# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 1 Introduction
This is the reason why MFS are usually established in less-favored areas, e.g., mountains or sloppy areas, rough landscapes and heterogeneous terrains, where conventional farming or breeding does not usually take place [24]. In order to achieve a satisfied level of sustainability under these conditions, MFS should be able to effectively monitor the farm area, the animals and the grasslands. That is a difficult task, as MFS are extremely complex systems that include interactions between climate and weather, surface and sub-surface soil, crops, pastures, animal production and human management with economic components [25]. Critical features such as income stability and sustainability need special quality dimensions, criteria and indicators for the evaluation of trans-disciplinary processes [26]. Moreover, the climate, through weather patterns, as time progresses, play a decisive role, as rainfall and temperature drive the productivity, profitability and environmental health of the system. In addition, there is a need for reducing the energy needed to maintain the MFS operation. Another challenge is related to the role of research on MFS and the need for multi-disciplinary knowledge integration. A lack of integration between the research of different disciplines, such as agronomists, veterinarians and social scientists, limits the necessary integral vision and makes MFS difficult to implement [27]. Moreover, science lacks influence mainly because of biases towards academic research rather than practical applications [28]. It should be noticed, however, that this lack of knowledge integration is not always the case. There are countries that present significant progress in integrated farming systems from both practical and research perspectives [18]. The complexity of using MFS is increased when combining crop and livestock production due to the increase of the management demands of organizing multi-tasking activities. Considering that finding reliable labor with the required skills in specialized farming systems is difficult enough [29]; the problem becomes even more acute in a diversified farming system that requires the aggregation of various kind of knowledge and skills. Additionally, for a mixed farming system, the bureaucratic and administrative workload, which requires expertise that especially older farmers do not possess [29], is expected to increase compared to a specialized farm. The success of MFS depends heavily on the aggregation of data, which is either produced by or affects the MFS, given that new technologies and solutions are effectively applied in order to collect, process and use it during decision making [30]. Precision agriculture is a method in which farmers optimize inputs such as water and fertilizer to enhance productivity, quality and yield. The fact that farmers are more precise with planting, harvesting, fertilizing leads to higher efficiency and productivity of the farm while ecological standards are respected [31]. Today, mobile applications [32], smart sensors [33], unmanned aerial vehicles (UAVs) [34], cloud computing [35] and edge computing [31] make precision agriculture possible for farming cooperatives. It goes without saying that the implementation of MFS requires extended technological features, such as sophisticated equipment, extended monitoring range, real-time processing capabilities and specialized artificial intelligence (AI) models. Although precision agriculture is characterized by high complexity and depends heavily on cutting edge technologies, it constitutes a method that soon or later is going be used across the globe. Developed countries, such as USA, Australia, Canada and some European countries, including Germany, Finland, Sweden and Denmark, have made significant progress towards this area and already show some level of adoption of precision agriculture [36]. In developing countries instead, the acquisition of cutting edge technologies and the lack of suitable infrastructure constitute major impediments to precision agriculture exploitation.