Innovation Uncertainty Impacts the Adoption of Smarter Farming Approaches

Eastwood, C. R.; Renwick, Alan

doi:10.3389/fsufs.2020.00024

Cited by 42 publications

(31 citation statements)

References 34 publications

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“…Another important aspect to consider in addition to technical feasibility is whether these particular types of technologies would likely be adopted by farmers since the widespread uptake of precision technologies thus far has been rather slow, including in dairy farming as a result of "innovation uncertainty" (Eastwood and Renwick, 2020). In their study, Vigors and Lawrence (2019) interviewed farmers on their perception of positive animal welfare and found that as a whole, farmers prioritized the reduction of negative experiences, and mostly considered that by doing so, positive welfare would arise as a result.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

2021

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The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health, and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers' duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type [sensors, bolus, image or sound based, Radio Frequency Identification (RFID)], their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare.

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Section: Discussionmentioning

confidence: 99%

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

2021

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“…[ 13 ]). Effectively addressing them is nonetheless crucial for realizing the full potential and societal benefits of agricultural digitalization [ 1 , 8 ]. Inter- and transdisciplinary approaches are promising, but they can be hampered by practical and conceptual challenges, including entrenched attitudes, biases and institutionalized disciplinary inequalities.…”

Section: Discussionmentioning

confidence: 99%

“…7: 201511 Box 1 provides a legal analogy of the three-level framework we propose. 8 Such an analogy is equally applicable to agricultural regulation where policymakers frequently decide regulatory measures based on uncertain modelled information and myriad other contextual factors-all of which can introduce diverse uncertainties for policymakers, modellers and farmers. While the example in box 1 illustrates our three-level conceptual distinction, two points warrant clarification before reflecting on the usefulness of this framework in interdisciplinary research.…”

Section: ]mentioning

confidence: 99%

“…Our conceptual framework helped to clarify some of this confusion and formed the basis for more meaningful dialogue and system-based thinking. Specifically, the outlined meta-categories of direct, indirect and contextual uncertainty allowed us to move towards a shared appreciation of the 8 The conceptual framework can also be represented schematically (see [92]).…”

Section: Reflection: Ordering Interdisciplinary Research Through Collaborative Problem Framingmentioning

confidence: 99%

“…Both modellers and model users frequently refer to uncertainty, but it is often unclear which specific aspects of a model are deemed ‘uncertain’ or whether discussions even relate to the actual modelling. Communication around the uncertainties associated with modelling (hereafter ‘modelling uncertainty’) can consequently remain vague and ineffective, which poses challenges for realizing the full potential and societal benefits of agricultural digitalization [ 1 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization

et al. 2020

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Agricultural digitalization is providing growing amounts of real-time digital data. Biophysical simulation models can help interpret these data. However, these models are subject to complex uncertainties, which has prompted calls for interdisciplinary research to better understand and communicate modelling uncertainties and their impact on decision-making. This article develops two corresponding insights from an interdisciplinary project in a New Zealand agricultural research organization. First, we expand on a recent Royal Society Open Science journal article (van der Bles et al . 2019 Royal Society Open Science 6 , 181870 ( doi:10.1098/rsos.181870 )) and suggest a threefold conceptual framework to describe direct, indirect and contextual uncertainties associated with biophysical models. Second, we reflect on the process of developing this framework to highlight challenges to successful collaboration and the importance of a deeper engagement with interdisciplinarity. This includes resolving often unequal disciplinary standings and the need for early collaborative problem framing. We propose that both insights are complementary and informative to researchers and practitioners in the field of modelling uncertainty as well as to those interested in interdisciplinary environmental research generally. The article concludes by outlining limitations of interdisciplinary research and a shift towards transdisciplinarity that also includes non-scientists. Such a shift is crucial to holistically address uncertainties associated with biophysical modelling and to realize the full potential of agricultural digitalization.

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

Lowenberg‐DeBoer

Behrendt

Ehlers

et al. 2021

Applied Eco Perspectives Pol

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Autonomous equipment for crop production is on the verge of technical and economic feasibility, but government regulation may slow its adoption. Key regulatory issues include requirements for on‐site human supervision, liability for autonomous machine error, and intellectual property in robotic learning. As an example of the impact of regulation on the economic benefits of autonomous crop equipment, analysis from the United Kingdom suggests that requiring 100% on‐site human supervision almost wipes out the economic benefits of autonomous crop equipment for small and medium farms and increases the economies‐of‐scale advantage of larger farms.

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Innovation Uncertainty Impacts the Adoption of Smarter Farming Approaches

Cited by 42 publications

References 34 publications

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization

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

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