Publicising Food: Big Data, Precision Agriculture, and ...

Publicising Food: Big Data, Precision Agriculture, and Co-Experimental Techniques of Addition Michael Carolan

Abstract This article draws upon data taken from the following: 18 interviews of Iowa farmers who utilise big data when making farm management decisions; 14 interviews of those engaged within big data industry, those involved in the sale and promotion of largescale data acquisition, predictive analytic software, and/or precision agriculture technologies for conventional agriculture applications; and 19 interviews of regional food system entrepreneurs, those looking to create and encourage the adoption of technological platforms that enhance the capacities of regional food systems. A central aim of this article is to help reshape the debate around agro food-based technologies, from one that asks what technology is to one that looks at what these socio-technical forms engender. As described, alternative foodscapes are not looking for alternatives to technology but rather to technologies that engender specific effects. The empirical findings are organised around three themes that emerged out of the qualitative interviews. The technological assemblages investigated all exhibited the following three engendering qualities, which are (1) anticipatory, (2) moralising, and (3) a movement that multiplies absent presences. Precisely how these qualities were expressed, however, varied greatly across foodscapes.

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griculture is often romanticised for its embodiment of agrarian ideals (Berry 2015) while simultaneously situated at the forefront of reimagining itself under the guise of technological utopianism, first with the industrial revolution (where labour was substituted for capital), followed by the green revolution, biotechnology revolution, and more recently the big data revolution. Perhaps that is why critics of the status quo have a tendency to emphasise the ‘softer’ technologies embodied within alternative foodscapes, as evidenced in arguments supporting civic agriculture-type forms (e.g., Lyson 2004; Obach and Tobin 2014) where, for example, social capital, community participation, indigenous and tacit knowledge, biocultural diversity, and trust are valorised, with the aim of moving agriculture away from those monolithic ‘revolutionary’ tropes. This analytic approach is not meant to cast proponents of alternative foodscapes as being anti-utopians in a technological sense (Stock

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Sociologia Ruralis, Vol 00, Number 00, Month 2016

DOI: 10.1111/soru.12120

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et al. 2015), though their attention to socially appropriate technology could be mistaken for a wholesale rejection of technology per se, which would explain why they are occasionally cast as ‘culinary luddites’ (e.g., Laudan 2015). Agrofood scholarship could also do a better job unpacking the ‘hardened’ techniques encountered in its studies by experimenting with alternative analytic frameworks, seeking especially those equipped to speak to the various technological forms that all foodscapes employ. The point made ought to be that alternative foodscapes are not looking for alternatives to technology but rather to technologies that engender specific effects. This article offers precisely such an intervention, and suggests further what those specific effects might be and thus what technologies we would do well to avoid. To do this the argument draws upon multi-sited qualitative research involving the following: 18 interviews of Iowa farmers who utilise big data when making farm management decisions; 14 interviews of those engaged within big data industry (from around the USA), those involved specifically in the sale and promotion of large-scale data acquisition, predictive analytic software, and/or precision agriculture technologies for conventional agriculture applications; and 19 interviews of regional food system entrepreneurs (from around the USA), namely, those looking to create and encourage the adoption of technological platforms that enhance the capacities of regional food systems. A number of technological forms are thus investigated: e.g., big data (big soil data, big climate data, etc.), precision agriculture, and a variety of internet-based platforms utilised by self-described activists and proponents of more local and regional based foodscapes. One of the main aims of this article is to help reshape the debate around how we think and talk about agro food-based technologies, from one that asks what technology is to one that looks at what these socio-technical forms engender. The spirit of this project thus falls in line with the relational approach described previously in this journal by Carolan (2013); an approach embodied by assemblage thinking, vibrant materialism, geographies of care, enactive politics, and other styles of scholarship that look to describe how materialities, practices, and discourses matter in terms of their effects and affectivities. While representative of numerous recent ‘turns’ in agro food scholarship, a relational approach is grounded within a much longer tradition in the critical social sciences; a point recently echoed by Gibson-Graham (2014) while pointing to the style of theorising practiced by Clifford Geertz. Decades ago Geertz (1973, p. 23) issued the following warning, about how ‘small facts speak to large issues, winks to epistemology, or sheep raids to revolution, because they are made to’. When we assume totalising narratives we ‘find’ evidence of that totality – neoliberalism, discipline/bio-power, surveillance, etc. – everywhere. In short, we tend to find what we are looking for. We also then tend to ignore those encounters that do not fit with a pre-assumed pattern. Conversely, ‘weak theory’, as Gibson-Graham (2014, p. S149) write, ‘does not elaborate and confirm what we already know, it observes, interprets, and yields to emerging knowledge’. The processes of unpacking what agro food-based technologies do (a performative-as-process claim), versus what they are (a fixed ontological claim), embodies the spirit of this weak theorising: e.g., it does not, a priori, cast technological forms as inherently ‘bad’ but rather moves the making of judgements to what those forms engender; it does not attempt to draw lines around a technology in terms of where ‘it’ starts and ends; C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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and it does not presume that the effects engendered of any agro food-based technological form are monolithically felt across space and time, thus leaving our scholarship open to the element of surprise and novelty. When approached this way, the forms studied are found to engender the following three effects. In no particular order, those effects are (1) anticipatory, (2) moralising; and (3) a movement that multiplies absent presences. Precisely how these qualities were expressed, however, varied greatly across foodscapes. Overview: big data, precision agriculture, and internet platforms Talk about ‘big data’; it is hard to escape. Its arrival has been celebrated in venerable news outlets the world over, from Forbes, where its status as kicking off a ‘revolution’ was recently affirmed (Marr 2015), to Wired, where readers were told big data mark the ‘end of theory’ (Anderson 2008), and The Guardian, where predictive analytics are described as being ‘crucial’ to eradicating poverty (Shearman 2015). Big data are also frequently referred to as the next ‘big thing’ in many agricultural circles, in no small part due to the expanding amount of information collected relating to farm level crop production (big soil data) combined with extensive weather data (big climate data), which together form the backbone of precision agriculture technology. According to United States Department of Agriculture’s (USDA) Agricultural Resource Management Survey (ARMS), in 1997, only 17 per cent of corn acres were cultivated using precision agriculture equipment. In 2010, the most recent year available for corn production practices, 72 per cent of corn acres were planted with this technology (USDA 2015). The precision farming market reached e2.3 billion in 2014 on a global level, with an estimated annual growth rate of 12 per cent through 2020 (Michalopoulos 2015). In a recent report, the Joint Research Center of the European Commission wrote that ‘precision agriculture can play a substantial role in the European Union in meeting the increasing demand for food, feed, and raw materials while ensuring sustainable use of natural resources and the environment’ (ZarcoTejada et al. 2014, p. 9). As evidence of its growing popularity among EU producers take the case of the Netherlands, where precision techniques are now used to managing 65 per cent of the country’s arable farmland, compared to just 15 per cent in 2007 (Michalopoulos 2015). While having become a cause cele`bre, big data remains ill defined. This makes sense; after all, the modifier ‘big’ is context dependent. What was ‘big’, data-wise, 100 years ago is ‘small’ by today’s standards. And no doubt what is ‘big’ today will pale in comparison to what will be considered ‘big’ a century from now. Just look at the data assembled by President’s Obama’s team for his 2008 and 2012 elections, which would have been inconceivable two decades ago: hundreds of randomised, large-scale polling experiments; cookie data tracking visitors to their websites; voter registration, census and other government data; commercial data; credit rating data; and data from cable television companies and social media sites. The result: ‘a set of interrelated, massive databases about every voter in the country consisting of a minimum of 80 variables, and often many more, relating to a potential voter’s demographic characteristics and location, their voting history, their social and economic C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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history, their patterns of behavior and consumption and expressed views and opinions’ (Kitchin 2013, p. 263). Big data is finding widespread commercial applications due to elevating correlations over theory, such as, famously, Target’s ‘pregnancy prediction’ score, which is based on the purchase of vitamin supplements and unscented lotions, or FICO’s Medical Adherence Score (based on patients’ employment, homeownership and living situations), which predicts the likelihood patients will fill a prescription and take all their pills on schedule. At the same time, and not unexpectedly, concerns are being expressed over the ‘dataifcation’ of everything (Cukier and MayerSchoenberger 2013), from issues dealing with, for instance, surveillance (Lyon 2014), epistemology (Kitchin 2014a), and governance (Chun 2015). Curiously, while talk of big data in the context of agriculture dates back to the early 1990s agro food scholars have yet to critically unpack the subject. (Using Google Ngram – coincidently, a form of big data – we find the term ‘precision agriculture’ beginning to be used in the English language with some frequency starting in 1990 and its use increased exponentially in the years to follow). For example, save for a few pieces published in the 1990s (e.g., Wolf and Buttel 1996; Wolf and Wood 1997), which examine precision farming through a critical political economy lens, the social science scholarship on the subject has been largely survey-based (e.g., Silva et al. 2011). This relative silence among critical agro food scholars is made even more pronounced when one considers how much research colleagues in the information and crop sciences do on the subject, whom all evaluate practices through a distinctly productivist lens. A survey of soybean farmers utilising precision agriculture, for instance, recorded an immediate 15 per cent savings on seed, fertiliser, and chemicals (Johnson 2012). Elsewhere, the adoption of these techniques increased farm yield by 16 per cent and reduced water use by 50 per cent (Khosla 2013). Similar conclusions where reached by researchers examining precision agriculture (PA) technologies in the Brazilian sugarcane industry: ‘The main conclusions of this research suggest that companies that adopt and use PA practices reap benefits, such as managerial improvements, higher yields, lower costs, minimization of environmental impacts and improvements in sugarcane quality’ (Silva et al. 2011, p. 67). Some words now about how these techniques – big data and precision agriculture – are actually put to work in the context of agriculture. John Deere has recently released several products that can connect its equipment with each other as well as to dealers and consultants – interconnectivity, John Deere claims, that enhances productivity and increases efficiency (Hoffman 2012). The company has incorporated sensors to all their latest farm equipment. To make the data even ‘bigger’, this real time soil level information is combined with historical data and weather predictions, among other things. The information is presented in the MyJohnDeere.com platform. Farmers can also observe the output on their iPads and iPhones, using the Mobile Farm Manage app – what is also called ‘on-the-go big data’. This information then instructs producers about which crops to plant (and even which variety), where and when to plant, and, later, when and where to cultivate. Monsanto is an even bigger player in all of this, as evidenced by their acquisition of numerous farm data analytic companies since 2012. The company paid, for C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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instance, US$930 million in 2012 to acquire a single firm: Climate Corporation (Kanaracus 2013). Climate Corporation produces a popular software platform. An even more data heavy programme than the above mentioned product offered by John Deere is Climate Pro, which costs farmers US$15 per acre. The company promises producers that this platform will increase their profits by an average of US$100 per acre (Gilpin 2014). This programme gives users access to algorithms that show historical trends of soil moisture and crop level weather patterns going back 30 years. The product allows farmers to plug in different seeds and receive as output, before planting season has even commenced, what their likely yields will be that fall. Monsanto has stated that its Climate Pro sensors on harvesting equipment generate roughly seven gigabytes of data per acre – do the maths, recognising there are close to 100 million acres of corn in the USA and 80 million acres of soybeans (Bobkoff 2015). Monsanto’s rapid investment into the big data industry might surprise some, initially. But remember, the company has long been interested in large datasets and complex analyses for commercial applications. After all, biotechnology is also big data. Locating the genes for favourable, and profitable, traits in plants in order to create new seed varieties means sifting through the billions of base pairs in a genome. Even before the company’s acquisition of companies like Climate Corporation it had assembled arguably the world’s most extensive agricultural databases, built on thousands of field tests of countless seed varieties under every imaginable experimental field condition. Enter Climate Corporation: an opportunity to integrate this new company’s climate database with Monsanto’s unequalled crop data. As for what precision agriculture looks like on the ground for producers: one output familiar to all who have adopted this technology is the ‘yield map’. There is a common saying among precision agriculture proponents: ‘If you’re farming based on your farm’s average, you’re going to continue to produce average yields’. Without precision agriculture, the logic goes, farm management decisions based on ‘management zones’ are not possible, and so, for example, you risk under-applying inputs on highly productive ground and over-applying on land plagued by highly compacted soils with poor tilth and lacking microorganism activity. (‘Why waste money on land that yields 130 bushels an acre when you could be investing it in those zones that consistently crank out 250 bushels on a per acre basis’, to quote one of my proprecision farming respondents). At the moment, the resolution of these systems allows producers to map ‘zones’ that are somewhere between 12 and 30 square-feet in size (Pearce 2015). At this level, producers can tailor their input applications at a per unit scale that is about the size of their bedrooms, recognising that this scale is only going to shrink in the years ahead. Big Ag. is not the only place you will find 1 s and 0 s, technocratic discourses, and satellite and information technologies used to produce food. Far from being ‘culinary luddites’ (e.g., Laudan 2015), food entrepreneurs looking to build more civic foodscapes are turning increasingly to internet-based and social media platforms to enact novel alternatives to the status quo. A pilot project is underway in Colorado, for example, where a web-based platform is being developed to connect aspirational food entrepreneurs (cooks and the like) with idle kitchen space. Inspired by Uber – the now global transportation phenomenon – the aim of this project is to grow regional C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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food system capacity by building upon existing (underutilised) resources. Historically, the transaction costs of making these connections were too great. With the Internet, smart phone, and global positioning satellite technology, however, those with kitchen space and those in need of it can now be connected with just a few taps of one’s iPhone. Or take AgSquared: a software package specifically designed to help diversified farms plan, manage, and monitor everything that happens from a farm management standpoint. As for when it comes round to the time to market products, smaller, diversified farms can turn to platforms like Local Orbit, which are designed to co-ordinate and support the flow of local food from many sellers to many buyers. At this point, it would be difficult to provide further background without touching on subjects discussed in the context of the empirical findings. To avoid such redundancies, I turn now to an overview of methods, followed by a discussion of the empirical results of those qualitative interviews. Methods This article draws upon data collected from in-depth person interviews, involving 18 Iowa farmers, 14 individuals from big data industry (those involved in the sale and promotion of large-scale data acquisition, predictive analytic software, and/or precision agriculture technologies), and 19 interviews of regional food system entrepreneurs (those looking to create, and encourage the adoption of, technological platforms that enhance the capacities of regional food systems). Interviews occurred from May 2014 through to July 2015. Respondents were obtained through a random snowball sampling technique, where individuals known to the researcher from these three populations were approached for an interview and then asked to supply the names of additional possible participants. All of the farmers interviewed managed at least 1,200 acres, which reflects a combination of both land owned and leased. When farm households were first contacted the researcher asked to ‘speak to an individual of the household that would be comfortable talking about their operation’s use of big data, cloud technology, and precision technology’. The purpose of this question was to allow households to self-select for who ultimately gave the interview, as those contacted were all headed by married, heterosexual couples. This query in each instance directed me to the male figure of the household, which is to say that all 18 of the ‘farmers’ interviewed were men (while interesting in itself this point will not be addressed further in this article). The primary crop commodities raised on these farms were corn and soybeans. Questions centred on what technologies they used, when they adopted them (and why), what they thought of them, and what their perceived benefits (and potential risks) were. Growers were also engaged in broader conversations about what the future of agriculture looks like, in terms of what will be raised, who will be doing it, and the management practices involved. Interviews lasted between 70 and 120 minutes and were tape recorded and later transcribed. All respondents were promised anonymity and pseudonyms are used below with that promise in mind. Among the 14 participants from the big data industry: five were employed by implement/equipment firms; four employed by firms that produce and sell predictive C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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analytic software; three worked as technology/big data consultants for various firms; one worked for a US federal agency; and one was employed by a large university with a specialty in precision agriculture who also did consulting for the industry. The protocol utilised for this population closely mirrored that described above concerning farmers. Questions centred on asking what technologies they study/produce/sell/ promote, for some backstory to explain how they arrived at where they are today concerning their views on these technologies and techniques, and the perceived benefits (and potential risks) associated with them. Time was also taken to engage these individuals in broader conversations about what the future of agriculture looks like, in terms of what will be raised, who will be doing the farming, and the management practices involved. Interviews lasted between 60 and 120 minutes and were tape recorded and later transcribed. All respondents were promised anonymity and pseudonyms are used below to ensure that end. The 19 regional food system entrepreneurs interviewed can be placed into roughly two categories: those currently working with these products, as either end-users or creators (n514) and those working (and hoping) to bring their own platforms to market (n55). Questions asked during interviews spanned from inquiring about the technologies they are using to grow their business and regional food system capacity to asking about how they arrived at where they are today concerning their views toward these technologies and the perceived benefits (and potential risks) associated with them. As with the other populations, time was taken to engage respondents in broader conversations about what the future of agriculture looks like, in terms of what will be raised, who will be doing the producing, and the management practices involved. Interviews lasted between 80 and 120 minutes and were tape recorded and later transcribed. All respondents were promised anonymity and pseudonyms are used to protect their identities. A total of 51 individuals were therefore conducted for this research. For all three populations, interviews occurred until the point of theoretical saturation was reached. Theoretical saturation – a practice that also embraces the spirit of weak theorising – is an iterative style of doing qualitative research where the researcher continues sampling and analysing data until no new themes emerge and concepts and linkages between concepts that form the theory are verified and well-developed. At this point it can be argued that no new data are needed (Glaser and Straus 2012). The remainder of the article interrogates those emergent themes.

Findings and discussion: three emergent themes As noted earlier, the aim of this research is to investigate what the abovementioned technological forms engender, in terms of the thoughts and feelings they enact, the relationships they make possible, the forms of governance they encourage, and the ways of life valued. In no particular order, those effects, which emerged as I learned to be affected by the socio-technical foodscapes studied, are (1) anticipatory, (2) moralising, and (3) a movement that multiplies absent presences. Precisely how these qualities were expressed, however, varied greatly across foodscapes. C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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Anticipatory ‘Two words: 9 billion. I’m doing this so we don’t all starve’. (Mark, farmer) ‘We’re going to have 9 billion people on this planet by 2050. We’re just doing our part to make sure we have the techniques in place to feed all those people’. (Jerry, big data industry) ‘The current system isn’t sustainable. The future will inevitably involve more regional food systems and I think technology will play an important role in co-ordinating those resources and people. As we move toward shorter supply chains we’re going to need to become more adept at co-ordinating everyone, growers and buyers. That’s where some of these platforms can play a huge role’. (Noel, regional food system entrepreneur)

The future was ever-present in my conversations with respondents. Everyone interviewed from the ‘farmer’ and ‘big data industry’ groups made some reference to a growing population that needs to be fed, with the assumption that current alternative practices (agroecology, organic agriculture and the like) are insufficient to achieve this end. Conversely, the ‘regional food system entrepreneurs’ interviewed all talked about the unsustainability of the food status quo, suggesting an ‘inevitability’, to requote Noel from above, for more place-based foodscapes. All of their arguments give the future a strange ontological presence in the here and now – ‘strange’ because we are ultimately talking about some-Thing that has not and may never happen (Massumi 2007; Anderson 2010).1 As techniques of what might or never be, big data evoke, as Anderson (2010, p. 778) writes on anticipatory action, ‘a seemingly paradoxical process whereby a future becomes cause and justification for some form of action in the here and now’. Yet these anticipatory acts are far from innocent (Rose 1999; Foucault 2007). To do this means to anticipate which lives are to be valued and thus value those lives, which, in a performative turn, enacts the very ends that were originally used to justify particular actions in the now. And so: ‘anticipatory action will only provide relief, or promise to provide relief, to a valued life, not necessarily all of life’ (Anderson 2010, p. 780). This ultimately means, to again quote Anderson (2010, p. 780), that ‘certain lives may have to be abandoned, damaged or destroyed in order to protect, save or care for life’. To protect and care for certain forms of life at the expense of others: big data and precision agriculture certainly have this risk. ‘I’m not interested in eradicating organic agriculture or small-scale artisan producers. I appreciate a finely crafted cheese as much as the next guy. This is about feeding the world, about producing more; not about making sure that those already well fed will continue to have access to their expensive cheeses and wines, though I believe strongly that organic growers could benefit from this technology too – or at least those that get they’ll need to up their yields in the future to meet future demand’. (Nickolas, big data industry)

This quote comes from Nickolas, who owns a consultant company that specialises in agricultural predictive analytics. Nickolas’ sentiments were commonly expressed during interviews among those in industry; about how big data techniques are not looking to ‘eradicate’ different forms of life (e.g., those tied to organic or artisan agriculture). But in the same breath, those ways of becoming were not equally valued C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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either, at least not within those assemblages interested in a productivist imaginary. And so in these techniques there appears to be a real risk of abandoning certain forms of life in order to protect others. We see this in the above quote from Nickolas, where what are valued include ‘producing more’, ‘yields’, and ‘future demand’, which imply also a valuing of current (agro-industrial based) consumption patterns and everything that enacts them. This is not to suggest that anticipatory action is inherently ‘bad’. Any normative judgement should instead hinge on understanding what is protected, and conversely destroyed and abandoned, by whom, and with what effects (Anderson 2010). From such an assessment such judgements could then be made, which perhaps could take the form proposed by Carolan (2013, 2015a, 2015b) and his call for a politics of addition – an embracing of those practices, discourses, and affects that engender novelty and multiplicity over those that seek to reduce (a politics of subtraction). It was interesting to learn that big data is enrolling unexpected allies in the struggle over climate change, as it is helping individuals see ‘it’ – not a minor accomplishment when you consider the relationally complex ontological status of this process (Carolan and Stuart 2014). Two individuals from the industry group showed me historical data illustrating how the US corn belt, running from Kanas north to North Dakota, has migrated northward by roughly 200 miles over the last three decades. ‘I know climate change is happening because I’ve seen it with my own two eyes thanks to the data we’ve assembled’, to quote one of these individuals (Larry, big data industry). Some farmers too pointed to their extensive historical crop level data as evidence for the existence of climate change. To quote one farmer who made this point rather powerfully: ‘The soil gets warmer now earlier than it did 10 years ago and the precipitation patterns are totally different. I have the data. They’re compelling. And they matter to my bottom line. [. . .] I may be a Republican but I can’t ignore something that’s detrimental of my family’s livelihood. [. . .] Just a couple degrees difference; that’s all it takes to change when insects hatch, which changes how I manage my crops’. (Craig, farmer)

The enrolling of climate change admittedly complicates questions around what forms of life big data value, as discourses and practices wrapped up in this process touch upon a host of human and non-human actors, many of whom have yet to even exist. For instance, it appears as though farmers and those from industry had learned to be affected by certain distant ‘others’ of climate change through big data techniques. The anticipatory imaginaries engendered by big data and precision techniques made real ‘things’ like temperature and precipitation change. These assemblage were less effective, however, at making real every-Thing else associated with climate change – anthropogenic drivers, ways of life lost, etc. So we can say their emancipatory and multiplicative effects in this respect appear limited. Those enmeshed within more alternative foodscapes also worked with techniques that were anticipatory, but with different effects. As indicated by the quote opening this sub-section from the individual (Noel) associated with this group (‘the future will inevitably involve more regional food systems. . .’), their anticipated futures involved more place-based foodscapes. Yet more importantly than scale – after all, you cannot ontologically disentangle, say, regional from global and vice versa (Massey 2007) – was what these alternative food imaginaries hoped to engender. C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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Take the following exchange with Jeff, the owner-operator of a diverse farm who sells to various markets – a public school, restaurants, and a local food co-operative. In recent years he has experimented with numerous so called short supply chain platforms (like the aforementioned Local Orbit) to help him, in his words, ‘distribute through more local and more transparent networks’. He later added: ‘We produce plenty of food. The problem isn’t one of production, and besides, when they [conventional agriculture] talk about producing more they’re just interested in producing more calories, which comes with its own problems. We don’t need to produce more food, we need to produce and distribute food more wisely’. ‘How do these platforms do that?’, I asked. ‘They allow us to imagine a future beyond the direct to market model most people think of when they think about alternative food networks. That model has its place but by itself it isn’t going to challenge conventional markets. We [alternative food proponents] have to get over our aversion to technology. It doesn’t have to be bad. As I said, in this case it’s allowing those of us using it the ability to see alternative distribution networks that we couldn’t see before.” I quickly injected: ‘Can you say a bit more about that point? How is it allowing you to see these new futures?’ To which Jeff replied: ‘Take the example of scale. Before the internet scale was a function of operation size. Now with it people are able to co-ordinate in ways never before imaginable, that old view is being thrown out the window. [. . .] Today, you can scale up without anyone getting any bigger thanks to the ability to access and share resources – that sharing economy model. That’s revolutionary and makes room for actors that before were squeezed out’.

Note in this exchange reference to how certain internet-based techniques allowed those enrolled to ‘see alternative distribution networks that we couldn’t see before’ and how in turn this ‘makes room for actors that before were squeezed out’. In terms of lives these techniques value, therefore, the assemblages appear to engender a politics of addition, which is to say rather than reducing life – to, say, productivist imaginaries – the techniques employed in these foodscapes sought to multiply them. Moralising No technology is neutral; all are political. Some, however, are more veiled than others (Winner 1986). There was a distinct veil of neutrality given to the opaque algorithms and predictive tools used in conventional agriculture. And yet, those very techniques shape what farmers plant and when, how they manage their crop during the growing season (fertiliser and pesticide applications, cultivation decisions, etc.), and when they harvest. It was not uncommon to hear among those from the big data industry comments such as the following: ‘With our software we’re able to give producers the information they need to do better than educated guesses, which is what they’ve been making until now. [. . .] These C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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systems give farmers an objective assessment of the facts, irrespective of what their neighbour might be doing’. (Julian, big data industry)

In truth, these systems are anything but neutral. Even lurking in the prior comment we find subtle normative judgements being cast as a result of these techniques. ‘Irrespective of what their neighbour might be doing’ is a warning, after all, you would not want to follow the lead of someone making non-objective farm management decisions. In other words, good farmers do not follow their gut, they follow data. And more specifically, they follow the data bundled in these commercial platforms. To quote another individual from the big data industry: ‘It takes a lot of the guesswork out of farming. Farmers use precision technology to make better farm management decisions. It makes a good farmer better’. (Sonia, big data industry)

While this was the only instance were the term ‘good farmer’ came up during interviews, the implication that those using precision technologies are ‘good’, and ‘better’ than those who do not, was clear (adding another empirical dimension to an already extensive literature, see e.g., Burton 2004; Saunders 2015). To quote another from industry, who was not shy in implying that those who ‘get the importance’ of these applications are somehow better than those who do not: ‘Those [farmers] that understand what we’re up against, in terms of resource scarcity on the input side and population growth on the consumption side, get the importance of these technologies. And those who don’t probably aren’t going to remain viable’. (Ralph, big data industry)

Some producers did express anxiety over how this seemingly ‘objective’ technology amplifies particular characteristics while undermining other qualities that they believe are essential for creating healthy rural communities. The two quotes below offer representative examples of such sentiments. ‘Yields are great but I worry about how technologies like this distract from those other things that we’re growing, biodiversity, trust, strong communities. If we all start evaluating each other based on what we’re hauling to the elevator every fall, that’s not the culture that attracted me to farming’. (Paul, farmer) ‘Thanks to these big data companies what’s to keep someone from viewing a farmer as just a number that grows next to another number? And what if those numbers get out? What if one of my landlords got their hands on that data and they see that another neighbour might be yielding more, or they’re able to see trends – yields on their land going down, yields on neighbour’s land going up? What’s to keep them from leasing their land to them, hoping to get more for their lease?’ (Eric, farmer)

This last quote adds an interesting temporal dimension to understanding how conceptions of ‘good farmer’ are playing out through big data and precision techniques. Not only are those 1 s and 0 s being used to define what farmers might be – a good farmer or not – but what they might become, through, for instance, ‘trends’. It also speaks to a process noted by Deleuze, building upon the work of Foucault – a style of modulation and control that moves beyond disciplinary power. Deleuze (1992) was C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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especially worried about those elements of contemporary societies that mould people into data – ‘dividuals’. He wrote especially about how conventional institutions, from prisons to schools, medicine, and business, are being reimagined through practices associated with dataifcation. These changes may be presented as giving individuals’ tailored services – such as medical treatment based on specific individual genetic markers or farm management prescriptions designed around a farm’s soil makeup. But they also threaten broader solidarities, as evidenced by Paul’s comment above about ‘how technologies like this distract from those other things that we’re growing, biodiversity, trust, strong communities’. In addition, Deleuze worried about how these techniques can coerce actors into certain forms of ‘communication’ (Marks 2005), which is in reference to how ‘the data’ in these assemblages seem to do the speaking for themselves and how those impacted by ‘it’ have little (if anything) to say about what and whom they should be or become. The techniques employed by regional food system entrepreneurs were no less moralising but their respective politics seemed less tied up in a single conception of ‘the good’. While these technologies were premised on conceptions of what ought to be those imaginaries were generally more open, in the sense of being left to be decided at a later date by stakeholders. One statement exemplifying these feelings includes the following: ‘Many of today’s global community chains just aren’t going to be around in 100 years. That we know. What I’m less sure about is what’s going to take their place. My guess is that the solution lies in diverse supply chains, some direct – farmers’ markets aren’t going anywhere – some short; some medium. To co-ordinate these diverse networks is going to require technology. [. . .] Many of these platforms open up possibilities, which is why I’m hesitant to say THIS is how the future is going to look. We don’t know, other than knowing the solution isn’t singular’. (Harold, regional food system entrepreneur)

Knowing the solution isn’t singular: this suggests a diverse understanding of ‘the good’. I heard this a lot during interviews with this group, moralising claims that still left room for possibilities and novelty. The techniques themselves were in many respects an expression of this diversity, in that rather than enabling an end (higher yields) they enabled processes that still largely left stakeholders in the driver’s seat to determine what those ends ought to be. For an example of this, take the following exchange between myself and an individual hoping to connect cooks with idle kitchen space through an app. ‘Food entrepreneurs, those with a sense of grassroots energy, are interested in building community capacity. I think the word “enable” is apt. We don’t know what the ideal food system looks like. But we do know what some of the barriers are keeping people in the local and regional food scene from doing what they want to do. We’re just trying to remove barriers’. (Lisa, regional food system entrepreneur) ‘“Enable”: can you say a bit more about that? How does your platform enable?’, I enquired. ‘When you see all the idle kitchen space in a metropolitan area and then you hear from upstart food entrepreneurs about how kitchen space, or more specifically the capital to acquire it, is beyond the reach of most – that’s what I mean when I say enable. I’m just trying to help people do what they’ve wanted to do all along’. C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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Multiplication of absent presences I have already touched on the absent presences enrolled through these techniques as a result of their anticipatory tendencies: those inhabiting ‘the future’ that have yet and may never exist, like those nine billion people who eat a diet dependent heavily on agro-industrial inputs. It was also indicated in the previous subsection how these technologies veil practices, oughts, and politics. But beyond that, their very focus on data is itself a veiling discourse, as it obscures from whence the data – those 1 s and 0 s – came. ‘This is the ultimate green technology: cloud computing substituting for petrochemicals; data instead of soil erosion. It’s the next revolution in agriculture’. (Alex, big data industry)

This was a common sentient, that those 1 s and 0 s are somehow untethered from the material constraints that complicate the rest of the world: another absence presence – the natural world – within these assemblages. But big data does not represent the overthrowing of matter. If only that were true, that we could substitute cloud computing for petrochemicals and soil erosion. If ‘the cloud’ were a country it would have the fifth largest electricity demand in the world, at more than 700 billion kWh (Cook 2012). And the source of that energy: there is nothing ‘clean’ about it. The anti-virus software firm McAfee calculates that the electricity required to transmit the trillions of spam emails sent annually is equivalent to the amount required to power more than two million homes in the USA and produces the same greenhouse gas emissions as over three million cars (Farrar 2009). The US based consulting firm McKinsey & Company analysed energy use by data centres used by pharmaceutical companies, military contractors, banks, and government agencies and found them to be using, on average, only 6 per cent to 12 per cent of the electricity powering their servers to perform computations. The remaining energy – approximately 90 per cent of it – went to idle servers (Glanz 2012). In certain respects, then, all those interviewed were enmeshed within discourses and practices that veiled aspects of the natural world, namely, those (often distant) material assemblages – server farms, electricity, coal mines, etc. – that make those 1 s and 0 s possible. But discernable differences can be made between the big data and precision agriculture and the other techniques envisioned by the regional food system entrepreneurs interviewed in terms of the absent presences they engender. As discussed earlier, all groups interviewed where enmeshed within techniques that enrolled bodies. The distinction is not one of quantity. Where they seem to differ, rather, is in terms of the kind of connections each effect, what I call the difference between dependency and co-experimentation. Exchanges like the following were common during interviews with individuals from industry: ‘Farmers needn’t worry about losing control of the data. They can rest assured knowing they keep ownership over it. What we provide, and what we want farmers coming back for year in and year out, are our tools, our platforms, algorithms, and our expertise’. (Fred, big data industry) C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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Others in this group made reference to were treadmill-like (Cochrane 1958) logics, as illustrated in the following exchange. ‘Farmers have always had farm level data and climate data; I admit, not as rich as what’s available now, but what’s really new is the ability to bring all that data together into a product that’s tested, accurate, and valuable. And because it’s valuable farmers are willing to pay for it. They increasingly need these services, and the more who buy-in the more who will need it in the future’. (Mitchell, big data industry) I then asked: ‘I don’t understand, can you elaborate on that last point, about how buy-in now increases buy-in in the future’. After pausing a few seconds, I assume to choose his words, he responded with the following: ‘Look, these products make your operation better, and over time I think it will become the new norm – you’ll have to adopt these technologies and make them part of your management portfolio in order to be competitive with your peers’.

Yet, whereas techniques tied to precision agriculture appeared to make farmers dependent upon certain absence presences those linked to enacting novel alternatives were designed, I was told repeatedly, with an eye toward making those absence presences co-experimenters in the future. A few examples will help illuminate this admittedly nebulous point, such as this one from Noel, whom we have heard from already: ‘This isn’t about bringing people together physically. When I talk about bringing people together through internet-based platforms and social media I’m talking about bringing people together virtually. And I’m not just talking about connectivity. It’s not just about connecting people; it’s about getting these [distant] people together to share ideas in the hope of building something new [clear emphasis in voice]’. (Noel, regional food system entrepreneur)

Or this one, from earlier-quoted Lisa: ‘These are grassroots technologies; they’re a response to the needs of a community. We don’t need to convince people they need them – the typical pathways of most new innovations. So I’m not as interested in the product as in the question of whether I’m heading in a direction that removes barriers. This is a team effort, and I mean that in the broadest sense’. (Lisa, regional food system entrepreneur)

Conclusion: the very public nature of food and agriculture Before concluding I wish to speak briefly about the ‘publics’ that these technologies engender. The public I have in mind, however, goes beyond that understood by C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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conventional western thought. I am talking about a more-than-human understanding of the public (Marres 2012; Rubio and Fogue 2013; Carolan 2015a). Rather than a simple collection of autonomous human actors, discourses, and practices a morethan-human public foregrounds the constitutive role that different entities play in constraining, enabling, and enacting ways of becoming. With this conceptual move we can say all of the techniques alluded to above attempt to publicise food, which is to say each speaks to assemblages that generate alternative imaginations for dwelling together. What each publicises, however, varies. Precision agriculture techniques make public, for instance, climatological trends, soil fertility levels, short-term weather patterns, and absence presences who are spoken for – such as in terms of what those future nine billion people eat. Techniques to build regional food system capacity, conversely, seem more trained on publicising possibilities (versus the certainty of nine billion people eating agro-industrial commodities) and absence presences who are given space to speak for themselves (versus being spoken for), as illustrated in the last quotes in the previous section. Just to be clear, by ‘publicise’ I am not referring to a mere unveiling – to make public in the sense of making visible. John Dewey (1946), the great American philosopher and an early developer of pragmatism, worried about the interests, beliefs, and ideologies of elites becoming ‘fixed’ and assuming a taken for granted status within dominant political and social cultures. To combat this he prescribed the technique of ‘experimentalism’, which essentially involves the recruiting of the broader public to constantly reflect upon and question conventional habits and beliefs. Dewey believed this constituted an important first step in breaking up imposed rules of order and action that is necessary if meaningful social change is to occur. How publics do this, however, is where I part company with many contemporary pragmatists, as they tend to place too much faith in the power of talk. Habermas (1987), for instance, develops his pragmatic insights by way of the concept of communicative rationality. For him, a vibrant public sphere composed of people talking and actively listening has the power to break the stranglehold on rationality by elites. What these communication-centred arguments miss is that publics also involve a material coming together, not just a talking together (Carolan 2015a, 2015b). And that is where these divergent assemblages differ, in terms of their politics, their styles of ‘communication’, and in what those coming-togethers engender. The techniques prevalent in conventional agriculture appear to be directed at a style of communication more interesting in telling than listening, in directing rather than following, and in effecting rather than in learning to be affected. Conversely, those technological discourses and practices embedded within alternative foodscapes seemed genuinely committed to expanding possibilities rather than imposing inevitabilities. In this sense, building on Isabelle Stengers (2000), the aim of such publics ‘does not belong to the future’ (p. 155) but rather ‘belongs to the present as a vector of becoming or an “experiment of thought”, that is, as a tool of diagnosis, creation, and resistance’ (p. 155). This is not to suggest that these techniques are perfect or somehow ‘right’. In terms of what they engender, however, particularly in contrast to the discourses and practices surrounding applications in conventional agriculture, they seem to give rise to certain vectors of becoming that would not otherwise be imaginable. And in that sense, they are helping to enact an additive politics. C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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All of this also has implications in regards to the varying styles of technological governance embedded within real and imagined foodscapes. For example, big data and precision techniques show parallels with neoliberal forms of agro-governance in terms of the effects engendered. Each advance market-led and technological solutions, presuming all aspects of agriculture can be measured, monitored, and when possible privatised (e.g., commodification of information). Complex social issues, then, become de-socialised; effectively ‘disassembled into neatly defined problems that can be solved or optimised through computation’ (Kitchin 2014b, p. 9). This is not to suggest, however, that this particular style of governance is monolithic; after all, there are ‘cracks’ (Carolan 2015b, p. 7), or a ‘margin of manoeuvrability’ (Massumi 2002, p. 212), that litter even the most massive (Latour 1992) assemblages. One hopeful example not discussed above involved two farmers who were actively seeking ways to reduce the technological lock-in, centralisation of power, and dependency that precision systems seem to promote by belonging to a community called Farm Hack. As described on the group’s website, ‘[we are] a worldwide community of farmers that build and modify our own tools. We share our hacks online and at meet ups because we become better farmers when we work together’ (http://farmhack.org/app/). ‘We used to be able fix damn near anything with spit and sweat, a couple bungee cords, and bailing wire’. This quote comes from John. He continued, ‘Not anymore. Now, when my combine or tractor breaks down, mine mind you, I own it, but when it breaks down I not only can’t fix it I’m not even legally allowed to, even if I could, which I can’t’.

This is another dependency linked to today’s ‘high tech’ precision farm implements – farmers are dependent on dealers and manufacturer technicians. The (US) Digital Millennium Copyright Act, passed in 1998 to prevent digital piracy, declares it a breach of copyright to break a technological protection – to break into, in other words, a tractor’s engine control unit (tECU), which is essentially the brains of any ‘smart’ piece of farming equipment. That is what John was referring to when he referenced not being ‘even legally allowed to fix’ his equipment. This brings to mind something noted by Kitchin and Dodge (2011), in their discussion of what they call ‘code/spaces’ – the blending of software and everyday life creating spaces where the tacit knowledge of the old analogue systems is being lost and the dependencies these systems create after that occurs. Farmers take great pride in being a DIY (Do It Yourself) group. But that is becoming increasingly difficult, in part because it is becoming increasingly illegal to do so. As farmers lose that tacit DIY knowledge they are becoming increasingly dependent upon others to do repairs for them. There is where Farm Hack comes into the picture. Started in 2010, Farm Hack seeks to ‘set farmers free’, in the words of Josh, the other Farm Hack member that I interviewed. ‘We encourage farmers to purchase analogue farming implements that they can actually fix without having a Master’s degree in computer science and a password that only John Deere knows’. Josh went on to tell me about the Slow Tools Project, involving, for instance, a solar-powered ‘horse tractor’ for regions of the world dependent on draft animals or a senor system designed with open source code for recording the temperature and moisture of active compost. Again Josh: ‘Farming C 2016 The Authors. Sociologia Ruralis V C 2016 European Society for Rural Sociology. V


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is in my blood. [. . .] That way of life is being taken away from me by corporations who are more interested in profiting off of me than anything. First they made us farm out of can [referencing a dependence on agro-industrial inputs]; now we’re literally someone who just pushes buttons, and we can’t even fix the buttons when they’ve broke. I feel like I’m in a straightjacket. [. . .] Farm Hack sets farmers free’. I mention Farm Hack simply to put a final spotlight on a theme that has run throughout this article: that we cannot (and should not) say much about technological forms when abstracted from their broader web of networks, practices, effects/affectivities, and discourses. For example, taking the above compose sensing system, there is nothing inherently ‘bad’ about code. When embedded within (nonproprietary) assemblages it can engender ‘freeing’ effects, while in other assemblages it can evoke feelings of being in ‘a straightjacket’. In sum, using a relational approach this article attempts to provide agrofood scholars with a way of speaking about the various technological forms that we encounter in our research. The point made is not that alternative foodscapes are looking for alternatives to technology but rather to technologies that engender specific effects. By speaking about effects engendered allows us to pivot and engage in a normative politics that talks about the morality and ethics of food production. This then leads to productive conversations about what specific effects ought to be encouraged, namely, those engaging in politics of addition, and those we would do well avoiding, namely, practices that reduce our options and imaginations. Note 1

The Latourian Thing: ‘much too real to be representations and much too disputed to play the role of stable, obdurate, boring primary qualities, furnishing the universe once and for all’ (Latour 2000, p. 119).

Acknowledgement The author wishes to thank Dr. Bettina Bock and the anonymous reviewers for their helpful comments. This research was supported in part by the National Research Foundation of Korea (NRF-2013S1A3A2055243) and by the National Institute of Food and Agriculture (NIFACOL00725).

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Michael Carolan Department of Sociology Colorado State University Fort Collins CO USA e-mail: [email protected]

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