Landscape Change Programme, Macaulay Institute, United
The greatest use of crop/soil models so far has been by the research community, as models are primarily tools for organising knowledge gained in experimentation. The use of models in decision-support systems has had major impacts in the areas of irrigation scheduling and pest management, although this has been more as heuristic tools rather than operational systems. Models have a useful role to play as educational tools, both as aids to learning principles of crop and soil management, and also in helping students to develop a `systems' way of thinking. However, crop modelling as a research area is at a cross-roads; mathematical representations for most of the major crop processes have now been developed, and, although there is scope for further refinement of some of these, this is unlikely to contribute significantly to improving the accuracy and reliability of the models at the crop level. It is suggested that there are two opposite directions in which crop modelling research can develop.
In one direction, the rapidly expanding field of genomics means that links between information at the gene level and performance at the phenotype level need to be established, and models developed to describe these. Such models could help to improve the efficiency of crop improvement programs by providing more efficient ways of identifying and evaluating desirable plant characteristics.
In the other direction, crop models can be incorporated into higher-order systems such as the whole farm, catchment or region. At one level, linking crop growth models with other physical process models, such as those describing soil processes influencing gaseous emissions, for example, is a logical next step, and is occurring to some extent already. At another level, the role of humans in these systems also needs to be made explicit, so that the decisions that they take to sustain and improve their livelihoods and the influence these have on their environment can be taken into account. For both levels, ways must be found of incorporating knowledge from such diverse fields as agronomy, soil science, livestock, sociology, economics, artificial intelligence, and policy-making into workable and realistic simulation models.
Keywords: Crop modelling