Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems
2. Related Work
2.1. Data Aggregation Systems
2.2. Socio-Technical and Socio-Economic Modeling
2.3. MFS Integration Analysis and Evaluation
2.4. Knowledge-Based Management System in Agriculture
2.5. MFS Services
2.6. Decision Support Systems in Agriculture
3. MiFarm-CSA Architecture
- Easier to understand: architecture will be easier to understand, not only for the architects who designed it, but also for the technicians who are going to build and maintain it and for the people who are going to participate in it.
- Easier to test: having a layered architecture makes it possible to define independent components much better, which makes component testing easier.
- Easier to extend: adding new features or changing current ones is easier in a layered architecture.
3.1. Integrated Data Collection Network
3.2. Evidence-Based Assessment Methodology
3.3. The Social-Ecological Conceptual Framework
3.4. Mixed Farming as a Service
- The Soil Manager (SOMA) aims provide optimal decisions, recommendations and case studies on how the soil quality will be maximized. While each of these decisions, recommendations and case studies represent possible techniques to effectively manage soil resources, each practice will be adequately assessed to identify possible constraints or drawbacks.
- The Manure Manager (MAMA) aims to provide optimal decisions, recommendations and case studies on the manure management in Mixed Farming Systems. The utilization of livestock manure to add nutrients back to the soil is one of the key crop-livestock interactions in MFS. Manure when used as a soil amendment can benefit the soil, resulting in crop production and resilience benefits for smallholders via increased nutrient supply to crops and improved soil structure and water holding capacity. Manure has well-documented impacts on soil chemical and physical properties.
- The Water Manager (WAMA) will be able to make optimal decisions on the water management on the MFS. Improving water use efficiency and water management on mixed farms is arguably the most important and high potential improvement for farmers to be climate-smart. Optimal water management strategies increase net returns and purchasing power parity of households much more than any other and perhaps presents the only viable pathway to help transition smallholder farmers out of poverty.
- Aggregate accurate and timely climate and market information (weather, demand, supply and prices);
- Process and analyze it in a way such that it will be transformed into usable knowledge (intelligence) using big data analytics;
- Disseminate it to mixed farming stakeholders through web and mobile applications.
4. The Expected Benefits of Mifarm-CSA Architecture
- Increasing agricultural productivity and income while also enhancing resilience or adaptation of livelihoods and ecosystems towards climate extremes. Through the IDCON, SECF and MFaS layers, the proposed architecture exploits multi-disciplinary, on-field and off-field data, to train the novel DSS, thereby enabling it to make optimal decisions, which are expected to boost MFS productivity and stakeholders’ incomes. Moreover, through DiMaRa, MiFarm-CSA is anticipated to provide MFS the flexibility to face uncertain climate and market conditions.
- Increasing synergies among different farming systems by adopting new data collection and monitoring technologies, such as smart sensors, robots, UAVs, advanced tracking systems, long-range IoT-enabled sensors, middleware and gateways. The IDCON layer provides the cutting-edge technologies for applications in MFS: IoT sensors, devices, modern UAVs and FANETs, which aspire to be fully autonomous and multi-collective, supporting, advanced processing and integration functionalities and are capable of covering large-scale areas for providing big volumes of data in near real time. The autonomy is twofold: the energy autonomy will be accomplished by deploying energy harvesting techniques for recharging sensors and UAVs and by defining a pilot-sensitive energy-budget approach for each data collection technology.
- Speeding up the establishment of a community of practice and dynamic methodology in designing, developing and evaluating mixed and integrated farming systems. MiFarm-CSA adopts innovative human factor approaches and methodologies to champion social and behavioral aspects and to prioritize the role of the human in the technological development. By exploiting SCOPE in the proposed architecture, the aim is to facilitate a comparison between the current ways in which mixed farming and agroforestry are conducted and future ways which better account for the socio-technical system as described by SCOPE. This whole approach enables MiFarm-CSA to assess the inadequacies of the current treatment of social and behavioral issues and ensure that the emerged outcomes are built around the identified social and behavioral aspects of mixed farming and agroforestry. CoP will involve a multi-actor and trans-disciplinary group of end users. SLO relates to the continued acceptance of a set of business practices or operating procedures by a stakeholders. Moreover, SLO ensures that the technical innovations can be supported by sustained social and behavioral change.
- Forming a basis for the creation of a conceptual framework to analyze crop-livestock integration by fostering core interactions among animals, grasslands and crops, developing metabolic analysis of material flows, devising a biodiversity approach for the development of integrated crop-livestock systems and promoting social resources and stakeholder interactions.
- Prescribing a rich suite of user-friendly MFS applications and services for fostering the optimal combination of production, accurate climate change estimations and optimal decisions on local and national levels.
5. Case Study
- Climate change has affected the biodiversity of the area, having negative impacts on the yield production and the soil quality.
- High operational and labor costs (energy needs, animal feeding) add a lot of pressure to farming businesses.
- The extensive use of chemical inputs (fertilizers and pesticides) has negatively affected the water cycle.
- The animal waste management is not sufficient, leading to negative environmental effects and waste of resources.
- The lack of forestry woodland management results in high risks of things such as wildfires.
- Low cooperation between local farming businesses due to a poor local market.
- A wide skill gap between each kind of system, deteriorating the objective of collaboration considerably.
- Technological deficiencies, as farmers are not aware of modern monitoring and surveillance methods to reduce their labor costs.
- Absence of local professional groups in MFS rules out the possibility of combining local agricultural and livestock practices.
Informed Consent Statement
Conflicts of Interest
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|Climate Smart Agriculture||Agriculture that boosts productivity, enhances resilience, minimises greenhouse gas emissions, and facilitates achievement of national and international food security and development goals|
|Integrated Farming Systems||A biologically integrated system, which integrates natural resources in a regulated mechanism into farming activities to achieve maximum replacement of off-farm inputs and sustain farm income.|
|Mixed Farming Systems||A type of farming which involves crop cultivation and livestock rearing together in an integrated form that is managed as a single farming system.|
|Precision Agriculture||A farming management concept based on observing, measuring and responding to inter and intra-field variability in crops, aiming to lay down a decision support system for the whole farm management with the goal of optimizing yield while preserving resources.|
|"hicle||An aircraft without a human pilot on board.|
|Internet of Things||A network of physical objects, “things”, that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet|
|Flying Ad-Hoc Network||An Ad-Hoc network structure which is formed by a set of UAVs of which at least one must be connected to a ground control station or satellite|
|Radio-frequency Identification||A technology that uses electromagnetic fields to automatically identify and track tags attached to objects.|
|Machine Learning||An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.|
|Low-Power Wide-Area Network||A type of wireless telecommunication wide area network designed to allow long-range communications at a low bit rate among things, such as sensors operated on a battery.|
|Geographic Information System||A conceptualized framework that provides the ability to capture and analyze spatial and geographic data.|
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Kakamoukas, G.; Sarigiannidis, P.; Maropoulos, A.; Lagkas, T.; Zaralis, K.; Karaiskou, C. Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems. Telecom 2021, 2, 52-74. https://doi.org/10.3390/telecom2010005
Kakamoukas G, Sarigiannidis P, Maropoulos A, Lagkas T, Zaralis K, Karaiskou C. Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems. Telecom. 2021; 2(1):52-74. https://doi.org/10.3390/telecom2010005Chicago/Turabian Style
Kakamoukas, Georgios, Panagiotis Sarigiannidis, Andreas Maropoulos, Thomas Lagkas, Konstantinos Zaralis, and Chrysoula Karaiskou. 2021. "Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems" Telecom 2, no. 1: 52-74. https://doi.org/10.3390/telecom2010005