Information systems used in farming systems are characterized by high complexity. They should be composed of inter-related economic and biological components capable of working in a dynamic and continuous manner, receiving data and producing results within an organized production process. Taking this complexity into account, in this study we propose a novel conceptual macromodel with a system approach of the agricultural and livestock production environment to be adopted as information system in order to support the decision making process. This model is capable of representing the innumerable aspects of a farm production system aiming to help farm producers understand and manage their production system. To better understand the general model and its nuances, several submodels (input models) were built based on adaptation of pre-existing research, among which we mention: meteorological, pasture, animal, crop–livestock integration, crop, soil, pasture-animal, and pasture-soil submodels. The combination of these submodels originates and configures the farm production system structure. Among the main outputs of the proposed model are the economic results, based on agricultural and livestock productivity, the environmental impact assessment, and the analysis of operational risk. A qualitative approach was used with an exploratory descriptive design to carry out this research, based on literature review, interviews, and meetings with experts to refine and validate the proposed model. The refinement of the conceptual model was based on the Delphi method, which allowed the collection of data and peculiarities of the object under study, guided its development to achieve the goals of this research, and allowed the register of several issues for further studies. The validation of the model, also using a qualitative approach, was performed employing conceptual, face, and subsystem validation procedures, also applying the Delphi method. This way, we aimed to identify the weak and strong points of our conceptual model, its main shortcomings and limitations, and the variables that should be optimized, in theoretical and practical perspectives, so that this model can be improved.
