1. INTRODUCTION
Land evaluation and land use planning are often handicapped by a lack of suitable information on the
performance of candidate species (or cultivars, provenances, etc.) under different climates, soil types, and
management strategies. Often the problem is not so much the absence of information but rather deficiencies
within available decision support systems that rely on empirical models calibrated to plot-based data. Such
decision support systems are unable to utilize alternate sources of information, such as informal data and
expert knowledge. However, expert systems and other approaches enable such data to be incorporated into
models that are compatible with prevailing planning systems.
Systems approaches are an appropriate means for meeting the complex challenges facing managers of
agricultural systems. Crop modelling can play a significant part in systems approaches, by providing a
powerful capability for scenario analyses. Crop modelling has developed extensively over the past 30 years
such that a diverse range of crop models is now available. However, it is argued that the tendency to
distinguish between so-called “scientific” and “engineering” challenges and approaches in crop modelling
has constrained the maturation of model development and application. Consideration is therefore given here
to effective crop modelling that combines a scientific approach that enhances understanding with an
applications orientation that retains a focus on prediction and problem-solving. The major issue in effective
crop modelling is therefore seen as how to achieve the appropriate balance between simplicity and
complexity in combining the biological, physical, and prediction requirements for each specific task.
Avoidance of unnecessary complexity and maintaining transparency of design are considered as guiding
principles.
Given a comprehensive crop model with robust predictive capability, there are many opportunities for
applications ranging from research into management practices to crop improvement. In a controlled
environment of protected cultivation, seeking optimal combinations of environmental control and crop
management strategies to maximize profitability is feasible, using optimization algorithms in conjunction
with controlled experiments including climate and soil controlled with crop models. It is argued that a
participatory approach that includes managers as partners in this process is required to effect change.