Assessing the effect of using different APSIM model configurations on model outputs

Article

Chapagain, Ranju, Huth, Neil, Remenyi, Tomas A., Mohammed, Caroline L. and Ojeda, Jonathan J.. 2023. "Assessing the effect of using different APSIM model configurations on model outputs." Ecological Modelling. 483. https://doi.org/10.1016/j.ecolmodel.2023.110451
Article Title

Assessing the effect of using different APSIM model configurations on model outputs

ERA Journal ID3233
Article CategoryArticle
AuthorsChapagain, Ranju, Huth, Neil, Remenyi, Tomas A., Mohammed, Caroline L. and Ojeda, Jonathan J.
Journal TitleEcological Modelling
Journal Citation483
Article Number110451
Number of Pages15
Year2023
PublisherElsevier
Place of PublicationNetherlands
ISSN0304-3800
1872-7026
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ecolmodel.2023.110451
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0304380023001825
AbstractThe three major sources of uncertainty in crop models are model inputs, structure and parameters. Model structure is one of the major contributors to this uncertainty, however, its quantification is difficult due to limitations in controlling confounding effects from parameter and input uncertainty. The objective of this study was to quantify the contribution of structural uncertainty to the variance in model outputs produced by the Agricultural Production Systems sIMulator (APSIM). Outputs investigated were yield, irrigation requirements, partial gross margin, drainage and nitrogen (N) leaching. Eight model structures differing in choice of soil water model, crop model and irrigation model were developed within a single APSIM version (v.7.10) and tested under three contrasting environments (climate × soil) across 120 years. We quantified: (i) the model structure uncertainty (from soil water, crop and irrigation models) using analysis of variance (ANOVA) and deviation analysis; and (ii) the variability of outputs due to model structure and climate using the coefficient of variation. Confounding effects from inputs, parameters and model users were controlled. Most structural uncertainty resulted from first order effects of the choice of model components (crop model: 12.2–98.9%, irrigation model: 0–78.4%, soil water model:1–33.7%) rather than second order interactions between components (0.1–18.9%). Furthermore, uncertainty from choice of sub-model/model used was not necessarily related to the structural complexity of these components. The effects of structural uncertainty on predictions commonly used to inform agronomic, ecological or policy decision making were strongly impacted by site and climate conditions i.e., high rainfall site (?1330 mm year?1) had less uncertainty and variability as compared to low rainfall site (?610 mm year?1), highlighting the need for any uncertainty assessment to cover the entire range of conditions for model application. Here we show the value of a component-based modelling framework for quantifying uncertainty in crop modelling studies. © 2023
KeywordsAPSIM
ANZSRC Field of Research 20203001. Agricultural biotechnology
Byline AffiliationsUniversity of Tasmania
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Centre for Sustainable Agricultural Systems
Permalink -

https://research.usq.edu.au/item/z257q/assessing-the-effect-of-using-different-apsim-model-configurations-on-model-outputs

Log in to edit

Download files

Published Version
1-s2.0-S0304380023001825-main.pdf
License: CC BY-NC-ND 4.0
File access level: Anyone

  • 41
    total views
  • 26
    total downloads
  • 4
    views this month
  • 1
    downloads this month

Export as

Related outputs

Agricultural diversity, farmers’ definitions and uses: The case of Tasmanian farms
Tacconi, Francesco, Lefroy, Darcy, Waha, Katharina, Jonathan, Jonathan Jesus, Leith, Peat and Mohammed, Caroline. 2024. "Agricultural diversity, farmers’ definitions and uses: The case of Tasmanian farms." Journal of Rural Studies. 108. https://doi.org/10.1016/j.jrurstud.2024.103266
Filling the agronomic data gap through a minimum data collection approach
Tenorio, Fatima A.M., Edreira, Juan I. Rattalino, Monzon, Juan Pablo, Aramburu-Merlos, Fernando, Dobermann, Achim, Gruere, Armelle, Brihet, Juan Martin, Gayo, Sofia, Conley, Shawn, Mourtzinis, Spyridon, Mashingaidze, Nester, Sananka, Alex, Aston, Stephen, Ojeda, Jonathan J. and Grassini, Patricio. 2024. "Filling the agronomic data gap through a minimum data collection approach." Field Crops Research. 308. https://doi.org/10.1016/j.fcr.2024.109278
Field and in-silico analysis of harvest index variability in maize silage
Ojeda, Jonathan Jesus, Islam, M. Rafiq Islam, Correa-Luna, Martin, Gargiulo, Juan Ignacio, Clark, Cameron Edward Fisher, Rotili, Diego Hernan and Garcia, Sergio Carlos. 2023. "Field and in-silico analysis of harvest index variability in maize silage." Frontiers in Plant Science. 14. https://doi.org/10.3389/fpls.2023.1206535
Perceptions of co-design, co-development and co-delivery (Co-3D) as part of the co-production process – Insights for climate services
Fleming, A., Bohensky, E., Dutra, L.X.C., Lin, B.B., Melbourne-Thomas, J., Moore, T., Stone-Jovicich, S., Tozer, C., Clarke, J.M., Donegan, L., Hopkins, M., Merson, S., Remenyi, T., Swirepik, A. and Vertigan, C.. 2023. "Perceptions of co-design, co-development and co-delivery (Co-3D) as part of the co-production process – Insights for climate services." Climate Services. 30. https://doi.org/10.1016/j.cliser.2023.100364
Investigating the effects of APSIM model configuration on model outputs across different environments
Chapagain, Ranju, Remenyi, Tomas A., Huth, Neil, Mohammed, Caroline L. and Ojeda, Jonathan J.. 2023. "Investigating the effects of APSIM model configuration on model outputs across different environments." Frontiers in Agronomy. 5. https://doi.org/10.3389/fagro.2023.1213074
Comparison of machine learning methods emulating process driven crop models
Johnston, David B., Pembleton, Keith G., Huth, Neil I. and Deo, Ravinesh C.. 2023. "Comparison of machine learning methods emulating process driven crop models ." Environmental Modelling and Software. 162. https://doi.org/10.1016/j.envsoft.2023.105634
Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions
Della Nave, Facundo N., Ojeda, Jonathan J., Irisarri, J. Gonzalo N., Pembleton, Keith, Oyarzabal, Mariano and Oesterheld, Martin. 2022. "Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions." Agricultural Systems. 201, pp. 1-14. https://doi.org/10.1016/j.agsy.2022.103459
Advancing a farmer decision support tool for agronomic decisions on rainfed and irrigated wheat cropping in Tasmania
Phelan, David C., Harrison, Matthew T., McLean, Greg, Cox, Howard, Pembleton, Keith G., Dean, Geoff J., Parsons, David, do Amaral Richter, Maria E., Pengilley, Georgie, Hinton, Sue J. and Mohammed, Caroline L.. 2018. "Advancing a farmer decision support tool for agronomic decisions on rainfed and irrigated wheat cropping in Tasmania." Agricultural Systems. 167, pp. 113-124. https://doi.org/10.1016/j.agsy.2018.09.003
Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas
Ojeda, J. J., Pembleton, K. G., Caviglia, O. P., Islam, M. R., Agnusdei, M. G. and Garcia, S. C.. 2018. "Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas." European Journal of Agronomy. 92, pp. 84-96. https://doi.org/10.1016/j.eja.2017.10.004
The effects of coal seam gas infrastructure development on arable land in southern Queensland, Australia: field investigations and modeling
Antille, D. L., Huth, N. I., Eberhard, J., Marinoni, O., Cocks, B., Poulton, P. L., Macdonald, B. C. T. and Schmidt, E. J.. 2016. "The effects of coal seam gas infrastructure development on arable land in southern Queensland, Australia: field investigations and modeling." Transactions of the ASABE. 59 (4), pp. 879-901. https://doi.org/10.13031/trans.59.11547
Evaluation of the agricultural production systems simulator simulating lucerne and annual ryegrass dry matter yield in the Argentine pampas and south-eastern Australia
Ojeda, J. J., Pembleton, K. G., Islam, M. R., Agnusdei, M. G. and Garcia, S. C.. 2016. "Evaluation of the agricultural production systems simulator simulating lucerne and annual ryegrass dry matter yield in the Argentine pampas and south-eastern Australia." Agricultural Systems. 143, pp. 61-75. https://doi.org/10.1016/j.agsy.2015.12.005
Assessing erosion processes associated with establishment of coal seam gas pipeline infrastructure in Queensland, Australia
Vacher, Cameron A., Antille, Diogenes L., Huth, Neil I. and Raine, Steven R.. 2016. "Assessing erosion processes associated with establishment of coal seam gas pipeline infrastructure in Queensland, Australia." 2016 ASABE Annual International Meeting. Orlando, Florida 17 - 20 Jul 2016 United States. American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.20162461210
Quantifying potential yield and lodging-related yield gaps for irrigated spring wheat in sub-tropical Australia
Peake, A. S., Huth, N. I., Carberry, P. S., Raine, S. R. and Smith, R. J.. 2014. "Quantifying potential yield and lodging-related yield gaps for irrigated spring wheat in sub-tropical Australia." Field Crops Research. 158, pp. 1-14. https://doi.org/10.1016/j.fcr.2013.12.001
Quantifying the impacts of coal seam gas (CSG) activities on the soil resource of agricultural lands in Queensland, Australia
Vacher, C. A., White, S., Eberhard, J., Schmidt, E., Huth, N. I. and Antille, D. L.. 2014. "Quantifying the impacts of coal seam gas (CSG) activities on the soil resource of agricultural lands in Queensland, Australia." American Society of Agricultural and Biological Engineers Annual International Meeting (ASABE 2014). Montreal, Canada 13 - 16 Jul 2014 St. Joseph, MI. United States.