IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v222y2011i1p131-143.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380010004564
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2010.09.001?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. 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.
    3. Liu, Junguo & Williams, Jimmy R. & Zehnder, Alexander J.B. & Yang, Hong, 2007. "GEPIC - modelling wheat yield and crop water productivity with high resolution on a global scale," Agricultural Systems, Elsevier, vol. 94(2), pages 478-493, May.
    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. 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.
    2. Chauhdary, Junaid Nawaz & Li, Hong & Akbar, Nadeem & Javaid, Maria & Rizwan, Muhammad & Akhlaq, Muhammad, 2024. "Evaluating corn production under different plant spacings through integrated modeling approach and simulating its future response under climate change scenarios," Agricultural Water Management, Elsevier, vol. 293(C).
    3. Schlindwein, Sandro L. & Eulenstein, Frank & Lana, Marcos & Sieber, Stefan & Boulanger, Jean-Philippe & Guevara, Edgardo & Meira, Santiago & Gentile, Elvira & Bonatti, Michelle, 2015. "What Can Be Learned about the Adaptation Process of Farming Systems to Climate Dynamics Using Crop Models?," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 4(4).
    4. 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.
    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. 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.
    7. 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.
    8. 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.
    9. Pogson, Mark, 2011. "Modelling Miscanthus yields with low resolution input data," Ecological Modelling, Elsevier, vol. 222(23), pages 3849-3853.
    10. Pasquel, Daniel & Cammarano, Davide & Roux, Sébastien & Castrignanò, Annamaria & Tisseyre, Bruno & Rinaldi, Michele & Troccoli, Antonio & Taylor, James A., 2023. "Downscaling the APSIM crop model for simulation at the within-field scale," Agricultural Systems, Elsevier, vol. 212(C).

    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. Balkovič, Juraj & van der Velde, Marijn & Schmid, Erwin & Skalský, Rastislav & Khabarov, Nikolay & Obersteiner, Michael & Stürmer, Bernhard & Xiong, Wei, 2013. "Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation," Agricultural Systems, Elsevier, vol. 120(C), pages 61-75.
    2. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2021. "A comprehensive analysis of water productivity in natural vegetation and various crops coexistent agro-ecosystems," Agricultural Water Management, Elsevier, vol. 243(C).
    3. Dono, Gabriele & Cortignani, Raffaele & Doro, Luca & Giraldo, Luca & Ledda, Luigi & Pasqui, Massimiliano & Roggero, Pier Paolo, 2013. "Adapting to uncertainty associated with short-term climate variability changes in irrigated Mediterranean farming systems," Agricultural Systems, Elsevier, vol. 117(C), pages 1-12.
    4. Zamani, Omid & Azadi, Hossein & Mortazavi, Seyed Abolghasem & Balali, Hamid & Moghaddam, Saghi Movahhed & Jurik, Lubos, 2021. "The impact of water-pricing policies on water productivity: Evidence of agriculture sector in Iran," Agricultural Water Management, Elsevier, vol. 245(C).
    5. Scheierling, Susanne M. & Treguer, David O. & Booker, James F. & Decker, Elisabeth, 2014. "How to assess agricultural water productivity ? looking for water in the agricultural productivity and efficiency literature," Policy Research Working Paper Series 6982, The World Bank.
    6. Schreefel, L. & de Boer, I.J.M. & Timler, C.J. & Groot, J.C.J. & Zwetsloot, M.J. & Creamer, R.E. & Schrijver, A. Pas & van Zanten, H.H.E. & Schulte, R.P.O., 2022. "How to make regenerative practices work on the farm: A modelling framework," Agricultural Systems, Elsevier, vol. 198(C).
    7. Viaggi, Davide & Raggi, Meri & Gomez y Paloma, Sergio, 2011. "Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 127-145, January.
    8. Arjen Y. Hoekstra, 2017. "Water Footprint Assessment: Evolvement of a New Research Field," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3061-3081, August.
    9. Gerrard, Catherine L. & Padel, Susanne & Simon, Moakes, 2012. "The use of Farm Business Survey data to compare the environmental performance of organic and conventional farms," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 2(1), pages 1-12, October.
    10. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 61(01), pages 1-14, February.
    11. Gärtner, Dominique & Keller, Armin & Schulin, Rainer, 2013. "A simple regional downscaling approach for spatially distributing land use types for agricultural land," Agricultural Systems, Elsevier, vol. 120(C), pages 10-19.
    12. Dono, Gabriele & Cortignani, Raffaele & Giraldo, Luca & Doro, Luca & Roggero, Pier Paolo, 2014. "Assessing the awareness of climate change as a factor of adaptation in the agricultural sector," 2014 Third Congress, June 25-27, 2014, Alghero, Italy 173110, Italian Association of Agricultural and Applied Economics (AIEAA).
    13. Thomas, Timothy S., 2015. "US maize data reveals adaptation to heat and water stress:," IFPRI discussion papers 1485, International Food Policy Research Institute (IFPRI).
    14. Britz, Wolfgang & Ciaian, Pavel & Gocht, Alexander & Kanellopoulos, Argyris & Kremmydas, Dimitrios & Müller, Marc & Petsakos, Athanasios & Reidsma, Pytrik, 2021. "A design for a generic and modular bio-economic farm model," Agricultural Systems, Elsevier, vol. 191(C).
    15. Xiong, Wei & Balkovič, Juraj & van der Velde, Marijn & Zhang, Xuesong & Izaurralde, R. César & Skalský, Rastislav & Lin, Erda & Mueller, Nathan & Obersteiner, Michael, 2014. "A calibration procedure to improve global rice yield simulations with EPIC," Ecological Modelling, Elsevier, vol. 273(C), pages 128-139.
    16. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    17. María Jesús Beltrán & Esther Velázquez, 2011. "Del metabolismo social al metabolismo hídrico," Documentos de Trabajo de la Asociación de Economía Ecológica en España 01_2011, Asociación de Economía Ecológica en España.
    18. Valdivia, Roberto O. & Antle, John M. & Stoorvogel, Jetse J., 2012. "Coupling the Tradeoff Analysis Model with a market equilibrium model to analyze economic and environmental outcomes of agricultural production systems," Agricultural Systems, Elsevier, vol. 110(C), pages 17-29.
    19. Leng Liu & Bo Liu & Wei Song & Hao Yu, 2023. "The Relationship between Rural Sustainability and Land Use: A Bibliometric Review," Land, MDPI, vol. 12(8), pages 1-25, August.
    20. Louhichi, Kamel & Flichman, Guillermo & Blanco Fonseca, Maria, 2009. "A generic template for FSSIM," Reports 57463, Wageningen University, SEAMLESS: System for Environmental and Agricultural Modelling; Linking European Science and Society.

    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:222:y:2011:i:1:p:131-143. 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.