Modelling regional land use: the quest for the appropriate method

The demand for spatially-explicit predictions of regional crop-yield patterns is increasing. An approach to assess a priori and/or future ranges of alternative scenarios spatial yield patterns at the regional scale is the application of mechanistic crop growth simulation models (CGSMs) (e.g. Launary, 2002). However, two main problems emerge in the application of field-level CGSMs at regional scales. Firstly, the required input data on weather, soils, and management are often not available; and secondly, if they are, generally not at the required level of detail. There are two possible approaches to address the identified problems. One is replacing the CGSM by a metamodel (Kleijnen and Sargent, 2000). The second approach is a simple empirical model (e.g. Lobell et al., 2008). The modelling-approach choices and performances are context dependent. The context conditions that determine the best approach are input data requirements, problem definition, study sub-objective, the scale at which output results are expected, model end-users, and utilization of the output. The selection of the modelling approaches can be considered as one of the most difficult, and often ignored, steps to model crop yield at the regional level. However, a structured, systematic way of modelling-approach selection is lacking. In order to address this issue this thesis aimed to develop a framework for recommendable practices to model regional patterns of crop yield. We reviewed literature for existing approaches that have been used to overcome the problem of data availability for the application of CGSMs at the regional level. Then we used the review to formulate decision rules as to what approach to take under different circumstances. Which of the approaches should be used depends on the following questions: (i) do observations of the input variable allow to estimate semivariograms?; (ii) are there auxiliary data correlated to the target variable?; (iii) do the input variables exhibit spatial c