An emerging discourse about automated agricultural machinery imagines farms as places where farmers and workers do not need to be, but also implicitly frames farms as intolerable places where people do not want to be. Only autonomous machines, this story goes, can relieve farmers and workers of this presumed burden by letting them ‘farm at a distance’. In return for this distanced autonomy, farmers are promised increased control over their work-life balance and greater farm productivity from letting ‘smart’ robots assume (...) control over the operational environment. Drawing upon the ways that these machines are promoted by manufacturers in various media, we trace the nascent contours of what we term a liberatory sociotechnical imaginary for agricultural automation across three cases—automated milking systems, self-driving tractors, and robotic strawberry pickers. We show how promises for new freedoms and autonomy are flexibly deployed to respond to distinct frictions that farmers, workers, and even farm animals experience in different modes of industrial agriculture. However, underlying these promises is the purposefully understated self-interest of manufacturers, who stand to gain further control over farms if automated technologies assume a central role in agriculture. Through the liberatory rhetoric, we contend, the imaginary seeks to enroll farmers into a socio-technical network that creates new relations of dependence upon the companies who design, sell, maintain, and often retain ownership over automated technologies. While potentially powerful, this imaginary may nonetheless fail to coalesce as farmers, workers, and agroecosystems exert their own agency on automated imaginaries and technological futures for agriculture. (shrink)

Smart farming has largely been driven by productivity and efficiency aims, but there is an increasing awareness of potential socio-ethical challenges. The responsible research and innovation approach aims to address such challenges but has had limited application in smart farming contexts. Using smart dairying research and development in New Zealand as a case study, we examine the extent to which principles of RRI have been applied in NZ smart dairying development and assess the broader lessons for RRI application in smart (...) farming. We draw on insights from: a review of research on dairy technology use in NZ; interviews with smart dairying stakeholders; and the application of an analytical framework based on RRI dimensions. We conclude that smart dairying R&D and innovation activities have focused on technology development and on-farm use without considering socio-ethical implications and have excluded certain actors such as citizens and consumers. This indicates that readiness to enact RRI in this context is not yet optimal, and future RRI efforts require leadership by government or dairy sector organisations to fully embed RRI principles in the guidelines for large R&D project design. More broadly, enacting RRI in smart farming requires initial identification of RRI readiness in a given sector or country and devising a roadmap and coherent project portfolio to support capacity building for enacting RRI. Additionally, methods for operationalising RRI must be adapted to the context of the national or sectoral innovation system in which smart farming is being developed. (shrink)

Research addressing the challenges emerging from the development and diffusion of digital technologies has grown rapidly in recent years. However, much of this literature tends to overlook the immersion of these technologies into our everyday lives. This everyday digitalization cannot be reduced to specific technological innovations and is obviously a crucial aspect of the social changes introduced by digital technologies. This themed issue sets out to explore the everyday dimension of digitalization, in the specific context of agri-food systems. We propose (...) a collection of papers that contribute to opening new approaches to digitalization in food and agriculture, by concentrating the attention on the mundane and ordinary aspects of digitalization and exploring their potential contributions to profound transformations in agri-food systems. The various forms of everyday digitalization that are presented in this symposium offer two particular ways of critically engaging with the digitalization of agriculture and food. Firstly, our everyday digitalization looks beyond examining the impacts of digitalization and positioning digital technologies in opposition to farmers, consumers, government, or other actors. Secondly, we shed light on the politics of uncertainty, which enables us to make anticipatory actions and see things beyond success and failure. What we observe is how these imaginaries participate in the co-production of digital realities through their encounter with the complexity of everyday life. Therefore, it remains essential to engage critically with both the imaginary and the everyday dimensions of digitalization in their multiplicity. (shrink)

We describe how the set of tools, practices, and social relations known as “precision agriculture” is defined, promoted, and debated. To do so, we perform a critical discourse analysis of popular and trade press websites. Promoters of precision agriculture champion how big data analytics, automated equipment, and decision-support software will optimize yields in the face of narrow margins and public concern about farming’s environmental impacts. At its core, however, the idea of farmers leveraging digital infrastructure in their operations is not (...) new, as agronomic research in this vein has existed for over 30 years. Contemporary discourse in precision ag tends to favour emerging digital technologies themselves over their embeddedness in longstanding precision management approaches. Following several strands of science and technology studies research, we explore what rhetorical emphasis on technical innovation achieves, and argue that this discourse of novelty is a reinvention of precision agriculture in the context of the growing “smart” agricultural economy. We overview six tensions that remain unresolved in this promotional rhetoric, concerning the definitions, history, goals, adoption, uses, and impacts of precision agriculture. We then synthesize these in a discussion of the extent to which digital tools are believed to displace farmer decision-making and whether digital agriculture addresses the biophysical heterogeneity of farm landscapes or land itself has become an “experimental technology”—a way to advance the general development of artificial intelligence. This discussion ultimately helps us name a larger dilemma: that the smart agricultural economy is perhaps less about supporting land and its stewards than promising future tech and profits. (shrink)

A common and driving assumption in agricultural research is that the introduction of research trials, new practices and innovative technologies will result in technology adoption, and will subsequently generate benefits for farmers and other stakeholders. In Lao PDR, the potential benefits of introduced technologies have not been fully realised by beneficiaries. We report on an analysis of a survey of 735 smallholder farmers in Southern Lao PDR who were questioned about factors that influenced their decisions to adopt new technologies. In (...) this study, we have constructed measures or states of adoption which identify key elements of an adoption decision-making nexus. Analysis was conducted to statistically group explanatory factors of adoption. The key explanatory factors represented attributes of the farmer, the factors considered when undertaking production decisions and elements of the agricultural value chain that present as opportunities or constraints. We describe the combination of farmer’s personal attributes, perceptions of the value chain, and the introduction of new technologies by external actors as an “agricultural research value chain”, where agricultural research activities intervene to derive greater benefits for local farmers. A generalised linear model, via Poisson regression analysis on the identified explanatory factors, was applied to explore how they influence adoption measures and we found several significant relationships. (shrink)