Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization
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 mo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2020-12-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.201511 |
| _version_ | 1818566226888097792 |
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| author | M. Espig S. C. Finlay-Smits E. D. Meenken D. M. Wheeler M. Sharifi |
| author_facet | M. Espig S. C. Finlay-Smits E. D. Meenken D. M. Wheeler M. Sharifi |
| author_sort | M. Espig |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T01:51:03Z |
| format | Article |
| id | doaj.art-735c95f6b5444071a9d12c90ca740827 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-14T01:51:03Z |
| publishDate | 2020-12-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-735c95f6b5444071a9d12c90ca7408272022-12-21T23:21:23ZengThe Royal SocietyRoyal Society Open Science2054-57032020-12-0171210.1098/rsos.201511201511Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalizationM. EspigS. C. Finlay-SmitsE. D. MeenkenD. M. WheelerM. SharifiAgricultural 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.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.201511uncertaintybiophysical modellinginterdisciplinary researchsocial sciencestatisticsengineering |
| spellingShingle | M. Espig S. C. Finlay-Smits E. D. Meenken D. M. Wheeler M. Sharifi Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization Royal Society Open Science uncertainty biophysical modelling interdisciplinary research social science statistics engineering |
| title | Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization |
| title_full | Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization |
| title_fullStr | Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization |
| title_full_unstemmed | Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization |
| title_short | Uncertainty in and around biophysical modelling: insights from interdisciplinary research on agricultural digitalization |
| title_sort | uncertainty in and around biophysical modelling insights from interdisciplinary research on agricultural digitalization |
| topic | uncertainty biophysical modelling interdisciplinary research social science statistics engineering |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.201511 |
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