Hidabu Abote, Dera, Wera Jarso and Debra Libanos districts of North Shewa zone are potential in honey production. To enhance this potential, different organizations disseminate improved beehives technologies for the smallholder farmers. However, the impact of the disseminated technologies on household income has not been evaluated. Thus, this study aimed to evaluate the impact of improved beehive adoption on household income. Purposive and two stage sampling technique was used to select 384 sampled households. The study used logistic regression model to identify the determinants of adoption decision of modern beehive technology while propensity score matching to evaluate the impact of modern beehive technology adoption on household income. The result of logistic regression model shows that age of household head, family size, households experience in beekeeping, frequency of extension contact, access to credit services, access to training and access to beehive demonstration site visit had positive and significant effect on household adoption decision of modern beehive technology. The result of propensity score matching indicates that the adopters of improved beehive technology were earned Birr 2690.383 than non-adopter. The difference in household income between the two groups shows that there is considerable room for improvement of household income through increasing the number of adopter of improved beehives technology in the study area. This should be done through provision of training, credit, extension and expansion of beehive demonstration site among the others.

Hidabu Abote, Dera, Wera Jarso and Debra Libanos districts of North Shewa zone are potential in honey production. To enhance this potential, different organizations disseminate improved beehives technologies for the smallholder farmers. However, the impact of the disseminated technologies on household income has not been evaluated. Thus, this study aimed to evaluate the impact of improved beehive adoption on household income. Purposive and two stage sampling technique was used to select 384 sampled households. The study used logistic regression model to identify the determinants of adoption decision of modern beehive technology while propensity score matching to evaluate the impact of modern beehive technology adoption on household income. The result of logistic regression model shows that age of household head, family size, households experience in beekeeping, frequency of extension contact, access to credit services, access to training and access to beehive demonstration site visit had positive and significant effect on household adoption decision of modern beehive technology. The result of propensity score matching indicates that the adopters of improved beehive technology were earned Birr 2690.383 than non-adopter. The difference in household income between the two groups shows that there is considerable room for improvement of household income through increasing the number of adopter of improved beehives technology in the study area. This should be done through provision of training, credit, extension and expansion of beehive demonstration site among the others.