Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming

The Fourth Agricultural Revolution

Some would argue that the “fourth agricultural revolution” (Lejon and Frankelius, 2015) or “agriculture 4.0” (Bartmer in Frankelius et al., 2017) has already begun. Each previous agricultural revolution was radical at the time—the first representing a transition from hunting and gathering to settled agriculture, the second relating to the British agricultural revolution in the 18th century, and the third relating to post-war productivity increases associated with mechanization and the Green Revolution in the developing world. While technological innovation is thus not new to agriculture, emergent technologies, such as the Internet of Things, Cloud Computing, robotics, and Artificial Intelligence (AI), have the potential to change farming beyond recognition (Wolfert et al., 2017), hence the shift toward agriculture 4.0 (see section Technological Re-scripting of Society).

There is certainly not a shortage of uses for these technologies; smart farming approaches are being used to increase the precision of fertilizer, pesticide, and herbicide application (Carolan, 2016). Smart farming approaches, including the use of Microsoft's Cortana Intelligence Suite, are currently being used to determine optimal planting dates for crops around the world, such as in India and Colombia (López and Corrales, 2018). Unmanned aerial vehicles, or drones, are being used to aid weed identification (Lottes et al., 2017), and robots are helping farmers to milk their cattle (Driessen and Heutinck, 2015) and remove weeds (Fennimore, 2017).

Signals from policy and the private sector suggest that there will be growing momentum behind agriculture 4.0. The UK Secretary of State for Business has recently announced a £90 million investment to generate a “technical revolution” to transform food production, which would put the UK at the “forefront” of forward-thinking sustainable agriculture (Department for Business Energy Industrial Strategy et al., 2018). Greece has announced the digitisation of agriculture, with big data and the internet of things, placed at the forefront of farming (Euractiv.com, 2018). Much of this investment would be made in developing smart technologies, the use of which has been projected to grow exponentially in the next 10 years.¹ Similar agri-tech revolutions have been predicted globally, such as in Japan (Japan Times., 2017), other parts of Asia (“Second Green Revolution,” The Economist, 2014), Ireland (Irish News, 2017), and Australia (Financial Review, 2016).

Prominent concepts within sustainable agriculture, particularly “sustainable intensification” (SI), are also embracing emergent smart technologies. Since appearing as a concept in the 1990s, SI has been defined in various ways (Gunton et al., 2016), and it remains contested. Smith et al. (2017) trace the origins of SI to the work of Pretty (1997) whose work sought to establish collaborative projects between researchers and farmers for the benefit of productivity, profitability, the environment, and society. These pillars of sustainability are still commonly included in definitions of SI; for example, Gunton et al. (2016) consider it to be a process by which productivity is increased without damaging the environment, and where possible, also generating social, and environment benefits. It is also associated with eco-efficiency (Gadanakis et al., 2015), as a trade-off between economic and ecological performance, and may be considered as a goal, a process, or a set of principles (see Firbank et al., 2018). Regardless of its definition, the idea of sustainable intensification is becoming ever more popular in research and policy. Reviews of how the idea is evolving are finding that it is embracing technological approaches to achieving its goals (Mahon et al., 2017; Smith et al., 2017), while attempts to define a series of standard SI practices also favor high-tech, smart approaches (Dicks et al., 2018).

In light of increased policy attention on funding smart agri-tech, and prominent research and policy concepts such as sustainable intensification which embrace smart approaches, we consider how the fourth agricultural revolution can be socially responsible. This is an especially pertinent question given that new innovations have the potential to change the nature of farming in a radical way relative to past technology revolutions (Nordmann, 2014). As outlined in the following section, some of the smart technologies may have undesirable impacts on-farm, as well as to wider society. They are thus likely to be resisted in a similar way as technologies like genetic modification have been (Macnaghten, 2015). In this perspective, we encourage policy-makers, technology companies, funders, and researchers to develop, test, and refine, responsible innovation frameworks to guide the development of smart farming technologies.

The work of Eastwood et al. (2017) on responsible innovation in smart dairying, and associated suggestions by Bronson (2018), provide a useful starting point for us to think about how to develop a plan. Yet, both of these contributions focus on translating existing concepts into an agricultural context without seeking to develop them further. In addition, there is little evidence that responsible innovation frameworks make a difference in practice. Here, we discuss how we can build a more comprehensive framework for responsible innovation, which is appropriate to the wider contexts of sustainable agriculture and learns from its application in practice.

Technological Re-scripting of Society

We do not dispute that smart farming technologies can play an important role in sustainable agricultural production, although the need for a technical revolution would be reduced if there were societal changes elsewhere.² The potential for improved productivity is likely to provide social benefits (e.g., greater food/income security), and environmental benefits, as less land is put into production. Precision agriculture, in combination with more productive crop varieties/livestock and the use of decision support systems to foster evidence-based decision-making (see Rose et al., 2016; Rose and Bruce, 2017), can lead to the smarter use of inputs with greater rewards. Furthermore, robotic technology could provide benefits to farming communities in compensation for lost labor, which is becoming a serious problem in the developing world³ as the population migrates to urban centers (Struik and Kuyper, 2017). Technology-based SI could enhance social sustainability by supporting the profitability of farming businesses, and by providing different high-tech jobs.

Despite the potential benefits of a new technology revolution, the dominant techno-centric narratives associated with smart farming should be treated with caution (Whitfield et al., 2018). Technology is a double-edged sword because it has the potential to cause harm, as well as provide benefit (Stilgoe et al., 2013). In agriculture, we have witnessed several controversies over the use of chemicals, including DDT (Carson, 1962) and the ongoing issue of neonicotinoids (Dicks, 2013), as well as intense debates over genetic modification (Macnaghten, 2015). We have seen, for example, how previous technology revolutions have caused mass rural unemployment (Goodman et al., 1987). The potential side-effects of smart technology like AI are being seriously considered now in policy (e.g., House of Lords., 2018).

Studies have shown that technology shapes agricultural society at several scales; on-farm, and across farming and non-farming communities (Eastwood et al., 2017). Rose et al. (2018a) argued that the requirement to use decision support tools would change, or “re-script,” the ways in which farmers interacted with their land (see also Higgins et al., 2017 on how technology “orders” agricultural society). Other studies have looked at the impacts of robotic milking technologies on-farm (Holloway et al., 2014; Bear and Holloway, 2015; Driessen and Heutinck, 2015; Holloway and Bear, 2017). Driessen and Heutinck (2015) argued that robotic technologies could change what it is to be a “good” farmer. The introduction of technologies to aid livestock management will mean that farming becomes less “hands-on” and this may change the nature of stockmanship and the relationship between animals and farmers (Driessen and Heutinck, 2015; Holloway and Bear, 2017). Bear and Holloway (2015) argue that such technologies could create new rural subjectivities, in other words what it is to be a farmer or advisor.

Negative impacts on-farm also influence imaginaries at wider scales. Rose et al. (2018a) found evidence that the requirement to use emergent technologies are mismatched with the expectations of farmers about what farming is. Wolf and Wood (1997) argued that a focus on precision farming legitimates a narrative of chemical-based agriculture, and Wolfert et al. (2017) suggested that the emphasis on big data could further move decision-making power from the farmers into the hands of private companies who have control over such data (see also Carbonell, 2016). In terms of wider impacts on society, the use of emergent technologies may not chime with societal expectations of sustainable food production. Using GM and other emerging technologies as analogs, it is likely that similar controversies will occur during this technology revolution if the views of publics are not adequately addressed (Macnaghten and Chilvers, 2014). It is true that some smart technologies, such as precision agriculture, have so far been embraced with little societal “backlash,” yet it is argued that large-scale use of AI, robotics, and other emergent innovations have the clear potential to cause unintended, unforeseen, and unwanted societal consequences. Indeed, Hartley et al. (2016) use the same precedent of the GM controversy to argue for the responsible governance of agricultural biotechnology.

We have been here before. Past controversies have reinforced the notion that science and technology are socially and politically constituted, and that emergent technologies can be met with societal resistance (Stilgoe et al., 2013; Asveld et al., 2015). Since there is often an institutional void for new technologies (Hajer, 2003), it can be difficult to know how to govern implementation. Yet, past experiences highlight the peril of ignoring risks, the danger of becoming “seduced” by innovation (Nordmann, 2014), and the fallacy of not seeking the views of publics in an effort to construct a shared vision of the future.

Broadening Responsible Innovation for the Fourth Agricultural Revolution

The concept of responsible innovation should underpin the fourth agricultural revolution; simply, ensuring that innovations designed to improve productivity and/or eco-efficiency also provide social benefits, meet human needs and are socially responsible. Noting that the concept is not well-developed in agriculture, Eastwood et al. (2017) sought to develop a framework for responsible innovation in the context of smart dairy farming, while suggesting its possible applicability to other agricultural contexts (also see Bronson, 2018 who interprets the framework of Eastwood et al. in a Canadian context). Eastwood et al. (2017) build on the framework by Stilgoe et al. (2013) by suggesting mechanisms to guide technology development organized around four dimensions—anticipation, inclusion, reflexivity, and responsiveness.

To summarize the useful suggestions by Eastwood et al. (2017), it is argued that the impacts of technological innovation need to be anticipated through various techniques, such as foresight exercises and scenario building. In the context of the fourth agri-tech revolution, responsible innovation would mean anticipating impacts at all scales: on-farm, across farming landscapes, throughout the food chain, as well as considering effects on rural communities and publics as a whole. Sometimes these impacts may be difficult to anticipate, but Nordmann (2014) argues that the precautionary principle should guide actions. Eastwood et al. (2017) also argue that the responsible innovation process should seek to include all affected actors, such as technology companies, farmers, and local communities. Since sustainable food production has impacts across society, inclusion should be considered holistically. Indeed, we argue that inclusion should underpin all aspects of responsible innovation frameworks since anticipation, reflexivity, and responsiveness, cannot be undertaken without the inclusion of diverse voices.

In addition, Eastwood et al. (2017) also suggested that agri-tech innovation projects should adopt structures to guide reflexivity, in other words there should be many opportunities to assess whether mutually beneficial trajectories are being followed. If not, then it is argued that innovation projects should be able to respond to challenges and be adaptable. Agri-tech innovators should also be able to respond quickly to problems caused by a new technology.

While these signs of progress are to be encouraged, and the recent interventions of Eastwood et al. (2017) and Bronson (2018) have made good progress in directly translating principles from the existing responsible innovation literature to smart farming, we argue that this agenda needs to go further. There are signs that responsible innovation is being taken more seriously in policy and funding circles.⁴ But, the specificities of the smart farming revolution create challenges for responsible innovation frameworks that have largely been developed in other fields, and have not been tested in practice in an agricultural context. For the purposes of the current discussion we highlight three main areas where we feel further work is needed.

Responsible Innovation Systems

First, is the need to develop a more comprehensive and systemic approach to responsible innovation for the fourth agricultural revolution. The proposals by Eastwood et al. (2017) and Bronson (2018) mainly focus on possible tools and actions to enhance responsible innovation at the level of “R&D projects” and discrete technological innovations in the areas of smart dairying and smart agriculture. This partly reflects the frame of established responsible innovation frameworks which have been, for good reason, drawn to focus attention on controversial emerging technologies where one might expect to see significant negative consequences and questions of responsibility. We suggest, however, that in addition the frame of responsible innovation needs to be broadened. The fourth agricultural revolution is associated with many innovations in sustainable agriculture, some emerging and some more established, which are interacting and co-evolving in a wider “ecology of innovation”; or, as Klerkx et al. (2012) argue, throughout “agricultural innovation systems” in which many different actors (e.g., farmers, advisors) are influential. Such an ecology of innovation includes “big” emerging smart technologies (e.g., AI, Internet of Things, Cloud Computing, robotics), as well as “smaller” farmer and/or community-led innovations (Waters-Bayer et al., 2015; e.g., Tambo and Wünscher, 2017) through to more mundane or low-tech sustainable agriculture solutions (e.g., Kerr, 2015). In the rush to embrace smart agri-tech, we are in danger of forgetting the wider network of other innovations that play an important role, but may also affect societies in different ways.

We should ensure that the concept of responsible innovation is not attached solely to “big” emergent smart technologies. This would help to deliberately foreground questions over possible alternatives and the directionality of innovation pathways (Leach et al., 2010) in addressing problems of sustainable agriculture, which have been shown time and time again to be associated with deeply held public concerns about emerging science and technology (Macnaghten and Chilvers, 2014). In addition, a more systemic perspective would enable responsible innovators to consider interrelations between multiple co-existing innovations in sustainable agriculture. This could promote the cultivation of distributed responsibilities across wider innovation ecologies, as opposed to thinking about particular smart technologies in isolation. The more systemic thinking we advocate here demands methods capable of mapping multiple innovations—and tracking their emergence and interactions—across fields of innovation in sustainable agriculture. It also facilitates (and in turn demands) more strategic levels of governance and coordination between public, private, and civil society actors involved in steering innovations in the agri-tech revolution toward more socially responsible and humane ends.

Broadening Notions of ‘Inclusion'

Second, there is a need to broaden notions of “inclusion” in responsible innovation to attend to the diverse ways that societal actors are already engaging with smart farming innovations on their own terms. This is a particular blind spot in frameworks for responsible innovation and an important omission in the proposals for “inclusion” put forward so far in relation to smart farming. Agricultural research is still dominated by top-down, non-inclusive approaches, and rarely includes relevant stakeholders, such as farmers, at an early stage (Macmillian, 2018). The power to shape research trajectories, including innovation design, rests with research institutions, and project narratives are rarely opened up in consultation with stakeholders (Paul, 2018; Pimbert, 2018; Rose et al., 2018b). Responsible innovation encourages us to think of what technologies are for, who they are serving, and who is driving the process (Crossley, 2018). Inclusion has sometimes been regarded as problematic by innovators, who see public involvement as potentially increasing time to market and releasing sensitive information publicly (Blok et al., 2018; Purwins and Schulze-Ehlers, 2018). But, research has also shown that open innovation and responsible innovation can be complementary (Long and Blok, 2018).

In order to include all relevant actors and address the “gap in comprehensive inclusion” in smart dairy farming, Eastwood et al. (2017) propose a range of tools and procedures including citizen forums, “workshops,” “surveys,” internet forums, interest group representation on steering committees and “user-centered design.” Bronson (2018) echoes these procedures for including “rights holders” and advocates deliberative processes to open up democratic debate over the social and ethical implications of smart agriculture technologies. Such moves are important and necessary to open up inclusive debate and that might socially shape these emerging technologies. However, as Chilvers and Kearnes (2016) have argued, notions of participation and inclusion in responsible innovation need to go further. These existing proposals for smart agriculture fit in with what they identify as a dominant imagination of inclusion in science and innovation—i.e., a drive to invite all relevant actors into inclusive fora and collectives to socially shape specific projects or innovations that are the target for responsible innovation. Chilvers and Kearnes (2016) suggest that while this is important, there is a need to also open up to wider “ecologies of participation” and already existing forms of societal engagement around these very same projects and innovations, which are legitimate sources of public concern, values, and actions that would otherwise be excluded from processes of responsible innovation. Such moves resist new forms of inclusion in responsible innovation succumbing to an “public acceptance” model of innovation, where the onus is on innovators to change public opinion to accept technologies, rather than making technological trajectories more responsive to the needs of society (see Gupta et al., 2012).

This calls for a new range of tools and methods in responsible innovation of smart farming and beyond, which are capable of mapping across diverse forms of societal engagement with emerging technologies and across innovation systems. This can draw on a number of promising mapping methods that are emerging in the social sciences and humanities, including digital methods (Rogers, 2013), issue mapping (Marres, 2015), systematic mapping (Chilvers et al., 2018) and forms of comparative case study and meta-analyses (Macnaghten and Chilvers, 2014). Such methods can produce new forms of social intelligence about diverse forms of societal and farmer engagement with smart farming, for example: more formal spaces like farmer networking events, farmer clusters, demonstration farms, demonstration test catchments, and consultations, through to a multitude of informal spaces where farmers and publics are interacting with sustainable agriculture including interactions with vets, advisors, seed merchants, and livestock markets, discussions on social media, more low-tech forms of agricultural practice, community-based agri-environment solutions, and so on. This forms a key part of a more comprehensive responsible innovation framework for the fourth agricultural revolution capable of mapping existing spaces of participation, finding out what innovations and ideas are being considered there, what alternative public concerns are being mobilized, and so on. This is not only a question of inclusion, but can also enhance reflexivity, anticipation, and responsiveness in responsible innovation frameworks. The field of sustainable agriculture can learn from other domains where such mapping approaches have been developed, and new institutional architectures of observatories have recently been proposed, for the responsible development of low carbon energy transitions (Chilvers et al., 2017) and gene editing (Burall, 2018; Jasanoff and Hurlbut, 2018).

Responsible Innovation in Practice

Third, research needs to assess whether responsible innovation frameworks make a difference in practice. This is a point that Macnaghten (2016, 282) makes in the context of genetic modification. He argues that responsible innovation frameworks must “demonstrate their capacities to shape existing technological trajectories.” We find little, if any, evidence that responsible innovation frameworks have been tested in practice in the context of smart farming, before then being refined on the basis of feedback. If we do not know whether and how they affect the innovation process—in other words forcing innovators to anticipate problems caused by their product, to respond to emergent problems, to include diverse publics in an attempt to accommodate concerns, and lastly to adopt a reflexive and flexible approach to development—then they serve little practical purpose. We argue, therefore, that the development of a refined responsible innovation framework for smart farming in the context of sustainable agriculture could be undertaken concurrently with practical trials. For example, prototypes of the framework could be introduced into innovation teams to see whether and how design practices are influenced, before then being refined based on observation and feedback from the innovators themselves. This iterative approach, which could also involve an assessment of how innovators themselves come to “know” diverse publics, would make a valuable contribution both to the broad field of responsible innovation as well as to the application of its principles to the fourth agricultural revolution.

Concluding Remarks

In the context of the fourth global agri-tech revolution, we should ask what the direction of travel is, and whether we want to go there as a society. This question is topical in the UK with current large investments in smart agri-tech, but also in many other countries across the world where smart agri-tech is also being prioritized. In light of controversial agri-tech precedents, it is beyond doubt that smart farming is going to cause similar controversy. Smart farming can of course provide enormous benefits to sustainable agriculture, increasing the efficiency and productivity of food creation, as well as potentially providing environmental and social benefits. This fourth agri-tech revolution also, however, brings potential environmental, ethical, and social costs. While the frameworks of responsible innovation proposed by Eastwood et al. (2017) and Bronson (2018) certainly need to be applied to the agri-tech revolution, we argue that they do not go far enough. Rather, further work is needed to map the wider ecology of innovations associated with the agri-tech revolution, including within ideas such as sustainable intensification, as well as the formal and informal spaces of participation where farmers, advisors, publics, and other key practitioners are sharing their views, hopes and concerns. Ultimately, a framework for the responsible innovation of smart technologies needs to prove capable of socially shaping innovation trajectories. This requires us to test responsible innovation frameworks in practice to determine whether they are capable of changing the trajectories of innovation design. Responsible innovation frameworks should allow questions to be led, and opened out, by those people who are (likely to be) affected by the use of new technologies; including farmers, advisors, vulnerable communities and wider publics. They should be able to question and contest whether benefits to productivity should supersede social, ethical, or environmental concerns, and be able to convince innovators and policy-makers to change the directions of innovations for sustainable agriculture.

Author Contributions

DR led the writing of this manuscript and JC contributed several sections of the final text.

Conflict of Interest Statement

DR is a review editor of the Sustainable Intensification and Ecosystem Services section of Frontiers in Sustainable Food Systems. He was not involved at all in the review process for this manuscript. The handling Editor declared a past co-authorship with one of the authors, DR.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

DR acknowledges the School of Environmental Sciences at the University of East Anglia for providing funding support to attend the conferences which shaped this perspective. Both authors also thank Prysor Williams for his useful comments on a draft of the manuscript, as well as the editor and two reviewers.

Footnotes

1. ^AI is projected to be globally worth $2.6bn in 2025 up from $518.7 million in 2017 (Farminguk, 2017).

2. ^E.g. reducing over-consumption, Satterthwaite (2009); especially of environmental damaging foods, Woolston (2014); less food waste; equitable food distribution in the developing world, Sen (1999).

3. ^And in the developed world too e.g., the potential loss of migrant agricultural labor in the UK after Brexit.

4. ^(E.g., underpinning research in Horizon 2020 projects https://ec.europa.eu/programmes/horizon2020/en/h2020-section/responsible-research-innovation and decision-making within the Engineering and Physical Sciences Research Council in the UK https://epsrc.ukri.org/index.cfm/research/framework/).

References

Asveld, L., Ganzevles, J., and Osseweijer, P. (2015). Trustworthiness and responsible research and innovation: the case of the bio-economy. J. Agric. Environ. Ethics 28, 571–588. doi: 10.1007/s10806-015-9542-2

CrossRef Full Text | Google Scholar

Bear, C., and Holloway, L. (2015). Country life: agricultural technologies and the emergence of new rural subjectivities. Geogr. Compass 9, 303–315. doi: 10.1111/gec3.12217

CrossRef Full Text | Google Scholar

Blok, V., Scholten, V., and Long, T. B. (2018). Responsible innovation in industry and the importance of customer orientation: introduction to the special issue. IFAMA 21, 455–461. doi: 10.22434/IFAMR2018.x001

CrossRef Full Text | Google Scholar

Bronson, K. (2018). Smart farming: including rights holders for responsible agricultural innovation. Tech. Innov. Manage. Rev. 8, 7–14. doi: 10.22215/timreview/1135

CrossRef Full Text | Google Scholar

Burall, S. (2018). Rethink public engagement for gene editing. Nature 555, 438–439. doi: 10.1038/d41586-018-03269-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Carbonell, I. (2016). The ethics of big data in big agriculture. Int. Policy Rev. 5, 1–13. doi: 10.14763/2016.1.405

CrossRef Full Text | Google Scholar

Carolan, M. (2016). Publicising food: big data, precision agriculture, and co-experimental techniques of addition. Soc. Ruralis 57, 135–154. doi: 10.1111/soru.12120

CrossRef Full Text | Google Scholar

Carson, R. (1962). Silent Spring. Boston, MA: Houghton Mifflin Company.

Google Scholar

Chilvers, J., and Kearnes, M. (2016). Remaking Participation: Science, Environment and Emergent Publics. Abingdon: Routledge.

Google Scholar

Chilvers, J., Pallett, H., and Hargreaves, T. (2017). Public Engagement With Energy: Broadening Evidence, Policy and Practice. London: Energy Research Centre.

Chilvers, J., Pallett, H., and Hargreaves, T. (2018). Ecologies of participation in socio-technical change: the case of energy system transitions. Energy Res. Soc. Sci. 42, 199–210. doi: 10.1016/j.erss.2018.03.020

CrossRef Full Text | Google Scholar

Crossley, D. (2018). Big Questions and Radical Change. Available online at: https://www.foodethicscouncil.org/uploads/For%20whom%20-%20questioning%20the%20food%20and%20farming%20research%20agenda_FINAL_1.pdf

Department for Business Energy Industrial Strategy Department for Environment Food Rural Affairs, Rt Hon Greg Clark MP, Business Secretary (2018). Calls for New Tech Revolution in Agriculture. Available online at https://www.gov.uk/government/news/business-secretary-calls-for-new-tech-revolution-in-agriculture (Accessed February 28. 2018).

Dicks, L. (2013). Bees, lies and evidence-based policy. Nature 494:283. doi: 10.1038/494283a

PubMed Abstract | CrossRef Full Text | Google Scholar

Dicks, L. V., Rose, D. C., Ang, F., Birch, N., Boatman, N., Bowles, E., et al. (2018). What agricultural practices are most likely to deliver “sustainable intensification” in the UK? Food Ener. Secur. doi: 10.1002/fes3.148. [Epub ahead of print].

CrossRef Full Text

Driessen, C., and Heutinck, L. F. M. (2015). Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms. Agric. Hum. Values 32, 3–20. doi: 10.1007/s10460-014-9515-5

CrossRef Full Text | Google Scholar

Eastwood, C., Klerkx, L., Ayre, M., and Dela Rue, B. (2017). Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. J. Agric. Environ. Ethics 1–28. doi: 10.1007/s10806-017-9704-5

CrossRef Full Text | Google Scholar

Euractiv.com (2018). Green Plan to Digitise Agriculture Wins Eu Approval. Available online at: https://www.euractiv.com/section/agriculture-food/news/greek-plan-to-digitise-agriculture-wins-eu-approval/.

Farminguk (2017). Ai in Agriculture Expected to Grow Exponentially by 2025, Report States. Available online at: https://www.farminguk.com/News/AI-in-agriculture-expected-to-grow-exponentially-by-2025-report-states_48161.html

Fennimore, S. A. (2017). “Automated Weed Control: New Technology to Solve an Old Problem in Vegetable Crops,” Conference presentation at ASA Section: Agronomic Production Systems.

Financial Review (2016). Why Agtech is Australia's Next $100 Billion Industry. Available online at: http://www.afr.com/technology/why-agtech-is-australias-next-100-billion-industry-20160907-grb1uz

Firbank, L. G., Attwood, S., Eory, V., Gadanakis, Y., Lynch, J. M., Sonnino, R., et al. (2018). Grand challenges in sustainable intensification and ecosystem services. Front. Sustain. Food Syst. 2:7. doi: 10.3389/fsufs.2018.00007

CrossRef Full Text | Google Scholar

Frankelius, P., Norrman, C., and Johansen, K. (2017). Agricultural innovation and the role of institutions: lessons from the game of drones. J. Agric. Environ. Ethics 1–27. doi: 10.1007/s10806-017-9703-6

CrossRef Full Text | Google Scholar

Gadanakis, Y., Bennett, R., Park, J., and Areal, F. J. (2015). Evaluating the sustainable intensification of arable farms. J. Environ. Manage. 150, 288–298. doi: 10.1016/j.jenvman.2014.10.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Goodman, D., Sorj, B., and Wilkinson, J. (1987). From Farming to Biotechnology of Agro-Industrial Development. Oxford: Wiley-Blackwell.

Google Scholar

Gunton, R. M., Firbank, L. G., Inman, A., and Winter, D. M (2016). How scalable is sustainable intensification? Nat. Plants 2:16065. doi: 10.1038/nplants.2016.65

PubMed Abstract | CrossRef Full Text | Google Scholar

Gupta, N., Fischer, A. R., and Frewer, L. J. (2012). Socio-psychological determinants of public acceptance of technologies: a review. Publ. Understand. Sci. 21, 782–795. doi: 10.1177/0963662510392485

PubMed Abstract | CrossRef Full Text | Google Scholar

Hajer, M. (2003). Policy without polity? Policy analysis and the institutional void. Policy Sci. 36, 175–195. doi: 10.1023/A:1024834510939

CrossRef Full Text | Google Scholar

Hartley, S., Gillund, F., van Hove, L., and Wickson, F. (2016). Essential features of responsible governance of agricultural biotechnology. PLoS Biol. 14:e1002453. doi: 10.1371/journal.pbio.1002453

PubMed Abstract | CrossRef Full Text | Google Scholar

Higgins, V., Bryant, M., Howell, A., and Battersby, J. (2017). Ordering adoption: materiality, knowledge and farmer engagement with precision agriculture technologies. J. Rural Stud. 55, 193–202. doi: 10.1016/j.jrurstud.2017.08.011

CrossRef Full Text | Google Scholar

Holloway, L., and Bear, C. (2017). Bovine and human becomings in histories of dairy technology: robotic milking systems and remaking animal and human subjectivity. BJHS Themes 2, 215–234. doi: 10.1017/bjt.2017.2

CrossRef Full Text | Google Scholar

Holloway, L., Bear, C., and Wilkinson, K. (2014). Robotic milking technologies and renegotiating situated ethical relationships on UK dairy farms. Agric Hum Values 31, 185–199. doi: 10.1007/s10460-013-9473-3

CrossRef Full Text | Google Scholar

House of Lords. (2018). AI in the UK: ready, willing and able?, Select Committee on Artificial Intelligence. Report of Session 2017–19. House of Lords.

Irish News (2017). Agri-Tech Revolution Transforming Traditional Farms. Available online at: http://www.irishnews.com/business/2017/03/28/news/agri-tech-revolution-transforming-traditional-farms-974766/

Japan Times. (2017). Japan's Farming Industry Poised for Automation Revolution. Available online at: https://www.japantimes.co.jp/news/2017/08/04/business/tech/japans-farming-industry-poised-automation-revolution/#.WpaHKhPFKb8

Jasanoff, S., and Hurlbut, J. (2018). A global observatory for gene editing. Nature 555, 435–437. doi: 10.1038/d41586-018-03270-w

PubMed Abstract | CrossRef Full Text | Google Scholar

Kerr, R. B. (2015). How Low-Tech Farming Innovations Can Make African Farmers Climate-Resilient, The Conversation. Available online at: https://theconversation.com/how-low-tech-farming-innovations-can-make-african-farmers-climate-resilient-47684.

Klerkx, L., van Bommel, S., Bos, B., Holster, H., Zwartkruis, J. V., and Aarts, N. (2012). Design process outputs as boundary objects in agricultural innovation projects: functions and limitations. Agric. Syst. 113, 39–49. doi: 10.1016/j.agsy.2012.07.006

CrossRef Full Text | Google Scholar

Leach, M., Scoones, I., and Stirling, A. (2010). Dynamic Sustainabilities: Technology, Environment, Social Justice. London: Earthscan.

Google Scholar

Lejon, E., and Frankelius, P. (2015). Sweden innovation power—Agritechnica 2015, Elmia, Jönköping, Sweden.

Long, T. B., and Blok, V. (2018). Integrating the management of social-ethical factors into industry innovation: towards a concept of Open Innovation 2.0. IFAMA 21:463–486. doi: 10.22434/IFAMR2017.0040

CrossRef Full Text | Google Scholar

López, I. D., and Corrales, J. C. (2018). A smart farming approach in automatic detection of favorable conditions for planting and crop production in the upper basin of Cauca River. Adv. Intell. Syst. Comput. 687, 223–233. doi: 10.1007/978-3-319-70187-5_17

CrossRef Full Text

Lottes, P., Khanna, R., Pfeifer, J., Siewart, R., and Stachniss, C. (2017). “UAV-based crop and weed classification for smart farming,” Proceedings – IEE International Conference on Robotics and Automation. 3024–3031.

Macmillian, T. (2018). Learning From Farmer-Led Research. Available online at: https://www.foodethicscouncil.org/uploads/For%20whom%20-%20questioning%20the%20food%20and%20farming%20research%20agenda_FINAL_1.pdf

Macnaghten, P. (2015). “A Responsible Innovation Governance Framework for GM Crops,” in Governing Agricultural Sustainability,” eds P. Macnaghten, and S. W. Carro-Ripalda (London: Earthscan from Routledge), 225–239. doi: 10.4324/9781315709468-19

CrossRef Full Text | Google Scholar

Macnaghten, P. (2016). Responsible innovation and the reshaping of existing technological trajectories: the hard case of genetically modified crops. J. Respons. Innov. 3, 282–289. doi: 10.1080/23299460.2016.1255700

CrossRef Full Text | Google Scholar

Macnaghten, P., and Chilvers, J. (2014). The future of science governance: publics, policies, practices. Environ. Plann. C 32, 530–548. doi: 10.1068/c1245j

CrossRef Full Text | Google Scholar

Mahon, N., Crute, I., Simmons, E., and Islam, M. M (2017). Sustainable intensification–“oxymoron” or “third-way”? A systematic review. Ecol. Indicat. 74, 73–97. doi: 10.1016/j.ecolind.2016.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Marres, N. (2015). Why map issues? On Controversy Analysis as a Digital Method. Sci.Tech. Hum. Values 40, 655–686. doi: 10.1177/0162243915574602

PubMed Abstract | CrossRef Full Text | Google Scholar

Nordmann, A. (2014). Responsible innovation, the art and craft of anticipation. J. Respon. Innov. 1, 87–98. doi: 10.1080/23299460.2014.882064

CrossRef Full Text | Google Scholar

Paul, H. (2018). UK Agricultural Research: A Different Approach Is Urgently Needed. Available online at: https://www.foodethicscouncil.org/uploads/For%20whom%20-%20questioning%20the%20food%20and%20farming%20research%20agenda_FINAL_1.pdf

Pimbert, M. (2018). Democratising Food and Agricultural Research. Available online at: https://www.foodethicscouncil.org/uploads/For%20whom%20-%20questioning%20the%20food%20and%20farming%20research%20agenda_FINAL_1.pdf

Pretty, J. N. (1997). The sustainable intensification of agriculture. Nat. Resour. Forum. 21, 247–256. doi: 10.1111/j.1477-8947.1997.tb00699.x

CrossRef Full Text | Google Scholar

Purwins, N., and Schulze-Ehlers, B. (2018). Improving market success of animal welfare programs through key stakeholder involvement: heading towards responsible innovation? IFAMA 21, 543–558. doi: 10.22434/IFAMR2017.0047

CrossRef Full Text | Google Scholar

Rogers, R. (2013). Digital Methods. Cambridge MA: MIT Press.

Rose, D. C., and Bruce, T. J. A. (2017). Finding the right connection – what makes a successful decision support system?. Food Energy Secur. 7:e00123. doi: 10.1002/fes3.123

PubMed Abstract | CrossRef Full Text | Google Scholar

Rose, D. C., Morris, C., Lobley, M., Winter, M., Sutherland, W. J., and Dicks, L. V. (2018a). Exploring the spatialities of technological and user re-scripting: the case of decision support tools in UK agriculture. Geoforum 89, 11–18. doi: 10.1016/j.geoforum.2017.12.006

CrossRef Full Text | Google Scholar

Rose, D. C., Parker, C., Fodey, J., Park, C., Sutherland, W. J., and Dicks, L. V. (2018b). Involving stakeholders in agricultural decision support systems: improving user-centred design. Int. J. Agric. Manage. 6, 80–89. doi: 10.5836/ijam/2017-06-80

CrossRef Full Text | Google Scholar

Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., et al. (2016). Decision support tools for agriculture: towards effective design and delivery. Agric. Syst. 149, 165–174. doi: 10.1016/j.agsy.2016.09.009

CrossRef Full Text | Google Scholar

Satterthwaite, D. (2009). The implications of population growth and urbanization for climate change. Environ. Urban. 21, 545–567. doi: 10.1177/0956247809344361

CrossRef Full Text | Google Scholar

Sen, A. (1999). Development as Freedom. Oxford; New York, NY: Oxford University Press.

Google Scholar

Smith, A., Snapp, S., Chikowo, R., Thorne, P., Bekunda, M., and Glover, J. (2017). Measuring sustainable intensification in smallholder agroecosystems: a review. Glob. Food Secur. 12, 127–138. doi: 10.1016/j.gfs.2016.11.002

CrossRef Full Text | Google Scholar

Stilgoe, J., Owen, R., and Macnaghten, P. (2013). Developing a framework for responsible innovation. Res. Policy 42, 1568–1580. doi: 10.1016/j.respol.2013.05.008

CrossRef Full Text | Google Scholar

Struik, P. C., and Kuyper, T. W. (2017). Sustainable intensification in agriculture: the richer shade of green. A Review. Agron. Sustain. Dev. 37:39. doi: 10.1007/s13593-017-0445-7

CrossRef Full Text | Google Scholar

Tambo, J. A., and Wünscher, T. (2017). Farmer-led innovations and rural household welfare: evidence from Ghana. J. Rural Stud. 55, 263–274. doi: 10.1016/j.jrurstud.2017.08.018

CrossRef Full Text | Google Scholar

The Economist (2014). A Second Green Revolution. Available online at https://www.economist.com/news/leaders/21601850-technological-breakthroughs-rice-will-boost-harvests-and-cut-poverty-they-deserve-support

PubMed Abstract

Waters-Bayer, A., Kristjanson, P., Wettasinha, C., van Veldhuizen, L., Quiroga, G., Swaans, K., et al. (2015). Exploring the impact of farmer-led research supported by civil society organisations. Agric. Food Secur. 4:4. doi: 10.1186/s40066-015-0023-7

CrossRef Full Text | Google Scholar

Whitfield, S., Challinor, A. J., and Reeds, R. M. (2018). Frontiers in climate smart food systems: outlining the research space. Front. Sustain. Food Syst. 2:2. doi: 10.3389/fsufs.2018.00002

CrossRef Full Text | Google Scholar

Wolf, S. A., and Wood, S. D. (1997). Precision farming: environmental legitimation, commodification of information, and industrial coordination. Rural Soc. 62, 180–206. doi: 10.1111/j.1549-0831.1997.tb00650.x

CrossRef Full Text | Google Scholar

Wolfert, S., Ge, L., Verdouw, C., and Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricu. Syst. 153, 69–80. doi: 10.1016/j.agsy.2017.01.023

CrossRef Full Text | Google Scholar

Woolston, C. (2014). Beef's big impact on Earth. Nature 511:511. doi: 10.1038/511511e

CrossRef Full Text | Google Scholar