IDEAS home Printed from https://ideas.repec.org/a/ags/ccsesa/230372.html
   My bibliography  Save this article

What Can Be Learned about the Adaptation Process of Farming Systems to Climate Dynamics Using Crop Models?

Author

Listed:
  • Schlindwein, Sandro L.
  • Eulenstein, Frank
  • Lana, Marcos
  • Sieber, Stefan
  • Boulanger, Jean-Philippe
  • Guevara, Edgardo
  • Meira, Santiago
  • Gentile, Elvira
  • Bonatti, Michelle

Abstract

The objective of this paper is to reflect and discuss how the use of crop models by aware practitioners might trigger learning of how to think and act differently about the adaptation process of farming systems to climate dynamics. The development of adaptation strategies is discussed from the perspective of contrasting metaphors, since the metaphors in use have distinctive practical implications regarding how crop models might be used for adaptation purposes. Further, in this paper it is pointed out that adaptation should be understood as the result of a learning process and therefore the use of crop models for adaptation purposes must be transformed. Instead of seeing them only as tools to secure yield of cropping systems under a changing climate they must be conceived as components of learning systems for adaptation of whole farming systems.

Suggested Citation

  • 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).
    • Handle: RePEc:ags:ccsesa:230372
    DOI: 10.22004/ag.econ.230372
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/230372/files/P13-p122-131.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.230372?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
    ---><---

    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. 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.
    3. Basso, B. & Ritchie, J. T. & Pierce, F. J. & Braga, R. P. & Jones, J. W., 2001. "Spatial validation of crop models for precision agriculture," Agricultural Systems, Elsevier, vol. 68(2), pages 97-112, 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. Lundström, Christina & Lindblom, Jessica, 2018. "Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT," Agricultural Systems, Elsevier, vol. 159(C), pages 9-20.
    2. Ara, Iffat & Turner, Lydia & Harrison, Matthew Tom & Monjardino, Marta & deVoil, Peter & Rodriguez, Daniel, 2021. "Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review," Agricultural Water Management, Elsevier, vol. 257(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. Tenreiro, Tomás R. & García-Vila, Margarita & Gómez, José A. & Jimenez-Berni, José A. & Fereres, Elías, 2020. "Water modelling approaches and opportunities to simulate spatial water variations at crop field level," Agricultural Water Management, Elsevier, vol. 240(C).
    2. 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).
    3. Abdul Rehman & Luan Jingdong, 2017. "An econometric analysis of major Chinese food crops: An empirical study," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1323372-132, January.
    4. Perielathu Mathunny Mathews Author-Workplace- Scientist, Soil Chemistry, Division of Agronomy and Soil Sciences, India, 2016. "Soil Chemistry: A Way Ahead," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 2(4), pages 115-122, September.
    5. 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.
    6. Louise Beveridge & Stephen Whitfield & Andy Challinor, 2018. "Crop modelling: towards locally relevant and climate-informed adaptation," Climatic Change, Springer, vol. 147(3), pages 475-489, April.
    7. 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.
    8. 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.
    9. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    10. Piewthongngam, Kullapapruk & Pathumnakul, Supachai & Setthanan, Kanchana, 2009. "Application of crop growth simulation and mathematical modeling to supply chain management in the Thai sugar industry," Agricultural Systems, Elsevier, vol. 102(1-3), pages 58-66, October.
    11. 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.
    12. 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.
    13. Everingham, Y. L. & Muchow, R. C. & Stone, R. C. & Inman-Bamber, N. G. & Singels, A. & Bezuidenhout, C. N., 2002. "Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts," Agricultural Systems, Elsevier, vol. 74(3), pages 459-477, December.
    14. Mavromatis, T., 2016. "Spatial resolution effects on crop yield forecasts: An application to rainfed wheat yield in north Greece with CERES-Wheat," Agricultural Systems, Elsevier, vol. 143(C), pages 38-48.
    15. 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).
    16. Leo, Stephen & De Antoni Migliorati, Massimiliano & Nguyen, Trung H. & Grace, Peter R., 2023. "Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates," Agricultural Systems, Elsevier, vol. 205(C).
    17. Podesta, Guillermo & Letson, David & Messina, Carlos & Royce, Fred & Ferreyra, R. Andres & Jones, James & Hansen, James & Llovet, Ignacio & Grondona, Martin & O'Brien, James J., 2002. "Use of ENSO-related climate information in agricultural decision making in Argentina: a pilot experience," Agricultural Systems, Elsevier, vol. 74(3), pages 371-392, December.
    18. Li, Runwei & Wei, Chenyang & Afroz, Mahnaz Dil & Lyu, Jun & Chen, Gang, 2021. "A GIS-based framework for local agricultural decision-making and regional crop yield simulation," Agricultural Systems, Elsevier, vol. 193(C).
    19. Che-Chen Xu & Wen-Xiang Wu & Quan-Sheng Ge & Yang Zhou & Yu-Mei Lin & Ya-Mei Li, 2017. "Simulating climate change impacts and potential adaptations on rice yields in the Sichuan Basin, China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(4), pages 565-594, April.
    20. Bezuidenhout, C.N. & Singels, A., 2007. "Operational forecasting of South African sugarcane production: Part 2 - System evaluation," Agricultural Systems, Elsevier, vol. 92(1-3), pages 39-51, January.

    More about this item

    Keywords

    Labor and Human Capital;

    Statistics

    Access and download statistics

    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:ags:ccsesa:230372. 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: AgEcon Search (email available below). General contact details of provider: http://www.ccsenet.org/sar .

    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.