Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/6987
Title: Evaluating the Performance of the GrazFeed Model for Predicting Sheep Productivity on a Range of Pasture Types
Contributor(s): Yao, Zhong Jun (author); Scott, James (supervisor); Blair, Graeme (supervisor)
Conferred Date: 1997
Copyright Date: 1996
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/6987
Abstract: There are increasing pressures on graziers to improve management to achieve sustainable and profitable development and wilisation of their grazing lands. Computerised decision support systems (DSSs) which assist graziers in dealing with complex planning problems, by allowing exploration of alternative strategies and selection of appropriate technology, are becoming increasingly important tools in supporting farm management. Before a DSS can be useful as an aid in making management decisions, it must be evaluated for the ecosystem in which it is being used. Comparing a model's prediction with experimental observation is one method of evaluating a model's performance. GrazFeed is a decision support system for the nutritional management system of grazing animals, aimed primarily at enterprises in temperate southern Australia. The aim of the studies in this thesis was to evaluate the model's performance in predicting sheep production in an environment similar to those for which the model was developed. Also it was evaluated as a potential model for nutritional management of sheep in north-west China by comparing the model predictions with field data obtained from a typical grazing production system of this region. ... A variety of possible causes of the discrepancies between the model predictions and the observations are discussed in detail in each experiment. To some extent, errors in the estimation of pasture quantity and quality may have resulted in some of the discrepancies. It also appears that some of the discrepancies might be associated with deficiencies in the intake module of the model. However, there were insufficient data to allow the cause to be clearly identified. Further studies, especially the evaluation of intake module in GrazFeed, may lead to further improvements in model predictions.
Publication Type: Thesis Masters Research
Rights Statement: Copyright 1996 - Zhong Jun Yao
HERDC Category Description: T1 Thesis - Masters Degree by Research
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