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Spatial Neighborhood Effects in Agricultural Technology Adoption: Evidence from Nigeria

Listed author(s):
  • Adjognon, Serge
  • Liverpool-Tasie, Lenis
Registered author(s):

    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.

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    File URL: http://purl.umn.edu/210934
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    Paper provided by International Association of Agricultural Economists in its series 2015 Conference, August 9-14, 2015, Milan, Italy with number 210934.

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    Date of creation: 2015
    Handle: RePEc:ags:iaae15:210934
    Contact details of provider: Web page: http://www.iaae-agecon.org/
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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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    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.
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    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.
    9. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
    10. Pramila Krishnan & Manasa Patnam, 2014. "Neighbors and Extension Agents in Ethiopia: Who Matters More for Technology Adoption?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 308-327.
    11. Anselin, Luc, 2002. "Under the hood Issues in the specification and interpretation of spatial regression models," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 27(3), November.
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    14. Bardhan, Pranab & Udry, Christopher, 1999. "Development Microeconomics," OUP Catalogue, Oxford University Press, number 9780198773719.
    15. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
    16. Jan K. Brueckner, 2003. "Strategic Interaction Among Governments: An Overview of Empirical Studies," International Regional Science Review, , vol. 26(2), pages 175-188, April.
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