Lessons to be learned in adoption of autonomous equipment for field crops

Lowenberg‐DeBoer, James; Behrendt, Karl; Ehlers, Melf-Hinrich; Dillon, Carl R.; Gabriel, Andreas; Huang, Iona Yuelu; Kumwenda, I.; Mark, Tyler; Meyer‐Aurich, Andreas; Milics, Gábor; Olagunju, Kehinde Oluseyi; Pedersen, Søren Marcus; Shockley, Jordan M.; Rose, David Christian

doi:10.1002/aepp.13177

Cited by 21 publications

(32 citation statements)

References 17 publications

(22 reference statements)

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“…If we take future adoption of autonomous robotics as an example, farmers with better 4G and 5G connectivity, existing suitable building and other infrastructure that could be adapted to storing and charging robots, and less restrictive regulation which could diminish value of labour substitution (e.g. laws requiring human supervision of robots, Lowenberg-deBoer et al 2021 ), are most likely to adopt these new tools. This leads us onto our final theme elucidating the winners and losers created by supporting one future vision of agriculture over another.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Management and farmer characteristics (e.g., experience, education and training, financial status, and attitude towards risk) are also shown to be important drivers of productivity, with the interpretation that these shape innovative capacity and the adoption of novel technology and practices [1,14,15]. The empirical evidence on how farm manager age impacts productivity indicates it may be sensitive to the regional context.…”

Section: Review Of Empirical Studiesmentioning

confidence: 99%

Unpacking Total Factor Productivity on Dairy Farms Using Empirical Evidence

et al. 2022

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This study examines the farm-level factors that influence differences in total factor productivity (TFP) on dairy farms. To this end, a fixed-effects regression approach is applied to panel data for dairy farms obtained from the Farm Accountancy Data Network for Northern Ireland over the period of 2005 to 2016. The findings are largely consistent with existing empirical evidence, showing that herd size, milk yield, stocking density, and share of hired labour have a positive and statistically significant impact on TFP, while labour input per cow, purchased feed input per cow, and share of direct payments in total farm output have a negative and statistically significant impact. The more complex relationships, namely age, education, and investment, have been unpacked using interaction terms and nonlinear approximation. The impact of age is negative, and the drag on productivity grows as age increases. Capital investment and education both have a positive impact on farm-level TFP, as well as on their interaction. Policy recommendations on strategies and best practices to help dairy farms tackle productivity constraints are suggested.

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“…The human operator limits conventional farm machinery to 13 h/day (Shockley et al, 2011). However, autonomous machinery can operate 22 h/day, allowing 2 h for repairs and maintenance (Lowenberg‐DeBoer, Behrendt, et al, forthcoming).…”

Section: Economic Modelmentioning

confidence: 99%

“…On the other hand, the United Kingdom has the opportunity to define and develop new policies regarding autonomous farm machinery operations since their recent departure from the European Union. (Lowenberg-DeBoer et al forthcoming;European Commission, 2010). In Australia, there are limited regulations for operating autonomous equipment on private property.…”

Section: Policy Review Of Autonomous Machinery In Agriculturementioning

confidence: 99%

How will regulation influence commercial viability of autonomous equipment in US production agriculture?

Shockley

Dillon

Lowenberg‐DeBoer

et al. 2021

Applied Eco Perspectives Pol

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Autonomous equipment for crop production is on the brink of commercialization in the United States but federal, state, and local policies could affect commercial viability and hinder adoption. This article examines the farm‐level implications of both a speed restriction and on‐site supervisory regulations. The rules reduce the profitability of autonomous machinery, and for some scenarios autonomous machines are no longer an economically viable alternative to conventional machinery. Regulations also increase the optimal number autonomous machines required and influence production practices. Smaller farms have more flexibility in supporting the rules because they have more to gain from the use of autonomous equipment.

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Lessons to be learned in adoption of autonomous equipment for field crops

Cited by 21 publications

References 17 publications

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

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

Unpacking Total Factor Productivity on Dairy Farms Using Empirical Evidence

How will regulation influence commercial viability of autonomous equipment in US production agriculture?

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