The research described in this thesis expresses the importance of quantitative and qualitative data types and how these can be incorporated and combined to produce an agricultural management decision support system (DSS). Researchers cannot solely depend on numerical data and relationships when designing, modelling and producing decision management tools. The relevance of the social sciences and peoples interpretations of these tools is equally important.

The DSS described here focuses on the management of rainwater harvesting (RWH) in Tanzania. Numerical data related to natural resources (water and nutrients) and yields of rice and maize have been collected for the production of the DSS. With regard to the social science factors, the DSS tackles the concept of common pool resources (CPR) of water and nutrients. The importance of CPR is well understood, however their inclusion in the production of models is a relatively new concept. Criteria related to social status is linked with the by laws that govern the allocation of natural resources in Tanzania to help derive a numerical method for including CPR within the DSS. The production of the DSS is a novel way of combining this research into a tool that aims to benefit all socio-economic community groups.

During the production of the DSS, a single generic approach for the inclusion of quantitative and qualitative information has developed. Particular focus was on the development of a model base (programming and mathematical relationship building), database (storage of the data used for the relationships) and a dialog system (the user-interface and communication strategy). This method is termed the ‘dialog, data, and models (DDM)’ paradigm (Sprague and Carlson, 1982).

From this research, a DSS has been produced that aims to optimise RWH management in Tanzania with the aim of alleviating poverty and enhancing sustainable agriculture for all community members. Also an overall strategy for the production of DSSs has been produced. It illustrates how both quantitative (numerical and physical data) and qualitative (socio-economic considerations) can be utilised individually and in combination for the production of DSSs and can be extrapolated for further research and to new areas.