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Effects of modelling detail on simulated potential crop yields under a wide range of climatic conditions

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  • Adam, M.
  • Van Bussel, L.G.J.
  • Leffelaar, P.A.
  • Van Keulen, H.
  • Ewert, F.

Abstract

Crop simulation models are widely applied at large scale for climate change impact assessment or integrated assessment studies. However, often a mismatch exists between data availability and the level of detail in the model used. Good modelling practice dictates to keep models as simple as possible, but enough detail should be incorporated to capture the major processes that determine the system's behaviour. The objective of this study was to investigate the effect of the level of detail incorporated in process-based crop growth models on simulated potential yields under a wide range of climatic conditions. We conducted a multi-site analysis and identified that by using a constant radiation use efficiency (RUE) value under a wide range of climatic conditions, the description of the process of biomass production may be over-simplified, as the effects of high temperatures and high radiation intensities on this parameter are ignored. Further, we found that particular attention should be given to the choice of the light interception approach in a crop model as determined by leaf area index (LAI) dynamics. The two LAI dynamics approaches considered in this study gave different simulated yields irrespective of the characteristics of the location and the light interception approaches better explained the differences in yield sensitivity to climatic variability than the biomass production approaches. Further analysis showed that differences between the two LAI dynamics approaches for simulated yields were mainly due to different representations of leaf senescence in both approaches. We concluded that a better understanding and modelling of leaf senescence, particularly its onset, is needed to reduce model uncertainty in yield simulations.

Suggested Citation

  • Adam, M. & Van Bussel, L.G.J. & Leffelaar, P.A. & Van Keulen, H. & Ewert, F., 2011. "Effects of modelling detail on simulated potential crop yields under a wide range of climatic conditions," Ecological Modelling, Elsevier, vol. 222(1), pages 131-143.
  • Handle: RePEc:eee:ecomod:v:222:y:2011:i:1:p:131-143
    DOI: 10.1016/j.ecolmodel.2010.09.001
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    References listed on IDEAS

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    1. Hansen, J. W. & Jones, J. W., 2000. "Scaling-up crop models for climate variability applications," Agricultural Systems, Elsevier, vol. 65(1), pages 43-72, July.
    2. van Ittersum, Martin K. & Ewert, Frank & Heckelei, Thomas & Wery, Jacques & Alkan Olsson, Johanna & Andersen, Erling & Bezlepkina, Irina & Brouwer, Floor & Donatelli, Marcello & Flichman, Guillermo & , 2008. "Integrated assessment of agricultural systems - A component-based framework for the European Union (SEAMLESS)," Agricultural Systems, Elsevier, vol. 96(1-3), pages 150-165, March.
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    Cited by:

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    2. Dzotsi, K.A. & Basso, B. & Jones, J.W., 2013. "Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT," Ecological Modelling, Elsevier, vol. 260(C), pages 62-76.
    3. Adam, M. & Wery, J. & Leffelaar, P.A. & Ewert, F. & Corbeels, M. & Van Keulen, H., 2013. "A systematic approach for re-assembly of crop models: An example to simulate pea growth from wheat growth," Ecological Modelling, Elsevier, vol. 250(C), pages 258-268.
    4. Shibu, M.E. & Van Keulen, H. & Leffelaar, P.A., 2012. "Long-term dynamics of soil C and N in intensive rice-based cropping systems of the Indo-Gangetic Plains (IGP): A modelling approach," Ecological Modelling, Elsevier, vol. 232(C), pages 40-63.
    5. Chauhdary, Junaid Nawaz & Bakhsh, Allah & Engel, Bernard A. & Ragab, Ragab, 2019. "Improving corn production by adopting efficient fertigation practices: Experimental and modeling approach," Agricultural Water Management, Elsevier, vol. 221(C), pages 449-461.
    6. Lutz, Femke & Stoorvogel, Jetse J. & Müller, Christoph, 2019. "Options to model the effects of tillage on N2O emissions at the global scale," Ecological Modelling, Elsevier, vol. 392(C), pages 212-225.
    7. Dzotsi, K.A. & Basso, B. & Jones, J.W., 2015. "Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model," Agricultural Systems, Elsevier, vol. 135(C), pages 31-47.
    8. Pogson, Mark, 2011. "Modelling Miscanthus yields with low resolution input data," Ecological Modelling, Elsevier, vol. 222(23), pages 3849-3853.

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