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Stochastic efficiency analysis of alternative basic grain marketing strategies

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  • M.M. Venter
  • D.B. Strydom
  • B. Grové

Abstract

The use of modern routine marketing strategies to minimize risk exposure is not a widely adopted practice among grain producers. The producers tend to use high risk strategies which include the selling of the crop on the cash market after harvest; while the high market risks require innovative strategies including the use of futures and options as traded on South African Futures Exchange (SAFEX). This is mostly due to a lack of interest and knowledge of the market. The purpose of the study is to examine whether the adoption of basic routine strategies is better than adopting no strategy at all. The study illustrates that by using a Stochastic Efficiency with Respect to a Function (SERF) and Cumulative Distribution Function (CDF) that the use of three basic strategies for each crop type, namely, a Put (plant time)-, (Three-segment-), (Critical Moment in production/marketing process) and Sell after pollination can be more rewarding. These strategies can be adopted by farmers without an in-depth understanding of the market and market signals. The results obtained from the study illustrate that each strategy is different for each crop. It also indicates that no strategy is worse than a specific strategy and that the choice between strategies depends on the risk aversion level of the producer. It is imperative to note that the use of hedging strategies is better than no strategy at all.

Suggested Citation

  • M.M. Venter & D.B. Strydom & B. Grové, 2013. "Stochastic efficiency analysis of alternative basic grain marketing strategies," Agrekon, Taylor & Francis Journals, vol. 52(sup1), pages 46-63, March.
  • Handle: RePEc:taf:ragrxx:v:52:y:2013:i:sup1:p:46-63
    DOI: 10.1080/03031853.2013.770952
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    References listed on IDEAS

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    1. Coble, Keith H. & Barnett, Barry J., 1999. "The Role Of Research In Producer Risk Management," Professional Papers 15803, Mississippi State University, Department of Agricultural Economics.
    2. Boisvert, Richard N. & McCarl, Bruce, 1990. "Agricultural Risk Modeling Using Mathematical Programming," Research Bulletins 183294, Cornell University, Department of Applied Economics and Management.
    3. Harwood, Joy L. & Heifner, Richard G. & Coble, Keith H. & Perry, Janet E. & Somwaru, Agapi, 1999. "Managing Risk in Farming: Concepts, Research, and Analysis," Agricultural Economic Reports 34081, United States Department of Agriculture, Economic Research Service.
    4. Varangis, Panos & Larson, Donald & Anderson, Jack R., 2002. "Agricultural markets and risks - management of the latter, not the former," Policy Research Working Paper Series 2793, The World Bank.
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    Cited by:

    1. Markus Arlindo Monteiro & Bennie Grové & Nicolette Matthews, 2022. "Developing a Moving Average Crossover Strategy as an Alternative Hedging Strategy for the South Africa Maize Market," Agriculture, MDPI, vol. 12(8), pages 1-14, August.
    2. Kayode Ayankoya & Andre P. Calitz & Jean H. Greyling, 2016. "Real-Time Grain Commodities Price Predictions in South Africa: A Big Data and Neural Networks Approach," Agrekon, Taylor & Francis Journals, vol. 55(4), pages 483-508, October.

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