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Its small size and the lack of need for battery make the passive EID well suited for sheep farming

In addition, these included many beneficial bacteria with antimicrobial features, degraders of contaminants and producers of extracellular polymeric substances which are known to improve soil structure and to promote plant growth and drought tolerance. In addition, results are comparable with earlier findings that Firmicutes, including well-known pathogenic Clostridium species, are typical of organically managed plots and are most likely linked to manure fertilization. In general, actinobacterial representatives were more prominent in the organic system for cereal crop rotation and in rotations with manure fertilization. Indeed, high abundance of actinobacteria have been reported in root samples from organic managed soils. Interestingly, our results showed that actinobacterial genus Nocardioides may have benefited from some other organic system specific practice than manure in the cereal rotation. Indeed, actinobacteria have been found to be indicators for no-tilled organic farming systems, and suggested as producers of exopolysaccharides and lipopolysaccharides, and to have relevance in soil aggregate stability in reduced tillage systems. Furthermore, genus Bosea which contains root-nodule endophytic bacteria capable of dinitrogen fixing was specific for the organic cereal rotation system with legumes. There were fewer changes in fungal representatives in the conventional system for the cereal crop rotation between farming systems compared to changes in bacteria. These fungi included soil saprotrophs and mycoparasites which are general opportunists that either benefit from or tolerate synthetic fertilizers or tilling or both. In general, conditions in autumn may favour fast-growing saprotrophic fungi that effectively make use of harvest residues. Conversely, mycelia of AMF are dependent on living plants but as spores AMF may persist in soil even after harvesting. Here, Archaeospora trappei and Archaeospora sp., Glomus mosseae, and Pacispora sp. were indicative mycorrhizal fungi for the cereal crop rotation.

Most of the specific fungi for the organic system for cereal crop rotation were typical of both seasons, indicating certain seasonal stability in the fungal communities in studied arable soils. Furthermore,hydropnic bucket the majority of these specific fungal representatives were the same as the species specific for the manure fertilized plots. Most of them affiliated to ascomycetes and especially to the order Sordariales. Thus, the indicative fungal representatives in both the organic system for cereal crop rotation and manure fertilized plots consisted of functionally a wide mixture of soil and litter organisms, including molds and yeasts acting as saprotrophs, pathogens and predators of other organisms. However, a species of Arthrinium serenense was indicative for both organic rotations but not to manure plots, indicating that it could benefit from some other organic farming practice than manure fertilization. Endophytic genus Arthrinium has been suggested to have various roles in extreme temperature tolerance, production of substances against other fungi and herbivores, as well as acting saprotrophic and pathogenic. Other taxa linked to organic cereal rotation included representatives of Apiosporaceae and Helotiales detected in spring, and the pathogenic Fusarium oxysporum and its antagonist mycotoxin producing Glarea lozoyensis in autumn. These fungi may have the ability to grow quickly and benefit from the second cut of the grass and clover ley which was left on the field as a green manure in the organic system for cereal crop rotation. Precision livestock farming is the application of the precision agriculture concept to livestock farming using a variety of sensors and actuators in order to improve the management capacity for big groups of animals. The PLF is based on real-time data collection and analysis which can be used for animal/flock management .Other innovative tools used for this goal include automats and new technologies . Such innovations become increasingly important as farms grow bigger and single animal monitoring is no longer possible without technological aid .

In intensive farming facilities, the systems achieve this goal through single animal monitoring, environmental microclimate management, feed efficiency rationing, treatment planning and software decision-making aids for the farmer . In the modern farming world of highly industrialized systems with extremely low ratio of farmers to farm animals, it provides a crucial component in the ability of the stock person to keep track of its animals . The levels of monitoring provided by electronic tags and animal-based sensors for a single animal improve the ability of stock persons to manage each animal individually and respond to health problems or welfare issues faster than manual detection . The efficiency granted by the application of PLF and other technologies is also important to the reduction of farm waste and the reduction of the number of animals needed in order to produce the same amount of product increasing farm environmental and economical sustainability. In the farming of ruminants, PLF application has seen the highest implementation in the dairy cow sector as farm intensification took place in the developed world. This sector also enjoys a high level of competition between PLF developers which tends to improve PLF products as well as technical services. Dairy cow farmers nowadays are aware of the variety of management tools at their disposal and of the need to understand and implement those products in an increasingly competitive market . Other ruminants, especially ones kept in the pasture, are less likely to benefit from such systems. An extensive pasture environment is more difficult to control in comparison to a closed barn, especially in regard to infrastructures and communication options . Extensive farmers prioritize methods of grazing with low financial investment and relative simplicity of management which provide a level of economic resilience to market fluctuations. Therefore, adding PLF systems would inevitably increase production costs and would add another layer of technological complexity to farm management . Nevertheless, technological solutions are being gradually incorporated in extensive pasture farming of cattle and small ruminants . A particular sector of extensive sheep farming is the dairy sheep farming around the Mediterranean which has unique characteristics tied to its climate and cultural conditions. This led to the development of a variety of local breeds specialized in milk, with yields more than double the world average.

The production supports a diverse consumption market of sheep dairy products with global exports and known trademarks such as the Greek ‘Feta’ or the Italian ‘Pecorino’. The market and farming systems of the area were recently described in a review by Pulina et al. which highlights the global relevance of the sector: around the Mediterranean and Black Sea regions are concentrated roughly 27% of the world milk yielding ewes, providing more than 40% of total sheep milk production. Almost half of it is concentrated in 4 south European countries – France, Italy, Greece and Spain with over 15 million ha of land used for grazing . From the farmer’s perspective, Mediterranean flocks are usually small to medium size with high levels of specialization for milk yield where meat production is usually limited to light lamb consumed during traditional events. Fibre production for wool is negligible and the income ratio of the production is usually 38:62 of meat: milk clearly favouring milk production . The FIGS production includes modern characteristics, with breed selection programmes, commercial processing and Protected Designation of Origin nominations for their traditional cheese products . Farming systems include traditional extensive farms based on pasture as well as intensive systems that take advantage of modern technologies and precise nutrition management. While the intensive systems are favoured for their higher yield, extensive systems are not neglected due to their lower maintenance costs and better resilience to milk price fluctuations. While the integration of PLF and new technologies is accruing, it is associated with intensive farms which adopt systems similar to ones practiced for dairy cows . Extensive dairy sheep farming is a unique farming system, where animals are grazed outdoors, while maintaining contact with the farmer during daily milking for 120–240 days a year. This intensive handling process has no equivalents in the meat and wool production process where animals are handled only in specific occasions. This contact can be used for data collection by dedicated technological solutions, data that could aid in the feeding,stackable planters breeding and management of the flock. The current paper aims to present the technologies currently developed for extensive sheep farming and their potential to be incorporated in a small to medium scale dairy specialized farming system typical to the Mediterranean area. Also discussed in this paper are the current trends of PLF implementation as well as sheep farmers’ attitudes towards innovation, technology and systematic management due to their inherent influence on the adoption of any new technology in the field.A literature review was performed in order to evaluate the current state of PLF and new technologies that can be adapted to the Mediterranean extensive dairy sheep farming sector.

Literature was reviewed in order to identify PLF systems, innovative technologies and automats available and under development. The search was carried out in a manner similar to Lovarelli et al. on Web of Science®, Google Scholar® and Scopus® databases, focusing on studies carried out in the last 20 years . The following keywords were matched for the search: ‘PLF’, ‘sheep’ and ‘dairy sheep’, ‘PLF’, ‘extensive farming’. As the search yielded various PLF systems, each one received a further search, for example, ‘RFID’, ‘sheep’ and ‘extensive’ or ‘WOW’, ‘sheep’ and ‘management’. This process was performed for each one of the described technologies. Articles regarding precision medicine, precision diagnosis, as well as advanced bacteriological and parasitological diagnostics were excluded from the evaluation process. Following this selection process, a total of 154 articles were included into the initial database. A panel of three independent evaluators were given 52, 51 and 51 of the articles respectively. The lists of articles were then exchanged until each panellist covered all the 154 initial articles, and each article had three independent evaluations. Technologies and PLF systems were therefore grouped and described according to the collected conclusions of the three panellists. The further search focused on technology adoption by farmers. As the number of articles for exclusively sheep farming is limited, other farming sectors were also considered. When Mediterranean references were not available, articles regarding the applicability of systems in other places were discussed. This included EU member states, as well as very different farming systems . Consideration regarding PLF and new technologies future role included financial, cultural and environmental trends. Market prices of products and commercial data were collected from official sites of the producers, distributors, online stores and local selling agents . Financial information was obtained from consultant websites dedicated to farmer finances while Common Agricultural Policy and payment schemes were obtained from extension services of farmer’s co-operative associations. The CAP payments are a result of a common policy for all EU member states, funded by EU’s budget while the management is mostly delegated to local authorities .Electronic identification systems are a key component in PLF setting of farms and the only technology currently mandatory under EU laws . Radio-frequency identification systems allow each animal to be identified independently and the data to be stored and used for various decision-making processes. It is also a key component in animal identification for other PLF systems such as the weighting scale or AD. Passive EID tags are based on the storage of simple information code and a copper coil which briefly charges the transmitter through the energy passed from an active reader .Under the EU legislation, the use of EIDs is obligatory for all sheep and goat farmers, and currently represents an opportunity for introduction of PLF system into extensive management systems. The RFID operates on different radio frequency levels which determine their transition distance and ability to pass materials : low frequency , high frequency and ultra-high frequency . In farming , three significant differences can be distinguished between them: while the HF and UHF are working in the upper level, allowing anti-collision, longer distance, less noise and stable connection they hindered by materials . The LF is less stable but allows better passage through obstacles with the disadvantage for the need of larger antenna. In some cases, farm metal can act as an antenna itself and have multiple reading . It is the most common method of application. Its ease of use and application makes the method very appealing to farmers.

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The system is deployed on the Puerto de La Luz seaport and applied to data from two system sensors

The degree that farming systems follow the principles of OA can be represented as continuous scale, however, a clear line can be drawn between farmers who complied with the minimum requirements of organic standards and those who do not . On this scale, also conventional farmers can be by the extent to which they come close to the boundary of organic compliance, based on the amount and frequency of chemical inputs they use . Furthermore, organic farmers can be grouped according to whether they practice organic farming because they do not have access to chemical inputs or whether they practice organic farming intentionally . In our case studies, organicby-default farmers, which were in the control and not in the intervention groups, were rather uncommon, as most farmers used chemical inputs from time to time, even though some used them only in small quantities. Both groups practice organic farming intentionally and can be further distinguished as farmers who manage their farm only passively and those who manage their farm organically in an active way. Among the latter group, we can further distinguish between farmers who merely substitute conventional inputs by organic ones and those who actively follow agroecological principles and design their farm accordingly for a sound organic nutrient and pest management. While the latter group can be considered closest to implementing the principles of OA, according to our data it is the absolute minority among smallholder farmers in SSA. This emphasises the necessity to view organic agriculture as a farming system that requires a systemic shift beyond the view of single practices that is increasingly taken up by agroecology or regenerative agriculture . Cultivating soil, producing crops, and preparation and distribution of the resulting products is a practice that dates back thousands of years, aeroponic tower garden system and since has been playing a vital role in contributing to the global economy.

In many developing countries, agriculture is a major source for income and employment in rural communities which constitute 45% of the world’s population. Around 26.7% of the world population secure their livelihoods from agriculture. Yet, despite its historical impact on food security, employment and socioeconomic development and stability, the sector still faces structural weaknesses and challenges. These include, but not limited to, pests, vulnerability to climate change, inadequate farming practices and uninformed decision making related to planning, support and protection. The lack of effective support for farmers to adopt good agricultural practices and prevention methods are yet another factors that hinder both the productivity and food security in large scale rural communities. Farmers need up-to-date advice on crops’ diseases, crop patterns and adequate prevention actions to face developing circumstances. Currently, farmers’ access to such information is limited due to current support system being inconsistent, unreliable and often not timely – hence delivered advice can become irrelevant. Over the last two decades, advancements in the agricultural industry has been made through the application of data analytic tools and decision support systems , with noticeable impact in irrigation management, precision agriculture and optimal farming. Though these systems are very useful in offering structured analysis and information to the farmers in a step by step manner, difficulty in usage due to their sophisticated nature, especially for farmers with low literacy in developing countries is often times a challenge. Several systems exist, including related informal forums, social networks, and interactive voice response systems where peers and experts interact with each other and exchange suggestions and opinions on issues raised by farmers. Governments have also tried to handle enquiries and concerns raised by farmers via establishing agri-centres at rural hubs where experts provide suggestions on farmers’ complaints and enquiries by telephony. Whilst this approach seems to facilitate reasonable results, nonetheless, due to the high user demand, it is practically not feasible to provide effective response to extremely large numbers of phone calls, and does not offer a structured way to keep track, and use, of the historic record of enquiries made, resolved and otherwise.

Moreover, providing adequate responses for farmers’ queries is difficult for domain experts as comprehensive information regarding the context of the problem and underlying issues may not be adequately communicated through conventional phone calls. For a sustainable farming practice, the development of an automated query/complaint management system is still an open problem. Mohit Jain et al. proposed a conversational agent for resolving farmer queries by using IBM Watson Speech-based system and Google Translator. However, there is still a high demand for efficient query/complaint management system to enhance the usability and acceptability aspects for farmers with limited literacy while keeping the system highly scalable, available around-the-clock and have manageable overheads. This study aims to resolve the problem of support and advice for farmers in place of the current manual system, deployed in Egypt, by presenting a framework for Complaint Management and Decision Support System for Sustainable Farming . It is based on the application of knowledge discovery and analytics on agricultural data and farmers’ complaints, deployed on a Cloud platform. The automated system is to provide adequate and timely advice for farmers upon their enquires/ complaints, and also to foresee near future development of circumstances by the experts. Consequently, enabling agricultural experts to broadcast early warning signals of threats, mainly pests and disease, and the needed prevention actions to be undertaken by farmers. The system can be deployed to serve villages around farming fields in Egypt and will aim at improving welfare and development in rural parts of the country, and open opportunities for further research and development in the field. The rest of the paper is structured as follows: In Section 2, a literature review of decision support and expert systems in agriculture is presented. Section 3 describes the system requirements and applications constraints. Section 4 presents the system architecture with an illustration of the services/features offered by AgroSupport Analytics system. In Section 5, we present the software application architecture.

The N-tiered architectural representation of the proposed system is described in Section 6. Section 7 offers the subsystem layering and component-level functionalities details. Section 8, presents the Applications of the AgroSupport Analytics system along with a brief case study of farmer query and complaint response that serves as a demonstrative proof of system. Section 9 concludes the paper.Agriculture in Egypt absorbs over 30% workforce and provides livelihood to more than 50% of rural population, but contributes only 11% to national GDP in 2019. This is mainly because each year a large portion of crops are wasted due to pests and diseases and also due to obsolete farming practices. It is believed, therefore, that timely farmers’ complaint resolution and access to information and expertise advice is vital to achieve sustainable and quality agriculture production. The existing farmers’ complaint management process follows a conventional query submission approach where farmers deliver, usually manually, their complaints and needs for support to their respective ‘agricultural associations’ distributed across Egypt. These, being in Arabic text, are received and then submitted to one of the national ‘centers’ distributed over the country to offer support for farmers in their villages. Several agricultural experts working at these centers subsequently process farmers’ enquiries, either instantly or by consulting the Agricultural Research Center via an interface designed for the purpose. A recommendation is usually provided. Most of the times, however, a ‘no known solution’ is delivered ‘ usually via phone calls. The portal provided by ARC offers access to a database of complaint-support pairs, which can sometimes features issues of inconsistency, redundancy, lack of structure, or missing value. The flow of the existing manual querying system is shown as Fig. 1. Even with a swift ‘‘round” of consultancy provided by the system, response from experts can get significantly delayed, mainly due to a large number of sent queries . Consequently, farmers, get an answer when it is too late for them to act. Similarly, the support provided by experts deals only with farmers’ instant complaints, lacking near future perspective on developing circumstances, and thus advice.For nearly two decades, decision support systems and data analytics have become efficient tools for providing precision agriculture and farming. Recently, Big data technologies are being widely adapted in agriculture domain mainly because the agriculture related data sets are becoming extremely large and complex that it is becoming difficult to process them using on–hand data management tools and/or traditional data processing applications.

CropSyst is a DSS developed into a suite of programs, including a crop simulator, a weather forecast generator, GIS modeler program, and a watershed utility program. CropSyst aims to simulate and optimize features like the soil water budget, soil–plant nitrogen budget, crop canopy and root growth, and yield. The AquaCrop model evaluates the production of maize crop under semi-arid climate conditions. García-Vila and Fereres later combined an economic model with the Aqua Crop simulator to optimized farm-level irrigation. Paredes et al. analysed and predicted the impact of irrigation management strategies against yield and economic returns of maize crop. Giusti and MarsiliLibelli introduced an inference based fuzzy DSS to optimally find irrigation actions based on the crop and site characteristics and conserving the water usage. Perini and Susi discussed the design and development aspects of a pest management DSS that can be used by the members of advisory services including pest experts and technicians. Xu et al.introduced an agricultural ecosystem management systems to extracts,dutch buckets for sale manage and analyze data regarding terrain, land utilization and planting. Kurlavicˇius et al. introduced a DSS for sustainable agriculture to predict the optimal crops grown and animals kept in particular regions, The system also predicts the resources required to carry out these activities under the varying environmental conditions. Antonopoulou et al. introduced a Web-based DSS to let farmers find the appropriate crops based on their regional and environmental conditions and also provide the best cultivation strategies and periods.Kaloxylos et al. later, proposed implementation of a cloud-based FMIS for managing a greenhouse. Fountas et al., Tayyebi et al. and Tan proposed perspectives of cloud computing as the key drivers in future development of FMIS and precision agriculture. Big data mining can facilitate the extraction of useful information from complex, variable, and large volume of the dataset, therefore can improve a DSS’s accuracy in various fields. The Millennium Project; for example, has identified many interesting challenges related to clean water, sustainable developments, climate changes, population and resources etc. This project has advocated the use of big geospatial data to save energy with eco-routing, i.e., avoiding congestion, stopping at red lights, turning points, and identifying elevation changes. Furthermore, a fuel consumption minimising technique has been proposed to achieve best travel time with reduced travel distance.

Recently, an unprecedented growth of Data Force Analytics enabled utilisation of big data technologies and digital sensors to manage data efficiently. Adopting such an approach in the field of agriculture can bring many benefits to support decisions. Nevertheless, data analytics still faces many challenges of handling extensive data and diverse data sets like semi-structured, unstructured, and streaming data. Therefore, in such Data Force Analytics developments there will be a strong need to effectively utilise datasets to facilitate users in finding their needs efficiently and effectively e.g. a qualitative study in points out a co-evolving tool to understand such needs/skills. Recently, organisations have started to use the concept of SelfService Analytics to encourage professionals or workers to perform queries with IT support and generate reports independently. The framework proposed in provides matrix called the governance of Self-Service Analytics , which uses the power of business intelligence tools and platform to support ITenabled analytic content development to help experts find the best solutions and get the decision rapidly. The geodatabase contains a visual analysis of tabular data to achieve the primary utilisation of practising BI system and GISs in data analytics. The Puerto de la Luz is a SmartPort solution, enabling real-time monitoring and collection of sensor data in a seaport infrastructure. It is a web-based GIS application, which uses an open-source big data architecture to achieve its functionality. The Spatial Decision Support System is an extension of DSS application, which supports an improvement in decision-making compared to non-spatial data. In particular, SDSS in agriculture has a positive impact on improving decision making.

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Economic viability at farm level is a relatively fast and measurable indicator

From a system dynamic perspective this could suggest that the studied farming systems have some buffering capacity to deal with disturbances . An example of this is the farm expansion in area and number of animals in many farming systems that compensates for the loss of farms from the system. From a methodological perspective, it could be argued that the participatory assessment of critical thresholds of challenges is easier than for system functions and resilience attributes. Critical thresholds of challenges are linked to important function indicators and resilience attributes and, therefore, may serve as warnings in the mental models of farming system stakeholders. Based on workshop results and further reflections, interactions between critical thresholds are expected to directly affect the economic viability at farm level, a central critical threshold observed in all farming systems .This gives another argument for monitoring income and other economic indicators in the monitoring frameworks such as the CMEF. The lack of a consistent pattern with regard to environmental thresholds indicates the importance of the local context. In all farming systems, exceeding the critical threshold for economic viability at farm level affects the attractiveness of the sector, the number of farm closures and the availability of farm successors, which in turn in about half of the case studies contribute to lower availability of labor and/or depopulation, which finally can reinforce low economic viability. Hence, a vicious cycle is initiated. This suggests that processes related to the economic and social domain can be driving dynamics of farming systems as well as being reinforced by those dynamics. This potentially can turn a relatively slow social process into a fast process. Social processes are therefore indeed important to monitor . This is already acknowledged in, for instance, in DE-Arable&Mixed, where participants emphasized the attractiveness of the area, hydroponic dutch buckets specifically regarding the development of infrastructure. Through its interactions with processes in other domains and levels, economic performance can be seen as an indirect driver as well as a warning signal for approaching critical thresholds in other domains and levels.

In all farming systems food production was perceived to directly impact economic viability. Therefore, from the perspective of many farming system actors participating in our workshops, focus on food production and economic viability , which are based on relatively fast and measurable processes , seems often more justified than focusing on the more slowly developing social functions such as providing an attractive countryside. However, this may be due to the fact that farmers were in most case studies the best represented stakeholder group, thus possibly masking the voices of other stakeholder groups that were represented less. In any case, social and environmental functions should not be overlooked as a focus on one domain will likely lead to missing important interactions with critical thresholds in other domains . For example, improving economic viability through scale enlargement and intensification, meaning fewer farms and often replacing labor by technology, often leads to a less attractive countryside. Regarding the environmental domain, focus on economic farm performance can even be dangerous as it could ignore externalized risk. For instance in UK-Arable and NL-Arable soil quality, the base of crop production and hence economic performance, was considered close to critical thresholds, while prohibition of certain crop protection products was seen as a challenge for the farming system, rather than the damage these products cause to surrounding ecosystems. Another example of externalized risk in one of our case studies is the pollution of water bodies in IT-Hazelnut. On their own, farmers may initially not have the willingness or capacity to look beyond the farm level. In IT-Hazelnut, farmers, through interaction with environmental actors, are now addressing these environmental issues. Building on this example, we argue that for instance societal dialogues and policy deliberations on improving sustainability and resilience need input from specific social and environmental actors, possibly even from outside the farming system. This seems necessary to counter-balance the bias towards economic performance at farm level by most of the participating farming system actors in most of our workshops. In the more remote case studies, e.g. DE-Arable&Mixed and BGArable, attractiveness of the area seems low anyway. Consequently, improving prices alone, for instance, may not improve the availability of the necessary labor, thus reducing the emphasis on economic performance. Extensive rural development seems necessary to maintain the functioning of these farming systems. Mitter and et al. , based on their mechanistic scenario development approach, expected no or negative developments regarding rural development in all future scenarios of EU agriculture.

The notion that both mechanisms at EU and farming system level are not wired to address rural development, shows how the low attractiveness of an area can persist once it has come about. Avoiding exceedance of critical thresholds without further adaptation or transformation, implies a performance at or below the current low to moderate levels for most system function indicators and resilience attributes . A potential exceedance of a critical threshold in the coming ten years is expected to lead to negative developments for most system function indicators and resilience attributes. Negative developments of function indicators are expected in the economic, social as well as the environmental domain. On average, across all farming systems, we did not observe any differences in the magnitude of the effect between domains for function indicators. This consistent development confirms the idea that the different domains are interacting. The consistent expected developments for function indicators and resilience attributes after exceeding critical thresholds suggest a perceived interaction between them. One could argue that a system needs resources to react to shocks and stresses , especially for adaptation and transformation. These resources can only be adequately realized when there is an enabling environment and when system functions are performing well. The other way around, resilience attributes can be seen as “resources” to support system functions on the way to more sustainability. For instance, existing diversity of activities and farm types makes visible what works in a specific situation, openness of a system helps to timely introduce improved technologies, and connection with actors outside the farming system may help to create the enabling environment for innovations to improve system functioning . Impact of challenges is primarily experienced at the farm level,resulting in the disappearance of farms from the farming system. In multiple case studies , participants indicated that identified critical thresholds would be perceived differently among farmers. As mentioned before, farm closure generally leads to a less attractive countryside, a long-term process that is currently not perceived the most important issue in most studied farming systems, according to stakeholder input. Increasing farm size could be seen as a solution to compensate for the loss of farms and farmers in the farming system. Increasing the farm size is often associated with the advantage of economies of scale. For multiple farming systems in our study , production margins are low, which could further stimulate this thinking. However, from the farm level perspective, beyond a certain size, further economies of scale are not realized in some of the studied farming systems, i.e. there are limits to growth dependent on the rural context. In BE-Dairy, for instance, increasing farm size seems to be limited due to environmental standards. In ES-Sheep, further reduction of the farmer population is perceived to be harming the farming system, e.g. through reduction of facilities such as farmer networks, agricultural research initiatives, etc., but also hospitals, schools, etc. Besides, to further increase farm size, farmers in ES-Sheep depend on extra labor that is not available because of low attractiveness of the countryside, bato bucket while investment in labor saving technology does not pay off with the current market prices.

This is an example of the reflection of Kinzig et al. that a seemingly reversible threshold becomes irreversible because a certain management option to reverse processes is not available anymore. Based on Fig. 1, we argue that this specific example may be true for more farming systems where a lack of labor force is experienced and investment in labor saving technology are not likely to pay off . The importance of the social domain of farming systems makes us argue that indicators in this domain should be monitored. The option for countries in CAP2021-27 to shift 25% of the budget from income support to rural development provides the opportunity to adapt policies and investments to rural development needs. For instance for the more remote farming systems such as DE-Arable&Mixed and BGArable. We argue that a large shift of budget across the two pillars is already an indication of the perceived need to improve rural living conditions and can thus be used for monitoring. Although relating to economic values, the allocation of budget to rural development can thus be seen as the importance that is attributed to support processes in the social domain. Caution is needed however, as Pillar II also supports processes related to the environmental domain. Surveys among experts at national and regional level that record how much of the budget should be shifted from pillar I to II is a further step in assessing the performance of farming systems in the social domain. This implies introducing subjectivity in the CMEF on the evaluation side, while the choice of the parameter is defined objectively, i.e. externally. Jones remarks that objectively defined and subjectively evaluated resilience assessments are relatively robust, easy and quick, while the limitations lay mainly in having to deal with bias, priming and social desirability. Other possibilities for objectively defined and subjectively evaluated indicators may lie in including indicators on living conditions and quality of life in rural areas based on Eurofound studies . These type of indicators also have the advantage of being entirely in the social domain, i.e. they don’t indirectly refer to economic values such as the shift in budget from Pillar I to Pillar II as discussed above.

A common reflection in the discussion section so far is that having adequate system resources seems essential for stimulating system resilience attributes and dealing with challenges. In cases of low farming system resilience, building system resources may initially depend largely on external resources. This implies a role for regional, national and EU government bodies, i.e. a pro-active role for actors in the institutional domain outside the farming system. Given the tendency to focus on economic performance at farm level, external resources in the form of economic subsidies should be increasingly conditional regarding environmental and social functioning of the farming system. The emphasis on resources for building resilience is also acknowledged in several recent resilience frameworks , for instance with regard to knowledge and innovation systems . To elaborate on the example of AKIS, we argue that, rather than only monitoring and evaluating the amount of budget and the number of people that benefit from improved AKIS , also the amount of this resource and stakeholders’ access to it should be known and evaluated regularly. Similarly, other social and institutional resources need to be monitored next to economic and environmental resources. Given the challenges regarding assessing and discussing critical thresholds in workshops , all identified critical thresholds could be seen as “Thresholds of potential concern” . In our case these TPCs would express the concerns of a selection of farming system stakeholders. TPCs can be seen as a set of evolving management goals that are aimed at avoiding critical thresholds that are expected, e.g. from experiences in other systems, but are not known. In case thresholds are considered beforehand as TPC’s, Q-methodology may be an interesting participatory method to define which TPC deserves most priority. Estimating main functions of a system by assessing critical thresholds as TPCs, reduces the presence of clear sustainability goals. This makes the threshold assessment less dependent on externally determined values and criteria than most sustainability assessments . Implicitly, the goal is to avoid a decline in sustainability and resilience levels of the current system, which may give the participating system actors the trust to provide details, expose interrelatedness between sustainability domains, and also come up with solutions. Regarding the latter, it should be noted that avoiding exceedance of critical thresholds does not automatically imply that a system is steering away from mediocre performance.