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Plants and AnimalsThe Farm Is Not An Algorithm

The Farm Is Not An Algorithm

The Farm Is Not An Algorithm

The inaccuracies of precision agriculture carry socio-environmental risks and produce inequalities.

This text provides an summary of the second interview in a three-part interview series that explores how digitalization is reshaping environmental governance. I spoke with Oane Visser, an Associate Professor in Agrarian Studies on the International Institute of Social Studies. Visser earned his Ph.D. in anthropology from Radboud University, Nijmegen, within the Netherlands. His research focuses on the intersection of digitalization and climate change, environmental degradation, and the longer term of food production.

His current project explores precision agriculture within the Netherlands, the US, Canada, and the UK. Visser argues that precision agriculture has been promoted as a method of addressing the issues with conventional agriculture. When practicing conventional agriculture, a farmer will typically apply the identical amount of fertilizer and pesticides across their entire field. With precision agriculture, each plant and animal ideally receive precisely the inputs it needs, thereby reducing water, energy, fertilizer, and pesticide use. This precise application of inputs is achieved through an assemblage of technologies including crop, animal, and soil sensors, satellites and distant sensing devices, geographic information systems (GIS), global positioning systems (GPS), variable rate technologies (VRT), and artificial intelligence, amongst others. Precision agriculture goes by several other names including, “smart farming,” “agriculture 4.0,” and “digital agriculture.”

Advocates of precision agriculture often frame technology as a seamless solution to the issues that ecological crisis and climate change pose to food systems. Approaching these technologies with an ethnographic perspective, Visser complicates that narrative and argues that these digitalized agricultural technologies will not be, in actual fact, seamless, and as a substitute remain depending on human motion. At its worst, precision agriculture may even extend or intensify the vulnerabilities that farmers experience and produce power dynamics that could be exploitative.

Precision agriculture originated on large farms within the Midwestern United States within the Nineteen Nineties, where the main focus was on staple crops like corn, soy, and wheat. Precision agriculture stays most prevalent in developed countries just like the Netherlands, Germany, the UK, France, Australia, and Canada, but has spread across the globe. Much of the investment in, and deployment of, precision agriculture stays targeted at large- and mid-sized farms.

Interest in precision agriculture has increased inside California over the previous few years. In 2020, the University of California, Merced joined the University of Pennsylvania, Purdue University, and the University of Florida in forming a research center on the Web of Things for precision agriculture. Moreover, the University of California, Davis now offers a minor in precision agriculture. In response to this system’s website, “the minor prepares students for difficult positions in site-specific crop management as we enter the ‘information age’ in agriculture.”

Visser sees this shift toward the “information age” of agriculture as being driven by three sets of actors. First, there’s the AgTech manufacturing industry — large, traditional manufacturers of agricultural equipment, like John Deere and AGCO — which is serious about branching out into software and cloud-based services. Second, precision agriculture is being promoted by the agribusiness sector — i.e. major chemical, pesticide, herbicide, and fertilizer corporations, like Bayer-Monsanto and Syngenta — which seeks to manage data about farming practices and develop the dominant digital platforms utilized by farmers. Finally, Big Tech firms like IBM, Google, and Microsoft are also boosters of precision agriculture. Through digitalization, all three sets of actors benefit from, and extend their control over, the management of farms in what Visser describes as an “off-farm choreography.”

Some environmental advocates see precision agriculture as a helpful tool for addressing climate change. They legitimately fear the unprecedented, unpredictable, and fast-moving environmental and economic disturbances that climate change will bring, and imagine that farmers will struggle to adapt in time. They argue that digital tools could provide farmers with the precise, accurate, and reliable data mandatory to adapt to climate change.

While there could also be some merit to this argument, Visser’s ethnographic research — which incorporates interviews with farmers — brings some much-needed nuance to this story. Visser believes the argument presents a very optimistic view of technology’s capabilities. The truth is, among the farmers he’s spoken with don’t see the guarantees of digital technologies materialize in practice. The AI-powered algorithms that drive precision agriculture require historical datasets to operate accurately. Visser notes that when the datasets are too small or the baselines are always shifting attributable to climate change, the algorithms can perform poorly. He argues that, in some cases, the lived experience and knowledge of a farmer who’s been in business for a long time is more useful for adapting to climate change.

Visser also highlights among the ways in which nature and the environment can affect technological systems and diminish their efficacy. Animals can destroy sensors on or around them, and environmental conditions, resembling wind, water, sunlight, and dirt, can degrade sensors. Sensors also can change the behavior of animals or make them ailing, which affects the standard of the information which can be collected. Visser provides the precise example of a farmer who invests in a barn floor that has valves that open and shut to separate urine from cow manure. In theory, this will dramatically reduce nitrogen emissions. Nevertheless, if the ground isn’t meticulously maintained, dirt and other matter can accumulate and cause the machinery to malfunction. This, Visser says, is why it’s necessary to really study technologies “within the wild.” Unfortunately, many technology developers are likely to treat the farm like a laboratory.

Visser pushes back against the narrative that a farmer’s knowledge isn’t any longer adequate to fulfill contemporary environmental crises by highlighting the numerous ways farmers have successfully and assuredly adapted to major disturbances throughout history. This history includes changing climates, population growth, droughts, and other disasters. And, he argues, in those instances where farmers have had difficulty adapting, it’s actually because of investments in earlier technologies. The big capital outlays for brand new systems, machines, and equipment, can send farmers into debt and leave them less flexible and nimble within the face of unexpected challenges. As an alternative of those tools being a path toward farmer empowerment, as they’re sometimes described, Visser argues they can lead to vulnerabilities, dependencies, and constraints upon the farmer.

Finally, Visser argues that, in those cases where digital farm technologies do perform accurately and precisely, it’s often the farmer, through his or her labor, who makes the technology work because it should. Visser explains that the farmer plays a pivotal role in calibrating, corroborating, and interpreting the information produced by digital technologies. They need to always assess whether or not the information that’s generated is logical and fix any errors. This reality stands in stark contrast to what the president of the European Agricultural Machinery Association said when he referred to farmers as “one in every of the weakest components” of digital agriculture (Visser et al., 2021, p. 629). Fairly than understanding precision agriculture as an interplay between humans and technology, the technology is glorified as continually moving toward perfection, limited only by human fallibility.

What’s the consequence of this tendency to overestimate the accuracy of digital tools? In his paper, “Imprecision farming? Examining the (in)accuracy and risks of digital agriculture,” Visser argues that it might probably result in a “precision trap.” A precision trap is the “exaggerated belief within the precision of huge data that over time results in an erosion of checks and balances,” resembling analog technique of quality assurance and direct farmer commentary (Visser et al., 2021, p. 623). He writes that three conditions can result in a precision trap: (1) the opacity of algorithms, (2) the increasing give attention to forecasting and prediction, and (3) the growing distance between farmers and the every day field operations on their farms (Visser et al., 2021, p. 624). It’s not that digital farming technologies have to be hyper-accurate to have value, he says. As an alternative, the issue is the dearth of scrutiny across the inaccuracies of those tools. These oversights can result in costly and environmentally harmful outcomes.

The speed with which digital technologies operate can compound the chance and impacts of a precision trap, Visser claims. While fast-paced, real-time, algorithmically-driven, decision-making carries a certain appeal, it also makes it difficult for farmers to evaluate, intervene, and proper a process gone awry. Visser interviewed a farmer who said that digital tools generate and transmit data and errors with lightning speed. If a farm operates as an Web of Things — where digital devices are interconnected — then it’s possible to have interactions between devices that produce cascading failures.

If and when precision agriculture produces costly operational failures, Visser argues that it’s often the farmer who’s blamed. For instance, he asks us to think about the fate of a farmer who invests a major sum of money in a robotic milking system for his or her cows. Visser says that the milk robots work well for about 98% of farmers. In each barn, there’ll inevitably be some cows that can’t adapt to the robots, so that they are slaughtered. This level of attrition is deemed acceptable. Nevertheless, there could be cases where the entire herd doesn’t adapt to the robots, or they adapt to start with only to change into intolerant some months later. When the cows don’t enter the milk robots, diseases and infections can quickly spread throughout the herd.

Then the query becomes: who’s accountable for the financial losses incurred by the farmer? The farmer would likely seek compensation from the manufacturer of the equipment. Nevertheless, when purchasing the equipment, the farmer has to sign a dense, lengthy contract. These contracts typically stipulate that the farmer shall be compensated only in the event that they can prove the problem wasn’t attributable to the weather, animals, farming or management style, buildings, etc. With so many aspects at play, this could be an onerous burden of proof. This instance illustrates the facility dynamics that exist between multinational corporations and farmers and the repercussions of that disparity. Digitalization inside agriculture pressures farmers to speculate in large, mechanized and digitalized systems, which shift greater control of farm management to corporations, yet leave the farmers responsible when issues arise.

Visser also raises the problem of a “precision divide.” In an agricultural context, the precision divide arises when there are differences inherent to the technology itself — on the extent of the hardware or software (i.e. the algorithm) — that privileges certain crops over others. AI-driven algorithms are only nearly as good because the datasets they’ve been trained on. On condition that precision agriculture originated within the Midwestern U.S., early algorithms were trained on the staple crops grown in that region. These commodity crops proceed to be the primary focus. Subsequently, the algorithms that drive precision agriculture produce higher-quality data for staple crops and privilege the farmers who grow those crops. Visser says that a farmer who attempts to grow a less common crop will likely receive lower-quality data and poorer outcomes.

A precision divide also can form between farming styles. For instance, Visser explains that algorithms are likely to have a much easier time capturing and analyzing data from a farmer who’s monocropping. In contrast, a farmer who engages in complex rotation schemes, regenerative agriculture, permaculture, or integrated crop and livestock farming will typically find the algorithms to be less accurate (Visser et al., 2021, p. 630). Sometimes the information collected for one crop could be corrupted by nearby crops. Or, the farmer may have multiple software packages to gather data on each crop. Visser argues that, at present, precision agriculture focuses on and reinforces monocropping on the expense of more experimental and sustainable types of farming.

Precision agriculture can produce precision traps and precision divides that expose farmers to risk, for which they are sometimes liable. Nevertheless, Visser sees opportunities for more just and equitable approaches to precision agriculture. Farmer-led movements and coalitions are developing open-source technologies, in partnership with engineers, software designers, and software developers. They’re producing tools adapted to farming styles largely ignored by profit-maximizing transnational corporations. This bottom-up, progressive approach is going on in places just like the U.S., especially Recent England. There’s also the Gathering for Open Agriculture Technologies (GOAT) and L’atelier Paysan — the Workshop of the Peasant — two collectives situated in France. Then there are global hubs of collaboration like Farm Hack. These are encouraging developments by way of promoting equity for farmers and more sustainable farming styles, says Visser. He hopes his research will help inform these movements and alert farmers to the risks and possibilities of precision agriculture.

By way of his future work, Visser now turns his attention to greenhouse-based horticulture. He points out that greenhouses and indoor farms are increasingly seen as a strategy to grow food on a planet made less hospitable by climate change. The concept is to “shut agriculture off from the environment,” he says. He sees a danger in “this concept of total controllability of agriculture.” Despite our biggest efforts, the farm resists algorithmic considering.



Visser, O., Sippel S.R., Thiemann, L. (2021). Imprecision farming? Examining the (in)accuracy and risks of digital agriculture. Journal of Rural Studies, 86, 623-632. https://doi.org/10.1016/j.jrurstud.2021.07.024


Previous article: “Digitalization and Predictive Policing in Conservation: Does technology shift focus toward “green policing” and away from integrated conservation and development?”

Final article: “Technology’s Role in Governing Sustainable Food Systems: Digitalization is altering how we understand the environment and act upon problems with sustainability.” (coming soon)

digitalization, environmental governance, food systems, precision agriculture, smart farming


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