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Spatial Neighborhood Effects in Agricultural Technology Adoption: Evidence from Nigeria

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  • Adjognon, Serge
  • Liverpool-Tasie, Lenis

Abstract

This paper uses plot level data to analyze network effects on the adoption of a targeted fertilizer application technique amongst rice producers in Niger State, Nigeria. A Spatial Durbin Model (SDM) of farmers’ probability of using the Urea Deep Placement (UDP) technology is estimated using a Spatial Instrumental Variable Probit estimation approach. The results reveal the existence of significant and positive spatial correlation in rice farmers’ adoption of UDP in the study area. These results have important implications for the design of appropriate and cost effective extension programs, in order to facilitate the diffusion of agricultural technologies in developing countries.

Suggested Citation

  • Adjognon, Serge & Liverpool-Tasie, Lenis, 2015. "Spatial Neighborhood Effects in Agricultural Technology Adoption: Evidence from Nigeria," 2015 Conference, August 9-14, 2015, Milan, Italy 210934, International Association of Agricultural Economists.
  • Handle: RePEc:ags:iaae15:210934
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    File URL: http://purl.umn.edu/210934
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    References listed on IDEAS

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    1. Loomis, John B. & Mueller, Julie M., 2013. "A Spatial Probit Modeling Approach to Account for Spatial Spillover Effects in Dicotomous Choice Contingent Valuation Surveys," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 45(01), February.
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    3. Oriana Bandiera & Imran Rasul, 2006. "Social Networks and Technology Adoption in Northern Mozambique," Economic Journal, Royal Economic Society, vol. 116(514), pages 869-902, October.
    4. Liverpool- Tasie, Lenis Saweda O. & Adjognon, Serge & Kuku-Shittu, Oluyemisi, 2015. "Productivity Effects of Sustainable Intensification: The Case of Urea Deep Placement for Rice Production in Niger State, Nigeria," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 10(1), March.
    5. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    6. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    7. Timothy Conley & Udry Christopher, 2001. "Social Learning Through Networks: The Adoption of New Agricultural Technologies in Ghana," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 668-673.
    8. Lenis Saweda O. Liverpool-Tasie & Alex Winter-Nelson, 2012. "Social Learning and Farm Technology in Ethiopia: Impacts by Technology, Network Type, and Poverty Status," Journal of Development Studies, Taylor & Francis Journals, vol. 48(10), pages 1505-1521, October.
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    Keywords

    Spatial; Adoption; Social network; agriculture; UDP; Agribusiness; Agricultural and Food Policy; Agricultural Finance; O13; O33; Q16;

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

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