# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 3 MiFarm-CSA Architecture
### The Social-Ecological Conceptual Framework  
The lessons learnt and the experience of farmers stemming from the existing MFS are of paramount importance for further development of MFS. The production of more knowledge on MFS and the knowledge acquisition by farmers are both required in order to develop sustainable, productive, viable and efficient future MFS. The SECF component of MiFarm-CSA architecture aims to foster core interactions among animals, forests and crops, while mitigating the high complexity of these interactions. SECF follows a participatory, learning and technology-oriented methodological approach based on knowledge-driven tools. This methodological approach is defined in five main pillars, namely, (a) the participatory knowledge building (PKB), (b) the mixed farming simulator (MFSim), (c) the experimentally-oriented co-design process (EC-P), (d) the peer learning network (PLN) and (e) decision-making engine (D-ME). PKB involves the critical knowledge extraction, which encloses the most important knowledge needed to support sustainable MFS. This knowledge can be extracted from meetings, seminars and workshops and recent literature findings. The contributions of international experts who will analyze and evaluate the current knowledge and remaining uncertainties about large-scale, commercial MFS are crucial. The PLN pillar encourages technical and organizational innovation; it enhances learning options, while reducing the isolated position of this kind of system in a specialized world. The PLN could address MFS, by (a) identifying learning issues, (b) supporting the exchange of (tacit) knowledge within the network and (c) organizing access to relevant sources of knowledge outside the peer network. The final step of the PKB is related to the knowledge-exchange beyond the PLN. The PLN ensures knowledge exchange among peers; e.g., it could also be valuable that experienced farmers and stakeholders organize access for other actors (advisors, research and policy makers) who are interested in MFS. Field visits, thematic networks and technological demonstrations constitute an immersive type of learning which can enhance significantly the process of knowledge exchange. The participatory knowledge building can be enriched with state-of-the-art technologies like virtual and augmented reality. Such technology could maximize the participants' engagement in the training material, by fostering the motivation of end users to be engaged to the PKB and the SECF activities in general. The MFSim is also introduced as another major pillar of SECF in order to mitigate the complexity of MFS. Recent trends in broader collaboration across institutions, across disciplines and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems [49; 94]. Specialized simulation modeling of crop-livestock integration among farms is the basis for achieving the up-scaling process involved in integrating beyond the farm level. Furthermore, computer simulation with environmental-economic models at the farm level provide a useful tool for integrating knowledge and information to predict production efficiency, environmental impacts and the effects of management policies on production performance [95]. The MFSim is introduced as the main modeling and simulation tool of designing, testing, validating and evaluating mixed farming modeling systems and complex simulation scenarios. The aim of MFSim lies in the fact that it allows farmers, stakeholders and supply chain actors to experiment with diversified cropping, animal and forest systems without having to invest or risk resources. MFSim could also enable stakeholders to (a) model and simulate different variations of forest-crop-livestock integrated farms, (b) model and simulate different use of resources (nutrients, land, manure management, etc.) and (c) model and simulate manure management practices and animal feeding.