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Stochastic efficiency optimisation of alternative agricultural water use strategies

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  • Grove, Bennie

Abstract

Mathematical programming was used to optimise utility efficient deficit irrigation schedules for maize in Vaalharts, based on certainty equivalence assuming an exponential utility function. Total gross margin risk resulting from production risk of alternative deficit irrigation practices was quantified using an irrigation simulation model and stochastic budgeting procedures. Results showed that the portfolio of irrigation schedules for a risk averse farmer may include schedules with high production risk, due to the interaction of resource use between alternatives when water is limited. Owing to the difficulty of implementing the best portfolio of irrigation schedules, the optimised schedules may best be used to benchmark the efficiency of second best alternatives that are easier to implement. Ignoring risk may underestimate the value decision-makers attach to the security of water supply and policy-makers should take cognisance of this result.

Suggested Citation

  • Grove, Bennie, 2006. "Stochastic efficiency optimisation of alternative agricultural water use strategies," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 45(4), pages 1-15, December.
    • Handle: RePEc:ags:agreko:10136
    DOI: 10.22004/ag.econ.10136
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    References listed on IDEAS

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    1. English, Marshall & Raja, Syed Navaid, 1996. "Perspectives on deficit irrigation," Agricultural Water Management, Elsevier, vol. 32(1), pages 1-14, November.
    2. Marca Weinberg & Catherine L. Kling & James E. Wilen, 1993. "Water Markets and Water Quality," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(2), pages 278-291.
    3. Hardaker, J. Brian & Lien, Gudbrand D., 2003. "Stochastic Efficiency Analysis With Risk Aversion Bounds: A Simplified Approach," Working Papers 12954, University of New England, School of Economics.
    4. Boisvert, Richard N. & McCarl, Bruce, 1990. "Agricultural Risk Modeling Using Mathematical Programming," Research Bulletins 183294, Cornell University, Department of Applied Economics and Management.
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