The types of simulation models used with agricultural systems vary widely in terms of scale, scope and purpose. They range from the micro-level (animal genetics and physiology), through paddock and farm-level models, to large regional or national systems. These various applications are illustrated, using published examples from this field. Next, the proven advantages and uses of systems research in agriculture are outlined. The choice of which variable to optimise depends on the purpose of the study, and include different measures of deviance (for model tuning applications), the gross production or margin (profitability) of the modelled system, or some utility function further incorporating risk or other important factors. This leads on to the consideration of multi-objective optimisations, where the multiple competing outcomes are frequently negatively correlated. By considering the types of results that end-users expect, the optimisation requirements of this approach are considered. Finally, the particular types of problems which agricultural systems models pose are listed and discussed. Practical methods of dealing with these problems are outlined, again using agricultural examples from the literature.
CITATION STYLE
Mayer, D. G. (2002). Agricultural Systems Models. In Evolutionary Algorithms and Agricultural Systems (pp. 9–17). Springer US. https://doi.org/10.1007/978-1-4615-1717-7_2
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