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.
Abstract via online journal. 2 pages., Technological innovation is vital to economic growth and food security in sub-Saharan Africa where agricultural productivity has been stagnant for a long time. Extension services and learning from peer farmers are two common approaches to facilitate the diffusion of new technologies, but little is known about their relative effectiveness. Selection bias, whereby well-motivated training participants would perform better even without extension services, as well as knowledge spillovers, where non-participants can indirectly benefit from extension services, are among the major threats to causal inference. Using a unique sequential randomized experiment on agricultural training, this study attempts to meet the dual objectives of executing rigorous impact evaluation of extension services and subsequent spillovers on rice production in Cote d’Ivoire. Specifically, to reduce selection bias, we randomly assigned eligibility for training participation; and to satisfy the stable unit treatment value assumption, control-group farmers were initially restricted from exchanging information with treated-group farmers who had received rice management training. Once some positive impacts were confirmed, information exchange between the treated and control farmers was encouraged. We found that the initial performance gaps created by the randomized assignment disappeared over time, due presumably to social learning from peer farmers. A detailed analysis concerning the information network and peer effects provided suggestive evidence that there were information and technology spillovers from treated to control farmers after removing the information exchange restriction. Overall, our study demonstrates that information dissemination by farmers can be as effective in improving practices as the initial training provided by extension services.
