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2.3 Managing within a variable climate

 Increased self-reliance by rural producers requires the ability to manage both crop and livestock enterprises exposed to a variable climate and to minimise the impact of drought. It also requires the ability to more effectively manage risk: production risk, environmental risk, financial risk and market risk (White 1997). Improved seasonal outlooks are but one approach to assisting farmers to become more self-reliant. Ways of offsetting the risks associated with climate variability in order to create opportunities require a systems approach (Hammer and Nicholls 1996).

2.3.1 Managing climate variability per se - grasslands

Variability in climate implies variability in the feed supply, and hence in the short-term carrying capacity of the sward. Managers usually respond to this by varying the number of livestock grazing their properties. Thus if recent rainfall and current paddock feed is well below average then decisions may be made to sell off classes of stock, usually starting with the oldest and least productive animals. Stock may also be put on agistment by transferring them temporarily to other properties that are experiencing a more favourable season. In areas where transport costs are high relative to the value of surplus stock, then livestock may be shot or even left to starve, although the latter may not be an option where animal welfare legislation is in place.

The majority of traded livestock are bought in good seasons and sold in adverse seasons, and this variation in supply and demand is reflected in their price. Determining the long-term optimal stocking rate, and when and which animals to buy and sell are therefore critical decisions affecting the overall profitability of the farm.

Fodder may be conserved in the form of hay, straw or grain. This can pose a problem in that the higher the stocking rate the greater the need for stored fodder, but the lower the capacity to grow surplus grass to provide hay or straw. Feed wheat can be the cheapest source of energy in a drought, although oaten grain has a higher fibre content so that livestock are less prone to acidosis.

Of course, the cheapest means of fodder conservation is often on the animal's back, so if livestock are in good condition prior to a drought then they should need less fodder reserves to keep them alive. However, higher liveweight is associated with higher maintenance requirements, so a prudent manager will be feeding supplements at a rate that causes them to lose weight to a level that is more appropriate for enduring a drought. If animals are too light in weight then there is a risk of substantial losses associated with malnutrition, disease or inclement weather. Weighing of livestock, and feeding on the basis of nutrient requirements (e.g. use GrazFeed software; Freer et al. 1997), are therefore appropriate tactics during drought.

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

  • Pasture and forage systems (GRAZPLAN) - Dr John Donnelly, CSIRO Plant Industry (AWRAPO; Australia and Pacific Science Foundation; MLA; 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
  • Systems overview of the northern Australian beef industry for optimal management of resources, Mr David Mayer, QDPI
  • Improved understanding of the population dynamics of perennial plants important for sustainable sheep grazing and for rangeland monitoring - Dr Ian Watson, Agriculture Western Australia
  • Strategies for maximising the persistence of perennial grasses through drought - Dr Jim Scott, University of New England (LWRRDC, MRC)
  • Exploiting drought opportunities: control of total grazing pressure of grasslands - Dr Ron Hacker, New South Wales Agriculture (LWRRDC, IWS)
  • Management strategies to maximise opportunities for sustainable economic gain in chenopod shrublands - Mr D. Atkins, Agriculture Western Australia (AWRAPO)
  • Reclaiming and sustaining productivity of Queensland bluegrass downs - Mr H. Bishop, QDPI
  • Improved pasture management and beef production through parthenium weed control - Mr H. Chamberlain, QDPI
  • Silage systems for beef production and drought feeding for northern Australia - Mr Ken Rich, Agresult, Buderim Qld (MRC, Pioneer Hi-Bred Australia)
  • Enhancing profits from poplar box country by tactical pasture management - Dr R. Silcock, QDPI
  • Evaluation of the impact of climate change on northern Australian grazing industries - Dr Greg McKeon, QDNR
  • Regeneration of drought-affected pastures - Mr G. Lambert, QDPI
  • Coping with rainfall variability in tropical savannas - Dr Peter O'Reagain, QDPI
  • Optimising phosphorus use in Victoria - Mr Cameron Gourley, DNRE, Vic.

References

1. Bowman, P.J., Fowler, D.G., Wysel, D.A. and White, D.H. (1989). Evaluation of a new technology when applied to sheep production systems: Part II. Real-time ultrasonic scanning of ewes in mid-pregnancy. Agricultural Systems 29, 287-323.

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. Donnelly, J.R., Moore, A.D. and Freer, M. (1997). GrazPlan: Decision support systems for Australian grazing enterprises-I. Overview of the GrazPlan project, and a description of the MetAccess and LambAlive DSS. Agricultural Systems 54, 57-76.

4. Freer, M., Moore, A.D. and Donnelly, J.R. (1997). GrazPlan: Decision support systems for Australian grazing enterprises-II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77-126.

5. 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.

6. Morley, F.H.W. and Daniel, G. (1992). Soil loss, drought and stocking rates. Proceedings of the Australian Society of Animal Production 19, 323-325.

7. 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.

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

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 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 David H White, ASIT Consulting, PO Box 328, Hawker, ACT 2614. Ph: (02) 6254 5936; Fax: (02) 6255 2455; dwhite@acslink.aone.net.au

2.3.2 Managing climate variability per se - crops

Establishing a crop is an expensive operation, yet significant financial returns to cropping are obtained in only about 30 per cent of seasons over much of Australia. Aids to estimating the likelihood of a successful crop are therefore in demand. There is therefore considerable interest in having reliable seasonal forecasts, in determining the most appropriate cultivars and sowing dates for different locations and seasons, in controlling weeds and other impediments to growth and yield, in determining appropriate fertiliser and pesticide applications, in monitoring soil moisture and, where a physical and financially viable option, in irrigating the crop. The number of crops in a pasture-livestock rotation, and the mix of cereal, legume and oilseed crops, can affect soil structure and nutrient status, crop health, grain yield and the financial viability of the farming system.

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 forecasting to improve industry competitiveness - Dr Russell Muchow, CSIRO Tropical Agriculture (LWRRDC)
  • 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. Abrecht, D.G. and Balston, J.M. (1996). Rules for managing the break of the season in Mediterranean environments. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 111-115.

2. Abrecht, D.G. and Robinson, S. (1996). TACT: a tactical decision aid using a CERES-based wheat simulation model. Ecological Modelling 86, 241-244.

3. Egan, J.P. (1995). Managing with our variable climate for crop production. In Proceedings of Agronomy Technical Conference, Adelaide, March 1995, pp. 175-180.

4. Egan, J.P. and Balston, J.M. (1996). Farming by season type. In Proceedings of Farming Systems Developments 1996 Workshop, Adelaide, March 1996, pp. 147-148.

5. Egan, J.P. and Balston, J.M. (1996). Managing climate risks, GRDC Southern Region Research Updates, Walpeup and Cummins, February 1996.

6. Egan, J.P. and Balston, J.M. (1996). Managing climate variability: helping farmers do it better. Paper for Sixth Community Landcare Conference, Renmark, SA, July 15-16 1996.

7. Egan, J.P. and Hammer, G.L. (1995). Managing climate risks in grain production. In Proceedings of Managing with Climate Variability Conference, Canberra, November 1995, LWRRDC Occasional Paper CV03/95, pp. 98-105.

8. Egan, J.P., Balston, J.M. and Abrecht, D.G. (1996). Adjusting cropping programs to match seasonal expectations in low rainfall districts of South Australia. In Proceedings of 2nd Australian Conference on Agricultural Meteorology, Brisbane, October 1996, pp. 308-311.

9. Egan, J.P., Balston, J.M. and Holton, I. (1997). Farming to season type - applying climate and yield risk information and seasonal forecasts to farm management decisions. In Proceedings of Farming Systems Developments 1997 Workshop, Adelaide, March 1997, p. 192.

10. Egan, J.P., Robinson, S.D., Abrecht, D.G., Hammer, G.L. and Huda, A.K.S. (1993). Climate variability management and its application in Property Management Planning. In Proceedings of National Conference of Soil and Water Conservation Association of Australia, Adelaide, October 1993, pp. 225-227.

11. Hammer, G.L. and Nicholls, N. (1996). Managing for climate variability - the role of seasonal forecasting in improving agricultural systems. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 19-27.

12. Hayman, P., Cox, P. and Huda, S. (1996). Will more climate information assist farmers better to assess and manage the risks of opportunity cropping on the Liverpool Plains. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 67-71.

13. Hayman, P.T., Freebairn, D.M. and Huda, A.K.S. (1996). Opportunity cropping on the Liverpool Plains: a comparison of risk assessment by farmers and simulation models. Proceedings of the 8th Australian Agronomy Conference, USQ, Toowoomba, Queensland, 30 January - 2 February 1996, pp. 293-296.

14. Huda, A.K.S., Rahman, M.S., Egan, J.P., Holloway, R.E. and Lymn, A.S. (1993). Assessing and managing cropping risks in low rainfall areas of southern Australia using agroclimatic data. South Australian Department of Primary Industries Technical Paper No. 34 (190 pp).

15. Huda AKS, Rahman MS, Holloway RE, Egan JP and Lymn AS (1994). Reducing the risk of cropping in low rainfall areas of southern Australia. Agricultural Systems and Information Technology 6 (2): 72-74.

16. Kingwell, R.S., Morrison, D.A. and Bathgate, A.D. (1992). The effect of climatic risk on dryland farm management. Agricultural Systems 39, 153-175.

17. Kingwell, R.S., Pannell, D.J. and Robinson, S.D. (1991). Tactical responses to seasonal conditions in whole-farm planning in Western Australia. Agricultural Economics 8, 211-226.

18. Truscott, M.A., Egan, J.P. and Balston, J.M. (1998). Managing climate variability. Crop harvest report 1997/1998, PIRSA, pp. 119-120.

19. Truscott, M.A, et al. (1998). Climate risk management in low rainfall environments of SA and WA. Proceedings of the 12th Australia and New Zealand Climate Forum, Nov 1998.

Contacts and institutions

Dr Doug Abrecht, Dryland Research Institute, PO Box 342, Merredin, WA 6415. Ph: (08) 9081 3106; Fax: (08) 9041 1138; Mobile: 0417 934 648; dabrecht@agric.wa.gov.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

 Mr Jim Egan, South Australian Research and Development Institute, PO Box 1783, Port Lincoln, SA 5606. Ph: (08) 8688 3424; Fax: (08) 8688 4327; egan.jim@pi.sa.gov.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 Peter Hayman, NSW Agriculture, Tamworth, NSW 2340; ph (02) 6763 1256; Fax: (02) 6763 1222; peter.hayman@agric.nsw.gov.au

Dr Samsul Huda, University of Western Sydney; Ph: (02) 4570 1390; Fax: (02) 4570 1750; s.huda@uws.edu.au

Dr Holger Meinke, Queensland Department of Primary Industries/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 2350. Ph: (07) 4688 1378; Mobile: 041 219 6534; Fax: (07) 4688 1193; mailto:MeinkeH@prose.dpi.qld.gov.au

Ms Melissa Truscott, Field Crop Improvement Centre, South Australian Research and Development Institute, GPO Box 397, Adelaide, SA 5001. Ph: (08) 8303 9639; Fax: (08) 8303 9378; Mobile: 017 875 651; truscott.melissa@pi.sa.gov.au

2.3.3 Decision support systems

Models and other Decision Support Systems (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. Many computerised DSS have a mathematical model at their core. This enables them to simulate the biological relationships and feedbacks between the major components of biological systems. They therefore often contain the information that is most relevant to management about the behaviour of a production system. DSS based on these models are able to mimic the dynamic nature of these biological systems in response to alternative management strategies, technological inputs, and continually varying environmental conditions.

It is nevertheless important to appreciate that DSS 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.

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 (Bywater 1990; Stuth and Stafford Smith 1993; White et al. 1993). 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.

DSS have applications in other areas of relevance to managing the land within a variable climate. These include land use decisions (choice of enterprise and integration of enterprises at property, catchment and regional scales), management of agricultural and horticultural crops, including diagnosis of crop pests and diseases and integrated pest management (IPM), livestock health and management, managing stream flow, and controlling irrigation systems to increase water use efficiency.

Climate

Australian RAINMAN allows users to determine the probability of rainfall of a given amount in any period of interest, based on the climatic records for different locations over the past 80 or more years. An important feature is the ability to adjust these probabilities according to the Southern Oscillation Index or Sea Surface Temperatures (Clewett and Drosdowsky 1996).

MetAccess allows users to summarise and analyse long-term daily recordings of weather events in a great variety of ways and to display the results in graphical or tabular form (Donnelly et al. 1997). A particularly useful feature is its facility for calculating an estimate of the long-term probability of the occurrence of specified weather events or patterns at any locality for which weather records are available. The software makes use of daily meteorological records, including rainfall, maximum and minimum temperatures, evaporation rate and frost occurrence.

Cropping systems

TACT is a tactical wheat sowing decision aid developed by Abrecht and Robinson (1996), initially for the eastern wheatbelt of WA. It incorporates a CERES-based wheat simulation model, a daily rainfall database, average monthly values of other climate data, and sorting and simple economic analysis routines. Outputs from the TACT program include probabilities for wheat yield and profitability under various management regimes, and a suite of rainfall probability statistics, for selected locations. It has been adapted for use in low rainfall cropping districts of SA by inclusion of local rainfall and climate data and modification of soil type options. Validation and modification of TACT for these districts is continuing.

PYCAL (Potential Yield Calculator) is a computer program developed by David and Shaun Tennant of Agriculture WA to monitor current seasonal rainfall against the historic record (rainfall deciles), and to estimate stored soil water and potential yield for a range of cereal, pulse and oilseed crops. Potential yield calculations are based on the growing season rainfall (April-October)/yield relationships developed by French and Schultz (1984). With local modifications and addition of local rainfall decile tables, PYCAL is applicable right across the southern Australian wheatbelt.

SOWHAT is an ExcelŽ spreadsheet template developed by Balston and Egan (in press) as a decision support tool for farmers in areas of South Australia receiving less than 350 mm average annual rainfall. It builds on the work of Huda et al (1994) which demonstrated the potential to identify years with a high probability of above average wheat yields (opportunity years) and those with a low probability of good yields (high risk years) in the low rainfall Upper Eyre Peninsula on the basis of total rainfall received between April 1 and June 15, and use this information to develop sowing strategy "rules of thumb". SOWHAT enables farmers to use their own farm records to develop their own "rules of thumb" relating early season rainfall to farm wheat yields. Analysis of a number of farmer data sets from low rainfall districts across SA using SOWHAT has confirmed the importance of early season rainfall in achieving high yields and the value of adjusting crop sowing programs on this basis.

The value of climate risk information generated by TACT and PYCAL has been tested in farmer decision support trials in various low rainfall districts of WA and SA during the crop sowing periods (April to July) of 1996 to 1998, using a "fax-back" method to exchange rainfall data and yield and rainfall probability information on a weekly basis. The success of these trials stimulated the development of the Climate Risk and Yield Information Service (CRYIS) by the Kondinin Group, in collaboration with Agriculture WA and PIRSA, and its testing as a pilot commercial service in the eastern wheatbelt of WA and on the Upper Eyre Peninsula in SA during 1997 and 1998. The automated data handling, processing and faxing systems developed for CRYIS also offer potential for delivery of other climate, market or technical information direct to farmers on a regular basis.

HOWOFTEN? manipulates rainfall records so that questions like the following may be answered:

  • How often do I get a planting rain in May? What is the chance of rain at harvest?
  • How long must I fallow to fill the soil profile? Does the SOI have any influence?

It is therefore useful for identifying when planning opportunities occur, when flooding rains are most likely, if sufficient rainfall has fallen to fill a soil profile, and whether more rain really did fall in the 1950s. Produced by APSRU (QDPI, and CSIRO), Toowoomba and the Queensland Department of Natural Resources, it is available from the QDPI Client Information Centres at Dalby and Toowoomba.

HOWWET? uses farm rainfall records to calculate how much fallow rainfall is actually stored in the soil. The accumulation of mineralised nitrogen is also estimated through the fallow. A better knowledge of soil water and nitrogen status may be useful when deciding how long to fallow, selecting crop type and plant density, choosing pre-crop irrigation requirements and estimating expected yields.

Daily rainfall totals are entered by the user. Weekly evaporation potential and some simple soil properties are retrieved from the program's own database. Calculations also track how stubble cover improves infiltration and reduces soil erosion. Nitrogen estimates are based on relationships between soil moisture, temperature and the amount of organic nitrogen in the soil. Produced by APSRU (QDPI, and CSIRO), Toowoomba and the Queensland Department of Natural Resources, it may be purchased from the Client Information Centre, Department of Primary Industries, PO Box 102, Toowoomba Qld 4350.

APSIM (Agricultural Production Systems sIMulator) has been developed by CSIRO and the Queensland Department of Primary Industries to assist the search for better farming strategies and the development of aids to better production decision making under risk (McCown et al. 1996). Changes in the status of soil state variables are simulated continuously in response to weather and management. Crops come and go, each finding the soil in a particular state and leaving it in an altered state. A large number of crop models have now been integrated into the APSIM framework

Temperate grasslands

The GrazPlan project of the CSIRO Division of Plant Industry (Donnelly et al. 1997) is targeting a range of environments differing in soil types and pasture species. Abiotic inputs required for the soil moisture budget and pasture growth models are maximum and minimum air temperature, precipitation, pan evaporation and daylength. Daily radiation is included where available; otherwise it is predicted from latitude, day of year and rainfall. Annual species, perennial grasses and perennial herbs are represented differently in the model on the basis of their morphology and ecology. The rate of net primary production is modelled as a maximum sward growth rate (dependent on the amount of light intercepted), scaled by the lesser of two factors governing the ability of plants in the sward to convert light into biomass. In annual swards, seeds pass from an unripe pool to which assimilate is directed, through a period of innate dormancy, to a pool of seeds in enforced dormancy.

For example, the GrassGro DSS of Moore et al. (1997) contains a library of information on pasture species such as Phalaris and subterranean clover (Trifolium subterraneum), including their responsiveness to soil moisture on different soil types, the onset of the reproductive and dormant stages in relation to daylength and their potential for growth in relation to temperature. Biomass is divided into four classes: live, senescing, standing dead and litter. Shoot material is further classified into five digestibility classes, ranging from 75-85 per cent digestible to 35-45 per cent digestible material. This provides inputs to another module that simulates diet selection by sheep and cattle and their subsequent productivity; this module also stands alone as the GrazFeed model for pasture assessment (Freer et al. 1997).

SheepO is a multi-paddock DSS designed to determine the likely outcome in terms of production and financial performance of a wide range of management decisions for sheep flocks in the winter rainfall areas of southern Australia (Whelan et al. 1987; Ransom 1992). The program uses pasture growth rates for individual years as the basis of its calculations. First developed in Victoria, its development has continued within New South Wales Agriculture (McPhee 1993).

Tropical grasslands

FEEDMAN is an easy-to-use computer program to help beef producers compare feeding options for growing cattle in terms of forage utilisation, animal performance, market options and economics (Rickert et al. 1996, Rickert 1998). It was designed for the 'endowed' zone of northern Australia, the region with relatively fertile soils and effective rainfall in both the warm and cool seasons.

After describing a farm in terms of paddocks, that may contain a range of soil types and forages, monthly forage growth and sustainable stocking rates are calculated in response to monthly rainfall, soil nitrogen status and tree density. Monthly rainfall can be entered directly or be selected from historical records. Also, growing cattle (steers and heifers of different breeds and age) can be allocated to each paddock and FEEDMAN estimates animal liveweight and market options for each mob. An economic assessment of a farm follows. Results appear on the screen and in printed reports. Thus, FEEDMAN evaluates a wide range of management options chosen by the user. Since the reliability of FEEDMAN depends on accurate inputs, key inputs can be changed by the user to reflect local conditions.

Farm descriptions can be stored and recalled for further use or modification. Extensive help notes provide directions and explanations for each component of FEEDMAN.

Rangelands

RANGEPACK (Stafford Smith and Foran 1989) is a strategy assessment tool that follows a herd or flock through successive years to evaluate the lagged effects of climatic fluctuations on herd numbers, allowing the user to follow the gradual implementation of a new strategy. The outcome of the biological and marketing strategies of a property can therefore be followed through good and bad years and linked to economic returns. It has been used to identify optimal drought management strategies for cattle and sheep pastoral properties, respectively (Foran and Stafford Smith 1991; Stafford Smith and Foran 1992).

Crop-livestock integration

Simulation models of agricultural systems are usually specific to one enterprise. However, many farms have more than one enterprise, mixed wheat and sheep farms being common in parts of Australia. Whole-farm models such as MIDAS are in fact mathematical programs that are useful for optimising land use to maximise profit, particularly if different soil types are involved (Morrison et al. 1986). Their reliability is greatest if seasonal variability is minimal and an average season is assumed.

Mathematical programs present difficulties in adequately representing the often non-linear nature of biological processes and the marked variation between seasons, particularly as these carryover into subsequent years. Considerable effort has therefore had to be put into MIDAS to make it biologically realistic. A more recent version, MUDAS (Kingwell et al. 1992; Kingwell and Schilizzi 1994; Schilizzi and Kingwell 1999), caters for 11 different types of seasons. Needless to say there is considerable interest by agricultural systems analysts in its performance. For sequences of seasons, use of other models is appropriate.

Mathematical programs have also been used to identify alternative, more profitable pasture-crop rotations in the wheat-sheep zones of Victoria (Oram 1985), New South Wales (Johnston and Matuska 1985) and South Australia (Hansen and Krause 1989).

In theory, rather than linear programming, various versions of non-linear programming could be used, particularly quadratic programming or, more generally, convex programming (but not non-convex as with logistic curves). It is probably more appropriate to deal with non-linear relationships, such as nitrogen-yield relationships, outside the linear program, and then insert the optimal nitrogen input level as a datum into the whole-farm model (S. Schilizzi, personal communication).

Ideally a number of pasture-livestock and crop models could be run simultaneously to predict changes in the productivity and profitability of multi-enterprise farming systems over a range of seasons, and with different combinations of management strategies. The anticipated integration of the GrazPlan and APSIM models should help bring this about (McCown et al. 1996). This would enable mathematical programs such as MIDAS to be used more effectively to optimise the land use, taking into account the variability in the climate.

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

  • Decision support for climatic risk management in dryland crop production. Mr J.P. Egan, South Australian Research and Development Institute, PIRSA (LWRRDC, GRDC and RIRDC).
  • Improving management of seasonal conditions for low rainfall crop production. Mr J.P. Egan, South Australian Research and Development Institute, PIRSA (GRDC).
  • Further development and application of AUSTRALIAN RAINMAN to improve management of climate variability - Dr J. Clewett, QDPI (LWRRDC, RIRDC)
  • Using climatic data for agricultural decision making - Dr D.G. Abrecht, Agriculture Western Australia
  • FEEDMAN - a pasture and crop decision support system - Dr K. Rickert, University of Queensland (AMRC; Australian Meat Research Committee)
  • Pasture and forage systems (GRAZPLAN) - Dr John Donnelly, CSIRO Plant Industry (AWRAPO; Australia and Pacific Science Foundation; MLA, Meat and Livestock Australia; Australian Pastoral Research Trust; LWRRDC)
  • Grazier-based profitable and sustainable strategies for managing climatic variability (DroughtPlan) - Dr Mark Stafford Smith, CSIRO Wildlife & Ecology (LWRRDC, MRC; Meat Research Corporation)

References

1. Balston, J.M., Abrecht, D.G. and Egan, J.P. (1996). Delivering relevant, timely seasonal information to farmers in Mediterranean dryland cropping areas. In Proceedings of 2nd Australian Conference on Agricultural Meteorology, Brisbane, October 1996, pp. 304-307.

2. Balston, J.M., Abrecht, D.G. and Egan, J.P. (1997). Development of a climate risk and yield information service. In Proceedings of Farming Systems Developments 1997 Workshop, Adelaide, March 1997, pp. 187-188.

3. Balston, J.M., Abrecht, D.G., Egan, J.P. (in press). Modification and evaluation of TACT as a decision support tool for the Murray Mallee of South Australia. SARDI Technical Note.

4. Balston, J.M. and Abrecht, D.G. (in press). Modification and evaluation of TACT as a decision support tool for the Eyre Peninsula of South Australia. SARDI Technical Note.

5. Balston, J.M., Abrecht, D.G., and Tennent, D. (in press). Farmer Decision support trial in the dryland agricultural areas of the Eyre Peninsula of South Australia. SARDI Technical Note.

6. Balston, J.M., Abrecht, D.G. and Tennent, D. (in press). Farmer Decision support trial in the dryland agricultural areas of the Murray Mallee of South Australia. SARDI Technical Note.

7. Balston, J.M. and Egan, J.P. (in press). Development and evaluation of SOWHAT, a decision support tool for sowing decisions based on early season rainfall. SARDI Research Report.

8. Bywater, A.C. (1990). Exploitation of the systems approach in technical design of agricultural enterprises. In Systems theory applied to agriculture and the food chain, (edited by J.G.W. Jones and P.R. Street), Elsevier Applied Science, London, pp. 61-88.

9. Clewett, J.F. and Drosdowsky, L. (1996). Study 1: Use of the Southern Oscillation Index as a drought management tool. In DroughtPlan: Building on participation by D.M. Stafford Smith, J.F. Clewett, A.D. Moore, G.M. McKeon, R. Clark and many others (1996b). Full project report, Working paper No. 10, pp 120-124.

10. Donnelly, J.R., Moore, A.D. and Freer, M. (1997). GrazPlan: Decision support systems for Australian grazing enterprises. I. Overview of the GrazPlan project, and a description of the MetAccess and LambAlive DSS. Agricultural Systems 54, 57-76.

11. Foran, B. and Stafford Smith, D.M. (1991). Risk, biology and drought management strategies for cattle stations in Central Australia. Journal of Environmental Management 33, 17-33.

12. Freer, M., Moore, A.D. and Donnelly, J.R. (1997). GrazPlan: Decision support systems for Australian grazing enterprises. II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77-126.

13. French, R.J. and Schultz, J.E. (1984). Water use efficiency of wheat in a Mediterranean-type environment. II. Some limitations to efficiency. Australian Journal of Agricultural Research 35, 765-775.

14. Hansen, B.R. and Krause, M.A. (1989). Impact of agronomic and economic factors on farm profitability. Agricultural Systems 30, 369-390.

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

16. Johnston, B.G. and Matuska, A.M. (1985). Simulating the farm-firm: impact and payoff from the adoption of new crops in the wheat-sheep zone of New South Wales. Mathematics and computers in simulation 27, 91-95.

17. Kingwell, R.S. and Schilizzi, S. (1994). Dryland pasture improvement given climatic risk. Agricultural Systems 45, 175-190.

18. Kingwell, R.S., Morrison, D.A. and Bathgate, A.D. (1992). The effect of climatic risk on dryland farm management. Agricultural Systems 39, 153-175.

19. 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.

20. McPhee, M. (1993). SheepO: a sheep management package for the 1990s and beyond. Agricultural Systems and Information Technology 5(1), 41-42.

21. 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.

22. Morrison, D.A., Kingwell, R.S., Pannell, D.J. and Ewing, M.S. (1986). A mathematical programming model of a crop-livestock farm system. Agricultural Systems 20, 243-268.

23. Oram, D.A. (1985). The profitability of alternative crop rotations in the Wimmera. Victorian Department of Agriculture and Rural Affairs, Research Project Series No. 205.

24. Ransom, K. (1992). Development of sheep grazing systems to utilize mixed pastures of sub clover, annual grasses and lucerne. Proceedings of the Australian Society of Animal Production 19, 221-224.

25. Rickert, K.G., Thompson, P.J.M., Pritchard, J.R. and Scattini, W.J. (1996). FEEDMAN - A feed-to-dollar beef management package. Department of Primary Industries, Brisbane, Queensland. QI96091.

26. Rickert, K.G. (1998). Experiences with FEEDMAN, a decision support package for beef cattle producers in south eastern Queensland. Proceedings of International Symposium on 'Applications of modelling as an innovative technology in the agri-food-chain', 29 November - 2 December 1998, Wageningen, The Netherlands.

27. Schilizzi S. and Kingwell R. (1999). Effects of Climatic and Price Uncertainty on the Value of Legume Crops in a Mediterranean-type Environment. Agricultural Systems (in press).

28. Stafford Smith, D.M. and Foran, B.D. (1989). Strategic decisions in pastoral management. Australian Rangelands Journal 8, 110-117.

29. Stafford Smith, M. and Foran, B. (1992). An approach to assessing the economic risk of different drought management tactics on a South Australian pastoral sheep station. Agricultural Systems 39, 83-105.

30. Stuth, J.W. and Stafford Smith, M. (1993). Decision support for grazing lands: an overview. In Decision Support Systems for the management of grazing lands: emerging issues, (edited by J.W. Stuth and B.G. Lyons), UNESCO and Parthenon, Carnforth, UK, pp. 1-35.

31. Truscott, M.A., Egan, J.P. and Balston, J.M. (1998). Climate risk and yield information service (CRYIS) trial. In Proceedings of Farming Systems Developments 1998 Workshop, Adelaide, March 1998, pp. 162-163.

32. Truscott, M.A., Balston, J.M., Abrecht, D.G. and Tennent, D. (in press). Farmer Decision support trial in the Upper North of South Australia. SARDI Technical Note.

33. Whelan, M.B., Bowman, P.J., White, D.H. and McLeod, C.R. (1987). SheepO: a sheep management optimization package for sheep industry specialists. In: Proceedings of the International Conference on Veterinary Preventive Medicine and Animal Production, Australian Veterinary Journal (special issue), pp. 142-143.

34. White, D.H., Howden, S.M. and Nix, H.A. (1993). Modelling agricultural and pastoral systems. In Modelling change in environmental systems, (edited by A.J. Jakeman, M.B. Beck and M.J. McAleer), John Wiley & Sons Ltd, Chichester, UK, pp. 267-292.

Contacts and institutions

Dr Jeff Clewett, Queensland Centre for Climate Applications, Queensland Department of Primary Industries, PO Box 102, Toowoomba Qld 4350. Ph: (07) 4688 1244; Fax: (07) 4688 1477; ClewettJ@prose.dpi.qld.gov.au

Mr Jim Egan, South Australian Research and Development Institute, PO Box 1783, Port Lincoln, SA 5606. Ph: (08) 8688 3424; Fax: (08) 8688 4327; egan.jim@pi.sa.gov.au

Ms Melissa Truscott, Field Crop Improvement Centre, South Australian Research and Development Institute, GPO Box 397, Adelaide, SA 5001. Ph: (08) 8303 9639; Fax: (08) 8303 9378; Mobile: 017 875 651; truscott.melissa@pi.sa.gov.au

2.3.4 Improving climate-based media services

Climate is now better monitored than ever through technologies ranging from satellites to automated weather stations and electronic fieldbooks. Telecommunication services enable the Bureau of Meteorology to quickly acquire information from numerous sources across Australia and around the globe, to process the vast amounts of data through high powered computers, and make the final products (forecasts, tables, maps, video sequences) available to the media and private citizens through fax services and the World Wide Web. As the community becomes more computer-literate, and tools (models and decision support services) are developed that can make use of current and historical data, the demand for such services increases rapidly.

The Bureau of Meteorology is planning to offer a web-based search facility that will allow users to find out what data are available in the Bureau's climate database. This will be linked to the main Bureau site: http://www.bom.gov.au/ and will enable users to submit data requests over the Internet for quotation of costs.

SILO is a web-based information system bringing agriculturally relevant meteorological information and derived products to rural Australia. Already it offers a wide range of agro-meteorological information and comment, with the first collaborative products, developed by QDNR and BoM in conjunction with CSIRO and QDPI, approaching completion. For basic data the Bureau of Meteorology can offer:

  • gridded (interpolated) data sites covering Australia at 0.25 degree resolution every day;
  • observations from their network of observers across Australia, either by the web or ftp;
  • meteogram output from the weather prediction program. This raw data, as used by meteorologists, might be used in research or development of short-term (1-5 days) forecasts, perhaps to assist irrigation scheduling, tactical pesticide application, or runoff containment.

QDNR has developed two additional data sets based on the BoM's basic data, and can offer:

  • the data drill, providing a continuous climate data set derived from interpolations. It is available across Australia every 5 km daily since 1957, or since the 1890s for rainfall data only;
  • the patched point data set combines observed data with the data drill to give a continuous daily dataset at all Rainman stations.

Some of these datasets are available automatically now, either free on the web, or by subscription. Subscription information is available at http://www.bom.gov.au/silo/subscriptions.html ***

QCCA is also developing systems to manage an Internet-based information technology service for rural and other clients on climate and related matters. This will be a user pays service, but subsidised by differential pricing for Queensland, Australian (other than Queensland) and overseas clients. Currently about 40 per cent of the internet accesses seeking information from QCCA are from the U.S.A.

QCCA is also producing a multi-media CD-Rom for distribution to client groups. It is now starting to provide media and expertise to foster training in and use of climate forecasting throughout Australia and the world.

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

  • SILO - Agrometeorological information systems - Mr A. Beswick, QNR (LWRRDC)

Contacts and institutions

Mr Alan Beswick, Climate Impacts and Applications, Queensland Department of Natural Resources, PO Box 631, Indooroopilly, Qld 4068. Ph: (07) 3896 9741; Fax: (07) 3896 9843; beswica@dnr.qld.gov.au

Ms Clare Mullen; Bureau of Meteorology, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4296; Fax: (03) 9669 4678; nnn@bom.gov.au

Mr Graham de Hoedt, Bureau of Meteorology, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4714; Fax: (03) 9669 4678; nnn@bom.gov.au

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