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Farm-Level Risk Management Using Irrigation and Weather Derivatives

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  • Lin, Shanshan
  • Mullen, Jeffrey D.
  • Hoogenboom, Gerrit

Abstract

An agronomic crop growth model—the Decision Support System for Agro-Technology Transfer—and a constant relative risk aversion utility function are used to examine corn irrigation strategies in Mitchell County, Georgia. Precipitation contracts are designed to help farmers manage risk. Three conclusions originate from the findings. First, the optimal irrigation strategy can greatly increase producers' certainty-equivalent revenue. Second, changes in water pricing policy would have a limited impact on the amount of water used. And third, across levels of risk preference, the precipitation contracts are not effective in increasing certainty-equivalent revenue or reducing cumulative water use.

Suggested Citation

  • Lin, Shanshan & Mullen, Jeffrey D. & Hoogenboom, Gerrit, 2008. "Farm-Level Risk Management Using Irrigation and Weather Derivatives," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(2), pages 485-492, August.
    • Handle: RePEc:cup:jagaec:v:40:y:2008:i:02:p:485-492_02
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    References listed on IDEAS

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    1. Escalante, Cesar L. & Rejesus, Roderick M., 2005. "Farmland Control Decisions under Different Intertemporal Risk Behavioral Constructs," 2005 Annual meeting, July 24-27, Providence, RI 19113, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Schnitkey, Gary D. & Sherrick, Bruce J. & Irwin, Scott H., 2002. "Evaluation Of Risk Reductions Associated With Multi-Periol Crop Insurance Products," 2002 Annual meeting, July 28-31, Long Beach, CA 19778, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Richards, Timothy J. & Manfredo, Mark R. & Sanders, Dwight R., 2004. "Pricing Weather Derivatives," Working Papers 28536, Arizona State University, Morrison School of Agribusiness and Resource Management.
    4. Olivier Mahul, 2001. "Optimal Insurance Against Climatic Experience," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 593-604.
    5. Harris, Thomas R. & Mapp, Harry p. Jr., 1980. "Qptimal Scheduling Of Irrigation By Control Theory: Oklahoma Panhandle," 1980 Annual Meeting, July 27-30, Urbana-Champaign, Illinois 278992, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Dwight R. Sanders, 2004. "Pricing Weather Derivatives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1005-1017.
    7. Harris, Thomas R. & Mapp, Harry P., 1980. "A Control Theory Approach to Optimal Irrigation Scheduling in the Oklahoma Panhandle," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 12(1), pages 165-171, July.
    8. Mario Miranda & Dmitry V. Vedenov, 2001. "Innovations in Agricultural and Natural Disaster Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 650-655.
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    Cited by:

    1. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    2. Barham, E. Hart Bise & Robinson, John R.C. & Richardson, James W. & Rister, M. Edward, 2011. "Mitigating Cotton Revenue Risk Through Irrigation, Insurance, and Hedging," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 43(4), pages 529-540, November.
    3. Brandon Schaufele & James R. Unterschultz & Tomas Nilsson, 2010. "AgriStability with Catastrophic Price Risk for Cow‐Calf Producers," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 58(3), pages 361-380, September.
    4. Finger, Robert, 2012. "Modeling the sensitivity of agricultural water use to price variability and climate change—An application to Swiss maize production," Agricultural Water Management, Elsevier, vol. 109(C), pages 135-143.

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    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

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