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Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania

Author

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  • Nakano, Yuko
  • Tsusaka, Takuji W.
  • Aida, Takeshi
  • Pede, Valerien O.

Abstract

Agricultural training is a potentially effective method to diffuse relevant new technologies to increase productivity and alleviate rural poverty in Sub-Saharan Africa (SSA). However, since it is prohibitively expensive to provide direct training to all the farmers in SSA, it is critically important to examine the extent to which technologies taught to a small number of farmers disseminate to non-trained farmers. This paper investigates the technology dissemination pathways among smallholder rice producers within a rural irrigation scheme in Tanzania. As an innovative feature, we compare the performance of three categories of farmers: key farmers, who receive intensive pre-season training at a local training center; intermediate farmers, who are trained by the key farmers; and other ordinary farmers. By collecting and analyzing a unique five-year household-level panel data set, we estimate difference-in-differences models to assess how the gap in performance evolve as the technologies spill over from the trained farmers to the ordinary farmers. To disentangle the technology spillover process, we also examine the extent to which social and geographical network with the key and intermediate farmers influences the adoption of technologies by the ordinary farmers, by incorporating social relationship variables into spatial econometric models. We found that the ordinary farmers who were a relative or residential neighbor of a key or intermediate farmer were more likely to adopt new technologies than those who were not. As a result, while the key farmers’ technology adoption rates rose immediately after the training, those of the non-trained ordinary farmers caught up belatedly. As the technologies disseminated, the paddy yield of the key farmers increased from 3.1 to 5.3 tons per hectare, while the yield of the ordinary farmers increased from 2.6 to 3.7 tons per hectare. Our results suggest the effectiveness and practical potential of farmer-to-farmer extension programs for smallholders in SSA as a cost effective alternative to the conventional farmer training approach.

Suggested Citation

  • Nakano, Yuko & Tsusaka, Takuji W. & Aida, Takeshi & Pede, Valerien O., 2018. "Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania," World Development, Elsevier, vol. 105(C), pages 336-351.
  • Handle: RePEc:eee:wdevel:v:105:y:2018:i:c:p:336-351
    DOI: 10.1016/j.worlddev.2017.12.013
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    as
    1. Weihua Guan, 2003. "From the help desk: Bootstrapped standard errors," Stata Journal, StataCorp LP, vol. 3(1), pages 71-80, March.
    2. Tsusaka, Takuji W. & Velasco, Ma. Lourdes & Yamano, Takashi & Pandey, Sushil, 2015. "Expert Elicitation for Assessing Agricultural Technology Adoption: The Case of Improved Rice Varieties in South Asian Countries," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 12(1), pages 1-15, June.
    3. Rokhaya Dieye & Habiba Djebbari & Felipe Barrera-Osorio, 2014. "Accounting for Peer Effects in Treatment Response," AMSE Working Papers 1435, Aix-Marseille School of Economics, France, revised Jul 2014.
    4. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 7(1), pages 71-83, February.
    5. 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.
    6. 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.
    7. 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.
    8. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    9. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    10. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
    11. Tripp, Robert & Wijeratne, Mahinda & Piyadasa, V. Hiroshini, 2005. "What should we expect from farmer field schools? A Sri Lanka case study," World Development, Elsevier, vol. 33(10), pages 1705-1720, October.
    12. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    13. Seck, Papa A. & Tollens, Eric & Wopereis, Marco C.S. & Diagne, Aliou & Bamba, Ibrahim, 2010. "Rising trends and variability of rice prices: Threats and opportunities for sub-Saharan Africa," Food Policy, Elsevier, vol. 35(5), pages 403-411, October.
    14. Yuko Nakano & Kei Kajisa & Keijiro Otsuka, 2016. "On the Possibility of Rice Green Revolution in Irrigated and Rainfed Areas in Tanzania: An Assessment of Management Training and Credit Programs," Natural Resource Management and Policy, in: Keijiro Otsuka & Donald F. Larson (ed.), In Pursuit of an African Green Revolution, edition 1, chapter 0, pages 39-64, Springer.
    15. Lambrecht, Isabel & Vanlauwe, Bernard & Merckx, Roel & Maertens, Miet, 2014. "Understanding the Process of Agricultural Technology Adoption: Mineral Fertilizer in Eastern DR Congo," World Development, Elsevier, vol. 59(C), pages 132-146.
    16. Patrick S. Ward & Valerien O. Pede, 2015. "Capturing social network effects in technology adoption: the spatial diffusion of hybrid rice in Bangladesh," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), pages 225-241, April.
    17. Otsuka, Keijiro, 2007. "Efficiency and Equity Effects of Land Markets," Handbook of Agricultural Economics, in: Robert Evenson & Prabhu Pingali (ed.), Handbook of Agricultural Economics, edition 1, volume 3, chapter 51, pages 2671-2703, Elsevier.
    18. Keijiro Otsuka & Kaliappa P. Kalirajan, 2005. "An Exploration of a Green Revolution in Sub-Saharan Africa," The Electronic Journal of Agricultural and Development Economics, Food and Agriculture Organization of the United Nations, vol. 2(1), pages 1-6.
    19. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    20. Christine M. Moser & Christopher B. Barrett, 2006. "The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar," Agricultural Economics, International Association of Agricultural Economists, vol. 35(3), pages 373-388, November.
    21. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    22. Keijiro Otsuka & Yoko Kijima, 2010. "Technology Policies for a Green Revolution and Agricultural Transformation in Africa," Journal of African Economies, Centre for the Study of African Economies, vol. 19(suppl_2), pages 60-76.
    23. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    24. Meyer, Bruce D, 1995. "Natural and Quasi-experiments in Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 151-161, April.
    25. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    26. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    27. Tsusaka, Takuji W. & Kajisa, Kei & Pede, Valerien O. & Aoyagi, Keitaro, 2015. "Neighborhood effects and social behavior: The case of irrigated and rainfed farmers in Bohol, the Philippines," Journal of Economic Behavior & Organization, Elsevier, vol. 118(C), pages 227-246.
    28. Muller, Daniel & Zeller, Manfred, 2002. "Land use dynamics in the central highlands of Vietnam: a spatial model combining village survey data with satellite imagery interpretation," Agricultural Economics, Blackwell, vol. 27(3), pages 333-354, November.
    29. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
    30. Davis, K. & Nkonya, E. & Kato, E. & Mekonnen, D.A. & Odendo, M. & Miiro, R. & Nkuba, J., 2012. "Impact of Farmer Field Schools on Agricultural Productivity and Poverty in East Africa," World Development, Elsevier, vol. 40(2), pages 402-413.
    31. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    32. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643.
    33. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    34. Anderson, Jock R. & Feder, Gershon, 2007. "Agricultural Extension," Handbook of Agricultural Economics, in: Robert Evenson & Prabhu Pingali (ed.), Handbook of Agricultural Economics, edition 1, volume 3, chapter 44, pages 2343-2378, Elsevier.
    35. Krishnan, Pramila & Patnam, Manasa, 2013. "Neighbours and Extension Agents in Ethiopia: Who matters more for technology diffusion?," CEPR Discussion Papers 9539, C.E.P.R. Discussion Papers.
    36. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
    37. Kurt J. Beron & Wim P. M. Vijverberg, 2004. "Probit in a Spatial Context: A Monte Carlo Analysis," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 8, pages 169-195, Springer.
    38. John DiNardo & Justin L. Tobias, 2001. "Nonparametric Density and Regression Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 11-28, Fall.
    39. Keijiro Otsuka & Takashi Yamano, 2005. "The Possibility of a Green Revolution in Sub-Saharan Africa: Evidence from Kenya," The Electronic Journal of Agricultural and Development Economics, Food and Agriculture Organization of the United Nations, vol. 2(1), pages 7-19.
    40. Rajagopal, 2014. "Technology Diffusion and Adoption," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 6, pages 148-173, Palgrave Macmillan.
    41. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    42. Lunn, Peter D., 2010. "The sports and exercise life-course: A survival analysis of recall data from Ireland," Social Science & Medicine, Elsevier, vol. 70(5), pages 711-719, March.
    43. David M. Drukker & Ingmar Prucha & Rafal Raciborski, 2013. "Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances," Stata Journal, StataCorp LP, vol. 13(2), pages 221-241, June.
    44. Yasuyuki Todo & Ryo Takahashi, 2013. "Impact Of Farmer Field Schools On Agricultural Income And Skills: Evidence From An Aid‐Funded Project In Rural Ethiopia," Journal of International Development, John Wiley & Sons, Ltd., vol. 25(3), pages 362-381, April.
    45. Gershon Feder & Rinku Murgai & Jaime B. Quizon, 2004. "The Acquisition and Diffusion of Knowledge: The Case of Pest Management Training in Farmer Field Schools, Indonesia," Journal of Agricultural Economics, Wiley Blackwell, vol. 55(2), pages 221-243, July.
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