Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Espig M

PROVIDER: S-EPMC7813261 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Espig M M Finlay-Smits S C SC Meenken E D ED Wheeler D M DM Sharifi M M

Royal Society open science 20201223 12

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 ...[more]

PMID: 33489287

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Project description:BackgroundTrainees in acute care specialties often grapple with the decision to perform an invasive procedure in a rapidly decompensating patient, for whom the benefits and risks are inherently uncertain. The difference between trainees who know when to act and when to seek supervision and those who do not is often linked to individual trainee psychological and cultural perceptions of uncertainty. But how much comfort with uncertainty relates to the situational context rather than the trainee traits is underexplored.ObjectiveThe objective of this study was to explore trainee actions around decompensating patients and assess the degree to which invasive intervention and supervision seeking depend on situational certainty or individual trait-based perceptions of uncertainty.MethodsA total of 41 internal medicine residents completed a survey to measure anxiety related to uncertainty using the Physicians' Reactions to Uncertainty (PRU) tool and to measure uncertainty avoidance using the Values Survey Module (VSM) before responding to 14 written emergency situations. Half of the scenarios contain sufficient diagnostic certainty to warrant aggressive intervention, and half lack sufficient diagnostic clarity to offset the risk of intervention. Mixed multivariable modeling was used to identify the relationship between planned invasive intervention, situational uncertainty, and trait-based perceptions of uncertainty measured in the PRU and VSM.ResultsTrainees' first actions were appropriate in 60% of cases. Multivariable modeling suggested that situational certainty was more predictive of upfront intervention (odds ratio [OR], 30.5; P < 0.0001) than trait-based PRU (OR, 1.22; P = 0.05) and VSM (OR, 1.73; P < 0.0001). Similarly, situational certainty was more predictive of reduced supervision seeking (OR, 0.20; P < 0.0001) than trait-based PRU (OR, 2.03; P < 0.001) and VSM (P = not significant).ConclusionsSituation-specific certainty was more strongly correlated with invasive intervention in cases of decompensated patients than individual trainee traits. Focusing on trainee contextual understanding of procedural risk-benefit ratios in decompensating patients holds more promise for improving trainee actions and supervision seeking than tackling their perceptions around uncertainty.

Dataset Information

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

Publications

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

Similar Datasets

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