Agriculture and Human Values

, Volume 31, Issue 3, pp 339–353 | Cite as

Effects of social network factors on information acquisition and adoption of improved groundnut varieties: the case of Uganda and Kenya

  • Mary Thuo
  • Alexandra A. Bell
  • Boris E. Bravo-UretaEmail author
  • Michée A. Lachaud
  • David K. Okello
  • Evelyn Nasambu Okoko
  • Nelson L. Kidula
  • Carl M. Deom
  • Naveen Puppala


Social networks play a significant role in learning and thus in farmers’ adoption of new agricultural technologies. This study examined the effects of social network factors on information acquisition and adoption of new seed varieties among groundnut farmers in Uganda and Kenya. The data were generated through face-to-face interviews from a random sample of 461 farmers, 232 in Uganda and 229 in Kenya. To assess these effects two alternative econometric models were used: a seemingly unrelated bivariate probit (SUBP) model and a recursive bivariate probit (RBP) model. The statistical evaluation of the SUBP shows that information acquisition and adoption decisions are interrelated while tests for the RBP do not support this latter model. Therefore, the analysis is based on the results obtained from the SUBP. These results reveal that social network factors, particularly weak ties with external support (e.g., researchers, extension agents, etc.), partially influence information acquisition, but do not influence adoption. In Uganda, external support, gender, farm size, and geographic location have an impact on information acquisition. In Kenya, external support and geographic location also have an impact on information acquisition. With regard to adoption, gender, household size, and geographic location play the most important roles for Ugandan farmers, while in Kenya information from external sources, education, and farm size affect adoption choice. The study provides insight on the importance of external weak ties in groundnut farming, and a need to understand regional differences along gender lines while developing agricultural strategies. This study further illustrates the importance of farmer participation in applied technology research and the impact of social interactions among farmers and external agents.


Social networks Strong and weak ties Adoption Information acquisition Kenya Uganda Groundnuts 



Appropriate technology


Full information maximum likelihood


Maximum likelihood


Non-research farmer


Peanut Collaborative Research Support Program


Recursive bivariate probit


Research farmer


Seemingly unrelated bivariate probit



The authors wish to thank Dr. Barry G. Sheckley for his review of this paper, and the researchers from Kenya Agricultural Research Institute (KARI-Kisii) and the National Semi-Arid Resources Research Institute (NaSARRI) Uganda for time spent in the fieldwork. The authors are also grateful for the comments received from two anonymous reviewers and the Editor-in-Chief, Harvey James. The study was supported by the United States Agency for International Development (USAID) under the Peanut CRSP Grant ECG-A-00-07-00001-00 2007–2012.


  1. Abdulai, A., P. Monnin, and J. Gerber. 2008. Joint estimation of information acquisition and adoption of new technologies under uncertainty. Journal of International Development 20: 437–451.CrossRefGoogle Scholar
  2. Amare, M., S. Asfaw, and B. Shiferaw. 2012. Welfare impacts of maize–pigeonpea intensification in Tanzania. Agricultural Economics 43(1): 27–43.CrossRefGoogle Scholar
  3. Anderson, J.R., and G. Feder. 2004. Agricultural extension: Good intentions and hard realities. The World Bank Research Observer 19(1): 41–60.CrossRefGoogle Scholar
  4. Bandiera, O., and I. Rasul. 2005. Social networks and technology adoption in Northern Mozambique. Unpublished paper. London, UK: London School of Economics and CEPR.Google Scholar
  5. Bandura, A. 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall Inc.Google Scholar
  6. Cameron, A.C., and P.K. Trivedi. 2009. Microeconometrics using Stata, vol. 5. College Station, TX: Stata Press.Google Scholar
  7. Carr, E.R. 2008. Men’s crops and women’s crops: The importance of gender to the understanding of agricultural and development outcomes in Ghana’s Central Region. World Development 36(5): 900–915.CrossRefGoogle Scholar
  8. Castello, J.V. 2010. Promoting employment of disabled women in Spain: Evaluating a policy. Labour Economics 19(1): 82–91.CrossRefGoogle Scholar
  9. Chang, H., and A. Mishra. 2008. Impact of off-farm labor supply on food expenditures of the farm household. Food Policy 33: 657–664.CrossRefGoogle Scholar
  10. Chambers, R. 2009. Foreword. In Farmer first revisited: Innovation for agricultural research and development, ed. I. Scoones, and J. Thompson, xix–xxv. Warwickshire, UK: Practical Action Publishing Ltd.Google Scholar
  11. Conley, T., and C. Udry. 2005. Learning about a new technology: Pineapple in Ghana. Working paper. New Haven, CT: Yale University.Google Scholar
  12. Dimara, E., and D. Skuras. 2003. Adoption of agricultural innovations as a two-stage partial observability process. Agricultural Economics 28(3): 187–196.CrossRefGoogle Scholar
  13. Diouf, W., B.G. Sheckley, and M. Kehrhahn. 2000. Adult learning in a non-western context: The influence of culture in a Senegalese farming village. Adult Education Quarterly 51(1): 2–44.CrossRefGoogle Scholar
  14. Doss, C.R. 2006. Analyzing technology adoption using microstudies: Limitations, challenges, and opportunities for improvement. Agricultural Economics 34(3): 207–219.CrossRefGoogle Scholar
  15. Feder, G., R.E. Just, and D. Zilberman. 1985. Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change 33(2): 255–298.CrossRefGoogle Scholar
  16. Foster, A.D., and M.R. Rosenzweig. 1995. Learning by doing and learning from others: Human capital and technical change in agriculture. The Journal of Political Economy 103(6): 1176–1209.CrossRefGoogle Scholar
  17. Genius, M., C.J. Pantzios, and V. Tzouvelekas. 2006. Information acquisition and adoption of organic farming practices. Journal of Agricultural and Resource Economics 31(1): 93–113.Google Scholar
  18. Granovetter, M. 1983. The strength of weak ties: A network theory revisited. Sociological Theory 1: 201–233.CrossRefGoogle Scholar
  19. Granovetter, M. 2005. The impact of social structure on economic outcomes. Journal of Economic Perspectives 19(1): 33–50.CrossRefGoogle Scholar
  20. Greene, W.H. 2007. Econometric analysis, 6th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  21. Hartwich, F., and U. Scheidegger. 2010. Fostering innovation networks: The missing piece in rural development? Rural Development News 1: 70–75.Google Scholar
  22. Haythornthwaite, C. 1996. Social network analysis: An approach and technique for the study of information exchange. Library and Information Science Research 18: 323–342.CrossRefGoogle Scholar
  23. Hoang, L.A., J.C. Castella, and P. Novosad. 2006. Social networks and information access: Implications for agricultural extension in a rice farming community in Northern Vietnam. Agriculture and Human Values 23: 513–527.CrossRefGoogle Scholar
  24. Hounkonnou, D., D. Kossou, T.W. Kuyper, C. Leeuwis, E.S. Nederlof, N. Röling, O. Sakyi-Dawson, M. Traoré, and A. van Huis. 2012. An innovation systems approach to institutional change: Smallholder development in West Africa. Agricultural Systems 108: 74–83.CrossRefGoogle Scholar
  25. Huth, P.K., and T.L. Allee. 2002. The democratic peace and territorial conflict in the twentieth century. New York, NY: Cambridge University Press.Google Scholar
  26. Katungi E., S. Edmeades, and M. Smale. 2006. Gender, social capital and information exchange in Rural Uganda. CAPRi Working Paper 59. Washington, DC: International Food Policy Research Institute.Google Scholar
  27. Kassie, M., B. Shiferaw, and G. Muricho. 2011. Agricultural technology, crop income and poverty alleviation in Uganda. World Development 39: 1784–1795.CrossRefGoogle Scholar
  28. Kidula N., N. Okoko, B.E. Bravo-Ureta, M. Thuo, and L. Wasilwa. 2010. A preliminary analysis of yield differences in groundnuts between research and non-research farmers in Kenya. In Paper Presented at the 12th KARI Biennial Scientific Conference, 8–12 November 2010, Nairobi, Kenya.Google Scholar
  29. Kipkoech, A.K., M.A. Okiror, J.R. Okalebo, and H.K. Maritim. 2007. Production efficiency and economic potential of different soil fertility management strategies among groundnut farmers of Kenya. Science World Journal 2(1): 15–21.Google Scholar
  30. Leckie, G.J. 1996. Female farmers and the social construction of access to agricultural information. Library and Information Science Research 18(4): 297–321.CrossRefGoogle Scholar
  31. Li, A., B.E. Bravo-Ureta, D.K. Okello, C.M. Deom, and N. Puppala. 2013. Groundnut production and climatic variability: Evidence from Uganda. Zwick Center Working Paper 17. Storrs, CT: University of Connecticut.Google Scholar
  32. Manski, C.F. 1993. Identification of endogenous social effects: The reflection problem. The Review of Economic Studies 60(3): 531–542.CrossRefGoogle Scholar
  33. Matuschke, I. 2008. Evaluating the impact of social networks in rural innovation systems: An overview. IFPRI Discussion Paper, 00816. Washington, DC: International Food Policy Research Institute.Google Scholar
  34. Monfardini, C., and R. Radice. 2008. Testing exogeneity in the bivariate probit model: A Monte Carlo study. Oxford Bulletin of Economics and Statistics 70(2): 271–282.CrossRefGoogle Scholar
  35. Monge, M., F. Hartwich, and D. Halgin. 2008. How change agents and social capital influence the adoption of innovations among small farmers: Evidence from social networks in rural Bolivia. IFPRI Discussion Paper, 00761. Washington, DC: International Food Policy Research Institute.Google Scholar
  36. Moreno, G., and D.L. Sunding. 2003. Simultaneous estimation of technology adoption and land allocation. In Paper prepared at the American agricultural economics association annual meeting, 27–30 July 2003, Montreal, Canada. Accessed 15 Oct 2011.
  37. Munshi, K. 2004. Social learning in a heterogeneous population: Technology diffusion in the Indian Green Revolution. Journal of Development Economic 73(1): 185–213.CrossRefGoogle Scholar
  38. Ntare, B.R., A.T. Diallo, J. Ndjeunga, and F. Waliyar. 2008. Groundnut seed production manual. Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT).Google Scholar
  39. Okello, D.K., M. Biruma, and C.M. Deom. 2010. Overview of groundnuts research in Uganda: Past, present and future. African Journal of Biotechnology 9(39): 6448–6459.Google Scholar
  40. Okoko, E.N.K., D.J. Rees, J.K. Kwach, and P. Ochieng. 1999. Participatory evaluation of groundnut production, Southwest Kenya. In Towards increased use of demand driven technology, ed. J.A Sutherland, 305-307. KARI/DFID NARP II PROJECT, End of Project Conference Proceedings, 23rd–26th March. KARI and DIFD, Nairobi, Kenya. Okoko, N., N. Kidula, F. Muriithi, G. O. Rachier, M. Shiluli, M. Okelo, M. Odendo, F. Simtowe and R. Kanda. 2011. Understanding the potential of groundnuts in Kenya. KARI-Kisii, Compiled Report. 54 pp.Google Scholar
  41. Pretty, J. 2003. Social capital and the collective management of resources. Science 302: 1912–1915.CrossRefGoogle Scholar
  42. Research into Use (RIU). 2011. Poor farmers in Uganda boost their income with new groundnut varieties: Commercial incentives for groundnut production and farmer led multiplication. Accessed 18 Sep 2011.
  43. Rivera, W.M. 2011. Public sector agricultural extension system reform and the challenges ahead. Journal of Agricultural Education and Extension 17(2): 165–180.CrossRefGoogle Scholar
  44. Rogers, E.M. 2003. Diffusion of innovations, 5th ed. New York, NY: Simon & Schuster Inc.Google Scholar
  45. Ruef, M. 2002. Strong ties, weak ties and islands: Structural and cultural predictors of organizational innovation. Industrial and Corporate Change 11(2): 427–449.CrossRefGoogle Scholar
  46. Sanginga, P.C., J. Tumwine, and N.K. Lilja. 2006. Patterns of participation in farmers’ research groups: Lessons from the highlands of southwestern Uganda. Agriculture and Human Values 23(4): 501–512.CrossRefGoogle Scholar
  47. Scandizzo, P.L., and S. Savastano. 2010. The adoption and diffusion of GM crops in United States: A real option approach. AgBioForum 13(2): 142–157.Google Scholar
  48. Sustainet. 2011. Sustainable agriculture: A pathway out of poverty for East Africa’s rural poor. Sustainable Agriculture Information Network. Accessed 20 Sep 2011.
  49. Valente, T.W. 1996. Social network thresholds in the diffusion of innovations. Social Networks 18(1): 60–89.CrossRefGoogle Scholar
  50. Wooldridge, J. 2001. Econometric analysis of cross section and panel data, 1st ed. Cambridge, MA: MIT Press Books.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Mary Thuo
    • 1
  • Alexandra A. Bell
    • 2
  • Boris E. Bravo-Ureta
    • 3
    • 4
    Email author
  • Michée A. Lachaud
    • 3
  • David K. Okello
    • 5
  • Evelyn Nasambu Okoko
    • 6
  • Nelson L. Kidula
    • 6
  • Carl M. Deom
    • 7
  • Naveen Puppala
    • 8
  1. 1.Department of Educational Planning and ManagementWolaita Sodo UniversityWolaita SodoEthiopia
  2. 2.Department of Educational LeadershipUniversity of ConnecticutStorrsUSA
  3. 3.Department of Agricultural and Resource EconomicsUniversity of ConnecticutStorrsUSA
  4. 4.Department of Agricultural EconomicsUniversity of TalcaTalcaChile
  5. 5.National Semi-Arid Resources Research Institute (NaSARRI)SorotiUganda
  6. 6.Kenya Agricultural Research Institute (KARI-Kisii)KisiiKenya
  7. 7.Department of Plant PathologyUniversity of GeorgiaAthensUSA
  8. 8.Agricultural Science Center at ClovisNew Mexico State UniversityClovisUSA

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