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Uncertainty around the characteristics of an innovation, related infrastructural implications, the level of adaptation required, and the impact on future options are all aspects of technological uncertainty (Meijer et al., 2007b; Klerkx et al., 2010; Tomy and Pardede, 2018). Relevant factors to AMS could be the support available to farmers when making investment decisions, the degree of technological lock-in (including the ability to expand herd sizes), and the specific challenges of adapting farming systems to AMS.

This factor focuses on the availability of resources, such as human, financial, and material, and also encompasses organization of the process of innovation (Meijer et al., 2007b). The uncertainty around forecasting resources and capital required for the innovation involves factors such as availability of knowledge and skills, required R&D expenditure, and potential revenue streams (Tomy and Pardede, 2018). In an AMS context, resource uncertainty could relate to the ability to get finance for the AMS investment, uncertainty around milk price and its impact on viability, along with other factors such as uncertain pricing of secondhand AMS technology and how to revert to previous milking methods.

The behavior of competitors in the innovation system can affect its success (Meijer et al., 2007b). Factors behind this uncertainty can include level of market share, the impact of leading competitors, and the type of competition in the market (Tomy and Pardede, 2018). This relates to the competition between retailers of AMS technology (i.e., is there sufficient competition in a market dominated by two main players?) and how each competitor might refer to each other's product. We assessed this by asking questions around the availability of independent advice on technology, the adequacy of advice provided by technology retailers, and what industry support was available to farmers.

This source of uncertainty relates to perceptions around the reliability of the supplier (Meijer et al., 2007b). In respect to AMS, we asked questions around the availability of AMS technology (was there sufficient supply to match demand), the access that farmers had to back-up service for both technical and learning support, and whether farmers knew where to access advice about farming with AMS.

Consumer uncertainty concerns the preferences consumers might have for an innovation, the characteristics of consumers, and the development of demand for the technology (Meijer et al., 2007b). It also includes factors associated with knowledge of consumer acceptance of the technology, and the potential changes in demographics of the target population and therefore potential market size (Tomy and Pardede, 2018). These features are more applicable to entrepreneurs looking to work with consumers (farmers) rather than the consumers themselves. Therefore, for this factor, we asked industry-related questions such as uncertainty around future patterns of AMS adoption, the nature of technological development, whether farmer ability to succeed was included in the design and sale process, and the fit of AMS with farmer typologies. This is one aspect of the framework that potentially has less applicability when taking the farmer perspective in an AMS innovation system.

The policy environment can have a major impact on the innovation process, for example, the interpretation of policy, existence of regulations, and uncertainty regarding government and policy changes (Meijer et al., 2007b). It also includes the potential impact of government support for the innovation, the impact of exchange rates, and taxation that may relate to the innovation (Tomy and Pardede, 2018). In respect to AMS, this may include the implications of milk quality and food safety regulations, general political and community support for AMS, the awareness of regulations, and the existence of incentives.

The survey design incorporated the framework derived from Meijer et al. (2007b) and Klerkx et al. (2010) to assess the impact of uncertainty in the AMS innovation system. For each of the uncertainty factors, a series of statements were developed by the project team (Table 2), and participants were asked to indicate to what extent they agreed or disagreed with each statement, based on a five-point Likert scale from 1 = strongly disagree to 5 = strongly agree.

A pilot of the survey was run with five experienced AMS researchers from New Zealand, The Netherlands, USA, England, and Australia. Feedback from the pilot group was incorporated into the final survey design. Participants in the full survey were chosen to represent a range of those in the network of practice of AMS farmers internationally including: AMS researchers, technology developers, and sales/support representatives. Actors in the research and service/support sectors were targeted primarily due to language differences across the countries surveyed. The project team decided that people from these sectors were more likely to engage and complete the extensive English-based survey. The survey was therefore designed for these actors to use their knowledge of both farmer experiences, and innovation system-wide issues, to answer the questions posed.

Contacts were sourced initially through researcher networks, and then a snowball method was used (Bryman, 2001). The study was approved by the University of Melbourne Human Research Ethics Committee (HREC), and a plain language statement outlining the project aims, funders, use of data, and key contacts was provided on the opening survey page. If participants consented to participate, they were invited to click “next” to enter the survey.

The survey weblink was sent out twice to those on the contact list. Results were exported as a.csv file and imported into Microsoft Excel™ Data were reviewed for quality and completeness and any erroneous responses were removed. The data were analyzed for interactions using multivariate statistics, but no strong associations were found; therefore, we focused the analysis on counts and summary statistics.

There were 84 survey responses of which three were removed due to incomplete responses; therefore, 81 responses were used in the analysis. The major countries represented were Canada (24), The Netherlands (14), USA (10), Denmark (7), and the UK/Ireland (6). Other countries represented were Germany (4), Sweden (3), Israel (2), Norway (2), and Switzerland (2). There was one representative from each of Finland, France, Ireland, New Zealand, India, Iceland, and Japan. The respondents were primarily male (89%) and 63% were aged 35–54.

There was a range among respondents when it came to their day-to-day experience with AMS farmers with 27 stating it was “a major part of my job,” 27 said it was “often part of my job,” and 26 a “small part of my job.” There was a similar mix in respect to years of experience that respondents had with AMS farms and farmers with 27 having over 10 years' experience, 27 had 5–10 years' experience, and 27 had <5 years' experience. There were 36 respondents from AMS retailers and 45 respondents not from AMS retailers (36 from public or industry funded research and advisory organizations, and 9 from privately funded advisors or consultants). In answering the survey questions, the respondents drew from experience that ranged from working with 1 farm to 1,000 farms. Most respondents interacted with between 20 and 100 farmers. The responses for each country, grouped by role (retailer vs. non-retailer) and experience, are shown in Table 3.

Of non-retailer respondents who were in a publicly funded research/advisory position, or identified as consultants, most were aligned with research. Many also provided general farm management advice to farmers and to a lesser extent helped farmers before and after AMS installation. Providing technical support was generally a small part of their role.

Survey responses in this current study indicated a range of influences on potential AMS adoption. While historical adoption had been negatively influenced by financial factors such as low milk prices and the 2008 financial crisis, there were examples of technological uncertainty affecting early installations and perceptions of these issues by other farmers. Respondents identified some examples of decommissioning, which created a level of uncertainty in the local farming population about the suitability of AMS. Farmers in early adopting countries (e.g., The Netherlands and Denmark) had some issues with learning to use AMS successfully, in some cases farmer skills and perceptions did not fit with AMS. The historical adoption factors associated with AMS identified in our study, such as reducing total farm labor, reducing hours spent milking, more family time, and reducing physical work, are supported by several authors (de Koning, 2010; Jacobs and Siegford, 2012; Hansen, 2015; Rodenburg, 2017; Vik et al., 2019).

In our study, we examined the predicted AMS adoption and the potential reasons for this among a group of experts. Almost all respondents predicted increased AMS adoption, with almost a quarter predicting a rapid increase. Understandably, this expectation of a rapid increase was higher among those selling AMS technology. Analyzed by country, respondents expected a slower adoption rate in Denmark and The Netherlands, when compared with Canada and USA. The data presented in Table 1 mostly agree with these predictions. Danish AMS installations have not increased from 2010 to 2018, and in recent years have actually decreased. However, installations in The Netherlands and Sweden doubled from 2010 to 2018, and there have been dramatic increases in countries such as Germany (650%), Canada (475%), and Norway (250%)—albeit off a relatively low 2010 base. Of the emerging markets in our survey, USA still shows a low level of adoption, with an estimated 3% of farms. This could be primarily due to the farm system types and sizes employed there, and relatively low labor costs. The number of large farms has previously been highlighted as a barrier to AMS adoption in USA by Jacobs and Siegford (2012), and increased farm size is also having an impact in countries such as Denmark and Norway (Sigurdsson et al., 2019; Vik et al., 2019). However, the information presented in our survey adds weight to the need for alternative automated milking approaches for larger farm systems, such as robotic rotaries and stand-alone robotic cup attachment systems that work in rotary parlors. We further explore the potential reasons for the different adoption trajectories in section The Impact of Uncertainty on AMS Adoption below.

Respondents were most positive toward factors associated with technological and supplier uncertainty. This indicates that at the time of the survey, the AMS technology was relatively mature and reliable, but in some countries, the knowledge associated with AMS use in different farm systems was not so developed. In this study, respondents felt that farmers were well-supported when making AMS investment decisions but were not always certain of the implications AMS had on farm systems challenges such as expanding their herd size, or reverting back to a conventional milking system (Hansen, 2015). The survey results therefore provide insights into the development and potential future adoption of AMS, including the need for greater certainty around issues of technological lock-in (where farmers face difficulties reversing technology investment decisions) and the forms of after-sales support required (for example, managing the farm systems changes related to AMS use).

There was most potential uncertainty around political factors, with ratings lowest among USA respondents. The political environment can have a large impact on innovations such as AMS through even apparently minor regulations or policies. One example highlighted in comments by respondents was food safety regulations in Europe requiring a person to be present at milking, which was not feasible under the 24 h milking cycle of AMS. Altering these regulations can take considerable effort and can act to discourage farmers. Other institutional arrangements can also be affected, for example, herd test protocols that require two milk samples at 12 h intervals have had impacts on the ability of AMS farmers to participate in herd improvement schemes (Eastwood et al., 2017b). Political factors have also been highlighted as driving industry structural change that can impact smart farming adoption such as AMS (Vik et al., 2019).

Respondents perceived a lack of awareness among farmers as to future regulations that may have an impact on AMS use and felt that farmers were moderately aware of the current regulations that are related to AMS use. There was a strong perception that current regulations did not act to make farming with AMS easier. There was also a perception that financial incentives at an industry level had not played a role in the uptake of AMS. In terms of the role of public and industry good organizations, respondents identified a lack of industry-level extension programs related to AMS use but perceptions were mixed as to whether there was a role for the dairy industry or public organizations in the learning support space. Smart farming technology is dominated by commercial interests, which has been shown to have implications for private and public R&D roles in terms of supporting adoption (Eastwood et al., 2017b; Klerkx et al., 2019). This tension was also highlighted by respondents in factors related to competitive uncertainty, with an indication that sourcing independent advice on AMS technology was difficult for farmers in the emerging markets of Canada, USA, and the UK. Farmer uncertainty about adopting AMS and the lack of service providers for technical support were also found to be an issue in USA by Jacobs and Siegford (2012).

Responses related to supplier uncertainty highlighted differences between retailers and non-retailers. Retailers were much more positive that farmers were receiving good technical and learning support. The ratings showed that while farmers could be certain about the extent of technical support they will receive, they may be less aware of the learning support available from their retailer. This retailer focus on technical support is common in the smart farming domain (Eastwood et al., 2016). However, this can be compensated by farmers having access to AMS farming system advice from other agents in the innovation system, but these skills take time to build. For example, a network of farm system advisors took two decades to develop in The Netherlands, primarily because a certain AMS market size was needed to make it worthwhile for advisory firms to upskill (Eastwood et al., 2017b).

When investing in smart farming technology, farmers need a clear value proposition and business case, which in turn requires more transparent sharing of financials and robot performance by farmers and retailers (Rojo-Gimeno et al., 2019). In terms of our factors related to resource uncertainty, respondents perceived relative certainty for farmers accessing finance for AMS, but that ascertaining the depreciation value of the technology was more difficult. This was more of an issue in the emerging markets, as the mature markets had more knowledge of AMS performance, and potentially more experience with use of secondhand AMS units. Additionally, in some countries, the uncertainty around future milk prices could impair the ability to create robust investment cases for smart technologies. It is interesting to note that AMS retailers indicated much more confidence in predicting milk prices than non-retailers, potentially indicating undue optimism in the sales process.

The framework of Meijer et al. (2007b) provided a good lens with which to look at the AMS issues internationally. While most of the factors of uncertainty were relevant to the “farmer as entrepreneur” perspective adopted in our analysis, some of the factors relate better to other actors in the network. For example, consumer uncertainty relates more to technology providers and their uncertainty of the farmers' needs or to consultants and their uncertainty whether there is a business case for them to become involved. Use of the framework highlighted clear differences between technology retailers in specific aspects, and therefore, the framework could be used with other smart farming technologies to assess where potential issues occur between retailer and non-retail actors.

Difficulties we encountered with an empirical investigation using the framework factors involved first determining robust factors that related to each area of uncertainty. While we developed these within the research team, and tested them in a pilot, they would benefit from further refinement subsequent to this study. Additionally, with such survey methods in a niche research area, achieving sufficient responses is difficult. In our study, we concentrated on results from countries with the most respondents, but further empirical studies using this framework would benefit from greater targeting of respondents.

This current study indicates some lessons for the configuration of smart farming innovation systems. In the case of AMS, the dominant forms of uncertainty uncovered across all respondents were in the resource and political domains, a finding supported by another study of uncertainty in technological start-ups (Tomy and Pardede, 2018). In particular, we identify a need for further discussion regarding the role of private providers of advice to farmers, and the related role of public or industry good AMS support programs. Our study indicates that development of commercial roles for consultants in providing advice to AMS farmers took some time to occur in the established markets in Europe, a finding supported by Eastwood et al. (2017b). In relation to smart farming technologies generally, the potential role of farm advisors in reducing innovation uncertainty has been highlighted in other studies (Ayre et al., 2019; Eastwood et al., 2019). There exists a significant opportunity for farm advisors to support farmers, so they get the most from their technology investments, requiring more focus from public R&D in the smart farming domain.

Our survey results highlighted a difference in perceptions between technology retailers and other actors in the technological innovation system, particularly around factors related to supplier and consumer uncertainty. The smart farming domain is dominated by private R&D (Eastwood et al., 2017b; Klerkx et al., 2019) and therefore the pressures of being first to market, providing a return on agtech venture capital, and achieving sufficient sales in a niche market can lead to ambitious marketing. This may result in development of smart technologies without a full understanding of market (farmer) needs (i.e., consumer uncertainty) and lack of focus on after-sales service that helps farmers integrate the technology into their farming system context (i.e., supplier uncertainty). It is therefore vital that commercial interests, farm advisors, and public R&D actors foster a collaborative approach to development and support of smart farming technologies (Ayre et al., 2019; Phillips et al., 2019). The need for collaborative approaches is especially the case where technologies are brought together in platforms (e.g., via artificial intelligence) to solve dynamic and complex agricultural problems (Hermans et al., 2019).

We identified an impact of immature AMS technology being marketed to farmers, and AMS technology being sold to farmers who did not have the capability or mindset to adapt their farm systems to suit. This resulted in instances of decommissioning, or reverting to conventional milking technology, and had a subsequent impact on farmer (and advisor) confidence in the technology. This experience highlights an important consideration for smart farming innovation uncertainty. Agricultural NGOs and governments are increasingly viewing smart farming as a tool for improvements in productivity and sustainability of agriculture. However, our study highlighted the potential impact of negative experiences associated with new technologies from farmers who struggle with the adaptation process as such occurrences may act to stall the uptake of smart farming technologies. If public policy organizations are to realize the desired impacts of smart farming technology, there needs to be greater focus on understanding where (and which) technologies can have an actual impact on farm (Shepherd et al., 2018), as opposed to technologies that only create greater farmer distrust and uncertainty (Jakku et al., 2018; Klerkx et al., 2019).

The approach used in this paper involved an online survey with a targeted snowball method where domain experts were first identified and then asked to distribute the survey among their networks. The target population (researchers and professionals with knowledge of AMS) was relatively small and dominated by commercial technology and service providers who are often difficult to access in research projects.

This research represented an exploratory approach to use the innovation uncertainty framework to describe major influences on AMS adoption in different countries. The questions were developed by the project team, and tested in a pilot survey, and therefore represent a best design of the appropriate questions. However, the questions could be open to interpretation of individual participants.

The authors declare that this study received funding from Dairy Australia and DairyNZ Inc. These funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.