So how does one select the "best" model to use? Smith et al. (1997) use many of the above statistical criteria to evaluate the effectiveness of different soil organic matter models to simulate soil organic matter dynamics at different sites around the world. This paper discusses the usefulness of these different statistical criteria and demonstrates that six of the nine models considered in the model comparison did an equally good job of representing the observed soil organic matter dynamics. Figure 16.11 shows the comparison of observed and simulated soil carbon levels for four of these models with the Tamworth lucerne/clover crop rotation system (Smith et al., 1997).

Some criteria that may be used in the selection of an appropriate model include (Rao et al., 1982): the intended use of the model, the spatial and temporal scales of application, the availability of input data, and the availability of computational facilities.

Once a model has been selected and applied, the output must be evaluated by comparing simulated or predicted values with actual measured or observed values from field experiments not used originally to develop the model. At its simplest, model evaluation can be done visually or graphically. This approach provides a rapid and easy means of evaluating whether a model is producing results close to those observed. One problem associated with the approach is that the difference between predicted and observed values cannot easily be quantified. Loague and Green (1991) suggest that model evaluation should include both qualitative graphical and quantitative statistical approaches. Several statistical criteria for evaluating simulated results are outlined in Table 16.8. Maximum error represents the

TABLE 16.8 Statistical Criteria for the Evaluation of Model Performance"




Maximum error

Root mean square error

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