Abstract : The objective of our work is to design a theoretical and methodological framework enabling decision support within a model of knowledge representation, illustrated by a case study from the cereal industry. The specific scope considered is the definition of food quality, for which we must consider different points of view (nutritional value, flavor quality, hygiene assurance of the products) and different stakeholders (industry workers, researchers, members of the general public) whose objectives diverge. The basis of our approach is the use of argumentation systems presented in AI literature. The argumentation systems are formal frameworks that aim to represent arguments, the interactions between these arguments and determine which statements are inferred by a set of arguments considered coherent. These statements may, for example, correspond to beliefs or to decisions to make. One of the most abstract formal frameworks, a benchmark in the field, is that proposed by Dung in 1995. In this context, an argumentation system is defined by a finite set of arguments and a binary relation on this set, called the attack relation. One can also view such a system as a labeled graph on which the vertices are the arguments and the edges represent the relationship of direct attack. An argument ''attacks'' another if the path from the first to the second is of an odd length and ''defends'' it if it is of an even length. An argument is inferable if it belongs to a set of arguments with some properties related to the notions of attack and defense. This is the basis for collective acceptability of arguments. Dung's argumentation framework was extended notably by the addition of preferences between arguments. These, aggregated to attacks, give a ''defeat'' relationship, changing the calculation of the collective acceptability of arguments. Thus, on the basis of collective acceptability, we propose a method for determining the similarities between two argumentation systems, in order to unify these abstract, preference-based argumentation frameworks. A contextual preferences-based argumentation framework is proposed (the preferences and the attacks between arguments have a contextual validity), methods of aggregations between attacks and preferences and the mergence between contexts are investigated in terms of consistency between the collectively accepted arguments. Consistency is obtained when such sets do not contain conflicts in terms of information conveyed and conclusions and/or decisions supported by their arguments. Our approach is based on three common trend of argumentation. Firstly, we propose a nested view of argumentation that meets the expectations of the ''micro'' trend, which attempts to define the internal structures of the argument. Secondly, we propose to generate attacks between arguments, based on the actions they support or reject. This allows us to investigate the concerns of the ''macro'' trend in the treatment of relationships between arguments in view of calculated collective acceptability. Finally, we investigate some aspects of the ''rhetoric'' trend, to determine the definition of audiences giving contextual strength to the argument and generating preferences. This last aspect allows us to establish such contextual recommendations. The entire approach, illustrated through situational examples and an application case, is included in an argumentation-based arbitration model, which in turn is implemented in a formalism of knowledge representation and reasoning (the conceptual graphs).