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Pluralistic agricultural advisory systems comprise diverse actors, including public and private research, development, and extension (RD&E) providers, and private sector agricultural advisors. Co-innovation, as a collaborative engagement involving the full range of actors, is regarded as useful when addressing issues arising from this complexity. However, what helps or hinders successful co-innovation with public and private actors in real-world situations is still under-described in the literature. A deeper understanding of the social interactions and practices of doing co-innovation in specific settings is required to achieve successful co-innovation outcomes. This paper asks: What key features of practice support co-innovation with public and private system actors? And: How can enhanced understanding of practice assist in strengthening co-innovation processes? We present findings from research funded by the Australian government from 2015 to 2018 to strengthen private sector extension roles and connections between the RD&E functions in Australia's pluralistic advisory system. Four action research pilots were co-designed with system actors to support co-innovation. These were: Involving agricultural supply chain actors in collaborative agricultural R&D; Increasing farm advisor capacity to engage with digital agricultural technologies; Improving career pathways for new entrants in the advisory and extension sector; and Improving knowledge flows in the advisory and extension system. We found that co-innovation requires potentially profound social change; a shaking up of expectations about roles, responsibilities, and institutionalised processes. The research also suggests that the requirement for social change is underacknowledged in current co-innovation practice, with implications for the success or failure of co-innovation partnerships. Applying a practice theory perspective, our research highlights features of practice that constrained and enabled co-innovation in the four pilots. In doing so, the research provides insights to assist in successful operationalisation of co-innovation involving the private sector in pluralistic agricultural advisory systems. • Co-innovation is complex but achievable in pluralistic agricultural advisory systems. • Social dynamics and practices matter when operationalising co-innovation processes. • Nuanced understanding of practices improves co-innovation design and implementation. • Co-innovation with public and private actors must accommodate commercial realities. • The practice perspective is relevant at the broader level of AIS scholarship. [ABSTRACT FROM AUTHOR]

This paper makes a contribution to understanding the impact of relational trust, as embodied within bonding, bridging and linking social capital, on rural innovation. Using cases of multi-stakeholder groups who work together on shared problems it explores how social capital and different forms of trust (companion, competence and commitment) influence rural innovation processes. Looking at both the 'bright' and 'dark' side of social capital, our focus is on how social capital and trust constrain and enable the process of innovation. The study highlights both positive and negative effects of social capital in the context of three fixed term projects that were part of New Zealand's Primary Innovation programme (2012–2017). Our findings show that there was a unique composition of social capital and trust at the outset of each project and that dark social capital was a critical constraint in each case. Enabling innovation processes required committed and dedicated brokers who provided bridging social capital and embodied competence trust to enable participants' confidence. Such brokers are capable of recognising and managing different 'shades' of social capital and trust in pursuing desired project outcomes. A main theoretical implication of this study is that a better understanding of social capital and trust is needed to enable innovation facilitators and project managers to design and undertake fixed term rural innovation projects effectively. This is because social capital determines whether the composition of relationships within networks of actors involved in innovation projects enables innovation, or to the contrary constrains innovation. Specifically the implications and implementation of bridging social capital and competence trust are key determinants of successful innovation processes. • We examine the influence of s hades of social capital ('dark' and 'bright') on fixed term innovation processes. • Our unique context is multi-stakeholder, participative, fixed term rural innovation projects. • The concept of trust is 'unpacked' drawing on notions of companion, competence, and commitment trust. • A key finding is that bridging social capital and competence trust are key determinants of successful innovation processes. [ABSTRACT FROM AUTHOR]

The concept of responsible innovation is gaining traction in the Smart Farming field to address emerging socio-ethical issues such as power asymmetries between farmers and technology development companies in farm data ownership, and animal welfare issues arising from the automation of livestock management. Responsible innovation involves the democratisation of science and decision making for societal control of innovations. The application of responsible innovation principles to the adoption phase of Smart Farming technologies is an under explored area in terms of defining what responsible adoption practices are for Smart Farming, and with what effect. This empirical research aims to provide insights into what responsible adoption practices might involve, based on application of a responsible innovation approach to designing a cross-industry adoption strategy for a virtual fencing technology in Australia. This case study also examines what responsible adoption practices mean for enacting responsible innovation of Smart Farming technologies more broadly. A participatory Technology Assessment (pTA) approach engaged a range of virtual fencing technology end-users (livestock producers) and stakeholders (agricultural advisers, natural resource managers, food retailers, a food processor, and state government department staff) (n = 80). The participants identified and considered the benefits and risks of implementing a specific virtual fencing product in various contexts. The engagement methods were 12 interactive workshops organized in peer/sector groups and one reflective and deliberative consultation process with 13 adoption specialists and practitioners to define an adoption pathway for virtual herding technology. The adoption of the virtual herding technology product requires multiple levels of support to ensure that the technology is used responsibly and generates private, industry and public goods. This prospective knowledge, built from the considerations of end-users and stakeholders, was integrated into a virtual herding adoption strategy, and accounted for animal welfare concerns, the empowerment of producers to lead their own adoption support network and the desire for some form of regulatory governance. The responsible adoption practices were limited by the lack of institutional pathways to mobilise the strategy beyond the life of the research project. The research indicates that there is value in further pursuing the notion of responsible adoption practices for Smart Farming technologies. These practices need to be designed for understanding the complexity of the adoption situation, testing the assumptions about what the 'right' adoption pathway should be, and inclusive participation in the ongoing governance of adoption pathways. [Display omitted] • Responsible innovation principles have not been extensively applied in the adoption phase of smart farming technologies. • A participatory Technology Assessment approach was used to the adoption of a virtual fencing technology in Australia. • Responsible adoption practices that engaged various stakeholders highlighted the complexities with adopting this technology. • Complexities were managed by integrating adoption values expressed by stakeholders into a 'responsible' adoption strategy. • More research into designing responsible adoption practices and ways to mobilize adoption strategies is needed. [ABSTRACT FROM AUTHOR]

The promise of integrated research for addressing complex problems of sustainability such as catchment management has been widely recognised. However, the mechanisms for achieving integration in research have been the subject of few empirical studies. A major challenge of doing integrated research is how to combine and effectively manage diverse disciplinary perspectives and other knowledge/s in the generation of new knowledge for practice and policy change. We examined this challenge within a large catchment management research project in Australia's Murray Darling Basin and asked, ‘What supports or enables integration in research?’ Addressing this question requires an attention to integration as a dynamic process of knowledge production. We propose a model of this dynamic process which is characterised by a changing demand for integration in five different phases: (1) establishing the imperative for integration; (2) coordinating different disciplinary and other knowledge commitments; (3) consolidating arrangements for integration; (4) prioritising outputs from integration; and, (5) representing outputs of integration. For researchers and research managers this model can help identify the mechanisms required to support effective integration within research projects. We identify these mechanisms as sites and devices for integration that must be both planned (as part of the original integrated research design) and emergent (iteratively created and supported) in research projects. [ABSTRACT FROM AUTHOR]

Abstract: Policymakers and innovation scholars share an increasing interest in how to operationalize innovation support given the increasing number and range of stakeholders engaged in co-producing innovation. Using comparative case study analysis, this article examines support initiatives for dairy sector innovation in The Netherlands and Australia, addressing common challenges such as environmental issues, cattle health, new technology, and human resources. To this end, a review was conducted of documented information and articles published on the initiatives. The qualitative analysis focused on how the co-production process was supported and the achievements and challenges associated with each case. Across both countries and between different initiatives, the main achievements were found to be the generation of very different ideas addressing dairy sector challenges and attempting to bridge public and private sector interests. The main challenges included maintaining effort and momentum for high ambition targets and the potential for duplication as stakeholders became enrolled in different initiatives sponsored by different organizations in an increasingly devolved institutional setting. Furthermore, without strong institutional support for innovation co-production processes, individual actors were less able to operate effectively in innovation co-production roles. It is concluded that dairy sector innovation policies should address institutional constraints (e.g. provision of leadership and rewards for involvement in co-production processes), recognize that facilitation of innovation co-production needs to be adequately resourced, enhance support for initiative coordination to avoid duplication of effort, and take into account the specific institutional setting of countries and sectors to guide the design of innovation co-production support initiatives. [Copyright &y& Elsevier]

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. • Farmer interest in data relates to business stage and the decision's importance. • Cooperative governance structures facilitate farmer trust and use of big data. • Support services reduce the impact of low digital literacy, capacity and capability. • Inter-dependence occurs between existing data processes and optimising data use. [ABSTRACT FROM AUTHOR]