This exploratory study assessed attitudes and perceptions of smallholder farmers towards agricultural technologies in Kakamega County, Kenya. Through a mixed-methods sequential design, the study evaluated the key variables predicting farmer adoption of agricultural innovations. While social sciences provide a clear human-driven pattern explaining the process of choices and behaviors regarding technology use, there is still little clarity on the influences of adoption decisions among smallholder farmers in rural Kenya. Using the diffusion of innovations theory, the study explored the attitudes and perceptions of smallholder farmers toward technology adoption in seven sub-counties of Kakamega County (Lurambi, Ikolomani, Shinyalu, Mumias East (Shianda), Malava Butere, and Khwisero). The study design utilized a quantitative survey of 245 smallholder heads of households, followed by focus group discussions to further probe attitudes, values and practices that could influence technology adoption. The survey questionnaire tested two hypotheses: (H1) socio-demographic characteristics are related to agricultural technology adoption; and, (H2) farmer access to extension services was related to agricultural technology adoption. A binary logistic regression model was used to quantitatively estimate socio-demographic variables presumed to influence the adoption of agricultural innovations. Subsequently, four informal focus group discussions of 28 discussants was conducted across representative sub-counties (Lurambi, Shianda, Malava and Ikolomani), to elicit an in-depth understanding of farmers’ perspectives on technology adoption. The focus group participants included farmers recruited from among survey participants. The qualitative research instrument sought to answer three questions, (RQ1) what are farmer attitudes and perceptions towards agricultural technologies; (RQ2) what socio-cultural values influence farmers’ choice of agricultural technologies; and, (RQ3) what sources do farmers use for obtaining information on agricultural technology? Quantitative results included a principal component analysis (PCA) in which 14 attitudes questions were reduced to five conceptual clusters. These clusters included: challenges in accessing modern agricultural technologies (explained 19.09% of the total variance); effectiveness of agricultural technologies (11.88%); enjoyment of agricultural technologies (10.02%); social influence in use of technology (9.47%); and experience with agricultural technologies (8.13%). A logistic regression model indicated that independently age (.07), education (.10), and off-farm income (.08) were significantly associated with adoption of technology at the 90% confidence level when controlling for all other variables in the model. However, agricultural extension (.42) was not a significant predictor of agricultural technology adoption in this model. Qualitative results provided rich insights which enhanced findings from the survey data. Key insights in the thematic analysis included: farmers’ ambivalence about agricultural technologies; lack of trust in agricultural agents; low levels of agricultural technology knowledge; extension services as the main source of information dissemination to farmers; predominance of gender in determining agricultural technology adoption; and gender inequity in agricultural decision-making. In conclusion, the study results suggested that a mixed-methods approach was valuable in probing the nuances of farmers’ perceptions of agricultural extension and technology adoption among smallholder farmers. The results supported the following recommendations: the agricultural extension efforts could be more effectively structured in order to support the dissemination of agricultural information; the issue of gender should be adequately addressed by engaging male and female in collaborative agricultural efforts to help break the barrier of gender inequity; and future research would benefit from disaggregating public and private extension services as a more robust method for determining their individual effects in the promotion of agricultural innovations among smallholder farmers.

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