Women Farmers and Barriers to Technology Adoption in Rural Uganda
Adopting modern agricultural technologies could improve productivity and reduce rural poverty in sub-Saharan Africa, but there is little evidence on the constraints that limit the diffusion of better practices. In this ongoing study, researchers partner with BRAC to roll out an agricultural extension program in Uganda in order to test how credit constraints, social networks, and expectations about the returns to technology affect adoption decisions.
Agricultural productivity in Africa is very low despite the existence of modern agricultural technologies such as improved seeds, fertilizers, pesticides, and improved livestock breeds. As a large fraction of the population works in agriculture, encouraging the adoption of these technologies could raise agricultural productivity and in turn reduce poverty and catalyze economic growth.
NGOs seeking to promote new technologies often do so through agricultural extension services, which provide farmers with information about agricultural practices and the opportunity to try out new techniques. Many existing studies look at the effectiveness of extension programs, but fewer examine the mechanisms that drive technology adoption. Adoption may be difficult without access to credit. Social networks may play an important role in the diffusion of new technologies. Finally, differences in expected returns to a new technology may also affect individuals’ adoption decisions. This study aims to disentangle how these constraints affect technology adoption, thereby allowing agricultural extension workers to fine-tune their efforts.
Context of the evaluation
Uganda’s agriculture is representative of most of rural Africa. Two-thirds of the population worked in agriculture in 2009, and the majority of farmers (71 percent) are subsistence farmers. Very few use modern inputs such as improved seeds, fertilizers, or pesticides. Many households raise poultry, but few have improved chicken breeds and the poultry mortality rate is among the highest in the world.
BRAC, one of the world’s largest NGOs, has 125 branch offices throughout Uganda and reaches over 800,000 poor female farmers. The organization’s existing agriculture and livestock extension program aims to improve productivity through training in modern agricultural practices such as line sowing, weeding, intercropping, and crop rotation; crop and poultry disease prevention; adoption of improved seeds and chicken breeds; introduction of new crops; and use of poultry vaccines. The program disseminates information about these new technologies and practices through local community members, exclusively women, called model farmers and community promoters. BRAC chooses these women for a six-day training based on their business skills, agricultural and livestock knowledge, and popularity in their villages. These women then take charge of training other farmers and selling seeds and vaccines.
Details of the intervention
Researchers introduced BRAC’s extension program in 168 villages or groups of villages that had not previously participated in agricultural extension in order to evaluate its effect on agricultural productivity, nutritional intake, and poverty levels. The villages were then randomly assigned to receive one of three programs:
BRAC’s traditional agricultural extension program (58 villages)
The agricultural extension program and an additional microfinance component (51 villages)
No BRAC services until 2015, when the full program will be expanded to all villages in the study (59 villages)
Within each of the 109 villages receiving an extension program, a model farmer was randomly chosen from among two eligible candidates.
The phased roll-out of program components will allow researchers to examine how credit, constraints, social networks, and expectations about the returns to technology affect adoption. Comparing the villages receiving the additional microfinance program to the villages only receiving traditional extension services will allow the researchers to evaluate the importance of credit constraints in the adoption process. Combining the random selection of model farmers with data on participants’ social networks will allow for the examination of the relevance of social networks for technology adoption. In addition, household surveys will ask farmers about their expectations about the returns to the technology, which will allow the researchers to study how adoption behavior varies according to expectations.
Results and policy lessons