An important goal of the European Commission-funded ECOGEN project is to develop a computer-based decision support system (DSS) for the assessment of economic and ecological impacts of using genetically modified crops. In ecological impact assessment, special emphasis is put on soil biology and estimating soil quality from the observed biological parameters. Methodologically, we have addressed this goal using model-based DSS, taking the approach of qualitative multi-attribute modelling. The approach is based on developing various hierarchical multi-attribute models that consist of qualitative attributes and utility (aggregation) functions, represented by decision rules. The model presented in this paper assesses the impact of cropping systems on soil quality and can subsequently be used to assess crop management options. It was developed as a collaboration between ECOGEN soil experts and decision analysts, and consists of 34 attributes, hierarchically structured into eight levels. There are 21 input attributes, most of which measure the richness and biomass of representative soil organisms. The assessment of soil quality is based on two main indicators: (1) soil diversity (assessed through microfauna, mesofauna and macrofauna richness) and (2) soil functioning (in terms of leaching, comminution, mineralisation and plant growth). In the paper, we address the methodological aspects of the model and its development, and describe the components of the model (attributes, value scales, decision rules and weights). We present initial results of its application to the assessment of crop management on soil quality in the case of using conventional and Bacillus thuringiensis (Bt)-maize at three field sites in France and Denmark. Our finding was that soil quality was unaffected even though several input attributes had changed. We also analyse the sensitivity of the model and discuss its current and potential contribution to soil quality management. The model is operational (available through a web page) and can be used for the assessment, comparison and analysis of realistic management scenarios. Furthermore, it provides an effective means of synthesising a lot of data, encoding knowledge about soil-quality factors, supporting communication among experts, answering 'what-if' and 'so-what' questions, and supporting the evaluation of new agricultural practices.