In pursuit of responsible innovation for precision agriculture technologies

Gardezi, Maaz; Adereti, Damilola Tobiloba; Stock, Ryan; Ogunyiola, Ayorinde

doi:10.1080/23299460.2022.2071668

Cited by 18 publications

(11 citation statements)

References 78 publications

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“…Recognizing that society engages the making of technological futures, and the reciprocal nature of this process, emphasizes how technology shapes societal outcomes. In agricultural work, we might consider how economic ideologies inform the ‘need’ for farms to scale, while at the same time, automated ‘smart’ technologies and data-driven practices such as precision agriculture enable and encourage that scaling, co-constituting one another [ 10 , 58 , 122 , 123 , 124 ]. These constructions are not imposed from above.…”

Section: Social Science’s Contributions To the Environmental-technolo...mentioning

confidence: 99%

Automation, Climate Change, and the Future of Farm Work: Cross-Disciplinary Lessons for Studying Dynamic Changes in Agricultural Health and Safety

Comi

Becot

Bendixsen

2023

IJERPH

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In this review, we first assess the state of agricultural health and safety research as it pertains to the dynamic challenges facing automating agriculture on a warming planet. Then, we turn to social science fields such as rural sociology, science and technology studies, and environmental studies to leverage relevant insights on the introduction of new technologies, environmental risks, and associated workplace hazards. Increased rates of automation in agriculture alongside new risks associated with climate change create the need for anticipatory governance and adaptive research to study novel mechanisms of worker health and safety. The use of the PRISMA framework led to the 137 articles for our review. We identify three themes in the literature on agricultural health and safety: (1) adoption outcomes, (2) discrete cases of health risks, and (3) an emphasis on care and wellbeing in literature on dairy automation Our review led to the identification of research gaps, noting that current research (a) tends to examine these forces separately, instead of together, (b) has not made robust examination of these forces as socially embedded, and (c) has hesitated to examine the broad, transferable themes for how these forces work across industries. In response to these gaps, we suggest that attention to outside disciplines may provide agricultural health and safety research with a toolset to examine needed inquiry into the multiplicity of experiences of rural stakeholders, the industry specific problems arising from automation and climate change, and the socially embedded aspects of agricultural work in the future.

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Section: Social Science’s Contributions To the Environmental-technolo...mentioning

confidence: 99%

Automation, Climate Change, and the Future of Farm Work: Cross-Disciplinary Lessons for Studying Dynamic Changes in Agricultural Health and Safety

Comi

Becot

Bendixsen

2023

IJERPH

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“…This re-shaping is influenced by the circumstances of individual farmers, for example their capacity to innovate, their situated knowledge (Lundström and Lindblom 2018 ) as well as their worldview (or imaginary) of why they farm and what they think farming is. As Gardezi et al ( 2022 , 15) write ‘[t]echnology always puts humans into a dance with it, and it is through these interactions that the agency continues to move like a pendulum between humans and machines’. An example of this is provided by Rose et al ( 2018 ) who showed how computer-based decision support systems changed how farmers spent time in different places on the farm (e.g.…”

Section: The Everyday and Histories Of Changementioning

confidence: 99%

“…Documenting what Gardezi et al ( 2022 ) term the ‘dance’ between farmers and technology requires a focus on the everyday and the creation of biographies. Using biographies of people and objects as a methodological approach in history is by no means new (though it is a novel approach within the context of agriculture 4.0 trajectories), as Gosden and Marshall ( 1999 ) have shown.…”

Section: The Everyday and Histories Of Changementioning

confidence: 99%

The old, the new, or the old made new? Everyday counter-narratives of the so-called fourth agricultural revolution

et al. 2022

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

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“…ML/AI technologies that provide answers to new questions have the potential to enhance crop production, accelerate economic growth, and reduce environmental impacts (Gardezi et al, 2022;Rose et al, 2016). However, obtaining these benefits depends on the willingness of farmers to adopt ML/AI technologies which may require additional financial resources, a learning and testing period, willingness to accept a black box that they do not understand, and ultimately a change in the decision process (Faundeen et al, 2013;Gardezi et al, 2022;Higgins, 2008;Demestichas & Daskalakis, 2020;Akaka et al, 2021;Baldin et al, 2021;Lowenberg-DeBoer & Erickson, 2019). Additional concerns about ML/AI technologies are that ML/AI technologies make humans dependent on machines for decision-making (Wolfert et al, 2017), dispossess farmers of their local and tacit knowledge (Gardezi et al, 2022), and displace farm labor.…”

mentioning

confidence: 99%

“…However, obtaining these benefits depends on the willingness of farmers to adopt ML/AI technologies which may require additional financial resources, a learning and testing period, willingness to accept a black box that they do not understand, and ultimately a change in the decision process (Faundeen et al, 2013;Gardezi et al, 2022;Higgins, 2008;Demestichas & Daskalakis, 2020;Akaka et al, 2021;Baldin et al, 2021;Lowenberg-DeBoer & Erickson, 2019). Additional concerns about ML/AI technologies are that ML/AI technologies make humans dependent on machines for decision-making (Wolfert et al, 2017), dispossess farmers of their local and tacit knowledge (Gardezi et al, 2022), and displace farm labor. This concern is valid.…”

mentioning

confidence: 99%

Will artificial intelligence and machine learning change agriculture: A special issue

Clay,

Brugler,

Joshi

2024

Agronomy Journal

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In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning.

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In pursuit of responsible innovation for precision agriculture technologies

Cited by 18 publications

References 78 publications

Automation, Climate Change, and the Future of Farm Work: Cross-Disciplinary Lessons for Studying Dynamic Changes in Agricultural Health and Safety

Automation, Climate Change, and the Future of Farm Work: Cross-Disciplinary Lessons for Studying Dynamic Changes in Agricultural Health and Safety

The old, the new, or the old made new? Everyday counter-narratives of the so-called fourth agricultural revolution

Will artificial intelligence and machine learning change agriculture: A special issue

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