Espig M, Finlay-Smits S C, Meenken E D, Wheeler D M, Sharifi M
AgResearch, Lincoln Research Centre, 1365 Springs Road, Lincoln 7674, New Zealand.
AgResearch, Ruakura Agricultural Centre, 10 Bisley Road, Enderley, Hamilton 3214, New Zealand.
R Soc Open Sci. 2020 Dec 23;7(12):201511. doi: 10.1098/rsos.201511. eCollection 2020 Dec.
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 journal article (van der Bles . 2019 , 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.
农业数字化正在提供越来越多的实时数字数据。生物物理模拟模型有助于解读这些数据。然而，这些模型存在复杂的不确定性，这促使人们呼吁开展跨学科研究，以更好地理解和传达建模不确定性及其对决策的影响。本文从新西兰一个农业研究机构的跨学科项目中得出了两点相应的见解。第一，我们在最近一篇期刊文章（范德布勒斯，2019年，181870（doi:10.1098/rsos.181870））的基础上进行拓展，提出了一个三重概念框架，以描述与生物物理模型相关的直接、间接和背景不确定性。第二，我们反思了这个框架的开发过程，以突出成功合作所面临的挑战以及更深入参与跨学科研究的重要性。这包括解决学科地位往往不平等的问题以及早期合作问题框架构建的必要性。我们认为，这两点见解对建模不确定性领域的研究人员和从业者以及一般对跨学科环境研究感兴趣的人都具有互补性和参考价值。本文最后概述了跨学科研究的局限性以及向包括非科学家在内的跨学科研究的转变。这种转变对于全面解决与生物物理建模相关的不确定性以及实现农业数字化的全部潜力至关重要。
