Publication Date
1989
Description
The management of grazing sheep and cattle in south-eastern Australia is confounded by the diversity between years in the annual pattern of pasture growth. Currently, the way in which a farm manager budgets or apportions pasture to the grazing animals is markedly influenced by personal experience of previous patterns of both pasture growth and animal response. This influence is usually heavily biased towards those enviromental conditions most recently experienced by the manager. The many deficiencies in this style of managing grazing systems have motivated scientists to investigate ways of supplementing this subjective « mental model » with a more quantitative technique. This can be achieved through the development of biologically-derived mathematical relationship, linking the various components of the system and combining these relationship in a way that emulates physiological processes and the flows of information in the « real » agricultural system.
Citation
White, D H. and Weber, K M., "The Use of Models of Sheep and Cattle Production Systems to Aid On-Farm Decision Making in Southern Australia" (2025). IGC Proceedings (1989-2023). 10.
https://uknowledge.uky.edu/igc/1989/session11/10
Included in
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The Use of Models of Sheep and Cattle Production Systems to Aid On-Farm Decision Making in Southern Australia
The management of grazing sheep and cattle in south-eastern Australia is confounded by the diversity between years in the annual pattern of pasture growth. Currently, the way in which a farm manager budgets or apportions pasture to the grazing animals is markedly influenced by personal experience of previous patterns of both pasture growth and animal response. This influence is usually heavily biased towards those enviromental conditions most recently experienced by the manager. The many deficiencies in this style of managing grazing systems have motivated scientists to investigate ways of supplementing this subjective « mental model » with a more quantitative technique. This can be achieved through the development of biologically-derived mathematical relationship, linking the various components of the system and combining these relationship in a way that emulates physiological processes and the flows of information in the « real » agricultural system.
