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Stochastic Technology In A Programming Framework: A Generalised Mean‐Variance Farm Model

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  • R. M. Hassan
  • A. Hallam

Abstract

Production uncertainty is important in studying behaviour of risk‐averse firms and developing successful agricultural policies. A model that extends the standard Mean‐Variance (E‐V) method to incorporate stochastic technology in a proscriptive programming framework is developed, and risk effects of factor inputs are measured for the irrigated multi‐crop farming system in the Sudan. Hired labour is found to be risk increasing in cotton and sorghum but risk reducing in groundnuts. Operator labour is found to be risk reducing in cotton and sorghum but risk increasing in groundnuts. Supply responses are derived from a nonlinear programming model of agricultural producer decisions and it is found that supply responses are more elastic when labour choices are allowed to influence production risks.

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  • R. M. Hassan & A. Hallam, 1990. "Stochastic Technology In A Programming Framework: A Generalised Mean‐Variance Farm Model," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(2), pages 196-206, May.
    • Handle: RePEc:bla:jageco:v:41:y:1990:i:2:p:196-206
    DOI: 10.1111/j.1477-9552.1990.tb00635.x
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    References listed on IDEAS

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    Cited by:

    1. Ogada, Maurice Juma & Nyangena, Wilfred & Yesuf, Mahmud, 2010. "Production risk and farm technology adoption in the rain-fed semi-arid lands of Kenya," Journal of Cooperatives, NCERA-210, vol. 4(2), June.
    2. Fufa, B. & Hassan, Rashid M., 2003. "Stochastic maize production technology and production risk analysis in Dadar district, East Ethiopia," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 42(2), pages 1-13, June.
    3. Ogada, Maurice Juma & Nyangena, Wilfred & Yesuf, Mahmud, 2010. "Production risk and farm technology adoption in the rain-fed semi-arid lands of Kenya," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 4(2), pages 1-16, June.

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