IDEAS home Printed from
   My bibliography  Save this article

Assessing the influence of neighborhood effects on the adoption of improved agricultural technologies in developing agriculture


  • Langyintuo, Augustine S.
  • Mekuria, Mulugetta


Researchers generally assume spatial homogeneity when assessing the factors that influence farmers to adopt improved agricultural technologies. However, the potential for spatial heterogeneity is high due to, for example, neighborhood effects such as farmers sharing information about new technology. Ignoring spatial heterogeneity can result in biased or inefficient regression estimates and make inferences based on t and F statistics misleading. Using data collected from 300 randomly selected farmers in three districts of Mozambique during the 2003/04 crop season, a spatial Tobit model was specified to estimate which factors determined the adoption of improved maize varieties, after an initial diagnostic test rejected the null hypothesis of spatial homogeneity. On the basis of the empirical evidence, the paper makes policy recommendations to increase Mozambican farmers’ adoption of improved maize varieties and concludes by emphasizing the need to test and correct for spatial heterogeneity in technology adoption modeling to improve the efficiency of the estimated results.

Suggested Citation

  • Langyintuo, Augustine S. & Mekuria, Mulugetta, 2008. "Assessing the influence of neighborhood effects on the adoption of improved agricultural technologies in developing agriculture," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 2(2), September.
  • Handle: RePEc:ags:afjare:56960

    Download full text from publisher

    File URL:
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Bell, Andrew & Parkhurst, Gregory & Droppelmann, Klaus & Benton, Tim G., 2016. "Scaling up pro-environmental agricultural practice using agglomeration payments: Proof of concept from an agent-based model," Ecological Economics, Elsevier, vol. 126(C), pages 32-41.
    2. Niedermayr, Andreas & Kapfer, Martin & Kantelhardt, Jochen, 2016. "Using Econometric Models To Analyse The Spatial Distribution Of Oil Pumpkin Cultivation In Austria," 56th Annual Conference, Bonn, Germany, September 28-30, 2016 244886, German Association of Agricultural Economists (GEWISOLA).
    3. Pede, Valerien O. & McKinley, Justin & Singbo, Alphonse & Kajisa, Kei, 2015. "Spatial Dependency of Technical Efficiency in Rice Farming: The Case of Bohol, Philippines," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205456, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    4. Tisorn Songsermsawas & Kathy Baylis & Ashwini Chhatre & Hope Michelson, 2014. "Can Peers Improve Agricultural Productivity?," CESifo Working Paper Series 4958, CESifo Group Munich.
    5. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.
    6. Fadare, Olusegun Ayodeji & Akerele, Dare & Toritseju, Begho, 3. "Factors Influencing Adoption Decisions Of Maize Farmers In Nigeria," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 2(3).
    7. Phiri, Innocent Pangapanga, 2011. "Modelling farmers’ choice of adaptation strategies towards climatic and weather variability: Empirical evidence from Chikhwawa district, Southern Malawi," Research Theses 134489, Collaborative Masters Program in Agricultural and Applied Economics.
    8. Chamberlin, Jordan, 2013. "Infrastructure, services, and smallholder income growth: evidence from Kenyan panel data," 2013 AAAE Fourth International Conference, September 22-25, 2013, Hammamet, Tunisia 161269, African Association of Agricultural Economists (AAAE).
    9. Cook, Aaron M. & Ricker-Gilbert, Jacob E. & Sesmero, Juan P., 2013. "How do African households adapt to climate change? Evidence from Malawi," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150507, Agricultural and Applied Economics Association.
    10. Pangapanga, Phiriinnocent & Thangalimodzi, Lucy Tembo, 2012. "Participation in pro poor agro based enterprises in Malawi: do households’ poverty levels change automatically?," MPRA Paper 39446, University Library of Munich, Germany.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:afjare:56960. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.