Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems
Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and intensity of excessive climatic events such as floods and droughts. Farmers need to adapt to climate change by developing advanced and sophisticated farming systems instead of simply farming at lower intensity and occupying more land. Integrated agricultural systems constitute a promising solution, as they can lower reliance on external inputs, enhance nutrient cycling and increase natural resource use efficiency. In this context, the concept of Climate-Smart Agriculture (CSA) emerged as a promising solution to secure the resources for the growing world population under climate change conditions.
Starvation is one of the biggest threats we are facing as humanity in the 21st century. The global demand for food is increasing, with recent studies forecasting that the global population will reach 9.5 billion people by 2050, and more than 80% of available land is already cultivated. Global demand for farming products is expected to increase by 70% for food production and double for livestock products by 2050. However, agricultural production is strongly affected by the changes in climate conditions, such as rising temperatures, changing rainfall regimes and variations in the frequency and intensity of extreme climatic events such as floods and droughts. The estimated impacts of the climate change indicates that the yield loss could be up to 35% for rice, 20% for wheat, 50% for sorghum, 13% for barley and 60% for maize, and livestock production will be also negatively affected.
Both farmers and breeders need to find efficient and affordable methods in order to strengthen the resilience of agriculture and livestock farming against climate change. The concept of Climate-Smart Agriculture (CSA) reflects an ambition to improve the resilience of agricultural systems against climate change. The Food and Agriculture Organization (FAO) of the United Nations defines CSA as agriculture that enhances productivity, improves resilience, reduces greenhouse gases (GHG) and facilitates the achievement of national food security and development goals . The CSA includes both traditional and innovative approaches; and technologies that promote agricultural productivity, increase the stability at the farm level and foster the sustainability of the relevant value chains. A system that adopts the concept of CSA is expected to increase its resilience against fluctuating climate conditions and therefore offer increased food production in the face of a changing climate and increased climate variability, while improving nutritional outcomes and reducing the carbon cost of farming and its contribution to GHG emissions.
It includes three major pillars: (a) increasing agricultural productivity; (b) increasing adaptive capacity at multiple scales (from farm to nation); and (c) reducing greenhouse gas emissions . While there is a consensus on the potential of the CSA to support global food and nutritional security in less-favored conditions, CSA scholars have different perspectives when approaching the scaling of CSA options.
Integrated systems can combine crop production, livestock and forestry, supporting the production of at least three types of product from the same land area over a defined period. These systems, based on inter-cropping, succession and/or rotation, can optimize the biological cycling of nutrients between plants and animals, and maintain long-term soil fertility. According to the FAO, Mixed Farming Systems (MFS) are defined as farming systems managed by households and/or enterprises where crop cultivation and livestock rearing together form integrated components of a single farming system. MFS offer a lot of advantages, such as efficient use of resources by using crops and grassland to feed animals and fertilize their fields with manure from the animals, complementarities between crops and livestock and a flexibility that allows the adjustment of crop/livestock ratios in anticipation of risks, opportunities and needs. Such context-relevant integration of crops and animals in the same system appears to support a biological, ecological and economic sustainability in the global food production chain.
Nevertheless, MFS is not a universal panacea. The economic results of MFS are not as optimal as those of dedicated systems, especially considering the remuneration of labor. 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. In order to achieve a satisfied level of sustain- ability 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. Critical features such as income stability and sustainability need special quality dimensions, criteria and indicators for the evaluation of trans-disciplinary processes. 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. Moreover, science lacks influence mainly because of biases towards academic research rather than practical applications. 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. 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; 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, 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. 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. Today, mobile applications, smart sensors, unmanned aerial vehicles (UAVs), cloud computing and edge computing 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.
In developing countries instead, the acquisition of cutting edge technologies and the lack of suitable infrastructure constitute major impediments to precision agriculture exploitation. Although precision agriculture, as it can be found in North America, Australia and Europe, differs considerably in developing countries, the need for accurate data and targeted interventions is actually greater there, due to the stronger imperative for change and the lack of resources. What is really encouraging is that over the past few years there has been considerable effort from developing countries to use some kinds of precision agriculture methods in various applications, such as yield monitoring and tractor auto-guidance.
In developing countries, MFS play an important role, as in some cases they act as the backbone of a sustainable agricultural policy, especially for individual farmers or small farming communities. During the last decades, research efforts in various developing countries have been redirected to integrated farming systems rather than dedicated ones, so as to cover several complementary enterprises under various agro-ecological situations. These efforts revealed that integrated farming systems can not only be profitable and productive but also eco-friendly, a countermeasure to unemployment and provide financial stability to the stakeholders.
Designing and applying integrated farming systems has severe constraints. Towards the technological direction, a severe constraint is the lack of long-term, structured, concrete data. In the best scenarios so far, end users’ actions heavily depend on short-term, biased data. As a result, data analytic services fail to operate on a larger scale since they are highly affected by area peculiarities and seasonality. That explains the fact that different frameworks and assessment schemes result in contradictory outcomes. Examining MFS implementations in view of the end-user, a new kind of barrier that lies in farmers’ willingness to cooperate, emerges. This barrier goes beyond technical solutions, adding a socio-economic dimension to the widespread exploitation of MFS.
MiFarm-CSA architecture aims at providing a CSA-based, multi-actor and community-oriented architecture, for advancing the current farming system to a smart, resilient and integrated/mixed farming ecosystem, aiming at increasing the resilience of the underlying farms, crops, livestock and forestry against the negative impacts of the climate change. Moreover, the proposed architecture envisions to foster cooperation between farmers and breeders through the provision of a sustainable MFS reference model.
This blog post is part of the paper Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems from Georgios Kakamoukas, Panagiotis Sarigiannidis, Andreas Maropoulos, Thomas Lagkas, Konstantinos Zaralis, and Chrysoula Karaiskou. The paper can be found in https://www.mdpi.com/2673-4001/2/1/5