How are data science systems made to work? It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. These negotiations laid the (...) foundation for how, why, and to what extent the system ultimately worked. We describe three main findings. First, how already-existing technologies are essential reference points to determine how and whether systems work. Second, how the situated resolution of development challenges continually reshapes the understanding of how and whether systems work. Third, how business goals, and especially their negotiated balance with data science imperatives, affect a system’s working. We conclude with takeaways for critical data studies, orienting researchers to focus on the organizational and cultural aspects of data science, the third-party platforms underlying data science systems, and ways to engage with practitioners’ imagination of how systems can and should work. (shrink)

Prevalent narratives of agricultural innovation predict that we are once again on the cusp of a global agricultural revolution. According to these narratives, this so-called fourth agricultural revolution, or agriculture 4.0, is set to transform current agricultural practices around the world at a quick pace, making use of new sophisticated precision technologies. Often used as a rhetorical device, this narrative has a material effect on the trajectories of an inherently political and normative agricultural transition; with funding, other policy instruments, and (...) research attention focusing on the design and development of new precision technologies. A growing critical social science literature interrogates the promises of revolution. Engagement with new technology is likely to be uneven, with benefits potentially favouring the already powerful and the costs falling hardest on the least powerful. If grand narratives of change remain unchallenged, we risk pursuing innovation trajectories that are exclusionary, failing to achieve responsible innovation. This study utilises a range of methodologies to explore everyday encounters between farmers and technology, with the aim of inspiring further work to compile the microhistories that can help to challenge robust grand narratives of change. We explore how farmers are engaging with technology in practice and show how these interactions problematise a simple, linear notion of innovation adoption and use. In doing so, we reflect upon the contribution that the study of everyday encounters can make in setting more inclusionary, responsible pathways towards sustainable agriculture. (shrink)

Artificial intelligence and robotics have increasingly been adopted in agri-food systems—from milking robots to self-driving tractors. New projects extend these technologies in an effort to automate skilled work that has previously been considered dependent on human expertise due to its complexity. In this paper, we draw on qualitative research carried out with farm managers on apple orchards and winegrape vineyards in Aotearoa New Zealand. We investigate how agricultural managers’ perceptions of future agricultural automation relates to their approach to expertise, or (...) the degree to which they think specialised skills and knowledge are required to perform agricultural work on their orchards and vineyards. Our research generates two insights: the perceived potential for work to be automated is related to the degree to which it is seen to require technical or embodied expertise, with technical expertise being more automatable; and, while embodied expertise is perceived to be more difficult to automate, it is sometimes attributed more exclusively to those in positions of power, such that embodied expertise can be highly valued while the majority of embodied work is viewed as non-expert and thus automatable. Our analysis illustrates that a robot can be an expert when expertise is technical. It also shows variability in the conceptualization of skilled or unskilled work, and that those conceptualizations can set the stage for the future effects of new technologies. This generates new insights into the conditions under which automation might reproduce existing inequalities in agriculture, and also raises new questions about responsibility in the context of automation. (shrink)

Data is essential to governing those emerging matters of concern that confront the agrifood every day. But data is no neutral intermediary. It disrupts, exposes, and creates new social, economic, political, and environmental possibilities, whilst simultaneously hiding, excluding, and foreclosing others. Scholars have become attuned to both the constitutive role of data in creating everyday worlds, and the need to develop critical accounts of the materialities, spatialities and multiplicities of data relationships. Whereas this emerging work develops insight to the capacity (...) for data topologies to reterritorialise the spatial performances of everyday life, it has largely reduced the associated temporal dimensions to matters of fact. The effect of these performances has been to naturalize the temporal quality of speed and elide the multiple temporalities required to enact contemporary data worlds. Applying the lenses of infrastructuring, performativity and ferality, this paper explores temporality and data in the everyday worlds produced through the New Zealand kiwifruit industry’s focus on dry matter. The paper argues that temporalities are deeply embedded in the kiwifruit industry’s data relations. We show that while temporal data relations are critical to the industry, we also highlight ways in which those relations introduce new, potentially destabilizing performances into kiwifruit relations. (shrink)

This article examines the construction of image recognition algorithms for the classification of plant pathology problems. Rooted in science and technology studies research on the effects of agricultural big data and agricultural algorithms, the study ethnographically examines how algorithms for the recognition of plant pathology are made. To do this, the article looks at the case of a German agtech startup developing image recognition algorithms for an app that aims to help small-scale farmers diagnose plant damages based on digital images (...) of their symptoms. The study posits that the construction of these algorithms can be grasped as a succession of layers, at each of which the startup’s employees carry out different selection practices. It is argued that these practices gradually inscribe a selective recognition of the phenomenon of plant pathology into the algorithms of the app. This selective recognition is reflected in the fact that the emerging algorithms are effective in identifying isolated plant damages on isolated crops, while ignoring most agroecosystem-related actors and relations through which plant damages arise. The article concludes that the app’s selective recognition of plant pathology is likely to perpetuate or even exacerbate the pesticide use of its users. This is because the app’s view of plant pathology as a phenomenon comprised of isolated plant damages on isolated crops is more compatible with the use of chemical pesticides than with approaches to crop protection that strive for a more moderate use of these substances (e.g., integrated pest management, agroecology, organic agriculture). (shrink)

Over recent decades, influential agri-food tech actors, institutions, policymakers and others have fostered dominant techno-optimistic, future visions of food and agriculture that are having profound material impacts in present agri-food worlds. Analyzing such realities has become paramount for scholars working across the fields of science and technology studies (STS) and critical agri-food studies, many of whom contribute to STSFAN—the Science and Technology Studies Food and Agriculture Network. This article introduces a Special Issue featuring the scholarship of STSFAN members, which cover (...) a range of case studies and interdisciplinary and transdisciplinary engagements involving such contested agri-food futures. Their contributions are unique in that they emerged from the network’s specific modus operandi: a workshopping practice that supports the constructive, interdisciplinary dialogue necessary for critical research and rigorous analyses of science and technology in agri-food settings. This introduction offers an overview of STS and critical agri-food studies scholarship, including their historical entanglements in respective studies of food scandals, scientific regimes and technological determinism. We illustrate how interdisciplinary engagement across these fields has contributed to the emergent field of what we term agri-food technoscience scholarship, which the contributions of this Special Issue speak to. After a brief discussion of STS concepts, theories and methods shaping agri-food policy, technology design and manufacturing, we present the eleven Special Issue contributions in three thematic clusters: influential actors and their agri-food imaginaries; obfuscated (material) realities in agri-food technologies; and conflictual and constructive engagements in academia and agri-food. The introduction ends with a short reflection on future research trajectories in agri-food technoscience scholarship. (shrink)

Despite increasing attention to the sensors, drones, robots, and apps permeating agri-food systems, little attention has been paid to social media, perhaps the most ubiquitous digital technology in rural areas globally. This article draws on analysis of farming groups on Myanmar Facebook to posit social media as appropriated agritech: a generic technology incorporated into existing circuits of economic and social exchange that becomes a site of agrarian innovation. Through analysis of an original archive of popular posts collected from Myanmar-language Facebook (...) pages and groups related to agriculture, I explore the ways that farmers, traders, agronomists and agricultural companies use social media to further agrarian commerce and knowledge. These activities evidence that farmers use Facebook not only to exchange market or planting information, but also to interact in ways structured by existing social, political and economic relations. More broadly, my analysis builds on insights from STS and postcolonial computing to disrupt assumptions about the totalizing power of digital technologies and affirm the relevance of social media to agriculture, while inviting new research into the surprising, ambiguous relationships between small farmers and big tech. (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)

Robots are widely expected—and pushed—to transform open-field agriculture, but these visions remain wedded to optimizing monocultural farming systems. Meanwhile there is little pull for automation from ecology-based, diversified farming realms. Noting this gap, we here explore the potential for robots to foster an agroecological approach to crop production. The research was situated in The Netherlands within the case of _pixel cropping_, a nascent farming method in which multiple food and service crops are planted together in diverse assemblages employing agroecological practices (...) such as intercropping and biological pest control. Around this case we engaged with a variety of specialists in discussion groups, workshops, and design challenges to explore the potential of field robots to meet the multifaceted demands of highly diverse agroecological cropping systems. This generated a spectrum of imaginations for how automated tools might—or might not—be appropriately used, ranging from fully automated visions, to collaborative scenarios, to fully analogue prototypes. We found that automating agroecological cropping systems requires finding ways to imbue the ethos of agroecology into designed tools, thereby seeking to overcome tensions between production aims and other forms of social and ecological care. We conclude that a rethinking of automation is necessary for agroecological contexts: not as a blueprint for replacing humans, but making room for analogue and hybrid forms of agricultural work. These findings highlight a need for design processes which include a diversity of actors, involve iterative design cycles, and incorporate feedback between designers, practitioners, tools, and cropping systems. (shrink)

The current research examines the emergent literature of Critical Data Studies, and particularly aligns with Michael and Lupton’s (2016) manifesto calling for researchers to study the Public Understanding of Big Data. The aim of this paper is to explore Irish stakeholders’ narratives on data sharing in agriculture, and the ways in which their attitudes towards different data sharing governance models reflect their understandings of data, the impact that data hold in their lives and in the farming sector, as well as (...) their preferences for how data should be governed within agriculture. Seven focus groups were held in 2019 with Irish stakeholders from a variety of backgrounds, including agri-researchers, those working in SMEs, and farmers of varying ages and sectors. The primary activities carried out during these focus groups centred upon asking participants to discuss four different data sharing governance models, and to work their way through a set of value cards relating to these models. Focus group results are studied using an inductive, data-driven form of thematic analysis (Braun and Clarke 2006). Five primary themes cross-cut these focus groups: 1) Desire for a data intermediary, 2) Reversing the value chain, 3) Categorisation of data, 4) The common good, and 5) Potential danger in data sharing. These themes are explored in the paper through a detailed discussion of the focus group results, in which the authors track the manifestation of these themes across focus groups, and the ways they sometimes morphed or changed depending upon the participating stakeholder group. (shrink)

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)