This thesis describes a systems analysis of the Queensland wool industry at a farm-enterprise level, and development of a computer simulation model designed to provide near real-time physical and financial information to policy makers at a regional level as an aid to decision making.
Extensive reviews of the main biological factors affecting the wool enterprise were completed: diet selection and feed intake, protein digestion and metabolism, energy digestion and metabolism, wool growth, reproduction and mortality.
A sensitivity analysis of three separate bio-economic models was carried out to identify system components having the greatest impact on the financial performance of wool enterprises. Fleece production, wool price and total variable costs were found to be the most important determinants in each model.
Grazfeed, a commercially available software package based on the Australian feeding standards for ruminants, was tested for its ability to simulate animal production as observed in Queensland sheep grazing experiments. A FORTRAN version of the required code for sheep and cattle was written and used in the subsequent analysis. These experiments varied greatly, both temporally and spatially. A number of problems in trial management and methodology were identified, these often required adjustment of recorded data. The collation of trial data was done in a manner that allowed ease of use in computer models. Optimisation software was used to modify parameters and equations within the model in an attempt to improve the agreement between predicted and observed values. Grazfeed was found to be unsuitable using the available test data.
Regression analysis techniques were then used to identify climate, soil water, pasture and dietary variables which were able to explain the observed grazing trial variation in annual fleece production and liveweight change. The dietary variables were estimated using a theoretical diet selection subroutine, and a feed intake subroutine based on Grazfeed equations. Pasture variables, such as number of growth days, green leaf availability, pasture growth and dietary nitrogen intakes were used in the models. Trial specific annual fieece and liveweight change models were able to explain approximately 70 - 91% and 82 - 93% respectively of observed variation in fleece and liveweight change. Pooling of Mitchell grass and mulga grassland trials resulted in 75 and 63% of the respective variation being explained. Australian Bureau of Statistics (ABS) data were used to test the ability of models developed from the grazing trials to operate at the shire level. The regression developed from the combined Toorak, Burenda and Arabella grazing trials was selected as most suitable for describing wool production throughout the state.
ABS data were used to estimate stocking rates and the number of sheep shorn. Fibre diameter was estimated from wool production and this in turn enabled an economic value to be assigned to the wool grown. Wool prices were the reported micron specific indicator prices where available, or extrapolated prices based on the mean annual market price and the relationship of the mean annual market price to the micron specific indicators. Data from the Australian Bureau of Agricultural and Resource Economics were used to estimate the variable costs per sheep shorn. This enabled a simple gross margin analysis to be carried out. The Queensland farmers index of prices paid was used to express all monetary values to a common base.
Various measures of physical and financial performance of wool enterprises were able to be generated in the format of coloured maps enabling easy comparison of regions within the state. Alternatively, each measure for a specific region was able to be examined relative to historical values predicted by model simulations over the period 1957 -1995.
The bio-economic model highlighted areas of the Queensland wool production system, such as diet selection and feed intake, where accurate and reliable data are lacking, and therefore, acts as a guide for future grazing sheep research. As more data does become available, those areas of the model judged to be lacking most in accuracy or failing to provide adequate variability will be able to undergo further refinement. Finally the usefulness and therefore success of the model will probably not be fully evaluated until the next major industry crisis (drought and / or commodity prices) occurs. Until then support from relevant funding bodies will be required to ensure data acquisition, and maintenance and development of the model is continued.
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