IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

Stochastic efficiency analysis of deficit irrigation with standard risk aversion

Listed author(s):
  • Grove, Bennie
  • Oosthuizen, Lukas Klopper
Registered author(s):

    The main objective of this research was to develop an expected utility optimisation model to economically evaluate deficit irrigation within a multi-crop setting while taking into account the increasing production risk of deficit irrigation. The dynamic problem of optimising water use between multiple crops within a whole-farm setting when intraseasonal water supply may be limited was approximated by the inclusion of multiple irrigation schedules into the optimisation model. The SAPWAT model (South African Plant WATer) was further developed to quantify crop yield variability of deficit irrigation while taking the non-uniformity of irrigation applications into account. Stochastic budgeting procedures were used to generate appropriately correlated matrixes of gross margins necessary to incorporate risk into the water use optimisation model. Special care was taken to represent risk aversion consistently between the alternatives through the use of a new procedure to standardise values of absolute risk aversion. The model was applied to study the impact of increasing levels of risk aversion on the profitability of deficit irrigation under limited water supply conditions. The main conclusion from the analyses was that although deficit irrigation was stochastically more efficient than full irrigation under limited water supply conditions, irrigation farmers would not willingly choose to conserve water through deficit irrigation and would be expected to be compensated to do so. Deficit irrigation would not save water if the water that was saved through deficit irrigation were used to plant larger areas to increase the overall profitability of the strategy. Standard risk aversion was used to explain the simultaneous increasing and decreasing relationship between the utility weighted premiums and increasing levels of absolute risk aversion and was shown to be more consistent than when constant absolute risk aversion was assumed.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-3774(09)00364-3
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Elsevier in its journal Agricultural Water Management.

    Volume (Year): 97 (2010)
    Issue (Month): 6 (June)
    Pages: 792-800

    as
    in new window

    Handle: RePEc:eee:agiwat:v:97:y:2010:i:6:p:792-800
    Contact details of provider: Web page: http://www.elsevier.com/locate/agwat

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as
    in new window


    1. Terrance M. Hurley & Paul D. Mitchell & Marlin E. Rice, 2004. "Risk and the Value of Bt Corn," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 345-358.
    2. Babcock, Bruce A. & Choi, E. Kwan & Feinerman, Eli, 1993. "Risk And Probability Premiums For Cara Utility Functions," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 18(01), July.
    3. J. Brian Hardaker & James W. Richardson & Gudbrand Lien & Keith D. Schumann, 2004. "Stochastic efficiency analysis with risk aversion bounds: a simplified approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), pages 253-270, June.
    4. Gillitt, C.G. & Nieuwoudt, W. Lieb & Backeberg, G.R., 2005. "Water markets in the Lower Orange River catchment of South Africa," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 44(3), September.
    5. Grove, Bennie & Nel, F. & Maluleke, H.H., 2006. "Stochastic efficiency analysis of alternative water conservation strategies," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 45(1), March.
    6. Vedenov, Dmitry V. & Barnett, Barry J., 2004. "Efficiency of Weather Derivatives as Primary Crop Insurance Instruments," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(03), December.
    7. Nieuwoudt, W. Lieb & Gillitt, C.G. & Backeberg, G.R., 2005. "Water marketing in the Crocodile River, South Africa," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 44(3), September.
    8. Scheierling, Susanne M. & Young, Robert A. & Cardon, Grant E., 2004. "Determining the Price-Responsiveness of Demands for Irrigation Water Deliveries versus Consumptive Use," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(02), August.
    9. Fausti, Scott W. & Gillespie, Jeffrey M., 2006. "Measuring risk attitude of agricultural producers using a mail survey: how consistent are the methods?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 50(2), June.
    10. Li, Jiusheng, 1998. "Modeling crop yield as affected by uniformity of sprinkler irrigation system," Agricultural Water Management, Elsevier, vol. 38(2), pages 135-146, December.
    11. Matthew Rabin & Richard H. Thaler, 2001. "Anomalies: Risk Aversion," Journal of Economic Perspectives, American Economic Association, vol. 15(1), pages 219-232, Winter.
    12. Pereira, Luis Santos & Oweis, Theib & Zairi, Abdelaziz, 2002. "Irrigation management under water scarcity," Agricultural Water Management, Elsevier, vol. 57(3), pages 175-206, December.
    13. Michael E. Gray & Kevin L. Steffey, 2004. "A Composed-Error Model for Estimating Pest-Damage Functions and the Impact of the Western Corn Rootworm Soybean Variant in Illinois," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 332-344.
    14. Boisvert, Richard N. & McCarl, Bruce, 1990. "Agricultural Risk Modeling Using Mathematical Programming," Research Bulletins 183294, Cornell University, Department of Applied Economics and Management.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:97:y:2010:i:6:p:792-800. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.