IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v221y2010i4p590-598.html
   My bibliography  Save this article

Semi-automatic reduction and upscaling of large models: A farm management example

Author

Listed:
  • Gibbons, J.M.
  • Wood, A.T.A.
  • Craigon, J.
  • Ramsden, S.J.
  • Crout, N.M.J.

Abstract

Research questions at the regional, national and global scales frequently require the upscaling of existing models. At large scales, simple model aggregation may have a prohibitive computational cost and lead to over-detailed problem representation. Methods that guide model simplification and revision have the potential to support the choice of the appropriate level of detail or heterogeneity within upscaled models. Efficient upscaling will retain only the heterogeneity that contributes to accurate aggregated results. This approach to model revision is challenging, because automatic generation of alternative models is difficult and the set of possible revised models is very large. In the case where simplification alone is considered, there are at least 2n−1 possible simplified models where n is the number of model variables. Even with the availability of High Performance Computing, it is not possible to evaluate every possible simplified model if the number of model variables is greater than roughly 35. To address these issues, we propose a method that extends an existing procedure for simplifying and aggregating mechanistic models based on replacing model variables with constants. The method generates simplified models by selectively aggregating existing model variables, retaining existing model structure while reducing the size of the set of possible models and ordering them into a search tree. The tree is then searched selectively. We illustrate the method using a catchment scale optimization model with c. 50,000 variables (Farm-adapt) in the context of adaptation to climatic change. The method was successful in identifying redundant model variables and an adequate model 10% smaller than the original model. We discuss how the procedure can be extended to other large models and compare the method to those proposed by others. We conclude by urging model developers to regard their models as a starting point and to consider the need for alternative models during model development.

Suggested Citation

  • Gibbons, J.M. & Wood, A.T.A. & Craigon, J. & Ramsden, S.J. & Crout, N.M.J., 2010. "Semi-automatic reduction and upscaling of large models: A farm management example," Ecological Modelling, Elsevier, vol. 221(4), pages 590-598.
  • Handle: RePEc:eee:ecomod:v:221:y:2010:i:4:p:590-598
    DOI: 10.1016/j.ecolmodel.2009.11.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009007522
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.11.006?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. Lawrie, Jock & Hearne, John, 2007. "Reducing model complexity via output sensitivity," Ecological Modelling, Elsevier, vol. 207(2), pages 137-144.
    2. Gibbons, James M. & Sparkes, Debbie L. & Wilson, Paul & Ramsden, Stephen J., 2005. "Modelling optimal strategies for decreasing nitrate loss with variation in weather - a farm-level approach," Agricultural Systems, Elsevier, vol. 83(2), pages 113-134, February.
    3. Giller, Ken E. & Rowe, Ed C. & de Ridder, Nico & van Keulen, Herman, 2006. "Resource use dynamics and interactions in the tropics: Scaling up in space and time," Agricultural Systems, Elsevier, vol. 88(1), pages 8-27, April.
    4. Ramsden, S. & Gibbons, J. & Wilson, P., 1999. "Impacts of changing relative prices on farm level dairy production in the UK," Agricultural Systems, Elsevier, vol. 62(3), pages 201-215, December.
    5. Chikowo, R. & Corbeels, M. & Tittonell, P. & Vanlauwe, B. & Whitbread, A. & Giller, K.E., 2008. "Aggregating field-scale knowledge into farm-scale models of African smallholder systems: Summary functions to simulate crop production using APSIM," Agricultural Systems, Elsevier, vol. 97(3), pages 151-166, June.
    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. Glithero, N.J. & Ramsden, S.J. & Wilson, P., 2012. "Farm systems assessment of bioenergy feedstock production: Integrating bio-economic models and life cycle analysis approaches," Agricultural Systems, Elsevier, vol. 109(C), pages 53-64.
    2. Troost, Christian & Berger, Thomas, 2015. "Process-based simulation of regional agricultural supply functions in Southwestern Germany using farm-level and agent-based models," 2015 Conference, August 9-14, 2015, Milan, Italy 211929, International Association of Agricultural Economists.

    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. Janssen, Sander & van Ittersum, Martin K., 2007. "Assessing farm innovations and responses to policies: A review of bio-economic farm models," Agricultural Systems, Elsevier, vol. 94(3), pages 622-636, June.
    2. Shrestha, Shailesh & Hennessy, Thia & Abdalla, Mohamed & Forristal, Dermot & Jones, Michael B., 2014. "Determining Short Term Responses of Irish Dairy Farms under Climate Change," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 63(3).
    3. Shrestha, Shailesh & Hennessy, Thia & Abdalla, Mohamed & Forristal, Dermot & Jones, Michael B., 2014. "Determining Short Term Responses of Irish Dairy Farms under Climate Change," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 63(03), pages 1-13, September.
    4. Janssen, Sander J.C. & van Ittersum, Martin K., 2007. "Assessing farmer behaviour as affected by policy and technological innovations: bio-economic farm models," Reports 9293, Wageningen University, SEAMLESS: System for Environmental and Agricultural Modelling; Linking European Science and Society.
    5. Louhichi, Kamel & Ciaian, Pavel & Espinosa, Maria & Colen, Liesbeth & Perni, Angel & Paloma, Sergio, 2015. "The Impact of Crop Diversification Measure: EU-wide Evidence Based on IFM-CAP Model," 2015 Conference, August 9-14, 2015, Milan, Italy 211542, International Association of Agricultural Economists.
    6. Nyakudya, Innocent Wadzanayi & Stroosnijder, Leo & Nyagumbo, Isaiah, 2014. "Infiltration and planting pits for improved water management and maize yield in semi-arid Zimbabwe," Agricultural Water Management, Elsevier, vol. 141(C), pages 30-46.
    7. Jayne, T.S., 2014. "Land dynamics and future trajectories of structural transformation in Africa," International Journal of Agricultural Sciences and Technology (IJAGST), SvedbergOpen, vol. 53(3), October.
    8. Piqueira, J.R.C. & de Mattos, S.H.V.L. & Vasconcelos-Neto, J., 2009. "Measuring complexity in three-trophic level systems," Ecological Modelling, Elsevier, vol. 220(3), pages 266-271.
    9. Louhichi, Kamel & Ciaian, Pavel & Espinosa, Maria & Colen, Liesbeth & Perni, Angel & Gomez y Paloma, Sergio, 2015. "EU-wide individual Farm Model for CAP Analysis (IFM-CAP): Application to Crop Diversification Policy," 2015 Conference, August 9-14, 2015, Milan, Italy 212155, International Association of Agricultural Economists.
    10. Berrueta, Cecilia & Giménez, Gustavo & Dogliotti, Santiago, 2021. "Scaling up from crop to farm level: Co-innovation framework to improve vegetable farm systems sustainability," Agricultural Systems, Elsevier, vol. 189(C).
    11. Jan Lietava & Risa Morimoto, 2019. "Regression tree analysis of soil fertility and agro-economic practices and the effects on yield in Tanzania," Working Papers 218, Department of Economics, SOAS University of London, UK.
    12. Klapwijk, C.J. & Bucagu, C. & van Wijk, M.T. & Udo, H.M.J. & Vanlauwe, B. & Munyanziza, E. & Giller, K.E., 2014. "The ‘One cow per poor family’ programme: Current and potential fodder availability within smallholder farming systems in southwest Rwanda," Agricultural Systems, Elsevier, vol. 131(C), pages 11-22.
    13. Rufino, M.C. & Dury, J. & Tittonell, P. & van Wijk, M.T. & Herrero, M. & Zingore, S. & Mapfumo, P. & Giller, K.E., 2011. "Competing use of organic resources, village-level interactions between farm types and climate variability in a communal area of NE Zimbabwe," Agricultural Systems, Elsevier, vol. 104(2), pages 175-190, February.
    14. Giller, Ken E. & Andersson, Jens & Delaune, Thomas & Silva, João Vasco & Descheemaeker, Katrien & van de Ven, Gerrie & Schut, Antonius G.T. & van Wijk, Mark & Hammond, Jim & Hochman, Zvi & Taulya, God, 2022. "IFAD Research Series 83: The future of farming: who will produce our food?," IFAD Research Series 322005, International Fund for Agricultural Development (IFAD).
    15. Le Gal, P.-Y. & Dugué, P. & Faure, G. & Novak, S., 2011. "How does research address the design of innovative agricultural production systems at the farm level? A review," Agricultural Systems, Elsevier, vol. 104(9), pages 714-728.
    16. Graham, Mary, 2008. "Biophysical Modelling and Performance Measurement," 2008 Conference (52nd), February 5-8, 2008, Canberra, Australia 6773, Australian Agricultural and Resource Economics Society.
    17. Guido van Hofwegen & Gertjan A. Becx & Joep A. van den Broek & Niek B.J. Koning, 2007. "Unraveling the unsustainability spiral in sub-Saharan Africa: an agent based modelling approach," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 5(2), pages 112-137.
    18. Gibbons, James M. & Sparkes, Debbie L. & Wilson, Paul & Ramsden, Stephen J., 2005. "Modelling optimal strategies for decreasing nitrate loss with variation in weather - a farm-level approach," Agricultural Systems, Elsevier, vol. 83(2), pages 113-134, February.
    19. Leonardo, Wilson & van de Ven, Gerrie W.J. & Kanellopoulos, Argyris & Giller, Ken E., 2018. "Can farming provide a way out of poverty for smallholder farmers in central Mozambique?," Agricultural Systems, Elsevier, vol. 165(C), pages 240-251.
    20. Waldman, Kurt B. & Ortega, David L. & Richardson, Robert B. & Snapp, Sieglinde S., 2017. "Estimating demand for perennial pigeon pea in Malawi using choice experiments," Ecological Economics, Elsevier, vol. 131(C), pages 222-230.

    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:221:y:2010:i:4:p:590-598. 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.