MASC, a qualitative multi-attribute decision model for ex ante assessment of the sustainability of cropping systems

International audience

Inglés. Realistic assessments of sustainability are often viewed as typical decision-making problems requiring multi-criteria decision-aid (MCDA) methods taking into account the conflicting objectives underlying the economic, social and environmental dimensions of sustainability, and the different sources of knowledge representing them. Some MCDA-based studies have resulted in the development of sustainable agricultural systems, but the new challenges facing agriculture and the increasing unpredictability of their driving forces highlight the need for faster ex ante ('Before-the-event') assessment frameworks. These frameworks should also (i) provide a more realistic assessment of sustainability, by integrating a wider range of informal knowledge, via the use of qualitative information; (ii) address alternative scales, such as cropping system level, improving granularity for the handling of sustainability issues and (iii) target a larger panel of decision-makers and contexts. We describe here the MASC model, which is at the center of a framework addressing these objectives. The MASC model has at its core a decision tree that breaks the sustainability assessment decisional problem down into simpler units as a function of sustainability dimensional structure (economic, social and environmental), generating a vector of 32 holistic 'mixed' (quantitative and qualitative) elementary criteria rating cropping systems. The assessment process involves the calculation of these criteria, their homogenization into qualitative information for input into the model and their aggregation throughout the decision tree based on 'If-Then' decision rules, entered by the user. We present the model and describe its first implementation for the evaluation of four cropping systems generated from expert knowledge, and discuss its relevance to the objectives cited above. The MASC model has several advantages over existing methods, due to its ability to handle qualitative information, its transparency, flexibility and feasibility.