Perception and determinants of agricultural technology adoption in North Shoa Zone, Amhara Regional State, Ethiopia - Solomon Estifanos Massresha; Tadesse Zenebe Lema; Markew Mengiste Neway; Wudineh Ayalew Degu (Cogent Economics & Finance, 2021)
Laying the ground for the agricultural sector to derive development is critical and urgent in relieving majority of the poor out of poverty trap. This in turn calls the modernization of the agriculture sector through agricultural technology adoption. The major objective of the study is to assess the perception and determinants of agricultural technology adoption in North Shoa zone, Amhara regional state, Ethiopia. Data were collected from 796 farming households from four districts namely, Angolela Tera, Menz Gera, Minijar Shenkora and Moretna Jiru. For analysis purpose, t-test and binomial logistic model was employed. The result indicates that the likelihood of adopting improved seed, chemical fertilizer and irrigation is higher among households with higher age, greater years of schooling, large farm size, large livestock ownership and many extension contacts. Additionally, the likelihood of adopting these agricultural technologies is higher for household participating in non-farm income generating activities, having membership status in various social group and having access to credit. The likelihood of adopting the prevailing agricultural technologies also found higher for male- headed households as compared to female-headed ones. Distance to the nearest market also negatively and significantly affects the decision to adopt various agricultural technologies. The study suggested that the awareness of farmers concerning the available agricultural technologies should be raised through membership of different social group and frequent extension contact. On the other, promoting farmers to participate in off-farm income generation activities and creating access to credit service can reduce the financial constraint in purchasing and possessing new agricultural technologies.
Keywords: 330; Adoption decision; agricultural technology; logistic regression model