13 pages., Article #: 3FEA2, via online journal., A multiple indicators, multiple causes, or MIMIC, modeling framework can be used for analyzing a variety of farmer decision-making situations where multiple outcomes are possible. Example applications include analyses of farmer use of multiple information sources, management practices, or technologies. We applied the framework to analyze use of multiple information sources by beef cattle farmers. We provide measures of how farmer demographics, farm characteristics, and risk attitudes influenced farmer use of information from Extension, producer groups, popular press, the U.S. Department of Agriculture, the Internet, and other farmers. Education and greater willingness to take risk positively influenced information use among the farmers we studied. Our process has implications for broader use within Extension.
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.
24 pages., via online journal., Biofortification of staple crops to combat micronutrient deficiencies is gaining global recognition. Projects promoting biofortified food crops use intensive agriculture-nutrition education and extension activities to increase adoption of such crops. This study examines the effect of such programs on the adoption and diffusion of orange-fleshed sweetpotato (OFSP). It finds that intensive agriculture-nutrition education and extension programs adopted by some of the biofortification projects increases the adoption and diffusion of OFSP. Specifically, participation in mother-to-mother nutrition support clubs and nutrition-focused health talks affect its adoption and diffusion, but with varying degrees of importance. The paper discusses the implications of these findings.
5 pages., via online journal., In India sustaining dairy farming as a rural livelihood and to meet the growing demand of milk, necessitates development and dissemination of technology for improving the farm’s output. There is also a need to understand how far existing innovations are adopted by farmers and factors influencing adoption and/or rejection. Hence, the factors that influence adoption and the extent of adoption were consolidated from past research using meta analysis and other techniques. It was found that at large-level farmer’s knowledge (true effect size r value +0.64) and at medium-level (true effect size r value ranging from +0.32 to +0.47) attitude, risk-taking behaviour and economic motivation, milk production and sales, education, extension agency contacts and mass media exposure influenced adoption of dairy innovation. Further, along with the above factors poor innovation attributes were limiting adoption to 55%.
