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Agribusinesses are investing in different forms of AI robots, as there is a lot of hope that these machines will help meet the challenges within the agricultural industry, which is to efficiently produce more food for a growing world population. AI robots are expected to enhance production, while compensating for lack of manpower, reducing production costs, taking over unattractive (risky, heavy, and dirty) jobs and reducing the burden of food production on the environment. In spite of these promises, however, AI robots for agri-food also give rise to ethical questions and concerns, which have been little researched and discussed until now. To fill this gap, we developed a research agenda for future research in this area. To do this, we opened our analysis to focus on ethics AI robots generally to specifically identify which of these issues are most relevant to agro-robots. The question we want to find an answer to is: what are the most relevant ethical questions raised about AI robots for robots developed for the agri-food sector? And which questions are not mentioned in the literature, which are particularly relevant for agro-robots? Our paper will provide an overview over the key issues and areas which deserve further elaboration to come to a more mature ethics of AI agro-robots.

These last years, the development of AI robots for agriculture, livestock farming and food processing industries is rapidly increasing. These robots are expected to help produce and deliver food more efficiently for a growing human population, but they also raise societal and ethical questions. As the type of questions raised by these AI robots in society have been rarely empirically explored, we engaged in four case studies focussing on four types of AI robots for agri-food ‘in the making’: manure collectors, weeding robots, harvesting robots and food processing robots which select and package fruits, vegetables and meats. Based on qualitative interviews with 33 experts engaged in the development or implementation of these four types of robots, this article provides a broad and varied exploration of the values that play a role in their evaluation and the ethical questions that they raise. Compared to the recently published literature reviews mapping the ethical questions related to AI robots in agri-food, we conclude that stakeholders in our case studies primarily adopt a relational perspective to the value of AI robots and to finding a solution to the ethical questions. Building on our findings we suggest it is best to seek a distribution of tasks between human beings and robots in agri-food, which helps to realize the most acceptable, good or just collaboration between them in food production or processing that contributes to realizing societal goals and help to respond to the 21 century challenges.

Digital twins provide a new paradigm for the integrated use of sensor data, process-based and data-driven modelling, and user interaction, to explore the behaviour of individual objects and processes. Digital twins originate from an engineering context and were developed for machines and mainly physical and chemical processes. In this paper, we further develop an understanding of digital twins for the green life sciences, which also include biological and social processes. We report on three use cases, in precision farming, greenhouse control and personalized dietary advice, focusing on practical benefits and challenges of digital twins compared with other research methods. This research extends earlier more conceptual research on digital twins in this domain. We find benefits in increased accuracy and impact because of the real-time data connection of digital twins to their real-life counterparts. Specification, availability and accuracy of relevant data sources are still major challenges. Specifically, when using digital twins for personalized advice, further research is needed on nontechnical aspects so that users will comply with the advice from the digital twins. We have outlined four directions of future research and expect that further research will include data-driven modelling to simulate the complex character of living objects and processes and at the same time develop approaches to limit the amount of required input data.

Anticipating the ethical impact of emerging technologies is an essential part of responsible innovation. One such emergent technology is the digital twin which we define here as a living replica of a physical system (human or non-human). A digital twin combines various emerging technologies such as AI, Internet of Things, big data and robotics, each component bringing its own socio-ethical issues to the resulting artefacts. The question thus arises which of these socio-ethical themes surface in the process and how they are perceived by stakeholders in the field. In this report we present the results of a qualitative study into the socio-ethical benefits and socio-ethical risks of using digital twins in healthcare. Employing insights from ethics of technology and the Quadruple Helix theory of innovation, we conducted desk research of white literature and 23 interviews with representatives from the four helixes: industry, research, policy and civil society. The ethical scan revealed several important areas where the digital twin can produce socio-ethical value (e.g., prevention and treatment of disease, cost reduction, patient autonomy and freedom, equal treatment) but also several important areas of socio-ethical risks (e.g., privacy and property of data, disruption of existing societal structures, inequality and injustice). We conclude with a reflection on the employed analytical tool and suggestions for further research.

Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.

Sensors, drones, weather satellites and robots are examples of technologies that make farming ‘smart’. In this article we present the results of our review of the literature that concerns the ethical challenges that smart farming raises. Our reading suggests that current ethical discussion about smart farming circles around three themes: (1) data ownership and access, (2) distribution of power and (3) impacts on human life and society. Discussions that fall under these themes have however not yet reached a satisfying conclusion, as there seem to be different ideas at work in the background regarding the purpose and function of digital farms in society. The pros and cons of these rivalling ideas are rarely foregrounded in the discussion. We suggest that future research should focus first on the content of these goals, especially on the content of societal and commercial goals and whether and how they can be combined in differing contexts. This will offer a lead to think about what data ought to be shared with whom, to set preconditions for trust between stakeholders and –eventually- develop appropriate guidelines and codes of conduct for future farming digitalization trajectories.

This study consists of a desk study on business models related to Ag data platforms and stakeholder consultation through interviews and stakeholder workshops (information on the interviews can be found in the Section References). An Ag data platform is interpreted as an IT-based interorganisational arrangement dealing with the collection, storage, exchange and use of Ag data. The scope of the desk study is limited to Ag data platforms with publicly available information on their key features. 8 | Wageningen Economic Research Report 2017-014 The objective of this study is to examine main types of business models that are used for developing Ag data platforms in agrifood chains and identify viable options that can be used for future development of the compliance platform envisaged by FarmDigital. More specifically, the research intends to provide answers to the following questions: What types of business models are being used for Ag data platforms? What features characterise the current landscape of Ag data platforms? What are the analogs and antilogs of the compliance platform envisaged by FarmDigital? What are the challenges and enabling options for future development of a compliance platform?

Campylobacter bacteria are an important cause of foodborne infections. We estimated the potential costs and benefits of a large number of possible interventions to decrease human exposure to Campylobacter by consumption of chicken meat, which accounts for 20-40% of all cases of human campylobacteriosis in the Netherlands. For this purpose, a farm-to-fork risk assessment model was combined with economic analysis and epidemiological data. Reduction of contamination at broiler farms could be efficient in theory. However, it is unclear which hygienic measures need to be taken and the costs can be very high. The experimental treatment of colonized broiler flocks with bacteriophages has proven to be effective and could also be cost efficient, if confirmed in practice. Since a major decrease of infections at the broiler farm is not expected in the short term, additional measures in the processing plant were also considered. At this moment, guaranteed Campylobacter-free chicken meat at the retail level is not realistic. The most promising interventions in the processing plant are limiting fecal leakage during processing and separation of contaminated and noncontaminated flocks (scheduling), followed by decontamination of the contaminated flock. New (faster and more sensitive) test methods to detect Campylobacter colonization in broilers flocks are a prerequisite for successful scheduling scenarios. Other methods to decrease the contamination of meat of colonized flocks such as freezing and heat treatment are more expensive and/or less effective than chemical decontamination.