Methane emissions represent over 40% of New Zealand’s total greenhouse gas emissions. Of these emissions, almost all are biogenic, meaning they come from living organisms such as ruminants. Given that methane has 27-30 times the warming potential of carbon dioxide, reducing methane emissions is an important part of addressing climate change. Many countries are beginning to legislate a reduction of methane emissions in a global effort to reduce the impact of climate change. In New Zealand, where livestock plays a large part in the economy, unique challenges have arisen around reducing biogenic methane emissions. One approach to mitigate methane in the dairy industry is the delivery of methane-inhibiting compounds, which, when added to the diet of ruminants, can reduce their methane emissions significantly. However, these compounds generally require frequent and precise delivery, a characteristic that is difficult to fulfil in New Zealand’s pasture-based sector. This thesis explores the potential adoption of a novel approach using emergent technology, in-paddock smart-feeders, and how they could deliver methane inhibitor compounds in a New Zealand dairy farming context. The research is conducted in two phases. Firstly, the likely adoption outcomes are assessed, which depends on how in-paddock smart-feeders fit into the range of New Zealand farm systems. In order to evaluate adoption outcomes, the Adoption Diffusion Outcome Prediction Tool (ADOPT) model is utilised in conjunction with a dairy expert consultation and farmer focus groups. After establishing a base case of adoption outcomes, the results are tested with a sensitivity analysis to determine those ADOPT variables impeding increased modelled adoption outcomes. Then, a scenario analysis is conducted to explore different technology performance levels. This chapter finds that the technology is unlikely to become widely adopted unless it is shown to be economically viable. The critical adoption factors are then shown to be trialability, ease and convenience and environmental performance. The third chapter explores the economic properties of in-paddock smart-feeders by developing a farm-level cost-effectiveness model. This model takes average farm data, introduces the expected costs of the approach and then apportions the associated costs of the quantity of mitigated methane to estimate the breakeven methane price at which the approach becomes viable. The main output of this model is the breakeven methane price, that is, the methane price, where it becomes viable to adopt IPSFs. Similarly to the first chapter, these results hinge on technology performance which is currently relatively uncertain as there is not existing technology to base performance. Therefore the results are explored at length using sensitivity and then scenario analysis. The scenario analysis shows that the breakeven price is most favourable in Northland out of the regions assessed, and the technology was viable only in a best-case scenario. Ultimately for this novel approach to become economically viable and, therefore, widely adopted by the dairy farmers of New Zealand, the manufacturers of both in-paddock smart-feeders and methane inhibitors will need to make improvements to their products. If these improvements eventuate, a clear path to reducing methane emissions in pasture-based dairy may result.