Uptake of technology and innovation is influenced by a range of social, cultural and economic factors.
Location, size and farm type further intensify the complexity of “predicting” technology uptake in agriculture.
So what does this mean for innovators, marketers and business in general within the agtech space? Can adoption rates for agtech be modelled and quantified?
Adoption and diffusion: agtech in Australia
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) published a paper in 2011 about a tool called “ADOPT” which can be used to predict adoption of agricultural innovations (Kuehne et al., 2011).
ADOPT stands for:
- Adoption
- Diffusion
- Outcome
- Prediction
- Tool
Specifically, the conceptual frame for ADOPT highlights important considerations for both innovators and farmers when it comes to creating agtech and going to market (Kuehne et al., 2011).
The ADOPT model allows for prediction of the diffusion curve of agtech or new practices in the agricultural industry (Kuehne et al., 2017).
Based on 22 variables, it is a tool for not only developing a deep understanding of the adoption process, but predicting the speed and peak level of adoption by farmers (Kuehne et al., 2017).
The model has been used to predict the diffusion of agricultural practices related to new crop types, new cropping technology and grazing options (Kuehne et al., 2017).
The CSIRO have even used the model as part of their Adoption and Diffusion Outcome tool to forecast agricultural adoption rates. It is now used by major research and development corporations and project teams in Australia and internationally (CSIRO, 2021).
The conceptual framework
The conceptual framework “Quadrant” highlights key influences over adoption of agricultural innovation (Kuehne et al., 2011).
This framework provides a break down of influences, barriers and opportunities when it comes to psychological, financial and environmental and social factors that influence farmers’ perception towards agtech and the ability of agtech to solve farmers’ problems.
Figure 1: The adoption influences quadrant (Kuehne et al., 2011)
Here is a summary of the conceptual framework variables and key questions related to each adoption influence the quadrant as outlined in the original report (Kuehne et al., 2011).
Farmer learnability characteristics
How easy is it for farmers to learn and understand agtech launched in the marketplace?
The concept of “learnability’ in this sense relates to the networks farmers have around them to advise and influence decision making, alongside their own existing knowledge and awareness of the agtech and the benefits it provides (Kuehne et al., 2011).
Here are some key questions to consider (Kuehne et al., 2011):
- What proportion of farmers are involved in farmer groups?
- What proportion of farmers are using paid advisory services or support?
- What proportion of farmers will need to develop (substantial) new skills and knowledge to use this agtech?
- What proportion of farmers are aware of the use or trialling of this agtech?
Agtech learnability characteristics
There are important considerations and questions to be asked about the agtech itself as the ease of trial and perceived complexity of agtech influences adoption rate:
- How easy is the agtech to trial on a small-scale before “full adoption”?
- Will this agtech cause complex changes to the farming system? To what extent?
- To what extent is use and benefits of the agtech “observable” to other farmers in the district?
Relative advantage for farmers
The relative scale of farmers who benefit from agtech will also influence the perception and adoption of other farmers (Kuehne et al., 2011).
Farmers’ orientation towards profit, risk and the environment also add complexity to predicting relative adoption and diffusion rates (Kuehne et al., 2011):
- What proportion of “major enterprise” farmers in the target population will actually benefit from this agtech?
- What proportion of farmers in the target population have a management horizon of 10 years or more?
- What proportion of the target population are driven/ motivated primarily by
a) Profit
b) the natural environment?
- What proportion of the target population
a) Is highly risk adverse
b) Have short term resource constraints?
Relative advantage of the agtech
The relative advantage of agtech relates to the proportion of farmers who will actually benefit from it, alongside the time frame between investment and receival of such benefits (Kuehne et al., 2011).
Understanding the upfront costs imposed by agtech and the relative lag in time for benefits to be realised is crucial, alongside the risk exposure this gap imposes on farm businesses (Kuehne et al., 2011):
- What is the relative upfront cost of this agtech in order to adopt it?
- How easy is it for farmers to “reverse” the adoption of this agtech? (To what extent can adoption be reversed?)
- To what extent will this agtech effect average farm business profitability? How likely is this effect?
- How long will it take post adoption of this agtech for major profit benefits to be realised?
- To what extent does adoption of this agtech expose the enterprise to business risk?
- To what extent will adoption and use of this agtech effect environmental advantages and disadvantages of the farm?
- How long will it take for the environmental impacts (positive or negative) to be realised on the farm post adoption of the agtech?
- To what extent will this agtech effect the ease and convenience of farm management?
Creating and launching agtech in Australia
This framework is a good example of key consideration any business in agtech should ask, be that a start-up creating a new innovation or more established business considering an expansion to their product line.
Half of the framework is directed towards understanding the target farmer population. Customer centricity is crucial to agtech development and proliferation, relying on market segmentation and farmer market research.
Our data provider, KG2, conducts extensive qualitative and quantitative market research accessing the KG2 proprietary farmer data base. If you’d like more information on using Australian agricultural data to hyper-target primary producers on your next advertising campaign, contact us.
Using KG2 data, we have access to highly specialised Australian agricultural market research.
This article was originally published on www.kg2.com.au on May 25, 2021.
SOURCES:
CSIRO. (2021). ADOPT: The Adoption and Diffusion Outcome Prediction Tool. https://www.csiro.au/en/about/corporate-governance/ensuring-our-impact/impact-case-studies/digital/adopt
Kuehne, G. Llewellyn, R. Pannell, D. Wilkinson, R. Dolling, P (2011). ADOPT: a tool for predicting adoption of agricultural innovations. 10.22004/ag.econ.100570
Kuehne, G., Llewellyn, R., Pannell, D. J., Wilkinson, R., Dolling, P., Ouzman, J., & Ewing, M. (2017). Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy. Agricultural Systems, 156, 115-125. doi:https://doi.org/10.1016/j.agsy.2017.06.007