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

Espig, Martin; Finlay-Smits, Susanna; Meenken, Esther D.; Wheeler, D. M.; Sharifi, Mostafa

doi:10.1098/rsos.201511

Cited by 6 publications

(10 citation statements)

References 76 publications

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“…These experiences show that reflexivity is needed throughout R&D programmes to achieve more RI outcomes. Both digital agtech programmes included projects where teams came to realise the value of interdisciplinary research and associated prompts for reflexive engagements with underlying assumptions and disciplinary limitations, despite initial time requirements (Espig et al., 2020; Fleming et al., 2021; Jakku et al., 2022). In addition to fostering an appreciation for reflexivity, relevant activities need to be adequately resourced and incentivised as core elements of R&D programmes.…”

Section: Resultsmentioning

confidence: 99%

“…After two years of proof‐of‐concept research that started in early 2019, NZBIDA is now considered a flagship integrative programme for developing digital agtech and new research approaches. Project teams have, for instance, trialled agile work principles, co‐designed activities with end users, and implemented inter‐/transdisciplinary approaches (Espig et al., 2020). These activities are aimed at realising the programme's ten to twenty‐year vision, as introduced by the programme leader in a PowerPoint presentation during the fourth workshop: The power of digital technologies is harnessed to enable the transformation of New Zealand food systems to meet the challenges the world faces by diversifying land use, creating new ways of doing business and new ways of adding value to products we produce.…”

Section: Resultsmentioning

confidence: 99%

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Responsible digital agri‐food innovation in Australian and New Zealand public research organisations

et al. 2022

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This article analyses digital agricultural technologies (agtech) innovation in two public research organisations in Australia and Aotearoa New Zealand through the lens of responsible innovation (RI), and how corresponding activities were realised in practice. Four virtual workshops explored the operationalisation of RI and its efficacy in digital agtech development. Thematic analysis of workshop materials provided scope to assess the recognised and realised value of RI in both digital agtech programmes, which we found lagging behind RI's full, perhaps idealised, potential. The value proposition of RI can, therefore, not be taken for granted without support for its operationalisation and institutionalisation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Responsible digital agri‐food innovation in Australian and New Zealand public research organisations

et al. 2022

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“…That can be attributed to what Wynne (1992) and Espig et al. (2020) call ignorance and indeterminacy. Simply put, the dimension of ignorance refers to the unknown unknowns: advisors’ inability to be aware of what they do not know and how these knowledge gaps can lead to unintended impacts.…”

Section: Discussionmentioning

confidence: 99%

“…Anticipation, in this vein, is narrowed to the impacts that Agriculture 4.0 technologies might have on farmers, whereas there is considerable difficulty in predicting what the societal impacts of Agriculture 4.0 will be ahead of time. That can be attributed to what Wynne (1992) and Espig et al (2020) call ignorance and indeterminacy. Simply put, the dimension of ignorance refers to the unknown unknowns: advisors' inability to be aware of what they do not know and how these knowledge gaps can lead to unintended impacts.…”

Section: Redefining Advisors' Responsible Professionalismmentioning

confidence: 99%

Farm advisors amid the transition to Agriculture 4.0: Professional identity, conceptions of the future and future‐specific competencies

et al. 2022

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The transition to Agriculture 4.0 creates new responsibilities for farm advisors and initiates changes to the professional trajectories. In this work, following a mixed research design, we examine how Greek advisors experience the transition to and anticipate the future of Agriculture 4.0. We also aim at identifying what elements they are changing in their professional identities to respond to the challenges associated with Agriculture 4.0, which are the new responsibility‐related competency needs that this transition generates, and how conceptions of the future of farming impact upon these needs. Our findings suggest that the transition to Agriculture 4.0 urges the emergence of a new culture, in which data and technology are considered more reliable than human advice, and creates new responsibility gaps. To cope with these changes, advisors add new roles in their professional identities, emphasising the principle of beneficence and paying limited attention to the societal externalities of transition. Advisors view Agriculture 4.0 as a threat or disruption more than as evolution or promise. In parallel, they prioritise different sets of competencies needed to responsibly facilitate agricultural digitalisation, depending on how they grasp the future of Agriculture 4.0.

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“…If decisions are made without adequate acknowledgment of uncertainty, the chances of unanticipated outcomes are either understated or overlooked. Quantitative and qualitative metrics of uncertainty also improve confidence in the validity of the information, and provide evidence for the underlying quality of the model (Espig, Finlay‐Smits, Meenken, Wheeler & Sharifi, 2020; Mastrandrea et al., 2010). Thus, the facility to report the degree of certainty in findings in way that exploits all available information is beneficial for informed decision making.…”

Section: Introductionmentioning

confidence: 99%

Bayesian hybrid analytics for uncertainty analysis and real‐time crop management

et al. 2021

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Dynamic, deterministic agricultural models, and current machine learning technologies based on sensor data, enable and support decision making for on-farm management. However, their predictions are subject to various sources of uncertainty. Hybrid analytics that leverage both modelled and sensor data provide predictive information that makes the best of both approaches in a timely fashion to inform operational decision making and enable inclusive uncertainty quantification. We describe and evaluate a probabilistic Bayesian data assimilation tool that combines the state variables from the Sirius wheat (Triticum aestivum L.) development model with time-series environmental and leaf count data. Additionally, the uncertainty associated with input parameters is quantified via expert opinion. The Bayesian approach obtained point estimates through time that were accompanied by inclusive, probabilistic, 95% credible intervals. At the end of simulation, a typical model predicted a final leaf number of 6.6 leaves, Sirius alone predicted seven leaves and the mean of the observed data was 6.7 leaves. The 95% credible interval was estimated as 5.1-8.4 leaves. Importantly, the tool was able to "redirect" simulated outputs if input parameters such as minimum leaf number or base phyllochron were incorrectly specified, with the implication that on-farm decision makers would have advance warning of variation in expected harvest date. Relatively few plants with time-intensive data were sufficient to fit the model, however, more plants would be desirable to reduce the rather wide range of credible intervals. Nevertheless, the tool shows potential and could be readily implemented with low resource requirements, providing more finely tuned harvest date information, with probabilistic uncertainty quantification built-in, for on-farm decisions.

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

Cited by 6 publications

References 76 publications

Responsible digital agri‐food innovation in Australian and New Zealand public research organisations

Responsible digital agri‐food innovation in Australian and New Zealand public research organisations

Farm advisors amid the transition to Agriculture 4.0: Professional identity, conceptions of the future and future‐specific competencies

Bayesian hybrid analytics for uncertainty analysis and real‐time crop management

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