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Modelling embedded risk in peasant agriculture: methodological insights from northern Malawi


  • Dorward, Andrew


Using a linear-programming model of farming systems in northern Malawi, the conditions under which peasant farmhousehold models may need to allow for embedded risk are investigated. Tactical, sequential responses to uncertainty are found to be more important to labour-scarce households with limited access to capital and to credit markets. Compared with semi-sequential programming, discrete stochastic programming (DSP) provided more efficient solutions for problems involving embedded risk. There may be intuitive advantages in presenting results from DSP models in terms of a semisequential strategy. © 1999 Elsevier Science B.V. All rights reserved.
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  • Dorward, Andrew, 1999. "Modelling embedded risk in peasant agriculture: methodological insights from northern Malawi," Agricultural Economics, Blackwell, vol. 21(2), pages 191-203, October.
  • Handle: RePEc:eee:agecon:v:21:y:1999:i:2:p:191-203

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    References listed on IDEAS

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    Cited by:

    1. Dorward, Andrew & Fan, Shenggen & Kydd, Jonathan & Lofgren, Hans & Morrison, Jamie & Poulton, Colin & Rao, Neetha & Smith, Laurence & Tchale, Hardwick & Thorat, Sukhadeo & Urey, Ian & Wobst, Peter, 2004. "Institutions and economic policies for pro-poor agricultural growth," DSGD discussion papers 15, International Food Policy Research Institute (IFPRI).
    2. Janssen, Sander J.C. & van Ittersum, Martin K., 2007. "Assessing farmer behaviour as affected by policy and technological innovations: bio-economic farm models," Reports 9293, SEAMLESS: System for Environmental and Agricultural Modelling, Linking European Science and Society.
    3. Janssen, Sander & van Ittersum, Martin K., 2007. "Assessing farm innovations and responses to policies: A review of bio-economic farm models," Agricultural Systems, Elsevier, vol. 94(3), pages 622-636, June.
    4. Torkamani, Javad, 2006. "Measuring and Incorporating Farmers’ Personal Beliefs and Preferences about Uncertain Events in Decision Analysis: A Stochastic Programming Experiment," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 61(2).
    5. Ridier, Aude & Chaib, Karim & Roussy, Caroline, 2016. "A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks," European Journal of Operational Research, Elsevier, vol. 252(1), pages 270-279.
    6. Marnie Griffith & Gary Codner & Erwin Weinmann & Sergei Schreider, 2009. "Modelling hydroclimatic uncertainty and short-run irrigator decision making: the Goulburn system ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 53(4), pages 565-584, October.
    7. Arriaza, M. & Gomez-Limon, J. A., 2003. "Comparative performance of selected mathematical programming models," Agricultural Systems, Elsevier, vol. 77(2), pages 155-171, August.
    8. Gicheha, M.G. & Edwards, G.R. & Bell, S.T. & Burtt, E.S. & Bywater, A.C., 2014. "Embedded risk management in dryland sheep systems II. Risk analysis," Agricultural Systems, Elsevier, vol. 124(C), pages 1-11.

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