Modelling the manager: Representing rule-based management in farming systems simulation models

We trace the evolution of the representation of management in cropping and grazing systems models, from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules. Attempts to define higher-level organizing concepts in management policies, and to analyse them to...

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Veröffentlicht in:Environmental modelling & software : with environment data news 2014-12, Vol.62, p.399-410
Hauptverfasser: Moore, Andrew D., Holzworth, Dean P., Herrmann, Neville I., Brown, Hamish E., de Voil, Peter G., Snow, Valerie O., Zurcher, Eric J., Huth, Neil I.
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Sprache:eng
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Zusammenfassung:We trace the evolution of the representation of management in cropping and grazing systems models, from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules. Attempts to define higher-level organizing concepts in management policies, and to analyse them to identify optimal plans, have focussed on questions relating to grazing management owing to its inherent complexity. “Rule templates” assist the re-use of complex management scripts by bundling commonly-used collections of rules with an interface through which key parameters can be input by a simulation builder. Standard issues relating to parameter estimation and uncertainty apply to management sub-models and need to be addressed. Techniques for embodying farmers' expectations and plans for the future within modelling analyses need to be further developed, especially better linking planning- and rule-based approaches to farm management and analysing the ways that managers can learn. •Representing farm management using scripts of rules is powerful and flexible.•“Rule templates” combine this power with ease of use for occasional model users.•We trace the history of this modelling technique and its software implementation.•Management sub-models face issues of parameter estimation and uncertainty.•Management rules can be used to represent learning and enhance farm typologies.
ISSN:1364-8152
DOI:10.1016/j.envsoft.2014.09.001