IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v266y2013icp42-57.html
   My bibliography  Save this article

Screening parameters in the Pasture Simulation model using the Morris method

Author

Listed:
  • Ben Touhami, Haythem
  • Lardy, Romain
  • Barra, Vincent
  • Bellocchi, Gianni

Abstract

Mechanistic vegetation models with large parameter sets and high temporal resolution are currently used in grassland studies. They need a parsimonious screening method to identify the most influential parameters for the grassland system in specific contexts (weather, soil, management). This is basic to better understand and make use of the outputs from these models. In this study, Morris’ method was applied to test the sensitivity of a variety of outputs of the Pasture Simulation model (PaSim) to its parameters in six European multi-year grassland sites (one of them run under both extensive and intensive management regimes). Twenty-eight parameters related to plant physiology and animal digestion were screened and ranked for their sensitivity (under two distributional assumptions of parameter values), with the objective of determining their stability across sites and the minimum requirements for parameter calibration. The sensitivity analysis results proved that PaSim response is fairly stable across European sites, with only a few differences. Key results are that (1) seven influential parameters of vegetation development, aboveground growth and carbon/nitrogen partitioning were globally identified with both uniform and Gaussian distributions of parameter values, (2) two additional parameters (associated with leaf and stem fibre content) were also recognized as relevant for animal CH4 emissions target output, (3) listing of key parameters differed, but not widely, across sites and targeted outputs, and between distributions (ranking was more plastic), (4) first-order sensitivity rank (strength, μ) was generally similar to (or higher than) higher-order sensitivity (spread, σ), indicating that parameters showing high interaction with other parameters or non-linearities are those with also a high direct effect on output. Overall, Morris’ method proved to be an effective and reliable tool to identify key vegetation parameters for the use of PaSim in the European conditions.

Suggested Citation

  • Ben Touhami, Haythem & Lardy, Romain & Barra, Vincent & Bellocchi, Gianni, 2013. "Screening parameters in the Pasture Simulation model using the Morris method," Ecological Modelling, Elsevier, vol. 266(C), pages 42-57.
    • Handle: RePEc:eee:ecomod:v:266:y:2013:i:c:p:42-57
    DOI: 10.1016/j.ecolmodel.2013.07.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013003372
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.07.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. DeJonge, Kendall C. & Ascough, James C. & Ahmadi, Mehdi & Andales, Allan A. & Arabi, Mazdak, 2012. "Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments," Ecological Modelling, Elsevier, vol. 231(C), pages 113-125.
    2. Confalonieri, R. & Bellocchi, G. & Bregaglio, S. & Donatelli, M. & Acutis, M., 2010. "Comparison of sensitivity analysis techniques: A case study with the rice model WARM," Ecological Modelling, Elsevier, vol. 221(16), pages 1897-1906.
    3. Pujol, Gilles, 2009. "Simplex-based screening designs for estimating metamodels," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1156-1160.
    4. Xu, C. & Gertner, G., 2007. "Extending a global sensitivity analysis technique to models with correlated parameters," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5579-5590, August.
    5. Confalonieri, R. & Bregaglio, S. & Acutis, M., 2012. "Quantifying plasticity in simulation models," Ecological Modelling, Elsevier, vol. 225(C), pages 159-166.
    6. Cariboni, J. & Gatelli, D. & Liska, R. & Saltelli, A., 2007. "The role of sensitivity analysis in ecological modelling," Ecological Modelling, Elsevier, vol. 203(1), pages 167-182.
    7. Confalonieri, R. & Bregaglio, S. & Acutis, M., 2010. "A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions," Ecological Modelling, Elsevier, vol. 221(6), pages 960-964.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wagener, Thorsten & Pianosi, Francesca, 2019. "What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling," Earth Arxiv g9ma5, Center for Open Science.
    2. Edoardo Bellini & Raphaël Martin & Giovanni Argenti & Nicolina Staglianò & Sergi Costafreda-Aumedes & Camilla Dibari & Marco Moriondo & Gianni Bellocchi, 2023. "Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)," Land, MDPI, vol. 12(2), pages 1-22, January.
    3. Trentin, Pedro Francisco Silva & Martinez, Pedro Henrique Barsanaor de Barros & dos Santos, Gabriel Bertacco & Gasparin, Elóy Esteves & Salviano, Leandro Oliveira, 2022. "Screening analysis and unconstrained optimization of a small-scale vertical axis wind turbine," Energy, Elsevier, vol. 240(C).
    4. Gambade, Julien & Noël, Hervé & Glouannec, Patrick & Magueresse, Anthony, 2023. "Numerical model of intermittent solar hot water production," Renewable Energy, Elsevier, vol. 218(C).
    5. Kipling, Richard P. & Bannink, André & Bellocchi, Gianni & Dalgaard, Tommy & Fox, Naomi J. & Hutchings, Nicholas J. & Kjeldsen, Chris & Lacetera, Nicola & Sinabell, Franz & Topp, Cairistiona F.E. & va, 2016. "Modeling European ruminant production systems: Facing the challenges of climate change," Agricultural Systems, Elsevier, vol. 147(C), pages 24-37.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gilardelli, Carlo & Confalonieri, Roberto & Cappelli, Giovanni Alessandro & Bellocchi, Gianni, 2018. "Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change," Ecological Modelling, Elsevier, vol. 368(C), pages 1-14.
    2. Francisco A. Buendia-Hernandez & Maria J. Ortiz Bevia & Francisco J. Alvarez-Garcia & Antonio Ruizde Elvira, 2022. "Sensitivity of a Dynamic Model of Air Traffic Emissions to Technological and Environmental Factors," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    3. Kanapaux, William & Kiker, Gregory A., 2013. "Development and testing of an object-oriented model for adaptively managing human disturbance of least tern (Sternula antillarum) nesting habitat," Ecological Modelling, Elsevier, vol. 268(C), pages 64-77.
    4. Song, Xiaodong & Bryan, Brett A. & Almeida, Auro C. & Paul, Keryn I. & Zhao, Gang & Ren, Yin, 2013. "Time-dependent sensitivity of a process-based ecological model," Ecological Modelling, Elsevier, vol. 265(C), pages 114-123.
    5. Paleari, Livia & Confalonieri, Roberto, 2016. "Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions," Ecological Modelling, Elsevier, vol. 340(C), pages 57-63.
    6. Priyadarshi, Anupam & Chandra, Ram & Kishi, Michio J. & Smith, S.Lan & Yamazaki, Hidekatsu, 2022. "Understanding plankton ecosystem dynamics under realistic micro-scale variability requires modeling at least three trophic levels," Ecological Modelling, Elsevier, vol. 467(C).
    7. Yi, Xuan & Zou, Rui & Guo, Huaicheng, 2016. "Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake," Ecological Modelling, Elsevier, vol. 327(C), pages 74-84.
    8. Melbourne-Thomas, J. & Johnson, C.R. & Fulton, E.A., 2011. "Characterizing sensitivity and uncertainty in a multiscale model of a complex coral reef system," Ecological Modelling, Elsevier, vol. 222(18), pages 3320-3334.
    9. Song, Xiaodong & Bryan, Brett A. & Paul, Keryn I. & Zhao, Gang, 2012. "Variance-based sensitivity analysis of a forest growth model," Ecological Modelling, Elsevier, vol. 247(C), pages 135-143.
    10. Confalonieri, R. & Bregaglio, S. & Acutis, M., 2012. "Quantifying plasticity in simulation models," Ecological Modelling, Elsevier, vol. 225(C), pages 159-166.
    11. Myrgiotis, Vasileios & Rees, Robert M. & Topp, Cairistiona F.E. & Williams, Mathew, 2018. "A systematic approach to identifying key parameters and processes in agroecosystem models," Ecological Modelling, Elsevier, vol. 368(C), pages 344-356.
    12. Zhang, Jing & Chen, Yi & Zhang, Zhao, 2020. "A remote sensing-based scheme to improve regional crop model calibration at sub-model component level," Agricultural Systems, Elsevier, vol. 181(C).
    13. Hanqing Ma & Chunfeng Ma & Xin Li & Wenping Yuan & Zhengjia Liu & Gaofeng Zhu, 2020. "Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
    14. Confalonieri, R., 2014. "CoSMo: A simple approach for reproducing plant community dynamics using a single instance of generic crop simulators," Ecological Modelling, Elsevier, vol. 286(C), pages 1-10.
    15. Zhang, Wang & Tian, Yong & Sun, Zan & Zheng, Chunmiao, 2021. "How does plastic film mulching affect crop water productivity in an arid river basin?," Agricultural Water Management, Elsevier, vol. 258(C).
    16. Morris, David J. & Speirs, Douglas C. & Cameron, Angus I. & Heath, Michael R., 2014. "Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: Factors affecting the biomass of fish and benthos," Ecological Modelling, Elsevier, vol. 273(C), pages 251-263.
    17. Xenia Specka & Claas Nendel & Ralf Wieland, 2019. "Temporal Sensitivity Analysis of the MONICA Model: Application of Two Global Approaches to Analyze the Dynamics of Parameter Sensitivity," Agriculture, MDPI, vol. 9(2), pages 1-29, February.
    18. Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
    19. Pelletier, Dominique & Mahevas, Stéphanie & Drouineau, Hilaire & Vermard, Youen & Thebaud, Olivier & Guyader, Olivier & Poussin, Benjamin, 2009. "Evaluation of the bioeconomic sustainability of multi-species multi-fleet fisheries under a wide range of policy options using ISIS-Fish," Ecological Modelling, Elsevier, vol. 220(7), pages 1013-1033.
    20. Kang, Fenni & Cournède, Paul-Henry & Lecoeur, Jérémie & Letort, Véronique, 2014. "SUNLAB: A functional–structural model for genotypic and phenotypic characterization of the sunflower crop," Ecological Modelling, Elsevier, vol. 290(C), pages 21-33.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:266:y:2013:i:c:p:42-57. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.