Impact of Modern Beehive Technology Adoption on Household Income: Evidence from North Shewa Zone, Oromia National Regional State, Ethiopia




Adoption , Household income , Impact , Logit , Propensity Score Matching


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.


Abadi AK, Burton M, Pannell DJ. 1999. More empirical evidence on the adoption of chick peas in Western Australia“, paper presented at the 43rd Annual Conference of the Australian Agricultural and Resource Economics Society.

Akinwumi AG, Adesina K, Baidu F. 2001. Farmers' perceptions and adoption of new agricultural technology: evidence from analysis in Burkina Faso and Guinea, West Africa Farmers' perceptions and adoption of new agricultural technology: evidence from analysis in Burkina Faso and Guinea, West Africa. ELSEVIER Agricultural Economics, 13: 1-9.

Becker SO, Ichino A. 2002. Estimation of average treatment effects based on propensity scores. The Stata Journal, 2(4), 358-377.

Bernard AB, Stephen JR, Peter KS. 2007. Comparative Advantage and Heterogeneous Firms. The Review of Economic Studies, Volume 74, Issue 1, January 2007, Pages 31–66.

Caliendo M, Kopeinig S. 2008. Some practical guidance for the implementation of propensity score matching. J Econ Surv 22(1):31–72.

Chilot Y, Shampiro BI, Demeke M. 1996. Factors influencing adoption of new wheat technologies in Wolmera and Addis Alem Areas of Ethiopia. Ethiopian Journal of Agricultural economics. Vol (1): pp 63-83

Cochran WG. 1977. Sampling techniques 3rd edition, New York: John Wiley & Sons

CSA. 2021. Report of Livestock Characteristics (Private Peasant Holdings), Volume II, Addis Ababa, Ethiopia.

Gujarati DN, Porter DC. 2009. Basic Econometrics. 5th Edition, McGraw Hill Inc., New York.

Kariyasa K, Dewi A. 2011. Analysis of Factors Affecting Adoption of Integrated Crop Management Farmer Field School in Swampy Areas. International Journal of Food and Agricultural Economics 1(2): pp 29-38.

Kassa T, Amenay A, Engida G. 2018. Adoption of Improved Beehive Technology in Ethiopia: Evidence from Kaffa, Sheka and Bench maji Zones. International Journal of Food and Agricultural Economics: Vol. 6, pp. 87-100 87

Mignouna DB, Manyong VM, Rusike J. 2011. Determinants of Adopting Imazapyr-Resistant Maize Technologies and its Impact on Household Income in Western Kenya. Agri-Bio-Forum, 14(3): 158-163.

MoA. 2015. Reports of Ministry of Agriculture, Addis Ababa, Ethiopia

MoA, ILRI. 2013. Apiculture value chain vision and strategy for Ethiopia, Addis Ababa, Ethiopia.

Musa HA, Hiwot MM, Seltene A, Wendmagegn M, Amare K. 2016. Adoption of improved groundnut seed and its impact on rural households’ welfare in Eastern Ethiopia. Cogent Economics Finance, 4:1–13.

Rahman S. 2007. Adoption of improved technologies by the pig farmers of Aizawal district of Mizoram, India. Livestock Research for Rural Development. Volume 19, No.5.

Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.

Sianesi B. 2004. An evaluation of the active labor market programs in Sweden. The Review of Economics and Statistics, 186(1):133-155.

Sisay Y, Malede B, Degsew M. 2013. Perception of Farmers towards the Use of Modern Beehives Technology in Amhara Region, Ethiopia. European Journal of Biological Sciences, 5(1): 01-08.

Tamrat G. 2015. Adoption of Modern Bee Hive in Arsi Zone of Oromia Region: Determinants and Financial Benefits. Agricultural Sciences, 6:382-396.

Workneh AW. 2017. Financial Benefits of Box Hive and the Determinants of its Adoption in Selected District of Ethiopia. American Journal of Economics, 1(1): 21-29.




How to Cite

Abera, N. T., & Girma, G. (2023). Impact of Modern Beehive Technology Adoption on Household Income: Evidence from North Shewa Zone, Oromia National Regional State, Ethiopia. Turkish Journal of Agriculture - Food Science and Technology, 11(10), 1871–1877.



Research Paper