IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v225y2012icp159-166.html
   My bibliography  Save this article

Quantifying plasticity in simulation models

Author

Listed:
  • Confalonieri, R.
  • Bregaglio, S.
  • Acutis, M.

Abstract

Different methodologies for evaluating aspects of model performance going beyond the pure agreement between measured and simulated data have been recently proposed. These indicators and criteria for the evaluation of, e.g., complexity and robustness can be used in conjunction with well-known metrics for the evaluation of model accuracy – such as root mean square error and modelling efficiency – to get a deeper knowledge about models structure and behaviour. The aim of this paper is to propose an indicator of model plasticity, defined as the aptitude of a model to change the sensitivity to its parameters while changing the conditions of application. Sensitivity was here analyzed using the Sobol’ method for sensitivity analysis (SA). Concordance among parameters relevance (total order effect) estimated under different conditions allowed to quantify changes in the way models react to different environments. The concordance among the different SA results was related to the variability of a normalized agrometeorological indicator used to characterize the explored conditions. The plasticity indicator was tested using three different crop models (WARM, CropSyst, WOFOST; rice was simulated), 10 European locations, and 10 years for each location, for a total of 5,939,200 simulations and 300 SA experiments. Results indicated WOFOST as the most plastic, both within location, year, and using all the combinations location×year, whereas WARM showed to be the less plastic across the conditions explored. Previous studies carried out on the same models in northern Italy seem to suggest a direct relationship between model complexity and plasticity, whereas model accuracy seems to be unrelated to these features. This consideration underlines that, in case of availability of different models with a similar degree of accuracy, different choices should be performed for different modelling studies, characterized by different aims and conditions of application.

Suggested Citation

  • Confalonieri, R. & Bregaglio, S. & Acutis, M., 2012. "Quantifying plasticity in simulation models," Ecological Modelling, Elsevier, vol. 225(C), pages 159-166.
  • Handle: RePEc:eee:ecomod:v:225:y:2012:i:c:p:159-166
    DOI: 10.1016/j.ecolmodel.2011.11.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380011005643
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2011.11.022?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. 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.
    2. 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.
    3. Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    4. Confalonieri, Roberto & Acutis, Marco & Bellocchi, Gianni & Donatelli, Marcello, 2009. "Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice," Ecological Modelling, Elsevier, vol. 220(11), pages 1395-1410.
    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. 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.
    2. Tadiello, Tommaso & Gabbrielli, Mara & Botta, Marco & Acutis, Marco & Bechini, Luca & Ragaglini, Giorgio & Fiorini, Andrea & Tabaglio, Vincenzo & Perego, Alessia, 2023. "A new module to simulate surface crop residue decomposition: Description and sensitivity analysis," Ecological Modelling, Elsevier, vol. 480(C).
    3. 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.
    4. 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.
    5. 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.

    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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
    8. 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.
    9. Nana, E. & Corbari, C. & Bocchiola, D., 2014. "A model for crop yield and water footprint assessment: Study of maize in the Po valley," Agricultural Systems, Elsevier, vol. 127(C), pages 139-149.
    10. Zhu, Xiufang & Xu, Kun & Liu, Ying & Guo, Rui & Chen, Lingyi, 2021. "Assessing the vulnerability and risk of maize to drought in China based on the AquaCrop model," Agricultural Systems, Elsevier, vol. 189(C).
    11. López-Benito, Alfredo & Bolado-Lavín, Ricardo, 2017. "A case study on global sensitivity analysis with dependent inputs: The natural gas transmission model," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 11-21.
    12. Masseroni, Daniele & Gangi, Fabiola & Galli, Andrea & Ceriani, Rodolfo & De Gaetani, Carlo & Gandolfi, Claudio, 2022. "Behind the efficiency of border irrigation: Lesson learned in Northern Italy," Agricultural Water Management, Elsevier, vol. 269(C).
    13. Liu, Min & He, Honglin & Ren, Xiaoli & Sun, Xiaomin & Yu, Guirui & Han, Shijie & Wang, Huimin & Zhou, Guoyi, 2015. "The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems," Ecological Modelling, Elsevier, vol. 303(C), pages 30-41.
    14. Bocchiola, D. & Brunetti, L. & Soncini, A. & Polinelli, F. & Gianinetto, M., 2019. "Impact of climate change on agricultural productivity and food security in the Himalayas: A case study in Nepal," Agricultural Systems, Elsevier, vol. 171(C), pages 113-125.
    15. Paleari, Livia & Movedi, Ermes & Zoli, Michele & Burato, Andrea & Cecconi, Irene & Errahouly, Jabir & Pecollo, Eleonora & Sorvillo, Carla & Confalonieri, Roberto, 2021. "Sensitivity analysis using Morris: Just screening or an effective ranking method?," Ecological Modelling, Elsevier, vol. 455(C).
    16. Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    17. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2014. "Sensitivity analysis of an energy-economy model of the residential building sector," CIRED Working Papers hal-01016399, HAL.
    18. Kitikidou, Kyriaki & Petrou, Petros & Milios, Elias, 2012. "Dominant height growth and site index curves for Calabrian pine (Pinus brutia Ten.) in central Cyprus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1323-1329.
    19. Alexandra M. Thorn & Jonathan R. Thompson & Joshua S. Plisinski, 2016. "Patterns and Predictors of Recent Forest Conversion in New England," Land, MDPI, vol. 5(3), pages 1-17, September.
    20. 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.

    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:225:y:2012:i:c:p:159-166. 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.