(2007b) identified the importance of technological, resource, competitive, supplier, consumer, and political uncertainty. Previous studies have highlighted the role that uncertainty plays in the functioning of technological innovation systems. To turn the opportunity of smarter farming into a reality on-farm, we need to better understand the wider issues affecting a farmer's investment decision making ( Rutten et al., 2018). Successful use of these new tools depends on aspects of technology fit-for-purpose, on-farm adaptation, learning about data-driven decision-making, and social learning within a farmer's network of practice ( Eastwood et al., 2012 Rose et al., 2016 Higgins et al., 2017 Klerkx et al., 2019). The uptake of smarter farming approaches often represents more than a “plug and play” process for farmers ( Jago et al., 2013). However, there is limited understanding of how the potential can be translated into widespread adoption in the farming sector, which has been slow to date ( Gargiulo et al., 2018). Potential management improvements are related to enhanced collection of data to manage animals, plants, and the wider farming environment ( Eastwood et al., 2017a). There are increasing opportunities to use smart farming technologies for improved management of farming systems ( Shepherd et al., 2018). Our study highlights that to reduce uncertainty with emerging smart technologies, greater public and private R&D collaboration is required to foster knowledge development and exchange. 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 as opposed to technologies that only create greater farmer distrust and uncertainty. 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. Political uncertainty also impacted adoption, with implications of food safety regulations or rules around herd testing systems. In terms of the impact of uncertainty, technological uncertainty was historically an important issue around the early development of AMS, with decommissioning occurring in some cases due to perceived technology issues. Respondents identified a range of adoption trends in their country and some of the reasons behind these adoption profiles were suppression of uptake due to low milk prices, financial markets, and issues with early installations and perceptions of these issues by other farmers. The main countries represented were Canada, The Netherlands, USA, Denmark, and the UK. We used an online survey of AMS experts globally and received 81 completed survey responses. The objective of this study was to review adoption of AMS internationally and propose lessons for developing institutional knowledge and effective networks of practice in emerging smart farming innovation systems. In this paper, we present the results of an international survey investigating the impact of innovation uncertainty on adoption of a smart farming technology, automatic milking systems (AMS). Previous studies have highlighted the role that uncertainty plays in technological innovation systems. However, there is limited understanding of how the potential can be translated into effective use in the farming sector. There are increasing opportunities to use smart farming technologies for improved management of farming systems.
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