# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 3 MiFarm-CSA Architecture
### Evidence-Based Assessment Methodology  
EVAM is defined as a major pillar of the proposed architecture, for fostering the involvement of end-users in the MFS landscape, consisting of the following components: (a) (SCOPE) methodology, (b) the SLO concept, (c) the behavioral decision-making (BDm) and (d) the Behavioral Intervention Engine (BIE). Oftentimes in technology, development projects the social, behavioral and even human aspects of the technology play a secondary role to the technological aspects. The proposed architecture intends to use innovative human factor approaches and methodologies to champion social and behavioral aspects and to prioritize the role of the human in the development of technology. Doing so will ensure that the implementation will not encounter barriers relating to user acceptance, usability and other human barriers. The starting point involves refining new methods for modeling the socio-technical aspects of farming systems and to examine the social and behavioral aspects of mixed farming and agroforestry. The SCOPE methodology examines and creates dynamic process maps focusing on three interlocking and interdependent levels of analysis: (i) process functionality (operational processes); (ii) social cohesion (trust and social networks); and (iii) the mediation of (i) and (ii) through collective knowledge and information cycles [88]. A further element of this layer is the SLO concept. This concept relates to the continued acceptance of a set of business practices or operating procedures by a company's employees, stakeholders and the general public. The concept of social license is closely related to the concept of resilience and sustainability, and the work to define and develop the SLO in this architecture will ensure that the technical innovations can be supported by sustained social and behavioral change. According to many MFS experts, the problems of the end users, mostly farmers, are not just about knowledge: it is about building the habit of cooperating. Over the last few decades, researchers have increasingly studied the factors that influence farmers' adoption of environmentally sustainable practices [89]. Previous academic attempts to take stock of the factors influencing farmers' adoption of sustainable practices [90] did not specifically focus on the roles of behavioral factors, often resulting in an incomplete overview and limited theoretical understanding of how and why these factors affect decision-making. There have been some efforts in policy circles to make an inventory of behavioral factors influencing farmers' adoption of sustainable practices, but their disciplinary scope was restricted to behavioral economics and communication sciences [91]. For this reason, MiFarm-CSA architecture introduces BDm which adopts the experimental research concept combined with laboratory experiments in parallel. The innovation of this concept lies in the following twofold statement: (a) it evaluates ex-ante farmers' choices when they are faced with voluntary versus mandatory schemes and with results based or collective schemes, and (b) it includes experiments in the field, which are carried out to inform agricultural policies by including a behavioral component, as the outcome variable generally consists of decisions made by farmers [92; 93]. To take a positive step forward, the proposed architecture is built on top of BDm the BIE. The latter aims at: (a) predicting the expected MFS-related behavior (behavioral intentions) of farmers on the basis of a set of socio-economic factors, (b) facilitating the selection and application of appropriate strategies through a number of communication tools and channels and (c) driving the desired MFS development widely. The main operating elements of the BIE break down as follows: farmers can be clustered according to their (socio-demographic) profiles, and the suitability of involvement strategies is defined both by the user profile and by additional socio-economic factors (e.g., country culture).