As enablers of change, it’s important that we keep up-to-date with the latest extension research. But sometimes it can be hard to get the journal article, let alone make the time to read and digest it! However, we thought we could help with this. In this episode we’re exploring adoption pathway analysis, thanks to a colleague of Denise’s… Oscar Montes.
Oscar recently published a paper from his PhD research that we both thought was brilliant! The paper is called Adoption pathway analysis: Representing the dynamics and diversity of adoption for agricultural practices. It was published in the journal Agricultural Systems, a great journal, but sadly is not open access. Oscar did his PhD research at the University of Western Australia, supervised by David Pannell and Rick Llewellyn—both of whom have made significant contributions to the way we understand extension.
Oscar’s research explored how to improve the understanding of adoption of agricultural innovations through predictive models. There were four components to the research:
- a review of conceptual models of adoption,
- a meta-analysis of numerical models,
- developing and applying adoption pathway analysis, and finally
- exploring the perceptions that extension professionals have about using predictive models of adoption to support planning.
So onto the paper that Oscar co-authored with David Pannell, Rick Llewellyn and Philip Stahlmann-Brown. They start by highlighting that often in research we treat the adoption of innovations as binary—meaning it either is or is not adopted. But there is actually more truth in the idea that adoption is a dynamic process. There has been good work with various researchers expanding the categories of adoption to include things such as being aware of an innovation, trialling, partial adoption, and so on. The research outlined in the paper starts to build the case that there’s a need for being able to measure adoption in a way that captures this dynamic process.
So what did Oscar do? The study he designed looked at the use of four practices: Body Condition Scoring, pasture management software, plantain and/or lucerne for summer grazing, and finally, an audited nutrient management plan. Oscar then designed a seven question survey that helped identify the journey through adoption of these practices. He was able to put these questions into a larger survey that was sent to farmers in New Zealand, and received 138 completed responses. And we know from our previous episode about designing a decent survey, that a good rule of thumb is to get 100 or more responses.
At this point we take a little detour and introduce Sankey diagrams. These diagrams date back to the 1800s and are very cool! They are a flow diagram that depicts flow rate as proportional to the width of the arrows in the diagram. You can find out more about them online here and here. Oscar used these to help visualise adoption as a proportion of adopters moving from one stage of the adoption process to the next. It’s such a great way of showing what’s really going on!
Oscar was able to use the diagrams to explore how various proportions of farmers move from awareness, to trialling a practice, to continuing using the practice, or disadopting the practice. You can see an example from the paper where the majority of farmers were aware of a practice, starting using it without a trial and then indicated they would maintain constant or increased use of it for more than five years. There was a small proportion who decreased their use or disadopted the practice.
Oscar was able to follow the adoption pathways from the past, through the present and into the future and comment on each of the practices explored. Oscar and his co-authors concluded that this approach did help improve understanding of the individual adoption journeys, and was useful to illustrate the common pathways followed. What they found was that the same farmers followed different journeys for each innovation that they explored, so this approach really facilitated comparison of pathways for these different practices. As Oscar points out, sometimes we put too much focus on finding out about the farmer’s attitudes, and then assuming that they will adopt all practices the same way!
They also realised while doing this analysis, that trialling (i.e. trying out an innovation or practice) is not as well understood as we think! It sounds straightforward, but in reality it can take a lot of time (and effort) to do in a farming system. They felt this was worthy of more work to unpack. But the use of adoption pathway analysis was helpful as a means of understanding and engaging with farmers to help provide appropriate extension activities and well targeted evaluation. They also wondered whether there would be common patterns that could be identified for particular groups of farmers in particular sectors using this approach. For example, we could identify farmers ‘sitting on the fence’ regarding one particular innovation—interested but not quite ready to make a move—so they could be better targeted by extension. This is something we think would be really useful to have in our toolbox as enablers of change!
Our thanks to Oscar (and his co-authors) for this great piece of research! And extra thanks to Oscar for checking the draft of this episode and making sure it was correct! Oscar says that if you cannot access his paper, chapter four of his thesis is pretty much the same and his thesis is freely available.
We’re interested to hear whether this process of highlighting relevant, recent research in our posts is useful for you. Add a comment below and let us know, if this adoption pathway analysis was interesting or helpful (or if you’ve used it). And let us know if you have a recent piece of research you’d like us to look into! We don’t want this to be just a one-way conversation—join in by sharing your thoughts and ideas with us!
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de Oca Munguia, O. M., Pannell, D. J., Llewellyn, R., & Stahlmann-Brown, P. (2021). Adoption pathway analysis: Representing the dynamics and diversity of adoption for agricultural practices. Agricultural Systems, 191, https://doi.org/10.1016/j.agsy.2021.103173