Effects of social network factors on information acquisition and adoption of improved groundnut varieties: the case of Uganda and Kenya
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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.
KeywordsSocial networks Strong and weak ties Adoption Information acquisition Kenya Uganda Groundnuts
Full information maximum likelihood
Peanut Collaborative Research Support Program
Recursive bivariate probit
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.
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