Efforts have been made on promoting improved chickpea technologies. However, the result is not that much impressive. This study aimed to analyze drivers of technology adoption. 224 respondents were used for this study. Multivariate Probit (MVP) and Seemingly Unrelated Regression (SUR) models were employed. The results from MVP model, marginal success probability of adoption decision were 60%, 19%, and 17% for a variety, bio-inoculant, and chemical fertilizer, respectively. Distance from FTC, farmland income, livestock TLU, and field day involvement have significantly affected for adoption of chickpea varieties. A social network, market information, and field day participation have affected significantly the adoption of bio-inoculant. In addition, household family size, physical asset owned, and field day participation has significantly influenced the adoption of chemical fertilizer. Field day participation was the most important variable for the adoption decision of chickpea technologies as a package. The results from SUR model, farmers’ training center distance, farm income, livestock holding, social network and agricultural training have significantly influenced adoption intensity of improved varieties. Age, farming experience, livestock holding, social network, education status, and field day and training participation have significantly influenced the intensity of bio-inoculant adoption. In addition, Age, education status, radio owned, training participation, asset owned and farmer’s perception have significantly influenced the intensity of chemical fertilizer adoption. Training participation is the most determinant factor for adoption intensity of improved technologies. Hence, government should emphasize on improving of extension services, provision of education, supporting livestock production, delivering credit service, rural infrastructure establishment, and considering community social network for adoption of chickpea technologies.

Keywords: Chickpea, Adoption, Multivariate Probit, Seemingly Unrelated Regression