Learning selection revisited: How can agricultural researchers make a difference?
AbstractTen years ago we developed, and published in this journal, the learning selection model to describe the development and early adoption of researcher-developed agricultural equipment in Southeast Asia. In this paper, we update the innovation histories of the three main technologies upon which the model was based and carry out some mapping and analysis of the post-harvest research networks in three countries. We find that the evolutionary algorithm based on interactive experiential learning remains valid. However, in the case of the most successful technology - the flat-bed dryer in Vietnam - the R&D team did not withdraw once a critical mass of manufacturers and users were familiar with the technology, as the model says should happen, but rather continued to champion the technology. In the process they developed major improvements to the original design, and a new type of dryer. They achieved far greater impact than any other team. They were successful largely because they were able to work with the same networks of partners, in the same innovation trajectory, for 25Â years. We find evidence of institutional support in working in this way. Their role was to make the major modifications while local users, manufacturers and promoters made local adaptations and 'bug fixes'. This way of working is similar to that of plant breeders working for the public sector and by many researchers in the private sector. However, current trends in international research towards 'projectization' on one hand, and the requirement to produce international public goods (IPGs) on the other means that researchers do not stay working for long enough with the same partners because funding keeps changing, nor do they work locally enough because of the expectation that they should generate new IPGs from scratch every one or two project cycles.
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Bibliographic InfoArticle provided by Elsevier in its journal Agricultural Systems.
Volume (Year): 103 (2010)
Issue (Month): 5 (June)
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Web page: http://www.elsevier.com/locate/agsy
Open source innovation Early adoption Agricultural machinery Experiential Learning Science and technology policy Innovation systems;
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- Ekboir, Javier, 2003. "Why impact analysis should not be used for research evaluation and what the alternatives are," Agricultural Systems, Elsevier, vol. 78(2), pages 166-184, November.
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