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Stochastic maize production technology and production risk analysis in Dadar district, East Ethiopia

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  • Fufa, B.
  • Hassan, Rashid M.

Abstract

A stochastic production technology that allows risk effects of factor inputs was estimated for maize farmers in Ethiopia. The results suggested that the promoted improved maize technology exhibits constant returns to scale whereas non-adopters use decreasing returns to scale technology. The study showed that timely planting is critical for maize yield stability among both adopters and non-adopters and suggests the importance of using oxen and higher efforts to achieve that. Nevertheless, the results showed that most of the factors under the control of the farmers do not offer powerful explanation to maize yield variability compared to natural factors such as rainfall, frost, pests and diseases.

Suggested Citation

  • 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.
    • Handle: RePEc:ags:agreko:9507
    DOI: 10.22004/ag.econ.9507
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    References listed on IDEAS

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    2. Jock R. Anderson, 1973. "Sparse Data, Climatic Variability, and Yield Uncertainty in Response Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 55(1), pages 77-82.
    3. Alain De Janvry, 1972. "Optimal Levels of Fertilization Under Risk: The Potential for Corn and Wheat Fertilization Under Alternative Price Policies in Argentina," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 54(1), pages 1-10.
    4. Richard E. Just & Rulon D. Pope, 1979. "Production Function Estimation and Related Risk Considerations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 61(2), pages 276-284.
    5. 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.
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    Cited by:

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    2. Gershom Endelani Mwalupaso & Shangao Wang & Sanzidur Rahman & Essiagnon John-Philippe Alavo & Xu Tian, 2019. "Agricultural Informatization and Technical Efficiency in Maize Production in Zambia," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    3. Khayyat, Nabaz T. & Heshmati, Almas, 2014. "Production Risk, Energy Use Efficiency and Productivity of Korean Industries," Working Paper Series in Economics and Institutions of Innovation 359, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    4. 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.
    5. Mujawamariya, Gaudiose & Medagbe, Florent M. Kinkingninhoun & Karimov, Aziz, 2017. "Integrating quantified risk in efficiency analysis: evidence from rice production in East and Southern Africa," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 56(4), December.
    6. Edren M. Panti & Gloria P. Gempes, 2018. "The Mediating Effect of Risk Management Strategies on the Relationship Between Attitude Constructs and Sustainability of Banana Production in Southern Philippines," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 4(2), pages 68-77.

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    Crop Production/Industries;

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