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A systematic approach to identifying key parameters and processes in agroecosystem models

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  • Myrgiotis, Vasileios
  • Rees, Robert M.
  • Topp, Cairistiona F.E.
  • Williams, Mathew

Abstract

Process-based agroecosystem biogeochemistry models are widely used to quantify the flow of water and nutrients in agricultural ecosystems and they have become important tools in the effort to address the twin challenges of reducing greenhouse gas emissions and improving agricultural sustainability. Model parameters require careful calibration, as they affect the simulated processes and outputs. Sensitivity analysis (SA) is commonly used to quantify the impacts of parameters on outputs, and guide the calibration process. Here we demonstrate a systematic approach for SA, which assures that (1) the role of time-dependency in the sensitivity indices is considered and (2) the SA is not biased by the edapho-climatic conditions at individual sites. Demonstrating this approach, we examine the parametric sensitivity of an advanced agroecosystem model (Landscape-DNDC) using a framework that is based on (1) the Sobol SA method, (2) model simulations at three UK arable sites and (3) the grouping of the model's parameters according to the processes they affect. The findings of this research identify the parameters and processes that should be carefully examined in order to minimise the impact of parametric uncertainty on model outputs. We show that a limited number of parameters are responsible for a large part of the sensitivity of model outputs. The description of soil microbial dynamics is identified as a key source of output sensitivity. Also, we show that individual management activities can significantly affect the time-dependency of the parametric sensitivity indices for certain model outputs.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:368:y:2018:i:c:p:344-356
    DOI: 10.1016/j.ecolmodel.2017.12.009
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    References listed on IDEAS

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    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. Ben D. MacArthur & Richard O. C. Oreffo, 2005. "Bridging the gap," Nature, Nature, vol. 433(7021), pages 19-19, January.
    3. 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.
    4. Li, Xi & Yeluripati, Jagadeesh & Jones, Edward O. & Uchida, Yoshitaka & Hatano, Ryusuke, 2015. "Hierarchical Bayesian calibration of nitrous oxide (N2O) and nitrogen monoxide (NO) flux module of an agro-ecosystem model: ECOSSE," Ecological Modelling, Elsevier, vol. 316(C), pages 14-27.
    5. Gilhespy, Sarah L. & Anthony, Steven & Cardenas, Laura & Chadwick, David & del Prado, Agustin & Li, Changsheng & Misselbrook, Thomas & Rees, Robert M. & Salas, William & Sanz-Cobena, Alberto & Smith, , 2014. "First 20 years of DNDC (DeNitrification DeComposition): Model evolution," Ecological Modelling, Elsevier, vol. 292(C), pages 51-62.
    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. Rafique, Rashad & Kumar, Sandeep & Luo, Yiqi & Kiely, Gerard & Asrar, Ghassem, 2015. "An algorithmic calibration approach to identify globally optimal parameters for constraining the DayCent model," Ecological Modelling, Elsevier, vol. 297(C), pages 196-200.
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