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ADOPT: a tool for predicting adoption of agricultural innovations

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  • Kuehne, Geoff
  • Llewellyn, Rick S.
  • Pannell, David J.
  • Wilkinson, Roger
  • Dolling, P.
  • Ewing, Michael A.

Abstract

A wealth of evidence exists about the adoption of new practices and technologies in agriculture but there does not appear to have been any attempt to simplify this vast body of research knowledge into a model to make quantitative predictions across a broad range of contexts. This is despite increasing demand from research, development and extension agencies for estimates of likely extent of adoption and the likely timeframes for project impacts. This paper reports on the reasoning underpinning the development of ADOPT (Adoption and Diffusion Outcome Prediction Tool). The tool has been designed to: 1) predict an innovation‘s likely peak extent of adoption and likely time for reaching that peak; 2) encourage users to consider the influence of a structured set of factors affecting adoption; and 3) engage R, D & E managers and practitioners by making adoptability knowledge and considerations more transparent and understandable. The tool is structured around four aspects of adoption: 1) characteristics of the innovation, 2) characteristics of the population, 3) actual advantage of using the innovation, and 4) learning of the actual advantage of the innovation. The conceptual framework used for developing ADOPT is described.

Suggested Citation

  • Kuehne, Geoff & Llewellyn, Rick S. & Pannell, David J. & Wilkinson, Roger & Dolling, P. & Ewing, Michael A., 2011. "ADOPT: a tool for predicting adoption of agricultural innovations," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100570, Australian Agricultural and Resource Economics Society.
    • Handle: RePEc:ags:aare11:100570
    DOI: 10.22004/ag.econ.100570
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    References listed on IDEAS

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    1. Amir K. Abadi Ghadim & David J. Pannell & Michael P. Burton, 2005. "Risk, uncertainty, and learning in adoption of a crop innovation," Agricultural Economics, International Association of Agricultural Economists, vol. 33(1), pages 1-9, July.
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    1. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    2. Rose, David C. & Sutherland, William J. & Parker, Caroline & Lobley, Matt & Winter, Michael & Morris, Carol & Twining, Susan & Ffoulkes, Charles & Amano, Tatsuya & Dicks, Lynn V., 2016. "Decision support tools for agriculture: Towards effective design and delivery," Agricultural Systems, Elsevier, vol. 149(C), pages 165-174.
    3. Schulz, Dario & Börner, Jan, 2021. "Context and Technology Traits Explain Heterogeneity Across Adoption Studies of Agricultural Innovations: A Global Meta-Analysis," 2021 Conference, August 17-31, 2021, Virtual 315003, International Association of Agricultural Economists.
    4. Kuehne, Geoff & Nicholson, Cam & Robertson, Michael & Llewellyn, Rick & McDonald, Cam, 2012. "Engaging project proponents in R&D evaluation using bio-economic and socio-economic tools," Agricultural Systems, Elsevier, vol. 108(C), pages 94-103.
    5. Dario Schulz & Jan Börner, 2023. "Innovation context and technology traits explain heterogeneity across studies of agricultural technology adoption: A meta‐analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 570-590, June.
    6. Lutengano Mwinuka & Khamaldin Daud Mutabazi & Frieder Graef & Stefan Sieber & Jeremia Makindara & Anthony Kimaro & Götz Uckert, 2017. "Simulated willingness of farmers to adopt fertilizer micro-dosing and rainwater harvesting technologies in semi-arid and sub-humid farming systems in Tanzania," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(6), pages 1237-1253, December.
    7. Martha Swamila & Damas Philip & Adam Meshack Akyoo & Stefan Sieber & Mateete Bekunda & Anthony Anderson Kimaro, 2020. "Gliricidia Agroforestry Technology Adoption Potential in Selected Dryland Areas of Dodoma Region, Tanzania," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    8. Moglia, Magnus & Alexander, Kim S. & Thephavanh, Manithaythip & Thammavong, Phomma & Sodahak, Viengkham & Khounsy, Bountom & Vorlasan, Sysavanh & Larson, Silva & Connell, John & Case, Peter, 2018. "A Bayesian network model to explore practice change by smallholder rice farmers in Lao PDR," Agricultural Systems, Elsevier, vol. 164(C), pages 84-94.

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