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Peasant farmer behavior and cereal technologies: Stochastic programming analysis in Niger

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  • Akinwumi A. Adesina
  • John H. Sanders

Abstract

Peasant farmers in Sahelian West Africa adjust to rainfall uncertainties in the agricultural season by making decisions sequentially as a function of the evolving rainfall patterns. Understanding such flexibilities in farmer decision‐making is central to technology introduction. This paper determines how sequential decision‐making under weather uncertainty affects the adoption and farm‐level effects of cereal technologies in Niger. The study also draws policy implications for a price floor to arrest the substantial fall in cereal prices in good rainfall years when farmers have more grains to sell. The methodology used is Discrete Stochastic Programming. This paper shows that the ability of peasant farmers to adapt cropping and resource‐management strategies to the rainfall patterns is the basis for their survival in this high‐risk environment. Model results show that by (a) carrying a portfolio mix of varieties of varying maturities, and (b) making sequential decisions based upon rainfall expectations, farmers can adapt to the production uncertainties. Breeding programs should therefore be diversified to develop not only early‐maturing cultivars, but also improved intermediate and long‐season varieties.

Suggested Citation

  • Akinwumi A. Adesina & John H. Sanders, 1991. "Peasant farmer behavior and cereal technologies: Stochastic programming analysis in Niger," Agricultural Economics, International Association of Agricultural Economists, vol. 5(1), pages 21-38, January.
    • Handle: RePEc:bla:agecon:v:5:y:1991:i:1:p:21-38
    DOI: 10.1111/j.1574-0862.1991.tb00133.x
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    Cited by:

    1. Andrew Dorward, 1999. "Modelling embedded risk in peasant agriculture: methodological insights from northern Malawi," Agricultural Economics, International Association of Agricultural Economists, vol. 21(2), pages 191-203, October.
    2. Mole, Paulo Nicua, 2000. "Smallholder Cashew Development Opportunities and Linkages to Food Security in Nampula Province, Mozambique: Summary of Findings and Implications for Policy, Research and Extension Efforts," Food Security Collaborative Working Papers 56043, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    3. Adesina, A. A. & Ouattara, A. D., 2000. "Risk and agricultural systems in northern Cote d'Ivoire," Agricultural Systems, Elsevier, vol. 66(1), pages 17-32, October.
    4. Ahumada, Omar & Villalobos, J. Rene, 2009. "Application of planning models in the agri-food supply chain: A review," European Journal of Operational Research, Elsevier, vol. 196(1), pages 1-20, July.
    5. Jaeck, Melanie & Lifran, Robert, 2011. "The Economic Determinants of the On-farm Management of Rice Cultivars in the Rhone River Delta (France)," 2011 Conference, August 25-26, 2011, Nelson, New Zealand 115508, New Zealand Agricultural and Resource Economics Society.
    6. Abdoulaye, Ibrahim Djido & Sanders, John H., 2013. "A Matching Approach to Analyze the Impact of New Agricultural Technologies: Productivity and Technical Efficiency in Niger," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150434, Agricultural and Applied Economics Association.
    7. Dono, Gabriele & Cortigiani, Raffaele & Dell'Unto, Davide, 2015. "Economic modelling of climate change scenarios and adaptation of Mediterranean agriculture," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207276, Italian Association of Agricultural and Applied Economics (AIEAA).
    8. Arno Maatman & Caspar Schweigman & Arjan Ruijs & Maarten H. van Der Vlerk, 2002. "Modeling Farmers' Response to Uncertain Rainfall in Burkina Faso: A Stochastic Programming Approach," Operations Research, INFORMS, vol. 50(3), pages 399-414, June.
    9. Worstell, James & Colley, Barry, 1993. "Enabling Resilience and Enhancing Assets: Famine Mitigation from a Seeds and Tools Perspective," USDA Miscellaneous 336999, United States Department of Agriculture.
    10. Dorward, A. R., 1996. "Modelling diversity, change and uncertainty in peasant agriculture in northern Malawi," Agricultural Systems, Elsevier, vol. 51(4), pages 469-486, August.
    11. Reis, Silvia Araújo & Leal, José Eugenio, 2015. "A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain," Journal of Transport Geography, Elsevier, vol. 43(C), pages 48-58.

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