Decision support systems as a part of agricultural operational management

The complexity of and highly capital intensive modern agricultural producti on systems stimulates the importance of operational management within agric ultural mechanisation systems. The development of model based decision supp ort systems related to the planning and execution of farm operations is ess ential considering a sustainable and profitable production. The planning mo dels generally applied in both industry and agriculture have been tradition al deterministic operational research models trying to provide the planner with exact optimal solutions. However, often these models have not been abl e to capture the inherent uncertainty and risks involved when planning futu re actions, and thereby prompting the need for models exceeding the limitat ions of traditional modelling approaches. In an attempt to provide for a more comprehensive modelling of planning as part of a decision support system, the use of probabilistic networks (Bayes ian networks) has been investigated as a method for building planning model s for scheduling field operations. The Bayesian approach includes principle s from Artificial Intelligence and provides for a normative framework to mo del decision making and reasoning under uncertainty. For instance, in the c ase of managing harvesting operations, the model will give the probable num ber of harvesting hours, probable state of the crop with regard to quantity and quality, probable machinery capacity, when evidence on area to be harv ested, weather prognosis, etc. are, provided.