The ability of farmers to make informed decisions is limited by the deficiencies which have been observed in the quality and applicability of the information available to them. These deficiencies are compounded by the lack of consistent data formatting or standards for the integration of data. Frameworks have been applied in the data mining and bioinformatics research disciplines as a means of facilitating integration of data, for example by the use of the Knowledge Discovery in Databases (KDD) methodology. This research describes a framework which has been created to assist growers in their decision making. The Farmer Decision Support Framework (FDSF) takes information needed by farmers and utilises processes that deliver this critical information. A series of steps which include data capture, analysis and data processing precede the delivery of integrated information to the farmer. Information is collected from disparate sources, captured and validated according to defined rules. It is then processed and integrated by data mining tools and technologies into a format that can be readily used by the farmer. This research paper describes the results of a case study of the proposed framework and the use of simulated data to identify any critical bottlenecks in the application of the framework. It will also speculate on ways in which the framework may be extended to be used in all farmer decision-making processes.