School of Agriculture, Food and Wine
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
I am a Lecturer and Research Program Lead in Decision Agriculture at The University of Adelaide.
My work aims to achieve sustainable agricultural futures. I use computer models and data analytics to help farmers, businesses and policy makers to better manage their resources in the face of uncertainty. I have worked extensively within multidisciplinary teams comprising agronomists, hydrologists, climate scientists, economists and engineers, and led the development of decision support tools that integrate data and concepts from across these various disciplines.
My current research is in Decision Agriculture. A key aspect of my research in this area deals with bridging the gap between data and decisions. There are more and more diverse data streams of different shapes and sizes rapidly becoming available in agriculture, and we now face the problem of how to best use all these data. I use computer models and data analytics to translate diverse data streams into actionable information for decision makers to improve their outcomes.
I recently led the prediction and advisory component of the $5M ‘VitiVisor’ project, funded by Wine Australia and Riverland Wine, involving a partnership of viticulturists, economists, computer scientists and engineers as well as grape growers in the Riverland region of South Australia. My research blended diverse data streams (e.g., plant and soil moisture sensors, financial benchmarks) with our current understanding of grapevine dynamics using innovative forecasting and data assimilation algorithms. This enabled growers an unprecedented level of ‘situational awareness’—what is happening in the vineyard and why, and which management strategies will likely lead to improved vineyard outcomes?
By integrating data and concepts from different disciplines such as agriculture, hydrology and economics, my research has also explored trade-offs between often-competing objectives; resolving these trade-offs is often necessary to solve complex, real-world problems. For example, I have combined optimization algorithms and process-based models to optimize dairy farm outcomes while satisfying ecological river health conditions under a range of risk tolerances.
Date Position Institution name 2022 - ongoing Lecturer (Decision Agriculture) University of Adelaide 2020 - 2022 Postdoctoral Researcher University of Adelaide 2016 - 2020 Modelling Scientist GNS Science 2015 - 2016 Research Associate Flinders University 2010 - 2016 Teaching Assistant Flinders University
Date Institution name Country Title 2012 - 2016 Flinders University Australia PhD
Year Citation 2022 White, J. T., Knowling, M. J., Fienen, M. N., Siade, A., Rea, O., & Martinez, G. (2022). A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty. Environmental Modelling and Software, 149, 12 pages.
DOI Scopus2 WoS1
2022 Alzraiee, A. H., White, J. T., Knowling, M. J., Hunt, R. J., & Fienen, M. N. (2022). A scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states. Environmental Modelling and Software, 150, 13 pages.
2022 Hessel, V., Liang, S., Tran, N. N., Escribà-Gelonch, M., Zeckovic, O., Knowling, M., . . . Burgess, A. (2022). Eustress in Space: Opportunities for Plant Stressors Beyond the Earth Ecosystem. Frontiers in Astronomy and Space Sciences, 9, 22 pages.
2022 Markovich, K. H., White, J. T., & Knowling, M. J. (2022). Sequential and batch data assimilation approaches to cope with groundwater model error: An empirical evaluation. Environmental Modelling and Software, 156, 105498.
2021 White, J. T., Hemmings, B., Fienen, M. N., & Knowling, M. J. (2021). Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses. Environmental Modelling and Software, 139, 105022.
DOI Scopus8 WoS7
2021 Knowling, M. J., Bennett, B., Ostendorf, B., Westra, S., Walker, R. R., Pellegrino, A., . . . Grigg, D. (2021). Bridging the gap between data and decisions: A review of process-based models for viticulture. Agricultural Systems, 193, 1-13.
DOI Scopus3 WoS3
2020 Knowling, M. J., White, J. T., Moore, C. R., Rakowski, P., & Hayley, K. (2020). On the assimilation of environmental tracer observations for model-based decision support. Hydrology and Earth System Sciences, 24(4), 1677-1689.
DOI Scopus11 WoS8
2020 Partington, D., Knowling, M. J., Simmons, C. T., Cook, P. G., Xie, Y., Iwanaga, T., & Bouchez, C. (2020). Worth of hydraulic and water chemistry observation data in terms of the reliability of surface water-groundwater exchange flux predictions under varied flow conditions. Journal of Hydrology, 590, 17 pages.
DOI Scopus11 WoS10
2020 Hemmings, B., Knowling, M. J., & Moore, C. R. (2020). Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example. Frontiers in Earth Science, 8, 22 pages.
DOI Scopus4 WoS3
2020 White, J. T., Foster, L. K., Fienen, M. N., Knowling, M. J., Hemmings, B., & Winterle, J. R. (2020). Toward Reproducible Environmental Modeling for Decision Support: A Worked Example. Frontiers in Earth Science, 8, 11 pages.
DOI Scopus11 WoS6
2020 Knowling, M. J., White, J. T., McDonald, G. W., Kim, J. H., Moore, C. R., & Hemmings, B. (2020). Disentangling environmental and economic contributions to hydro-economic model output uncertainty: An example in the context of land-use change impact assessment. Environmental Modelling and Software, 127, 13 pages.
DOI Scopus8 WoS7
2020 White, J. T., Knowling, M. J., Fienen, M. N., Feinstein, D. T., McDonald, G. W., & Moore, C. R. (2020). A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support. Environmental Modelling and Software, 126, 104657-1-104657-11.
DOI Scopus8 WoS8
2020 White, J. T., Knowling, M. J., & Moore, C. R. (2020). Consequences of Groundwater-Model Vertical Discretization in Risk-Based Decision-Making. Groundwater, 58(5), 695-709.
DOI Scopus7 WoS5
2019 Knowling, M. J., White, J. T., & Moore, C. R. (2019). Role of model parameterization in risk-based decision support: An empirical exploration. Advances in Water Resources, 128, 59-73.
DOI Scopus25 WoS24
2017 Knowling, M., & Werner, A. (2017). Transient recharge estimability through field-scale groundwater model calibration. Groundwater, 55(6), 827-840.
DOI Scopus4 WoS4 Europe PMC1
2016 Knowling, M. J., & Werner, A. D. (2016). Estimability of recharge through groundwater model calibration: insights from a field-scale steady-state example. Journal of Hydrology, 540, 973-987.
DOI Scopus22 WoS22
2015 Knowling, M. J., Werner, A. D., & Herckenrath, D. (2015). Quantifying climate and pumping contributions to aquifer depletion using a highly parameterised groundwater model: Uley South Basin (South Australia). Journal of Hydrology, 523, 515-530.
DOI Scopus23 WoS22
2013 Liggett, J. E., Knowling, M. J., Werner, A. D., & Simmons, C. T. (2013). On the implementation of the surface conductance approach using a block-centred surface-subsurface hydrology model. Journal of Hydrology, 496, 1-8.
DOI Scopus5 WoS5
Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2021 Co-Supervisor Creating Digital Twins of Space-Agriculture Systems Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Ms Shu Liang
Other Supervision Activities
Date Role Research Topic Location Program Supervision Type Student Load Student Name 2018 - 2018 Co-Supervisor Using predictive uncertainty analysis to optimise data acquisition for stream depletion and land-use change GNS Science (New Zealand) Master Full Time Tess op den Kelder
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