Skip to main content
Workforce LibreTexts

2.48: Perspective: Digital Agriculture

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)
    Digital Agriculture and the Promise of Immateriality

    Mascha Gugganig is a socio-cultural anthropologist and science and technology studies (STS) scholar who researches knowledge politics that both constitute and trouble ‘expertise’ on (Indigenous) land and environmental issues. Through ethnographic, multimodal research and policy analysis, she researches high- and (s)low-tech discourses and practices of sustainable agriculture. She is currently an Alex Trebek Postdoctoral Fellow in Artificial Intelligence and Environment at the University of Ottawa, and a Research Associate at the Department of Science & Technology Studies, Technical University Munich. .

    Kelly Bronson is a Canada Research Chair in Science and Society at the University of Ottawa in Canada. She is a social scientist studying and helping to mitigate science-society tensions that erupt around controversial technologies and their governance—from GMOs to big data. Her research aims to bring community values and non-technical knowledge into conversation with technical in the production of evidence-based decision-making. Kelly is the author of Immaculate Conception of Data: Agribusiness, activists and their shared politics of the future .

    Learning Outcomes

    After reading and discussing this text, students should be able to:

    • Describe the many different elements of digital agriculture, including both hardware and software elements.
    • Explain why the digitization of agriculture—and of many different practices and industries—has material effects in the world.
    • Articulate some of the broader and potentially problematic effects of the so-called digital revolution in agriculture.


    When thinking about food production , you likely imagine a muddy-booted farmer standing in a field using a notebook to log observations about crops . M aybe you can hear the sound of the grain rustling in the wind as you envision the farmer feel ing the wheat shaft and plung ing her hand in to the soil to assess its moisture level.

    Yet farming is also envisioned to becomedigitized,wherebyinsightson crop quality and soil moisture are determined usingdigital devices like sensors on tractors. The virtues ofdigital agriculture is its(supposed) immateriality, such as the precise (and thus reduced)useof fertilizers throughdata-driven advice stored in the cloud.Proponents argue that a ‘digital revolution’ will reduce farming’s negative material impactson the environment and on human health, while opponents raiseconcerns about digital tools displacinghuman labourers.

    In this chapter we ask: D oes the digitalization of agriculture mean that farming will become immaterial , that it will no longer involv e people , and that it will no longer generat e the material and environmental impacts of the farming practices of yester year ?

    We explore th ese question s and highlight some of the continued material aspects of digital tools in agriculture . We situate th is chapter with in new materialist social science , which has highlight ed the material effects of social processes ( e.g. , the ubiquitous use of plastic bags as a consumer convenience ) that have real material effects on the environment. To date , however, l ess attention has been given to digital agriculture.[1]

    We begin the chapter by outlining common terms connected to digital agriculture , show ing how it is often talked about as an escape from materiality. W e illustrate th e continued materiality of digital agriculture in two forms as physical matter, and as instantiated ideas and conclude that agriculture still depends on the material world , with significant impacts on people and the environment .

    What is digital agriculture?

    There are many termsthat relate to digital agriculture,and precision agricultureis arguably the most prominent one. With the help of sensors embedded in farm machinery, precision agriculturehas for two decadesbeen used to apply resources in a highly controlled and specificway.[2] The farm machinery collectsdata on local weather or soilconditions, which thendrive farm decisions. In recent years, such datahavebeen combinedwith remote sensing data,includingenvironmental (climate)satellite-collected data.[3]The resulting big datasets can be processed using sophisticated computing to create even more preciseinsights on farm management decisions, such as when to plant, apply chemicals, irrigate, and so on. The termdigital agricultureis often used to refer to the use of big datain food production,combined with thedeployment of internet of things (IoT), blockchain technology, artificial intelligence (AI), machine learning,cloud computing, as well asunmanned aerial vehicles (UAVs),and robotics.

    Another common term is smart farming . Compared to precision agriculture , scholars argue that smart farming and agriculture 4.0 are wider reaching terms, as the former includes digitization of whole food systems ( beyond farming ) , while the latter may include pre-production processes, like gene editing of crop s .[4] Another umbrella term is the fourth agricultural revolution , y et there is no agreement as to what constitutes its newness, whether it has started , and if it is even desired.[5]

    The immaterial ‘smart’ farm of the future

    A common way proponents talk about the benefits of digital agriculture is in regard to its decreased material impact on the environment . Indeed, the current systems of intensive, global , capitalist food production including its heavy reliance on agricultural chemicals has been the cause of tremendous greenhouse gas emissions and water pollution .[6]

    P roponents of digital agriculture predict that data-driven insights will lead to a dramatic reduction in chemical use . As Tobias Menne, head of Bayer/Monsanto’s Global Digital Farming Unit explained:

    Before, selling more products meant more business for a company like Bayer; whereas in future, the fewer products we sell the better, because we’re selling outcome-based services. With sensor devices, we can learn a lot more about what is and is not helping crops and livestock and create a better way of doing things .[7]

    Here, t he lead of the largest agribusiness corporation claims that conventional products like pesticides and seeds will no longer drive their business ; instead, they will focus on services . O ne Canadian agricultural economist likewise explain ed in an interview : “If we are to feed 10 billion people by 2100 while preserving our environment, the next green revolution must incorporate the virtual world . [8] Scholars often similarly argu e that precision agriculture will substitute environmental information and knowledge for physical inputs . [9]

    Yet m ateriality be it resources for software and machines , the climate , or labour do es not simply disappear . In the next section we explore the material precondition s of digital agriculture , and then look at labour, including h ow farmers interact with digital artifacts .

    The material preconditions of digital agriculture

    To consider the materiality of digital artifacts, it is helpful to distinguish two forms: materiality as physical substance ( e.g. , dron e s detect ing weeds ) and materiality as the manifestation of principles or values[10] ( e.g. , intellectual property rights that allow or restrict the use of farm management tools ) .

    Materiality as physical matter

    One key infrastructure supporting digital agriculture is e nergy , which requires materials like coal, gas, and oil, as well as water, wind, and solar infrastructure. When Monsanto boasts that its Climate Pro sensors generate seven gigabytes of data per acre ,[11] there are implications for the resources needed to manage that data. C onsider this: if the virtual cloud (where our data is stored and processed) were a country, it would have the fifth largest electricity demand worldwide .[12] In that context, d igital agriculture also requires reliable rural telecommunication infrastructure and broadband access .

    An other material dimension concerns the extraction of rare earth minerals to create microelectronics in microchips for computers and platforms , analytic software , and data storage systems . S cholars have explore d th is material dimension for social media[13] and information and communication technology (ICT) .[14] D ue to the heavy reliance on ICT and digital platforms , digital ag riculture share s many environmental impacts , including water and energy use , e-wast e , a s well as detrimental labour conditions .[15]

    A key material property in digital agriculture is computer infrastructure , especially for scientists and engineers in the public sector. Often , public sector computer scientists are limited by a lack of access to sophisticated computing .[16] Concurrently , spatial datasets compiled by public entities ( such as NASA) are used by industry actors to develop products that are subsequently blocked behind paywalls.

    Seeing the materiality of digital infrastructures the microchips, servers, computers, cell towers, or the electricity grid can be difficult in the farming context, where it seems distant from the immediate context .[17] However, these infrastructures have a n immediate effect on those that hav e to generate such materials . For instance, the demand for cobalt and other minerals has resulted in ongoing violence, slavery and labour exploitation in the Democratic Republic of Congo the world’s largest producer of cobalt[18] while businesses in Silicon Valley’s ICT manufacturing industry frequently contaminat e the environment and human bodies .[19]

    Materiality as instantiated ideas

    As mentioned above, m aterialit y is also the result of instantiated principles or values . G overnments around the world create policies and invest public money to develop telecommunication infrastructure , yet often for private corporations . [20] P olicies reflect ing the principle of equal rural access to the internet may therefore result in supporting industry actors who could not profit from selling digital farm tool s without this infrastructure . D igital infrastructure expansion and maintenance can also prove controversial ( e.g. , concerns over 5G technology’s environmental and health effects[21] or cell tower infrastructures interven ing with natural heritage protection[22] ).

    Legal infrastructures, in the form of intellectual property rights,are also principlesthat areinstantiated in material properties; theyregulatewho can have accessto data and machinery for developing and using sensing technologies, file formats, or metadata.[23] Exemplary is the agribusiness corporation John Deere, whichappliescopyright licenses to protect both data and sensing machines, whichin turn limits farmers’ access to their data and machineryeven preventing themfromfixing their tractors.[24]

    Further, interpreting agricultural data requires digital skills and expertise that many farmers often do not possess , but which would allow them to interpret data and acquire hands-on abilities to tinker, fix , innovate and build tools .[25] Concerns over corporate proprietary rights [26] have spurred such initiatives as the U . S . -based non-profit organization , Ag Data Coalition, which seeks to give farmers an option for storing all of their data in one secure location independent of supplier s or manufacturer s .[27] S tate authorities have also worked towards multi-stakeholder engagement s in the governance of digital agriculture ( e.g. , the Swiss Charter on the Digitalisation of Swiss Agriculture and Food Production[28] ).

    The materialization of values , in the form of private gain , is also visible in the design of digital agricultural technologies . Companies like John Deere develop tools with large commodity crop and capital-intensive farms in mind .[29] F arm technology developers , policymakers , and investors often imagine farmers as being minimally concerned with anything but economic profitability . [30] This result s in commercial systems like FarmCommand that follow economic logics, and provide an overview that is only useful for large-scale farm s . As one Prairie farmer, Dan, explained , precision tractors with GPS auto-steering are only worthwhile for farms like his because of the cumulative efficiency gains: “Say, you’re overlapping by two feet every time, it doesn’t take very long before you start to add up quite a bit of overlap.” As a result, s mall-scale farmers have had little to gain from the use of (very costly) digital agriculture tools . M ost v isual AI-driven tools trained to detect crop diseases are also not conducive to polyculture growth settings . Such farming systems are currently not captured by applications trained to collect big data .[31] The value of large-scale farming as business is thus instantiated into the materiality of digital technologies currently on the market .

    Implications of digital agriculture

    Because the re is a bias toward large-scale commodity producers, digital agriculture arguably furthers capital-intensive, industrial agriculture a system that has known material implications on people and the planet.[32] D igital agriculture extends histor ic processes of the industrial ization of agriculture[33] , potentially leading to a new digital food regime . [34] Adding the dimension of energy, extracted resources for developing microchips, digital storehouses , and rural network infrastructures, the environmental and health consequences of a digitized agriculture may in fact undo its own sustainability claims.[35]

    D igital tools such as robots may also alter farmers’ identity and relationship s to farm ing practice s .[36] Indeed, requir ing farmers to use decision support tools can re- write how farmers interact with their land.[37] A farm may turn into a control centre where the farmer becomes an office manager[38] or data labourer.[39] The ‘g ood farmer may be the one who trusts big data to be more objective than their neighbor, their gut intution , or their own tacit knowledge .[40]

    Yet for farmers , a digital monitoring system may also free up time for leisure activities , foster ing other forms of relationships , car ing for animals beyond service exchange s like cattle for milk , or improv ing communication with their consumers. Indeed, in practice, farmers engag e with precision technolog ies in many ways , sometimes by tinkering and repurposing them , or blending them with analogue tools .[41]


    Proponents of digital agriculture claim that digital tools in agriculture will require less chemical input , such as pesticides or fertilizer, as they can now be applied in a more precise way . The digitization of farm management, often imagined as data in the form of a distant ‘cloud,’ is portrayed as immaterial that is, requiring less machinery, chemical input, and land for food production. Digital agriculture is a lso imagined to decreas e the detrimental impacts on the environment due to decades of high-input industrial agriculture . Yet there are numerous material preconditions for , and consequences of the digitalization of agriculture , that has effects on land and people .

    To better understand such claims of immateriality , this chapter approached materiality not merely as physical matter but also as instantiated ideas . This is because the existence (or lack ) of policies, intellectual property rights, digital education programs , and the design of tools ha ve material implications regarding who is able to participate in the so-called digital revolution in agriculture. Likewise, the material preconditions of ICT-driven tools are , similar ly to other sectors , rel iant on the extraction of rare earth mineral s , e nergy resources for high-data drive sensors, or rural telecommunication infrastructures . They exemplify the very real material needs for the digitization of agriculture.

    Some questions left to consider are: D oes existing policy ( like broadband development programs ) and existing legislation ( like licenses protecting farm data as corporate property ) serve the public , industry , or both ? Who ought to hold the legal rights to develop, tinker with , and fix digital tools and machineries? What if digital tools were developed such that they reflect a broad array of farm values, like the environmental principles of agroecologists, or the relational knowledge of Indigenous farming ? What might the very material dimensions of digital agricultural tools look like if they were developed by farmers and DI Y -tool developers, based on their place-based knowledge and expertise , rather than merely industry scientists ? As you can see, there is still much research to be done!

    Discussion Questions

    • What are the key elements of digital agriculture?
    • How have digital technologies changed farming practices? How have digital technologies changed how and what we think about agriculture?
    • What are the potential benefits of digital agriculture? What are the potential problems?
    • This chapter identifies materiality as an important concept for examining the real-world impacts of digital agriculture. What is materiality and why is it important to identify the often-hidden material effects of digital agriculture?

    Additional Resources

    Disadvantaged by Digitization”: Technology, Big Data, and Food Systems. 2021. Handpicked: Stories from the Field S2E2 ( podcast).


    Bronson, K. and I. Knezevic. 2017. “Look twice at the digital agricultural revolution.” Policy Options.

    Blumenfeld, J. 2019. Meeting Data User Needs: A Look Behind the Curtain.

    Bongiovanni, R., and J. Lowenberg-DeBoer. 2004. “Precision agriculture and sustainability.” Precision Agriculture5 (4): 359–387.

    Bronson, K. 2018. “Smart farming: including rights holders for responsible agricultural innovation.” Technology Innovation Management Review8 (2): 7–14.

    Bronson, K. 2019. “Looking through a responsible innovation lens at uneven engagements with digital farming.” NJAS-Wageningen Journal of Life Sciences90: 100294.

    Bronson, K. and I. Knezevic. 2016. “Big Data in food and agriculture.” Big Data & Society3 (1): 2053951716648174.

    Bronson, K. and I. Knezevic. 2019. “The digital divide and how it matters for Canadian food system equity.” Canadian Journal of Communication44 (2): 63–68.

    Carolan, M. 2017. “Publicising food: big data, precision agriculture, and co‐experimental techniques of addition.” Sociologia Ruralis57 (2): 135–154.

    Chen, S. 2016. “The materialist circuits and the quest for environmental justice in ICT’s global expansion.” tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society14 (1): 121–131.

    Cobby, R.W. 2020. “Searching for sustainability in the digital agriculture debate: an alternative approach for a systemic transition.” Teknokultura17 (2): 224–238.

    Cook, G. 2012. How Clean is Your Cloud? Greenpeace International. Accessed May 16.

    De Schutter, O. 2015. “Don’t Let Food Be the Problem.” Foreign Policy.

    Delgado, Jorge A., Nicholas M. Short, Daniel P. Roberts, and Bruce Vandenberg. 2019. “Big Data Analysis for Sustainable Agriculture on a Geospatial Cloud Framework.Frontiers in Sustainable Food Systems 3.

    Driessen, C. and L.F.M. Heutinck. 2018. “Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms.” Agriculture and Human Values 32, no. 1 (2015): 3-20.Ensmenger, Nathan. “The environmental history of computing.” Technology and Culture59 (4): 7–33.

    Fitzpatrick, C., E. Olivetti, T.R. Miller, R. Roth, and R. Kirchain. 2015. “Conflict minerals in the compute sector: estimating extent of tin, tantalum, tungsten, and gold use in ICT products.” Environmental Science & Technology49 (2): 974–981.

    Fuchs, C. 2014. “Theorising and analysing digital labour: From global value chains to modes of production.” The Political Economy of Communication1 (2).

    Higgins, V., M. Bryant, A. Howell, and J. Battersby. 2017. “Ordering adoption: Materiality, knowledge and farmer engagement with precision agriculture technologies.” Journal of Rural Studies55: 193–202.

    Iles, A., Graddy-Lovelace, G., Montenegro, M., & Galt, R. 2017. “Agricultural systems: co-producing knowledge and food.” In Handbook of Science & Technology Studies, 4th ed.: 943–972.

    IPES Food (International Panel of Experts on Sustainable Food Systems). 2015. The New Science of Sustainable Food Systems.

    Klerkx, L., E. Jakku, and P. Labarthe. 2019. “A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda.” NJAS-Wageningen Journal of Life Sciences90: 100315.

    Kostoff, R.N., P. Heroux, M. Aschner, and A. Tsatsakis. 2020. “Adverse health effects of 5G mobile networking technology under real-life conditions.” Toxicology Letters323: 35–40.

    Lajoie-O’Malley, A., K. Bronson, S. van der Burg, and L. Klerkx. 2020. “The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents.” Ecosystem Services45: 101183.

    Legun, K., and K. Burch. 2021. “Robot-ready: How apple producers are assembling in anticipation of new AI robotics.” Journal of Rural Studies82: 380–390.

    Leonardi, P.M. 2010. “Digital materiality? How artifacts without matter, matter.” First Monday15 (6–7).

    Levidow, L. 1991. “Women who make the chips.” Science as Culture 2 (1): 103–124.

    Miles, C. 2019. “The Combine Will Tell the Truth: On precision agriculture and algorithmic rationality.” Big Data & Society6 (1): 2053951719849444.

    Mooney, P. 2018. Blocking the chain: Industrial food chain concentration, Big Data platforms and food sovereignty solutions. Berlin: ETC Group.

    Pellow, D. and L. Sun-Hee Park. 2002. The Silicon Valley of dreams: Environmental injustice, immigrant workers, and the high-tech global economy. Vol. 31. New York: NYU Press.

    Reading, A. 2014. “Seeing Red: A political economy of digital memory.” Media, Culture & Society36 (6): 748–760.

    Rose, D.C. and J. Chilvers. 2018. “Agriculture 4.0: Broadening responsible innovation in an era of smart farming.” Frontiers in Sustainable Food Systems (2): 87.

    Rose, D.C., C. Morris, M. Lobley, M. Winter, W.J. Sutherland, and Lynn V. Dicks. 2018. “Exploring the spatialities of technological and user re-scripting: the case of decision support tools in UK agriculture.” Geoforum89: 11–18.

    Rotz, S., E. Duncan, M. Small, J. Botschner, R. Dara, I. Mosby, M. Reed, and E.D.G. Fraser. 2019. “The politics of digital agricultural technologies: a preliminary review.” Sociologia Ruralis59 (2): 203–229.

    Strubenhoff, H., and R. Parizat. 2018. Can the Digital Revolution Transform Agriculture? Brookings Institute. February 28.

    Tsouvalis, J., S. Seymour, and C. Watkins. 2000. “Exploring knowledge-cultures: Precision farming, yield mapping, and the expert–farmer interface.” Environment and Planning A32 (5): 909–924.

    Vik, J., E.P. Stræte, B.G. Hansen, and T. Nærland. 2019. “The political robot–The structural consequences of automated milking systems (AMS) in Norway.” NJAS-Wageningen Journal of Life Sciences90: 100305.

    Wolf, S.A., and S.D. Wood. 1997. “Precision Farming: Environmental Legitimation, Commodification of Information, and Industrial Coordination 1.” Rural Sociology62 (2): 180–206.

    1. For an exception, see Cobby 2020 and Higgins et al 2017.
    2. Wolf & Wood 1997.
    3. Carolan 2017, 137.
    4. Rose & Chilvers 2018, 87; Klerkx et al. 2019, 100315; Overall, distinguishing agriculture into successive periods reflects a problematic evolutionary conception of agriculture.
    5. Rose & Chilvers 2018.
    6. IPES Food 2015.
    7. Quoted in Strubenhoff & Parizat 2004, para 8.
    8. Delgado 2019, n.p.
    9. Bongiovanni & Lowenberg-DeBoer 2004.
    10. Leonardi 2010, n.p.
    11. Carolan 2017, 139.
    12. Cook 2012.
    13. Reading 2014.
    14. Fitzpatrick et al. 2015.
    15. Cobby 2020; Chen 2016; Ensmenger 2018; Fuchs 2014.
    16. Bronson 2018.
    17. Carolan 2017, 147.
    18. Fuchs 2014.
    19. Pellow & Park, 1991.
    21. Kostoff et al. 2020.
    23. Blumenfeld 2019.
    25. Higgins 2017;
    26. Mooney 2018.
    29. Bronson 2018; Bronson 2019, 100294.
    30. Bronson 2019.
    31. Bronson & Knezevic 2016.
    32. De Schutter 2015.
    33. Bronson & Knezevic 2016; Bronson & Knezevic 2019.
    34. Klerkx et al. 2019, 10.
    35. Cobby, 2020; Lajoie-O'Malley et al. 2020, 101183.
    36. Driessen & Heutinck 2015. Legun & Burch 2021.
    37. Rose et al. 2018.
    38. Tsouvalis et al. 2000, 913.
    39. Rotz et al. 2019.
    40. Carolan 2017, 145; Miles 2019; Iles et al. 2017, 957.
    41. Vik et al. 2019, 100305; Higgins et al. 2017.

    This page titled 2.48: Perspective: Digital Agriculture is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Mascha Gugganig & Kelly Bronson (eCampus Ontario) via source content that was edited to the style and standards of the LibreTexts platform.