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Around the world, livestock producers are under pressure to curb greenhouse gas emissions to limit climate change. One potential avenue for grazing-based dairy enterprises involves adding substances that suppress methane production into the animals’ feed. A novel method for supplying these substances at the required regularity and accuracy is through smart feeders, a current smart farming tool already used to automatically dispense supplements to livestock while they are in the paddock. For this innovation to take hold, however, it must work within existing farm systems and deliver a clear, beneficial value proposition to farmers. This study set out to investigate the farm system and technology attributes that could shape the potential acceptance of in-paddock smart technologies for administering methane inhibitors in pasture-based settings. An adoption prediction instrument (ADOPT) was applied to simulate the diffusion patterns of smart feeders functioning as methane-inhibition delivery mechanisms on dairy operations, with contributions from industry specialists and farmers gathered through focus group discussions. The outcomes revealed that smart-feeder uptake within a pasture-based system framework would be modest. A deeper examination was conducted through a sensitivity analysis of seven decisive ADOPT variables identified as highly influential during the farmer focus group sessions. We modelled the effects of these seven essential ADOPT variables across two distinct smart-feeder designs to compare their adoption prospects. The adoption modelling indicated that, while considerations such as technological cost and capability were significant, uptake would also be profoundly affected by future regulatory directions, uncertainty surrounding the innovation, and how well it fits with farmers’ values and philosophies about their farm system. This investigation emphasized that the in-paddock delivery apparatus and the practical routines for its use on-farm constitute an early-phase innovation; consequently, farmers and other interested parties must be brought into the further refinement process to ensure that adoption barriers are properly addressed.

Smart farming increasingly operates through interconnected socio-technical ecosystems rather than through isolated technologies deployed at farm level. These ecosystems involve farmers, advisors, technology and platform providers, data intermediaries, equipment and input suppliers, cooperatives, public institutions, research and extension organizations, and agri-food supply-chain actors. This article develops a conceptual ecosystem model for examining how these stakeholders contribute to data-driven agricultural innovation and how value may be created, captured, and distributed through their interactions. The model-building approach integrates stakeholder roles, multidirectional data and knowledge flows, analytics and advisory services, platform coordination, institutional support, co-production, and co-innovation. It distinguishes farmer value, platform value, supply-chain value, public value, knowledge value, and sustainability value while treating value capture and distribution as separate analytical questions. Governance dimensions include data access, control, sharing, interoperability, portability, stewardship, transparency, accountability, trust, participation, digital inclusion, dependency, and power imbalance. The principal contribution is a structured framework that connects innovation processes with governance and distributional boundaries rather than assuming that technological deployment automatically produces shared value or farmer benefit. The model is intended to support conceptual analysis, stakeholder dialogue, and future empirical research. It does not establish the effectiveness, fairness, policy suitability, commercial viability, or implementation readiness of any particular smart farming ecosystem.