Predictive models in agricultural production

Abstract

Prediction may be envisaged as organized thinking about the possible. For this purpose, dynamic models of the state-variable approach are important tools because they combine basic knowledge on the physical, chemical and physiological processes that underlie crop growth and agricultural production. At one extreme are comprehensive models that claim to integrate all aspects of growth and to focus attention on the main gaps in present operational knowledge. As such, they are research tools. At the other extreme are summarizing models that are especially geared to answer ‘w hat-if’ questions and are used for evaluating regional production potentials and constraints, for irrigation management and integrated control of pests, diseases and weeds. Examples of these types of models are given and their usefulness for predictive purposes is discussed.