Influence of Traditional Media on Tea Productivity in Belgut Sub-County, Kericho County, Kenya

Traditional media remain an important means of disseminating agricultural information to smallholder farmers, yet their relationship with tea productivity warrants examination within specific local contexts. This study examined the relationship between traditional media and tea productivity among smallholder tea farmers in Belgut Sub-County, Kericho County, Kenya. A descriptive research design using a mixed-methods approach was adopted. The study targeted 13,096 smallholder tea-farming households and 32 agricultural extension officers. A sample of 385 tea-farming households was selected through proportionate stratified random sampling, while 12 agricultural extension officers were purposively selected to provide supplementary qualitative information. Quantitative data were collected using structured questionnaires, while qualitative data were obtained through semi-structured interviews. Quantitative data were analysed using descriptive statistics and Pearson’s product–moment correlation, while interview data were analysed thematically. Descriptive findings showed favourable assessments of agricultural newspapers and magazines (M = 4.41), the reliability of traditional media in communicating tea-production and marketing information (M = 4.17), printed extension materials (M = 4.15), and radio programmes (M = 3.97). Respondents were less definite about television programmes and the timeliness of information, with both items recording a mean of 3.25. Correlation analysis indicated a strong, positive and statistically significant relationship between traditional media and tea productivity (r = .624, p < .001). The findings suggest that favourable perceptions of traditional media are associated with higher reported tea productivity among the surveyed farmers. The study recommends strengthening agricultural information dissemination through radio and printed materials while improving the relevance and timeliness of television-based agricultural information.