G.T. and Dallwitz, R. (1996d). Relating AVHRR vegetation indices to in situ leaf area index. CSIRO, Division of Water Resources, Canberra, ACT, 54 pp.

18. Roderick, M., Smith, R.C.G. and Ludwick, G. (1996) Calibrating Long Term AVHRR-Derived NDVI Imagery Remote Sensing of the Environment 58, 1-12.

19. Smith, R.C.G. (1994). Australian vegetation watch. Final report to the Rural Industries Research and Development Corporation. RIRDC reference No. DOL-1A.

20. Smith, R.C.G. and Pearce, A.F. (1997). A bibliography of research into satellite remote sensing of land, sea and atmosphere conducted in Western Australia. Journal of the Royal Society of Western Australia 80, 29-39.

21. Smith, R.C.G., Adams, J., Stephens, D.J. and Hick, P.T. (1995). Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Australian Journal of Agricultural Research 46, 113-125.

22. Smith, R.C.G., Craig, R.L., Steber, M.T., Marsden, A.J., McMillan, C.E. and Adams, J. (1998). Use of NOAA-AVHRR for fire management in Australia's tropical savannas. In Proceedings of the Land AVHRR Workshop (edited by T.R. McVicar), 9^th Australasian Remote Sensing Photogrammetry Conference, 24^th July 1998, Sydney, pp. 1-10.

23. Tuddenham, W.G. and Le Marshall, J.F. (1996). The interpretation of 'NDVI' data and the potential use of a differential technique for monitoring sequential changes in vegetative cover. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 57-61.

24. Tuddenham, W.G., Le Marshall, J.F., Rouse, B.J. and Ebert, E.E. (1994). The real time generation and processing of NDVI from NOAA-11: a perspective view from the Bureau of Meteorology. Proceedings of the 7^th Australasian Remote Sensing Conference, March 1994, Melbourne, pp. 495-502.

25. Turner, P.J., Davies, H.L., Tildesley, P.C. and Rathbone, C.E. (1998). Common AVHRR Processing software (CAPS). In Proceedings of the Land AVHRR Workshop (edited by T.R. McVicar), 9^th Australasian Remote Sensing Photogrammetry Conference, 24^th July 1998, Sydney, 51-58..

26. Willmont, M.C. Griersmith, D.C. and Tuddenham, W.G. (1998). Remote sensing and the Bureau of Meteorology - where to now? In Proceedings of the Land AVHRR Workshop (edited by T.R. McVicar), 9^th Australasian Remote Sensing Photogrammetry Conference, 24^th July 1998, Sydney, pp. 19-31.

27. Wood, H., Hassett, R., Carter, J. and Danaher, T. (1996). Development of a National Drought Alert Strategic Information System. Volume 2. Field validation of pasture biomass and tree cover. Final report on QPI20 to Land and Water Resources Research and Development Corporation.

Contacts and institutions

Dr Shane Cridland, Environmental Resources Network, GPO Box 787, Canberra, ACT 2601. Ph: (02) 6274 1203; Fax: (02) 6274 1333; shane@erin.gov.au

Dr Dean Graetz, CSIRO Earth Observation Centre, GPO Box 3023, Canberra, ACT 2601. Ph: (02) 6216 7199; Fax: (02) 6216 7222; mailto:david.jupp@eoc.csiro.au

Dr David Jupp, CSIRO Earth Observation Centre, GPO Box 3023, Canberra, ACT 2601. Ph: (02) 6216 7203; Fax: (02) 6216 7222; david.jupp@eoc.csiro.au

Mr Tim McVicar, CSIRO Land & Water, GPO Box 1666, Canberra, ACT 2601. Ph: (02) 6246 5741; Fax: (02) 6246 5800; tim@cossa.csiro.au

Dr Richard Smith, Remote Sensing Applications Centre, PO Box 471, Floreat, W.A. 6014. Ph: (08) 9340 9330; Fax: (08) 9383 7142; richard_smith@notes.dola.wa.gov.au

Mr Guy Tuddenham, Bureau of Meteorology Research Centre, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4689; Fax: (03) 9669 4736; mailto:wgt@bom.gov.au

Mr Graeme Tupper, NSW Agriculture, Locked Bag 21, Orange NSW 2800. Ph: (02) 6391 3143; fax (02) 6391 3767; graeme.tupper@agric.nsw.gov.au

2.2.2 Agronomic modelling - grasslands

Biophysical models of agricultural systems are an effective means of determining the responsiveness of soil moisture, plants and livestock to changing climatic conditions, and for determining the effectiveness of the rainfall (White 1997). Since such models should also be realistically responsive to changes in management, the effects of both management and climate can be studied simultaneously. For example, Fouché et al. (1985) used a model to show how the frequency and duration of droughts on the South African veldt increased with stocking rate.

The more variable the climate, the more valuable models are as adjuncts to field experimentation. This is in part because a field experiment of say only three years in duration is likely to be an unrepresentative sample of the long-term behaviour of a farming system.

Agronomic models are probably the best tools for determining the severity of drought and the effectiveness of rainfall. They are also invaluable for estimating the productivity, environmental and financial consequences of different management strategies when applied to farming systems exposed to a variable climate. Vegetative cover is a major constraint to wind and water erosion, although soil type and topography are also important. The outputs of agronomic models are therefore important in assessing the agronomic and environmental condition of an area exposed to drought.

Agronomic models are usually specific to particular agroecosystems and vegetation types, and include:

• GRASP - tropical and subtropical rangelands (McKeon et al. 1990);
• GRASSGRO - temperate grasslands (Moore et al. 1997); available from Horizon Software;
• DYNAMOF - temperate grasslands, Victoria (Bowman et al. 1989, 1993), based on BREW (White et al. 1983);
• ARIDGROW - arid lands of central Australia (Hobbs et al. 1994);
• Forage flow model (arid lands) (Pickup 1995, 1996);
• SEESAW - sub-arid lands, western New South Wales (Ludwig and Marsden 1995a, b; Ludwig et al. 1994);
• SAVANNA-AU (Coughenour 1993; Ludwig and Tongway 1997)
• WAVES - hydrological/Leaf Area Index Model (Dawes and Short 1993);
• IMAGES - (Hacker et al. 1991; Yan and Wang 1996)

Several of these models are being compared within the Aussie GRASS project in terms of their ability to simulate specific pasture communities. GRASP is the model currently used for all communities in Australia. IMAGES (Hacker et al. 1991; Yan and Wang 1996) has recently been rewritten by Emma Raaff, in collaboration with David Stephens; improvements include the replacement of implicit variables with explicit variables. GRASP and IMAGES are being tested by Ian Watson at the Northam Office of AWA, in collaboration with Greg McKeon of QDNR. ARIDGROW is being compared with GRASP simulations for central Australia. Comparisons between the GRASP and GRASSGRO models in the high rainfall temperate zone of New South Wales are planned.

With the emphasis on managing risk, there is therefore a need to encourage farmers to use appropriate Decision Support Systems (DSS) to determine the likely consequences of different management strategies, and the associated risks. In many instances this will be done indirectly through a participatory dialogue with advisers who are familiar with the value and use of such tools. In this way farmers will become better informed about the variability in the environment in which they operate, and better able to make appropriate decisions to improve the productivity, sustainability and financial viability of the farming systems which they manage.

There are a number of DSS for analysing climate data, as well as a range of models and DSS to improve the management of the land. Increased use of these DSS will need a high level of field testing, extensive consultation with users as to their decision requirements, adequate software support, and careful monitoring of their adoption and use.

Models and other DSS have an important role in identifying those management strategies that are financially viable and exposed to minimum physical and financial risk, particularly in areas exposed to a variable climate. However, they are only of value if they are embedded in a broader vision, of acknowledging that DSS are not an end in themselves, but are a very useful, possibly essential tool, in achieving improved management and self-reliance.

Specific research projects currently (or recently) being undertaken in this area include:

• Pasture and forage systems (GRAZPLAN) - Dr John Donnelly, CSIRO Plant Industry (AWRAPO, Australian Wool Research and Promotion Organisation; Australia and Pacific Science Foundation; MLA, Meat and Livestock Australia; Australian Pastoral Research Trust; LWRRDC)
• Strategies to cope with climatic variability in the perennial pasture zone of south-eastern Australia - Mr Stephen Clark, Pastoral and Veterinary Research Institute, Hamilton, Vic. (LWRRDC)
• Grazier-based profitable and sustainable strategies for managing climatic variability (DroughtPlan) - Dr Mark Stafford Smith, CSIRO Wildlife & Ecology (LWRRDC, MRC)
• Estimating safe carrying capacities for grazing properties - Dr Peter Johnston, QDPI (NHT, QDPI, QDNR)
• Rangeland capability assessment in western Queensland - Mr David Cobon, QDPI

References

1. Bowman, P.J., Wysel, D.A., Fowler, D.G. and White, D.H. (1989). Evaluation of a new technology when applied to sheep production systems: Part I - Model description. Agricultural Systems 29, 35-47.

2. Bowman, P.J., White, D.H., Cottle, D.J. and Bywater, A.C. (1993). Simulation of wool growth rate and fleece characteristics of Merino sheep in southern Australia. Part 2 - Assessment of biological components of the model. Agricultural Systems 43, 301-321.

3. Coughenour, M.B. (1993). The SAVANNA landscape model - documentation and Users Guide. Natural Resource Ecology Laboratory, Fort Collins, Colorado.

4. Dawes, W.R. and Short, D.L. (1993). The efficient numerical solution of differential equations for coupled water and solute dynamics: the WAVES model. Technical Memorandum 93-18. Division of Water Resources, CSIRO, Australia.

5. Fouché, H.J., de Jager, J.M. and Opperman, D.P.J. (1985). A mathematical model for assessing the influence of stocking rate on the incidence of drought and for estimating the optimal stocking rates. Journal of the Grassland Society of South Africa 2(3), 3-6.

6. Hacker, R.B., Wang, K-M., Richmond, G.S. and Lindner, R.K. (1991). IMAGES: an Integrated Model of an Arid Grazing Ecological System. Agricultural Systems 37, 119-163.

7. Hobbs, T.J., Sparrow, A.D. and Landsberg, J.J. (1994). A model of soil moisture balance and herbage growth in the arid ra0ngelands of central Australia. Journal of Arid Environments 28, 281-298.

8. Hook, R.A. (1997). Predicting farm production and catchment processes. A directory of Australian modelling groups and models. CSIRO Publishing, Collingwood, Vic., 312 pp.

9. Ludwig, J.A. and Marsden, S.G. (1995a). A simulation of resource dynamics within degraded semi-arid landscapes. Mathematics and Computers in Simulation 39, 219-224.

10. Ludwig, J.A. and Marsden, S.G. (1995b). Modelling the impacts of climate change and degradation on semi-arid landscape systems. In Proceedings of the International Congress on Modelling and Simulation (edited by P. Binning, H. Bridgman and B. Williams), 27-30 November 1995, The University of Newcastle, Australia, 2, pp. 251-254.

11. Ludwig, J.A. and Tongway, D.J. (1997). Modelling scale-dependent processes and impacts of agricultural disturbances on tropical savanna ecosystems in northern Australia. Proceedings of the MODSIM 97 International Congress on Modelling and Simulation, (edited by A.D. McDonald and M. McAleer), 8-11 December 1997, Hobart, Tas., vol. 4, pp. 1875-1880.

12. Ludwig, J.A., Tongway, D.J. and Marsden, S.G. (1994). A flow-filter model for simulating the conservation of limited resources in spatially heterogeneous, semi-arid landscapes. Pacific Conservation Biology 1, 209-213.

13. Moore, A.D., Donnelly, J.R. and Freer, M. (1997). GrazPlan: decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS. Agricultural Systems 55, 535-582.

14. McKeon, G.M., Day, K.A., Howden, S.M., Mott, J.J., Orr, D.M., Scattini, W.J. and Weston, E.J. (1990). Management for pastoral production in northern Australian savannas. Journal of Biogeography 17, 355-372.

15. Pickup, G. (1995). A simple model for predicting herbage production from rainfall in rangelands and its calibration using remotely sensed data. Journal of Arid Environments 30, 227-245.

16. Pickup, G. (1996). Estimating the effects of land degradation and rainfall variation on productivity in rangelands: an approach using remote sensing and models of grazing and herbage dynamics. Journal of Applied Ecology 33, 819-832.

17. White, D.H. (1997). Roles and opportunities for using models to aid the management of agricultural and other natural resources within a variable climate. Proceedings of the MODSIM 97 International Congress on Modelling and Simulation, (edited by A.D. McDonald and M. McAleer), 8-11 December 1997, Hobart, Tas., vol. 4, pp. 1171-1175.

18. White, D.H., Bowman, P.J., Morley, F.H.W., McManus, W.R. and Filan, S.J. (1983). A simulation model of a breeding ewe flock. Agricultural Systems 10, 149-189.

19. Yan, Z.G. and Wang, K.M. (1996). IMAGES 2.1, an Integrated Model of an Arid Grazing System. Agriculture Western Australia, Technical report No 159.

Contacts and institutions

Dr John Donnelly, CSIRO Division of Plant Industry, GPO Box 1600, Canberra City, ACT 2601. Ph: (02) 6246 5106; Fax: (02) 6246 5800; john.donnelly@pi.csiro.au

Mr Ian Foster, Spatial Resource Information Group, Agriculture Western Australia, 3 Baron-Hay Court, South Perth, WA 6151. Ph: (08) 9368 3642; Fax: (08) 9368 3355; ifoster@agric.wa.gov.au

Dr Ron Hacker, Locked Bag 21, Orange, NSW 2800. Ph: (02) 6888 7404; ron.hacker@agric.nsw.gov.au

Dr Mark Howden, CSIRO Division of Wildlife & Ecology, P.O. Box 84, Lyneham, ACT 2602. Ph: (02) 6242 1679; Fax: (02) 6241 2362; mark.howden@dwe.csiro.au

Dr John Ludwig, CSIRO Division of Wildlife & Ecology, PMB 44, Winnellie, Darwin, NT 0821. Ph: (08) 8944 8413; Fax: (08) 8944 8444; john.ludwig@terc.csiro.au

Dr Greg McKeon, Climate Impacts and Grazing Systems, Queensland Department of Natural Resources, PO Box 631, Indooroopilly, Qld 4068. Ph: (07) 3896 9548; Fax: (07) 3896 9606; gregm@dnr.qld.gov.au

Dr Andrew Moore, CSIRO Division of Plant Industry, GPO Box 1600, Canberra City, ACT 2601. Ph: (02) 6246 5298; fax (02) 6246 5800; andrew.moore@pi.csiro.au

Dr Geoff Pickup, CSIRO Land and Water, GPO Box 1600, Canberra City ACT 2601. Ph: (02) 6246 5841; Fax: (02) 6246 5800; geoff.pickup@cbr.dwr.csiro.au

Ms Emma Raaff, Spatial Resource Information Group, Agriculture Western Australia, 3 Baron-Hay Court, South Perth, WA 6151. Ph: (08) 9368 3841; Fax: (08) 9368 3939; mailto:eraaf@agric.wa.agov.au

Dr Mark Stafford Smith, CSIRO National Rangelands Program, PO Box 2111, Alice Springs NT 0871. Ph: (08) 8950 7162; fax (08) 8952 7187; markss@dwe.csiro.au

Dr Ian Watson, Agriculture Western Australia, Northam, W.A. 6401. ph (08) 9690 2128; (08) 9622 1902; iwatson@agric.wa.gov.au

Dr David H White, ASIT Consulting, P.O. Box 328, Hawker, ACT 2614. Ph: (02) 6254 5936; Fax: (02) 6255 2455; dwhite@acslink.aone.net.au ; http://www.acslink.aone.net.au/asit

2.2.3 Agronomic modelling - crops

 A number of cereal, oilseed and legume crop models have now been developed in Australia, most of these being integrated into the Agricultural Production Systems Simulator (APSIM, McCown et al. 1996) being developed by the Agricultural Production Systems Research Unit (APSRU) at Toowoomba in Queensland. APSRU is a joint facility of the Queensland Department of Primary Industries and CSIRO Tropical Agriculture.

Crop models have already been shown to be of value in comparing the likely consequences of different management strategies when applied within a variable climate. These include choice of crop or cultivar, time of sowing, fertiliser input, and use of irrigation water. The value of seasonal forecasts has also been investigated. As highlighted in the following section, they can also be used in cropping areas in assessing and ranking the severity of individual droughts.

At the regional level, Stephens (1995) linked his STIN yield forecasting model with an Oracle database of meteorological data and ABS shire (county) level yield data. With this system, crop yields and soil moisture at sowing were able to be forecast in real-time (Stephens et al. 1994; Stephens 1997). Predicted yields can be plotted as colour maps (for publication on the Web or in farming journals) as a percent change from the 5-year means, or as a ranked percentile in comparison to all other years of historical data. The model calculates a water balance, and estimates Crop Water Use Efficiency and increases in yield attributable to technology (after the effect of climate has been removed), as well as the variability in yields and trends in climate as they relate to crop production.

Climate Risk and Yield Information Service

The timing and amount of early season rainfall has been identified as the most important climatic factor influencing farm management decisions. The ability to maximise profits in better seasons and minimise losses in the poorer seasons by adjusting crop area and inputs on the basis of early season information on rainfall and yields leads to increased long-term profitability.

During 1996 a trial involving farmers and advisers from South Australia and Western Australia determined the usefulness of relevant, timely information on yield and climatic risk in making decisions before and after sowing. Information was generated by two computer programs, Tact and Pycal, using farmers' own rainfall data which was faxed weekly to the relevant State Departments. The results were returned by fax within 24 hours.

In 1997 the trial was extended to encompass a larger area of the Eyre Peninsula of South Australia, and the eastern wheatbelt of Western Australia. The processes involved were automated to involve 150 participants for the 1997 season and the trial was run as a joint project between Primary Industries South Australia, Agriculture Western Australia and the Kondinin Group.

Specific research projects currently (or recently) being undertaken in this area include:

• Decision support for climatic risk management in dryland crop production - Mr Jim Egan, South Australian Research and Development Institute, PIRSA (LWRRDC, GRDC and RIRDC).
• Seasonal rainfall and winter crop yield forecasting for southern Australia - Mr Jim Egan, South Australian Research and Development Institute, PIRSA (LWRRDC, GRDC and RIRDC).
• Assessing and Forecasting Variability in Wheat Production in Western Australia - Dr David Stephens, Agricultural Western Australia
• The application of climate forecasts to crop management in northern Australia - Dr Peter Carberry, CSIRO/APSRU
• Evaluating the role of seasonal climate forecasting in tactical management of cropping systems in north-east Australia - Dr Roger Stone, QDPI/APSRU (LWRRDC)
• Seasonal climate variability and crop yield forecasting - Mr Ahmed Hafi, ABARE (LWRRDC)
• Effects of Improved Climate Forecasting on Competitiveness in the International Grain Market - Dr Graeme Hammer, QDPI/APSRU
• Improving management of seasonal conditions for low rainfall crop production - Mr Jim Egan, South Australian Research and Development Institute, PIRSA (GRDC)
• Developing and promoting systems for managing climatic and spatial risks in dryland crop production - Dr Doug Abrecht, Agriculture Western Australia (GRDC)
• Assessing and managing seasonal risks and opportunities in crop production - Dr Doug Abrecht, Agriculture Western Australia (GRDC)
• Climate risk options for managing frost risk in the eastern wheatbelt - Dr Doug Abrecht, Agriculture Western Australia (GRDC)
• Modelling cropping systems to ensure greater water use and water use efficiency - Dr Ian Fillery, CSIRO Plant Industry, Centre for Mediterranean Agricultural Research, Floreat Park, WA (GRDC)
• Application of a crop model to examine the efficiency and sustainability of wheat farming in Western Australia- Dr Ian Fillery, CSIRO Plant Industry, Centre for Mediterranean Agricultural Research, Floreat Park, WA (GRDC)
• Managing the risks associated with early sowing of lupins- Dr Doug Abrecht, Agriculture Western Australia (GRDC)
• Improved potential yield estimates for farmers and advisers - Dr David Tennant, Agriculture Western Australia (GRDC)
• Analysis of Cropping Systems in Northern NSW using Simulation Models - Dr H. Marcellos, APSRU (GRDC)
• FARMSCAPE - Farmer-adviser-researcher monitoring, simulation, and communication for best dryland cropping practice - Dr Bob McCown, CSIRO/APSRU (GRDC)
• Developing database and knowledge-based resources for commercial advisers using the cropping systems simulator APSIM - Dr Zvi Hochman, CSIRO/APSRU (GRDC)
• Whopper Cropper - a data base and graphics interface to connect crop management advisors with the simulation capacity of APSIM - Dr Graeme Hammer, QCCA/APSRU (GRDC)
• Better management of climate variability within the agribusiness service sector - Dr Peter Carberry, CSIRO/APSRU

References

1. Carberry, P.S.Muchow, R.C. and McCown, R.L. (1989). Testing the CERES-Maize simulation model in a semi-arid tropical environment. Field Crops Research 20, 297-315.

2. Carberry, P.S., Hammer, G.L. and Muchow, R.C. (1993). Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. III. Senescence and prediction of green leaf area. Field Crops Research 33, 329-351.

3. Fischer, R.A. (1979). Growth and water limitation to dryland wheat in Australia: a physiological framework. Journal of the Australian Institute of Agricultural Science 45, 83-94.

4. Hammer, G.L., Woodruff, D.R. and Robinson, J.B. (1987). Effects of climatic variability and possible climatic change on reliability of wheat cropping - a modelling approach. Agricultural and Forest Meteorology 41, 123-142.

5. Hook, R.A. (1997). Predicting farm production and catchment processes. A directory of Australian modelling groups and models. CSIRO Publishing, Collingwood, Vic., 312 pp.

6. Keating, B.A., Meinke, H., Probert, M.E., Huth, N.I. and Hills, I. (1997). Nwheat: Documentation and performance of a wheat module for APSIM. CSIRO Tropical Agriculture Technical Memorandum.

7. Littleboy, M., Silburn, D.M., Freebairn, D.M., Woodruff, D.R., Hammer, G.L. and Leslie, J.K. (1992). Impact of soil erosion on production in cropping systems. 1. Development and validation of a simulation model. Australian Journal of Soil Research 30, 757-774.

8. McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P. and Freebairn, D.M (1996). APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems 50, 255-271.

9. Meinke, H. and Hammer, G.L. (1995). A peanut simulation model. II. Assessing regional production potential. Agronomy Journal 87, 1093-1099.

10. Meinke, H. and Hammer, G.L. (1995). Climatic risk to peanut production: a simulation study for northern Australia. Australian Journal of Experimental Agriculture 35, 777-780.

11. Meinke, H., Hammer, G.L.and Chapman, S.C. (1993). A crop simulation model for sunflower. II. Simulation analysis of production risk in a variable sub-tropical environment. Agronomy Journal 85, 735-742.

12. Meinke, H., Hammer, G.L., van Keulen, H. and Rabbinge, R. (1998). Improving wheat simulation capabilities in Australia from a cropping systems perspective. III. The integrated wheat model (I_WHEAT). European Journal of Agronomy 8, 101-116.

13. O'Leary, G.J. and Connor, D.J. (1996). A simulation model of the wheat crop in response to water and nitrogen supply. 1. Model construction. Agricultural Systems 52, 1-29.

14. O'Leary, G.J. and Connor, D.J. (1996). A simulation model of the wheat crop in response to water and nitrogen supply. 2. Model validation. Agricultural Systems 52, 31-55.

15. O'Leary, G.J. and Connor, D.J. (1998). A simulation study of wheat crop response to water supply, nitrogen nutrition, stubble retention, and tillage. Australian Journal of Agricultural Research 49, 11-20.

16. O'Leary, G.J., Connor, D.J. and White, D.H. (1985). A simulation model of the development, growth and yield of the wheat crop. Agricultural Systems 17, 1-26.

17. Probert, M.E., Keating, B.A., Thompson, J.P. and Parton, W.J. (1995). Modelling water, nitrogen and crop yield for a long-term fallow management experiment. Australian Journal of Experimental Agriculture 35, 941-950.

18. Probert, M.E., Carberry, P.S., McCown, R.L. and Turpin, J.E. (1998). Simulation of legume-cereal systems using APSIM. Australian Journal of Agricultural Research 49, 317-327.

19. Probert, M.E., Dimes, J.P., Keating, B.A., Dalal, R.C. and Strong, W.M. (1997). APSIM's water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural Systems 56, 1-26.

20. Stapper, M. (1984). SIMTAG: A simulation model of wheat genotypes. Model documentation. ICARDA, Aleppo, Syria and University of New England, Armidale, NSW, Australia, 108 pp.

21. Stephens, D.J., Walker, G.K. and Lyons, T.J. (1994). Forecasting Australian wheat yields with a weighted rainfall index. Agricultural and Forestry Meteorology 71, 247-263.

22. Stephens, D.J. (1995). Crop yield forecasting over large areas in Australia. PhD thesis, Murdoch University, Perth, 317 pp.

23. Stephens, D.J. (1997). Assessing and forecasting variability in wheat production in Western Australia. Final report to Agriculture Western Australia, Perth, 59 pp.

24. Stephens, D.J. and Lyons, T.J. (1998). Rainfall-yield relationships across the Australian wheat belt. Australian Journal of Agricultural Research 49, 211-223.

Contacts and institutions

Dr John Angus, CSIRO Division of Plant Industry, GPO Box 1600, Canberra, ACT 2601; Ph: (02) 6246 5095; Fax: (02) 6246 5800; john.angus@pi.csiro.au

Dr Peter Carberry, CSIRO Tropical Agriculture/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 4350. Ph: (07) 4688 1200; Fax: (07) 4688 1193; Peter.Carberry@tag.csiro.au

Dr David Connor, Department of Crop Production, Institute of Land and Food Resources, The University of Melbourne. Ph: (03) 9344 5019; d.connor@landfood.unimelb.edu.au

Dr Graeme Hammer, Queensland Department of Primary Industries/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 2350. Ph: (07) 4688 1200; Fax: (07) 4688 1193; hammerg@prose.dpi.qld.gov.au

Dr Brian Keating, CSIRO/Agricultural Production Systems Research Unit, 306 Carmody Road, St Lucia, Qld 4067. Ph: (07) 3214 2373; Fax: (07) 3214 2420; brian.keating@cnetns.tcp.csiro.au

Dr Holger Meinke, Queensland Department of Primary Industries/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 4350. Ph: (07) 4688 1378; Mobile: 041 219 6534; Fax: (07) 4688 1193; MeinkeH@prose.dpi.qld.gov.au

Dr Garry O'Leary, South African Sugar Association, Durban, Republic of South Africa; gjoleary@yahoo.com

Dr Glynn Rimmington, Department of Crop Production, Institute of Land and Food Resources, The University of Melbourne. Ph: (03) 9344 5012; Fax: (03) 9349 4518; Mobile: 0412 810 529; gmr@landfood.unimelb.edu.au

Dr Maarten Stapper, CSIRO Division of Plant Industry, GPO Box 1600, Canberra City, ACT 2601. Ph: (02) 6246 5091; maarten.stapper@pi.csiro.au

Dr David Stephens, Spatial Resource Information Group, Agriculture Western Australia, South Perth WA 6151. Ph: (08) 9368 3346; Fax: (08) 9368 3939; david_s@agrsrv.agric.wa.gov.au

2.2.4 Drought assessment and the identification of exceptional droughts

Models have been used by the Bureau of Resource Sciences (BRS) and collaborating organisations to assess the effectiveness of rainfall and to improve the objective estimation of Drought Exceptional Circumstances (DEC) (White and O'Meagher 1995; O'Meagher et al. 1999). Rainfall and other climate data collected for approximately 100 years have been input into models of farming systems in order to characterise and rank past droughts, and determine appropriate indicators and criteria for estimating the severity and extent of future droughts.

Studies to date include using:

• • a model of a Merino ewe flock grazing an annual ryegrass and subterranean clover pasture near Heathcote in northern Victoria (White et al. 1998);
• • a model of an annual grass and subterranean clover pasture grazed by either Merino wethers or breeding ewes at Wellington in the Central Tablelands of New South Wales (Donnelly et al. 1998);
• • a rangeland model at Charleville (south-west mulga country, sheep) and Charters Towers (northern speargrass, cattle) in Queensland (Stafford Smith and McKeon 1998);
• • a Stress Index model to estimate long-term wheat yields and soil moisture accumulation at 16 representative sites across the Australian wheat belt (Stephens 1998);
• • an Agricultural Production Systems Simulator (APSIM, McCown et al. 1996) to assess the severity of drought on wheat-fallow, sorghum-fallow and wheat-sorghum (opportunity cropping) production systems at a range of sites throughout north-eastern Australia (Keating and Meinke 1998); and
• • a composite measure of the financial situation of farm families and generated regional estimates using ABARE survey data (Ockerby et al. 1996). They proposed that the estimates could be used in conjunction with rainfall and other seasonal data to generate benchmarks of one in 25 year economic events.

Associated with the above studies was a review of the role and values of remote sensing in determining the existence and spatial limits to exceptional droughts (McVicar and Jupp 1998).

A workshop at the conclusion of these studies (White and Bordas 1997) concluded that rainfall, soil moisture, grassland and crop production (or an index thereof, which may be derived from a model or remote sensing), liveweight gain, supplementary feed requirements, net farm income or a measure of financial stress are all useful indicators of DEC. Of these, the three most reliable indicators of rainfall deficit and effectiveness are simulated grassland and crop production, and the estimated requirements of livestock for supplementary feed, based on appropriate management regimes. The feasibility of taking account of significant long-term climate shifts was also demonstrated. An income-based approach to determining exceptional circumstances due to climatic and other events has been advocated by Thompson and Powell (1998), despite concerns as to the practicality of such an approach, and the extent to which it would limit structural adjustment and the capacity for rural industries to become more efficient (O'Meagher et al. 1998).

Drought monitoring in New South Wales

A study on the evaluation of the meteorological indices used in drought monitoring in various parts of the world was carried out by the NSW Agriculture in 1997 (Harpal Mavi, pers.comm.). The objective of the study was to identify and develop improved criteria for drought assessment and mitigation in NSW. Main findings and recommendations emerging from the study are summarised as follows.

Analysis of rainfall is the primary basis of identification of drought but there are a number of pitfalls if drought is assessed solely on rainfall criteria or indices. Rainfall measurements may vary considerably over relatively small distances. Rainfall occurring at one time of the year can be carried over using fallow to provide moisture at other times of the year, making irrelevant the average monthly rainfall values. Failure of rain at the optimum time may downgrade the otherwise average rainfall conditions in terms of production potential. Average rainfall conditions mask the influence of rainfall intensity and the duration of rainfall spells on the actual performance of crops and pastures.

Soil water balance and agronomic simulation models perform better than the rainfall indicators in the assessment of agricultural droughts. However, like rainfall indicators, they too have limitations. If wrong information about plant characteristics or soil parameters is used in running a simulation then these can greatly influence the output of the model. Some of models may tend to amplify minor events, and at other times suppress major events are presented in a suppressed form. These differing and far from reality results can create a confusing situation.

The study led to the conclusion that drought measurement is a difficult and complex task. No single measure, a rainfall index, a water balance model, an agronomic model or field surveys, can identify, effectively and objectively, the true extent and severity of a drought. Adopting a single measure for assessing drought is unrealistic and may not stand the test of time and the court of law. The Commonwealth Government recognised this in basing its decisions on Drought Exceptional Circumstances on six criteria, as well as visits by the Rural Adjustment Scheme Advisory Council, to affected areas. A combination of measures, including rainfall analysis, and soil moisture balance indices supported by field surveys - is proposed by NSW Agriculture to be the most realistic approach to assess the extent and intensity of drought.

Based on the monitoring criteria, an early warning system should be developed to appraise the community as well as State and Federal governments of the true nature of the situation and to counteract exaggerations and panic. A climatic information service developed and operated on an inter-disciplinary basis providing products tailored to planning requirements will be a great aid in the drought mitigation actions. A contingency drought mitigation plan should be prepared and activated as soon as the early warning system begins to send signals of the impending drought.

Contact: Dr Harpal Mavi; ph (02) 6391 3637; fax (02) 6369 3767; harpal.mavi@agric.nsw.gov.au

Exceptional Circumstances

BRS is currently working to enhance government responsiveness to Exceptional Circumstances (usually drought) by improving its capacity to deliver evidence-based assessments to AFFA (Agriculture, Fisheries and Forestry Australia, formerly DPIE, the Department of Primary Industries & Energy) and the Minister. This is being done through researching new approaches to the analysis of climate variability and its impacts on agriculture, and developing an Integrated Toolset that will integrate scientific evidence from a range of sources so as to provide customised delivery of assessments of Exceptional Circumstance applications (G. Laughlin, personal communication).

Although the EC declaration procedures are currently under review, they are still likely to be invoked according to the following criteria:

• a rare event with a severe impact on producers; with prolonged effects upon production and farm income;
• of sufficient scale to warrant government involvement; and
• impacts should be beyond the capacity of normal, responsible risk management and likely to cause unwarranted, undesirable and unnecessary structural change.

Current research by BRS and its collaborators is therefore being devoted to developing qualitative and quantitative descriptions of:

• different attributes of climate variability (short, medium and long term variations in the rainfall of major growing seasons) as causes of events; and
• the impacts on agricultural production and sustainability, such as through:
• combinations of enhanced AVHRR reflectances and brightness temperatures that are well correlated with useful measures of biomass production such as laser-based vegetation profiling;
• the outputs from pasture simulations and pasture experiments; and
• the principles of good risk management at regional scales.

Trends in the Incidence of Drought Exceptional Circumstances in New South Wales

This study was undertaken by Harpal Mavi (pers. comm.) in order to assess, understand and highlight:

i) Trends and frequency of drought exceptional circumstances events in NSW

ii) Seasonal comparison in the trends and frequency of drought exceptional circumstances.

iii) Adequacy of the statistical technique to assess a severe and rare drought event.

Rainfall records were collected for 62 well distributed stations for which long term uninterrupted records are available. These stations represent all the rainfall regimes and major agro-ecological regions of NSW.

The Excel macros written to analyse rainfall records for the assessment of Drought Exceptional Circumstances according to one of the Federal Government criteria were used to statistically analyse the historical rainfall records. For each of the stations, DEC events were counted for each decade for comparison. Eleven year running averages and averages for: i) the entire period of record, ii) the period up to 1900, iii) the period 1901 to 1950 and iv) the period 1951 to 1997 were worked out and plotted along with the actual rainfall to compare changes and trends in rainfall. The number of years with rainfall below the 20^th and above the 80^th percentiles were counted and compared. DEC events identified on the basis of different time series data at selected stations were compared to assess the sensitivity of the technique to identify a rare and severe drought event.

Preliminary results of the analysis of the last 100 years data show no consistency in the incidence of Drought Exceptional Circumstances. Three-quarters of the Drought Exceptional Circumstances events of the last 100 years occurred in the first half of the century. The decade 1935-1944 was the worst with 26 per cent of the total events. Substantially higher rainfall in the second half of the century, compared to the first half, has contributed toward the lesser number of the drought events in this period. There is a need to examine the current statistical method to assess a rare and severe event in the light of an increasing trend in rainfall due to the greenhouse effect.

Contact: Dr Harpal Mavi; ph (02) 6391 3637; fax (02) 6369 3767; harpal.mavi@agric.nsw.gov.au

Specific research projects currently (or recently) being undertaken in this area include:

• Objective criteria for exceptional circumstances declarations: improving scientific and economic inputs to decision making - Dr David White, Bureau of Resource Sciences (now with ASIT Consulting)
• Enhanced framework for analysing climate variability and its impacts for policy purposes - Dr Greg Laughlin, Bureau of Rural Sciences

References

1. Donnelly, J.R., Freer, M. and Moore, A.D. (1998). Using the GrassGro decision support tool to evaluate some objective criteria for the definition of exceptional drought. Agricultural Systems 57, 301-313.

2. Keating, B.A. and Meinke, H. (1998). Assessing exceptional drought with a cropping systems simulator: a case study for grain production in north-east Australia. Agricultural Systems 57, 315-332.

3. McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P. and Freebairn, D.M (1996). APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems 50, 255-271.

4. McVicar, T.R.. and Jupp, D.L.B. (1988). The current and potential operational uses of remote sensing to aid decisions on Drought Exceptional Circumstances in Australia: a review. Agricultural Systems 57, 399-468.

5. Ockerby, J., Proctor, W. and Corder, C. (1996). Economic criteria for Exceptional Circumstances declarations under the national drought policy. Report to the Bureau of Resource Sciences from the Australian Bureau of Agriculture and Resource Economics, ABARE Project Number 1287.

6. O'Meagher, B., du Pisani, L. and White, D.H. (1998). Evolution of drought policy and related science in Australia and South Africa. Agricultural Systems 57, 231-258.

7. O'Meagher, B., Stafford Smith, M. and White, D.H. (1999). Approaches to integrated drought risk management: Australia's national drought policy. In Drought (edited by D.A. Wilhite), Routledge, London (in press).

8. Stafford Smith, D.M. and McKeon, G.M. (1998). Assessing the historical frequency of drought events on grazing properties in Australian rangelands. Agricultural Systems 57, 271-299.

9. Stephens, D.J. (1998). Objective criteria for estimating the severity of drought in the wheat cropping areas of Australia. Agricultural Systems 57, 333-350.

10. Thompson, D. and Powell, R. (1998). Exceptional Circumstances provisions in Australia - Is there too much emphasis on drought? Agricultural Systems 57, 469-488.

11. White, D.H. and Bordas, V. (editors) (1997). Proceedings of a workshop on Indicators of Drought Exceptional Circumstances. Bureau of Resource Sciences, Canberra, 1 October 1996, 87 pp.

12. White, D.H. and O'Meagher, B. (1995). Coping with exceptional droughts in Australia. In Drought Network News, (edited by D.A. Wilhite), University of Nebraska, 7(2), 13-17; accessible at http://enso.unl.edu/ndmc/mitigate/policy/asutral.htm

13. White, D.H., Howden, S.M., Walcott, J.J. and Cannon, R.M. (1998). A framework for estimating the extent and severity of drought, based on a grazing system in south-eastern Australia. Agricultural Systems 57, 259-270.

2.2.5 Alert systems - national, regional, on-farm

Aussie GRASS

Aussie GRASS (Australian Grassland and Rangeland Assessment by Spatial Simulation), a spatial modelling framework for assessing the condition of Australia's grazing lands, has been developed by the Queensland Departments of Natural Resources and Primary Industries (Hall et al. 1997). This integrated climate data, natural resources data, remote sensing, historical agronomic research and simulation model to provide useful and objective assessment of drought conditions. The system has been operational in Queensland, using a pasture growth model (GRASP, Littleboy and McKeon 1997) to generate estimates of vegetative cover and condition on a monthly basis across the State of Queensland. The framework proved invaluable to the Rural Adjustment Scheme Advisory Council (RASAC) and the Commonwealth Government in their regular assessments of the severity and extent of drought across Queensland (White 1997).

The second stage of the project (Aussie GRASS) involves the national collaboration of State and Territory agencies and CSIRO Divisions, including an analysis of available regional models of plant growth, issues of extension, satellite data and biophysical data sets. For example, Agriculture Western Australia is to provide enhanced underlying datasets for that State that are then incorporated into the Aussie GRASS GIS to enable spatial and temporal changes in vegetative cover to be better simulated. As mentioned in Section 2.2.3 on grassland modelling, the GRASP model is being compared with IMAGES, ARIDGROW and SEESAW for grasslands in central and southern Australia, and with GrassGro for higher rainfall temperate pastures, to assess which model is the most appropriate for different areas. This is largely influenced by the relative proportion of C₃ and C₄ plants in different environments. Collaborators include Ian Watson, Greg Beeston, Matt Boland and Andrew Craig (W.A.), Roger Tynan and Russell Flavel (S.A.), Ron Hacker, Rob Richards, Judy Bean, Daryl Green, Allan McGuffiche, Graeme Tupper, John Crichton and Harpal Mavi (NSW), Rod Dyer (NT) and Col Paull (QDPI).

The national Aussie GRASS model currently produces products on a monthly timestep that are available to collaborators via a password-protected web site. Most of these products are yet to be fully calibrated and validated, except for Queensland. Calibration and validation will be conducted using field data collected using 'spider mapping' techniques. Further details are available at http://www.dnr.qld.gov.au/longpdk/agrass/proj.html

A series of training workshops is currently underway throughout Australia to educate researchers and extension officers about the project and available products. The workshops also provide an ideal forum for further product development.

STIN

When the regional wheat yield forecasting model (STIN) of Stephens (1995) is run with a constant technology assumption for all years of rainfall data, accumulated negative deviations in crop yields may be used to form a Drought Exceptional Circumstance Index (DECI) (Stephens 1998) which should help governments assess which areas in the cropping zone were experiencing drought exceptional circumstances and would therefore qualify for financial assistance from the Commonwealth Government.

An Automated Weather Station Network has also been set up in Western Australia to provide necessary data to underpin research into spatial and temporal crop and grassland modelling in that State (Ian Foster, personal communication). These ~20 AWSs are not in the BoM network, being deliberately set at a different height, with a different format and hourly measurements. They were originally installed for wind erosion research, but are now being used in the climate modelling program. http://www.agric.wa.gov.au/progserv/natural/climate/CigHome.htm

Soil water balance modelling based on WATBAL (Fitzpatrick and Nix 1969), with estimates at 5-day intervals (pentads) gives good estimates of duration of growth in the rangelands for areas where rainfall data are available. The usefulness of rainfall terciles to aid decision making in the cropping areas is also under investigation. Maps are also being produced for each month on the frequency of waterlogging for different soil types.

SLUIS

SLUIS, the Sustainable Land Use Information System, was developed for the Commonwealth Government as a system to provide integrated scientific information relating to the effects of climate variability on land surface processes (Lyons et al. 1997; Shao et al. 1997; Munro et al. 1998). Primary information sources include soils, vegetation, topography, and weather conditions. Outputs are primarily focussed on integrating atmospheric and land surface hydrology. Its development has been a collaborative project of the Bureau of Resource Sciences; the Centre for Advanced Numerical Computation in Engineering and Science, University of New South Wales; and the School of Mathematics of the University of New South Wales.

It works by integrating the outputs of a numerical weather prediction model with a land surface hydrological model. Soil moisture is estimated at a range of depths, along with surface runoff, taking into account soil temperature, wind speed and direction, radiation and humidity.

SLUIS has been constructed in such a way that it is capable of being coupled to Global Climate Models, providing effective downscaling to improve resolution in time and for regional and continental areas. It should therefore provide valuable information for analysing and predicting the impacts of climate variability and change.

Specific research projects currently (or recently) being undertaken in this area include:

• Development of a national drought alert strategic information system - Mr Ken Brook, QCCA (LWRRDC)
• Australian grassland and rangeland assessment by spatial simulation: Aussie GRASS - Dr Wayne Hall, QDNR (LWRRDC)
• Sustainable Land Use Information System (SLUIS) - Br Bob Munro, Bureau of Rural Sciences

References

1. Brook, K.D. and Carter, J.B. (1994). Integrating satellite data and pasture growth models to produce feed deficit and land condition alerts. Agricultural Systems & Information Technology 6(2), 38-40, 54-56.

2. Brook, K.D. and Carter, J.O. (1996). A prototype National Drought Alert Strategic Information Systems for Australia. In: Drought Network News: the Newsletter of the International Drought Information Center and U.S. National Drought Mitigation Center, Lincoln, Nebraska, pp. 13-16.

3. Brook, K.D., Carter, J.O., Danaher, T.J., McKeon, G.M., Flood, N.R. and Peacock, A. (1992). The use of spatial modelling and remote sensing to forecast drought-related land degradation events in Queensland. In Proceedings of the Sixth Australasian Remote Sensing Conference, 2-6 November 1992, Wellington, New Zealand, 1, pp. 140-149.

4. Brook, K.D., Carter, J.O., Danaher, T.J., McKeon, G.M., Flood, N.R. and Peacock, A. (1992). SWARD: Statewide Analysis of the Risks of Land Degradation in Queensland. Agricultural Systems and Information Technology 4(2), 9-11.

5. Carter, J.O. and Brook, K.D. (1995). Developing a national drought alert framework. In: Proceedings of the Managing with Climate Variability Conference, 'Of Droughts and Flooding rains', Canberra, 16-17 November 1995, LWRRDC Occasional Paper CV03/96, pp 53-60.

6. Carter, J.O., Brook, K.D., Danaher, T.J. and McKeon, G.M. (1992). A strategic information system for real-time management of rangelands during drought using spatial modelling methodology. In Proceedings of the Twentieth Annual International Conference of the Australasian Urban and Regional Information Systems Association Incorporated, Gold Coast, 25-27 November, 1992, pp 229-237.

7. Carter, J. Flood, N., Danaher, T., Hugman, P., Young, R. Duncalfe, F., Barber, D., Flavel, R., Beeston, G. Mlodawski, G., Hart, D., Green, D., Richards, R., Dudgeon, G., Dance, R., Brock, D and Petty, D. (1996). Development of a National Drought Alert Strategic Information System, Volume 3: Development of data rasters for model inputs, Final report on QPI20 to Land and Water Resources Research and Development Corporation, 76 pp.

8. Carter, J., Flood, N., McKeon, G., Peacock, A., Beswick, A. (1996). Development of a National Drought Alert Strategic Information System, Volume 4: Model framework, Parameter derivation, Model calibration, Model validation, Model outputs, Web technology, Final report on QPI20 to Land and Water Resources Research and Development Corporation, 42 pp.

9. Fitpatrick, E.A. and Nix, H.A. (1969). A model for simulating soil water regime in alternating fallow-crop systems. Agricultural Meteorology 6, 303-319.

10. Hall, W., Day, K., Carter, J., Paull, C. and Bruget, D. (1997). Assessment of Australia's grasslands and rangelands by spatial simulation. Proceedings of the MODSIM 97 International Congress on Modelling and Simulation, (edited by A.D. McDonald and M. McAleer), 8-11 December 1997, Hobart, Tas., vol. 4, pp. 1736-1741.

11. Hammer, G., Stephens, D. and Butler, D. (1996). Development of a National Drought Alert Strategic Information System, Volume 6: Wheat Modelling Subproject (a) Development of Predictive Models of Wheat Production, Final report on QPI20 to Land and Water Resources Research and Development Corporation, 41 pp.

12. Kuhnell, C. and Danaher, T. (1996). Development of a National Drought Alert Strategic Information System, Volume 6: Wheat Modelling Sub project (b) Mapping broadacre cropping areas in Queensland using Landsat TM and NOAA AVHRR imagery, Final report on QPI20 to Land and Water Resources Research and Development Corporation, 10 pp.

13. Lyons, W.F., Shao, Y., Munro, R.K., Hood, L.M. and Leslie, L.M. (1997). Soil moisture modelling and prediction over the Australian continent using the ALSIS land surface schema. In Climate prediction for agricultural and resource management, edited by R.K. Munro and L.M. Leslie, Australian Academy of Science Conference, Canberra, 6-8 May 1997, Bureau of Resource Sciences, Canberra, pp. 151-164.

14. McKeon, G., Paull, C. and Peacock, A. (1996). Development of a National Drought Alert Strategic Information System, Volume 5: Evaluation of model performance relative to rainfall, Inter-state model calibration, Extension, and State comments, Final report on QPI20 to Land and Water Resources Research and Development Corporation, 58 pp.

15. McKeon, G.M., Brook, K.D., Carter, J.O., Day, K.A., Howden, S.M., Johnston, P.W., Scanlan, J.C. and Scattini, W.J. (1994). Modelling utilisation rates in the Black Speargrass Zone of Queensland. In: Proceedings of the 8^th Australian Rangelands Society Conference, June 21-23, Katherine, NT, pp. 128-132.

16. Munro, R.K., Lyons, W.F., Wood, M.S., Shao, Y. and Leslie, L.M. (1998). A Sustainable Land Use Information System. Ecosystems and Sustainable Development, 1, 635-650.

17. Shao, Y., Leslie, L.M., Munro R.K., Irannejad, P., Lyons, W.F., Morison, R., Short, D. and Wood, M.S. (1997). Soil Moisture Prediction over the Australian Continent. Meteorology and Atmospheric Physics, 63, 195-215.

18. Stephens, D.J. (1995). Crop yield forecasting over large areas in Australia. PhD thesis, Murdoch University, 317 pp.

19. Stephens, D.J. (1998). Objective criteria for estimating the severity of drought in the wheat cropping areas of Australia. Agricultural Systems 57, 333-350.

20. White, D.H. (1997). Risk assessment and management: Case study - drought and risk. Proceedings of the National Outlook Conference: Commodity markets and resource management, 4-6 February 1997, Australian Bureau of Agriculture and Resource Economics, Canberra, pp. 98-103.

21. Wood, H., Hassett, R., Carter, J. and Danaher, T. (1996). Development of a National Drought Alert Strategic Information System, Volume 2: Field validation of pasture biomass and tree cover, Final report on QPI20 to Land and Water Resources Research and Development Corporation, 51 pp.

Contacts and institutions

Mr Greg Beeston, Spatial Resource Information Group, Agriculture Western Australia, South Perth WA 6151. Ph: (08) 9368 3272; Fax: (08) 9368 3939; gbeeston@agric.wa.gov.au

Mr Ken Brook, Climate Impacts and Spatial Systems, Queensland Department of Natural Resources, PO Box 631, Indooroopilly, Qld 4068. Ph: (07) 3877 9379; Fax: (07) 3896 9606; ken.brook@dnr.qld.gov.au

Mr John Carter, Climate Impacts and Spatial Systems, Queensland Department of Natural Resources, PO Box 631, Indooroopilly, Qld 4068. Ph: (07) 3877 9588; Fax: 3896 9606; john.carter@dnr.qld.gov.au

Mr Ian Foster, Spatial Resource Information Group, Agriculture Western Australia, South Perth WA 6151. Ph: (08) 9368 3642; Fax: (08) 9368 3355; ifoster@agric.wa.gov.au

Dr Wayne Hall, Climate Impacts and Grazing Systems, Queensland Department of Natural Resources, PO Box 631, Indooroopilly, Qld 4068. Ph: (07) 3896 9612; Fax: 3896 9843; john.carter@dnr.qld.gov.au

Dr David Stephens, Spatial Resource Information Group, Agriculture Western Australia, South Perth WA 6151. Ph: (08) 9368 3346; Fax: (08) 9368 3939; david_s@agrsrv.agric.wa.gov.au

Dr Bob Munro, Bureau of Resource Sciences, PO Box E11, Kingston, ACT 2604. Ph: (02) 6272 4035; Fax: (02) 6272 4687; robert.munro@brs.gov.au