14 pages., via online journal, The study evaluated the potential of 19 radio stations to promote new or improved agricultural technologies to strengthen agricultural extension services. Key informant interviews were conducted with the station and/or program managers of the selected radio stations. Two female respondents i.e. from UBC Radio, and Impact FM and 17 male respondents from the remaining radio stations were interviewed. The survey used semi structured questionnaire to determine broadcasting languages, radio transmitter capacity, geographical coverage and audience, major programs and scheduling, use of modern ICT, staff capacity and feedback mechanism from the audience as well as experience in agricultural programming using participatory radio campaign. The collected data was analyzed using content analysis. 16 of the radio stations are commercial while the remaining three belong to public, community and religious radio stations. The potential audience of the surveyed radio stations varied from one to ten million. Seven broadcasting languages (English, Luganda, Lugisu, Lusoga, Japadhola, Ateso and Samia) were predominant, while English and Luganda cut across all communities. The estimated number of audience for each radio station varies from one to ten million listeners. The results also indicate that agricultural programs are not a major component of radio program with time allocation for agrictural programs comprising only 15 percent of total time allocation. However, ten radio stations had previous collaboration with international, regional or national NGOs to promote specific agricultural technology. Radio broadcasters of these radio stations had some form of agricultural programming including participatory radio campaign. Building on this experience, it is possible to reach more farmers through radio to strengthen adoption of recommended agricultural technologies.
13th European Farming Systems Association (IFSA) Symposium, Greece., Provides a narrative review which identifies key functional characteristics that enable learning in demonstration farms.
