Enabling conditions in the social domain were e.g. related to rural demographics and/or availability of labor and more horizontal and vertical cooperation and social self-organization . Specifically, in BGArable and RO-Mixed emphasis was put on enabling conditions in the institutional and social domain. In all case studies, interacting thresholds across level and/or domain were observed . More details on the interacting thresholds are presented in the Supplementary Materials 3. Common interactions between critical thresholds occur between field environmental and field-economic, from field-economic to farm economic, from farm-economic to farm-social, from farm-social to farming system-social, and from farming system-social to farm-social . Generally, an environmental issue at field level, for instance, decreasing soil quality , pest diseases , wildlife attacks , or drought is so much of a shock or stress that it leads to yields that are too low to sustain an adequate level of farm income . In a majority of the farming systems, high input prices and decreasing output prices and sales further diminish the farm income. Too low incomes at farm level were in all case studies resulting in reduced attractiveness of farming, farmers quitting or the lack of finding a successor for the farm. In UK-Arable, also reduced farmer happiness due to lack of recognition was mentioned as a reason for quitting a farm. Farmers quitting their farm without having a successor was in multiple farming systems also considered to contribute to a smaller rural population at farming system level . Interestingly,hydroponic nft although socially oriented function indicators and resilience attributes were less often formally included in the discussions, they eventually appeared when explaining how challenges impact the farming system. Having less farms in the farming system was also associated with a lower maintenance of natural resources and a less attractive countryside.

Interactions with critical thresholds in the environmental domain at farm and farming system level were mentioned in a few other case studies. In NL-Arable, at farm level in the environmental domain a narrow rotation in which starch potato is grown every second year was expected to lead to increased pressure of plant parasitic nematodes . In UK-Arable, low income at farm level was expected to lead to declining soil health at field level . In IT-Hazelnut and SE-Poultry, environmental regulations were expected to improve the maintenance of natural resources at farming system level, but also to push farm income levels below a threshold through increased costs . Overall we observed that environmental thresholds certainly feature, but differ in the level at which they play a role and in what direction they evolve. In farming systems for which access to land is an issue , quitting of farmers may also be an opportunity, provided land becomes available on the market for sale or to be leased. In ES-Sheep, quitting of farmers was experienced as a serious issue. In IT-Hazelnut, the retention of young people on the farms was specifically mentioned as something that could support the rural life and vice versa . Both low economic viability at farm level and low attractiveness of farming and a smaller rural population were considered to reduce the access to labor at farm level in BG-Arable, SE-Poultry, PLHorticulture, DE-Arable&Mixed, RO-Mixed, and ES-Sheep. Access to labor in BG-Arable, PL-Horticulture and RO-Mixed was important for the continuation of activities on farms, as lack of labor was expected to push yields below acceptable levels . In BG-Arable lack of labor could be overcome by implementing new technologies, but this would require a labor force with higher levels of education and qualification which is even harder to find. Lack of labor was also expected to push production costs beyond critical thresholds in SE-Poultry and RO-Mixed. Hence, in multiple systems, low economic viability, attractiveness of farming, rural depopulation and low level of services at farming system level, and low access to labor seem to be part of a vicious cycle. Following from Fig. 1, it can be made plausible that after exceeding critical thresholds of challenges, a decline in performance of system’s main function indicators and resilience attributes was expected by workshop participants in most case studies . Across farming systems, the functions “Food production”, “Economic viability”, and the “Natural resources” were in most cases expected to decline moderately or strongly .In ES-Sheep, ongoing decline of function performance was expected to be aggravated.

When discussed in case studies, “Biodiversity & habitat” and “Animal health & welfare” were on average expected to be less impacted compared to other functions. When exceeding critical thresholds of challenges, also a decline in resilience attributes was expected in most case studies, mainly because of a decline in profitability, production being less coupled with local and natural capital, a declining support of rural life and lower levels of self organization . By contrast, participants in BG-Arable and SE-Poultry generally expected improvements in resilience attributes after critical thresholds are exceeded . For instance, infrastructure for innovation was expected to develop positively in BG-Arable and SE-Poultry, while it was expected to develop negatively in other case studies . In the case of BG-Arable, participants expected increased collaboration, leading to innovation, in case the system would collapse. In the case of ES-Sheep, participants expected that the current low profitability of farmers will not allow investment in new infrastructures for innovation. All studied farming systems were perceived to be “close” or “at or beyond” at least one critical threshold for challenges, function indicators or resilience attributes . The actual state of the system may be more or less close to a threshold than the participant’s perception. Obviously, for case studies that are perceived to be “at or beyond” critical thresholds while still continuing business as usual, the actual state must be at a different position than perceived. Still, perceived closeness can be seen as a clear stress signal, indicating that change is needed, expected or even already experienced. An example refers to the ban of crop protection products before alternatives are available. This stress signal could instigate a study about a reasonable time to phase in/ out regulations regarding the use of crop protection products before actually implementing them. Perceptions of being close to or at critical thresholds also indicate that, from the perspective of farming system actors, immediate action is needed to preserve the farming system or guide it in its transition, thus avoiding a situation where sustainability is even lower. Looking at multiple challenges puts individual challenges into perspective.

To give an example, climate change may be a problem causing regime shifts in many socio-ecological systems , but for the studied farming systems this is not the only challenge and often also not perceived to be the most urgent, except for some arable systems . This supports the notion that climate change should be studied in the context of other drivers . At a global level, reducing anthropogenically induced climate change is, of course, urgent and agricultural systems’ contribution to it must be reduced. Some challenges experienced by FS actors, especially farmers, may also be implicitly caused by climate change; for instance changing legislation and high input costs. For most of the farming systems in our study, climate awareness of some stakeholders, such as conventional farmers, is however not likely triggered due to the impact of climate change on their system per se. When deliberated in an appropriate manner with those stakeholders, new legislation in the context of fighting climate change may however have considerably more effect regarding changing stakeholder perceptions. Function indicators for food production and economic viability were often perceived to be close to critical thresholds. This confirms the need to closely monitor economic indicators as is done in the CMEF of the CAP . When discussed, social function indicators were generally perceived to be “not close” or “somewhat close” to a critical threshold, except for ES-sheep where participants experienced that a critical threshold was exceeded . Environmental function indicators were in most cases perceived to be “not close” or “somewhat close” to critical thresholds . Only in arable systems,hydroponic channel environmental functions were experienced “close” or “at or beyond” critical thresholds. This was mainly related to the capacity of soils to deal with an excess or lack of water, often due to climate change. Participants in workshops of arable systems indicated that a lot of effort was already required to maintain rather than to improve the current soil quality. Arable systems, in need for soil improvement to avoid critical thresholds, would benefit from enabling conditions at national and EU level that foster the maintenance of natural resources.

Mitter et al. , based on a mechanistic scenario development approach for EU agriculture, expect improved attention for natural resources only in a scenario following a “sustainability pathway” out of five possible future scenarios. Current conditions and their future development hence do not seem to support a resilient future of arable systems. Overall, perceived closeness to critical economic thresholds could explain the perceived lower importance of social and environmental functions compared to economic and production functions . Defining critical thresholds seemed most difficult for resilience attributes . According to Walker and Salt it is actually impossible to determine critical thresholds for resilience attributes because they all interact. However, function indicators also interact, but were easier to assess for participants. We argue that difficulties in determining critical thresholds are probably more an indication of the perceived redundancy of resilience attributes for system functioning: presence and contribution to resilience was low to moderate according to stakeholders’ perceptions . This could be related to a control rationale , in which keeping a relatively stable environment and improving efficiency is more important than increasing the presence of resilience attributes. It should be noted, however, that participants often could indicate enabling conditions that improve the resilience attributes. This could be an indication that participants are aware of the importance of resilience attributes, but are in need for more concrete, locally adapted indicators that represent the resilience attributes. In any case, suggesting improvements for resilience attributes could be seen as an implicit acknowledgment by participants that building capacities for adaptation or transformation is required. Perceived thresholds may be different than the real threshold. For the systems that are perceived to be “at or beyond” critical thresholds, it is not necessarily too late to adapt in case the real threshold is actually at a different level than the perceived one. The extensive sheep system in Spain was judged to be close to a collapse, but alternative systems and strategies to reach those have been proposed . In IT-Hazelnut, introduction of new machinery in the past has made farming more attractive for the younger generation, thus avoiding depopulation . Further developments in IT-Hazelnut regarding local value chain activities at farming system level rather than farm scale enlargement, are aimed to further stimulate economic viability and the retention of young people in the area . In PL-Horticulture, the case study is relatively close to Poland’s capital where access to land is limited, system actors aim at increasing the economic viability via vertical and horizontal cooperation at farming system level, which keeps re-attracting seasonal laborers from nearby Ukraine, where wages are lower, to the region. The common factor in these examples of adaptation is that resources are needed to implement them. Be it financial, human, social or other forms of resources. The examples above also suggest that coming back to a desired state, even after exceeding a critical threshold, is possible, provided the disturbance causing the exceedance does not last too long , and adaptation strategies are available . The notion of a critical threshold being a combination of magnitude and duration was not discussed much in the workshops but could help to further define critical thresholds. For instance with regard to the number of years the farming system can deal with extreme weather events as was done in NL-Arable. It is worth noting that challenges are perceived to be more often “at or beyond” perceived critical thresholds than function indicators and resilience attributes.

Looking more closely at the emergence of smart farming developments, another crucial consideration pertains to the innovation processes that try to generate and integrate new and striking configurations of firms, farmers, and research institutes. New networks of diverse stakeholders are formed; extant networks are re-made, potentially rendering invisible the role of some actors and their interests. In a sense, then, smart farming can resemble the notion of “innovation by withdrawal”,which departs from the view that innovation is “structured around the introduction of a new element, an artefact, a way of operating, a service, and its success is dependent on the number of adopters and the significance of the entities which are articulated with it” . Yet, as demonstrated by the case of no-till farming in France , withdrawal of one element relies on making visible hitherto invisible or overlooked elements , while maintaining problematic practices.To respond, an alternative to focusing on introduction or withdrawal is to recognize that agricultural innovation, like any other practice, is always a topological affair: it is about overseeing and managing configurations of humans and materials and how they flow through a system or across a specific domain, such as a field . Where there are blockages, conduits can be installed to increase flows; where there are leaks, plugs are required. Farmers shift and prod to adjust arrangements of materials or relations with a view to addressing problems, such as falling yields, vulnerabilities to climatic variability, or exposure to viruses. A mechanism of implementing innovations such as those associated with smart farming is to reconfigure topological arrangements. As demonstrated by literature on agricultural biosecurity,farming practice needs to be viewed as occurring against the topological backdrop of an “entangled interplay” , with numerous “contingent intra-actions” occurring across multiple risky “borderlands” .Proximities,distance,nft system connectivities,and modulations of presence/absence figure in the effort to create desired outcomes,with new insertions or removals pursued in efforts to control or steer activities in defined or experimental ways.

Whether the risk is a matter of falling yields or exposure to viruses, it pays to acknowledge the ongoing relationship between farming and innovation through a topological lens; that is, to dwell on farmers as active agents of topological transformation, even if they are rarely acting alone. One way to combine an analytical concern with digital life, corporate interests in establishing a certain type of smart farming, and topological transformations is highlighted by literature on smart farming innovation processes. Consider, for example, how new configurations come into the picture. As highlighted in research on smart farming in Canada, and as referenced by Relf-Eckstein et al.,the Canadian government has recently established innovation ‘superclusters’ to examine and exploit technological opportunities. A recent outcome is an industry-led consortia called Protein Industries Canada, which includes a partnership whereby Lucent BioSciences “will use the hulls of pea and lentil seeds which are a co-product from value-added processing completed by AGT Foods and Ingredients [to create] Soileos: a novel carbon-neutral micro-nutrient fertilizer that uses organic fibre as a carrier to provide micro-nutrients to plants” . As this case suggests, the ‘smart’ in smart farming can involve astute and imaginative arrangements to make new products and chase after profits in novel ways. A similar picture emerges in the Netherlands where a “golden triangle” of agricultural research, industry, and government aims to create “new business ecosystems consisting of focal firms, their suppliers, complementor firms, and customers” . A key feature is the leading role of the Dutch firm Philips, which occupies a prominent position in high-tech urban agriculture , a growing smart farming sector, by “providing the essential technologies, registration of patents, and creation of new business opportunities” . Meanwhile, in the larger and more traditional Dutch agricultural sector, the Food Valley Open Innovation Ecosystem includes “the Wageningen Campus and the planned World Food Centre in Ede” and creates ties between research and development centres run by large firms such as Friesland Campina and Unilever and wider networks of small-to-medium enterprises and startups.

There are 15,000 scientists across Food Valley, with twenty research institutes, 1440 food related, and 70 science related firms . Such configurations of firms, farmers and research institutes will likely create new smart farming products and services and build on Dutch successes in exporting around €9 billion worth of high-tech agrifood innovations, including “energy-efficient greenhouses, precision agricultural systems and new discoveries that make crops more resistant” . Put differently, the topologies of smart farming point toward new forms of “path creation” that involve but also often extend beyond farmers. It is instructive that smart farming today is bound up with efforts to use ‘open innovation’ processes that facilitate co-design or co-innovation between agricultural technology providers, farmers, and others. Such an approach can “blur the boundaries between scientists and agricultural system stakeholders, between agronomists and farmers, and between actors in the agricultural sector and those designing in other sectors” . The virtue of “participatory design processes involving farmers” is that it can yield new tools, such as dashboards , to help farmers understand agro-ecological conditions. There are, however, no guarantees that smart farming developments will yield effective configurations. Indeed, there is significant evidence that smart farming developments are hamstrung not only by the instrumental logics underpinning technology providers but also by ineffective coordination and inadequate arrangements of materials or skills. Consider here the push to develop automated body condition scoring and a soil water outlook tool for Australian dairy farmers. Noting that these versions of smart farming innovation involve “a unique innovation challenge [not least because of] the new knowledge demands for farmers in a highly dynamic, technology-driven environment” , one finding is that the new tools and practices confront limitations in the way agricultural relations are configured with respect to the wider institutional milieu. Making the most of the soil water outlook tool, for example, required but did not receive sufficient input from the Australian Bureau of Meteorology “to help farmers to link the SWO with seasonal climate outlooks” . Then, with regards to automated body condition scoring, the new technology led some farmers to think “maybe we don’t need the [farm] advisor as often,”,with the upshot that “some tools were potentially replacing the skills of advisors”.Yet, because “more remote monitoring of key performance indicator data via online software” can enable farm advisors to make fewer farm visits, smart farming in this context conceivably increases the sense of isolation many farmers already experience .

Elsewhere in Australia, smart farming developments call attention to a different dynamic between farmers and advisors. In some rice farming regions, advisors might be expected to be the “sense makers” who can explain and encourage farmers to adopt new technologies; but in fact one consultant respondent claimed “it’s mainly been the farmers dragging the agronomists along” .At the same time, “insufficient support structures” , for example regarding data compatibility or standards, can hold back adoption and frustrate farmers who are “prepared to use evolving and uncertainty-generating technologies” but find that their knowledge is not effectively tapped. In Canada, ‘broadacre’ smart farming developments occur amid the “critical constraint” of labour shortages and demographic change, but even here “adoption is lower than anticipated” , with one explanation focusing on tensions around what happens to data produced on farms. A problem yet to be overcome is industry self-regulation of data usage and a lack of certainty about the legal ramifications of smart farming. Thus, “[u]ntil clarity is brought to the issue of data, the industry is at risk of losing farmer’s trust and potentially hindering innovation opportunities at the farm level” . Although there are examples from the literature which demonstrate that smart farming innovation involves an ongoing process of trying to reconfigure arrangements of sociotechnical relations, I argue a more accurate and urgent conclusion is to emphasize the ‘mis-configured innovations’ of smart farming. One of the main features of smart farming concerns the limited parameters within which innovations operate. In Canada, for example, an element in smart farming arrangements is models and platforms designed for commodity farmers, not those “farmers working outside of the dominant industrial model” . In effect, “the maps created within those big data platforms developed by industry are made meaningful only if one adheres to a rigid conventional farming strategy of seeding in neat rows separated by areas of soil free of weeds” . A similar result emerges in Australia where observers note that farmers want autosteer technologies, new imagery services, levelling and GPS guidance because “if they’ve got efficient layouts, laser levelled, they’ll make significant water savings and they’ll have reduced labour inputs as well” . Smart farming therefore means that food producers contemplate, “standardizing the environment” in accordance with the commercial imperatives of farmers operating large holdings and using expensive machinery to generate predictable topographies that fit with the new topologies required to make smart farming technologies effective. Built-in biases pervade all algorithmic systems ; the biases in smart farming might only pertain to environments in the first place but they can have broader political-economic effects.

As such, the core problem with the various reconfigurations underpinning smart farming developments is not simply that the absence of one or other action or reform can limit their impact,hydroponic gutter but rather that smart farming innovation processes begin and proceed without adequately conceptualizing the underlying obstacles and limitations confronting food producers today. Technological innovations that reinforce power asymmetries regarding data ownership, for example, or that fail to challenge implicit biases toward certain types of environments, render some interests invisible while reifying specific types of logics, such as narrow measures of economic efficiency. Like any innovation, insertion, or reconfiguration, smart farming entails topological transformation; but problems emerge when the “quieter registers” of smart farming make it possible for “powerful actors to make their presence felt at one remove, to reach into the everyday life of distant others” , for example by dispossessing them of valuable data or establishing algorithmic biases toward standardized farm topographies.It can make sense to use devices or services in new arrangements that create efficiencies or give food producers new access to information that can inform decisions. However, because these developments always by necessity involve reconfiguring arrangements of sociotechnical relations, agricultural innovation processes will continue to introduce new misconfigurations when they pursue discrete solutions to specific problems, rather than integrated developments based on incremental adjustments in information-intensive iterative processes that target systemic or structural change. As insisted upon by scholarship on food sovereignty in critical agrarian studies , the urgent challenge today is to conceptualize a planetary land, agrarian, and food system in which food producers and consumers everywhere are confronted by, but examine ways of overcoming, the same problems of neoliberal capitalism dominated by transnational corporations, authoritarian governance, and climate change. In the shadow of the corporate food regime, producing food in the Netherlands or Canada is bound up with the realities of producing food in India or Kenya. Further, the dynamics of digital life mean smart farming innovations in one place will inform and conceivably move the ‘planetary cognitive ecology’ generally, with unpredictable but connected results playing out elsewhere. The products of smart farming will only reinforce problems if they yield new patents for agricultural technology providers in a place such as Ireland , a widening yield gap between capital- and labour-intensive agrarian systems , or if they increase the likelihood of ‘smart’ food production in one region leading to food dumping in another . Per the vision of a Common Food Policy in the European Union , rather than seeing smart farming developments “reinforcing existing production models, leading to trade-offs between different environmental impacts, or between environmental and social sustainability,” the task is to reorient innovation “towards low-input, diversified agroecological systems” . In the light of these challenges, a sustainable and successful smart farming innovation process requires what we might imagine as the coproduction of ambitious ‘topological repertoires’ that make ongoing assessments of absence, presence, proximity, and reach at the scale of a structure or system and then pursue appropriate technological solutions from the ground up.

All tests were performed using StataSE v12 software.Mushrooms are widely consumed foods whose high contents of bioactive compounds may provide antioxidant, anti-inflammatory, anti-obesity, and antidiabetic properties, among others.Thus, different in vitro and in vivo models have been used to identify and characterized the health benefits of these mushrooms for their potential application as food ingredients. Thereby, Grifola frondosa polysaccharides have been found to exert antioxidant and antidiabetic properties in different animal models . The regulatory pathways of energy homeostasis are highly conserved between C. elegans and mammals, making this nematode a powerful model for exploring the genetic bases of fatty acid synthesis and the regulation of fat storage. Thus, C. elegans has been widely used as a screening model for the identification and evaluation of BACs with healthy properties in the prevention of obesity-related disturbances, together with the characterization of the biological mechanism underlying these effects.

Moreover, thisnematode has been widely used for determining the antioxidant, anti-aging, and life-prolonging properties of BACs present in different food ingredients with beneficial properties in the prevention of aging-related diseases . Previous studies have suggested the anti-obesity properties of G. frondosa. For example, Aoki and colleagues demonstrated that supplementation with 0.4% G. frondosa extract in high fat-induced obese mice for 15 weeks significantly reduced body weight gain and visceral fat accumulation, ameliorated hepatic triglyceride storage, and improved glucose tolerance . They suggested that the anti-obesity and antidiabetic properties of this G. frondosa extract were attributed to its activity as a PPARδ agonist. As mentioned above, our GE represents an important source of different BACs, including beta-glucans, phenolic compounds, PUFAs, and MUFAs. Different studies have reported the lipid-reducing activity of different flavonoids and phenolic acids in C. elegans, including ours . For this reason, we aimed to determine if the combination of the bioactive compounds found in our GE could also affect the lipid homeostasis using the C. elegans model. The intestinal and hypodermal cells of this nematode accumulates lipids in the form of fat droplets, which can be detectable under microscopy using fat-soluble dyes, such as Sudan Black B, Oil red O, and Nile Red .

The quantification of the fluorescence of the fixative Nile Red lipophilic dye has been demonstrated to represent a reliable method to determine the fat content of this nematode, and has been widely used for evaluating the lipid-reducing activity of BACs, with potential uses in the prevention of and therapy for obesity-related diseases . For this experiment, L1 N2 wild-type worms were treated until reaching the L4 stage with and without the GE at the doses of 10 and 20 μg/mL, when nematodes were collected, fixed, and stained with Nile Red . As revealed by the quantification of the fluorescence of the worms , both doses of GE induced a significant reduction in the lipid content of C. elegans, in comparison with untreated control worms . Orlistat-treated worms were used as a positive control of fat reduction. In fact, the reduction induced by the high dose of GE was 18.64%, and a similar result was obtained after Oil Red O staining , confirming this effect on worm fat deposition. Although our extract did not exhibit in vitro genotoxicity in the SOS/umu test, these results could be related to an effect on nematode development. Thus, in order to dismiss this negative effect, we analysed the effect of GE extract treatment from L1 to day 1 of adulthood on worm length, size, and egg laying. No differences were observed in terms of worm length and size between GE-treated and untreated nematodes, suggesting that the lipid-reducing activity of GE is not accompanied by an effect on worm length and size. Furthermore, after 72 h of treatment, both GE and control plates exhibited the presence of both eggs and L1 larvae without differences in the time of appearance. All these results suggest that treatment with our GE from L1 to L4 significantly reduces the C. elegans fat content independently of any effect on worm development. Importantly, the effect of GE on fatty acid synthesis and breakdown was also accompanied by a tendency to higher expression of daf-2 , an ortholog of human IGF1R , INSR , and INSRR . This gene codes for the single receptor protein in the IIS pathway. Moreover, treatment with GE induced a pronounced overexpression of daf-16, the ortholog of human FOXO, which codes for a key transcription factor regulated by the IIS pathway. Daf-16 acts as a nutrient-sensing regulator of energy homeostasis and lipid metabolism .

The significant overexpression of GE on daf-16/foxo was also confirmed at a lower dose of the extract and would suggest that the anti-obesity properties previously observed with GE are mediated by the upregulation of this transcription factor. Finally, GE-treated worms exhibited a significant upregulation of skn-1 , an ortholog of the human NRF2 gene, an important transcription factor of the antioxidant and antiaging responses . No differences were observed in the expression of sod-3. Again, significant upregulation of skn-1 was observed after treatment with a lower dose of GE . SKN-1 activation has been previously shown to be involved in fat metabolism by depleting somatic lipids , so overexpression of this transcription factor by GE might also be involved in the fat-reducing activity observed in our Nile Red and Oil Red O analyses. Our findings demonstrate that our GE extract reduced the C. elegans lipid content when treated from L1 to L4; this effect is mediated by a reduction in the fatty acid biosynthesis and increased oxidation, together with a significant overexpression of the skn-1 and daf-16 transcription factors. In order to further investigate the potential implication of the daf-2/daf-16 and the skn-1/nrf-2 signalling pathways in the anti-obesity properties of the GE extract, we analysed the lipid-reducing activity of GE on C. elegans in a glucose-loaded medium.

Glucose has been used to establish a C. elegans obesity model in various studies , and has been demonstrated to affect both lipid accumulation and oxidative stress responses . Again, we observed that treatment with 20 μg/mL of GE induced a significant reduction in the fat content in comparison with untreated control worms in a glucoseloaded medium . A gene expression analysis performed after this assay demonstrated that, when the medium was supplemented with glucose, no differences were observed in skn-1 gene expression , suggesting that the skn-1 activation previously observed might be involved in the potential antioxidant activity of this extract, more than modulating the lipid accumulation in this model.