Smart farming (also referred to as digital farming, digital agriculture and precision agriculture) has largely been driven by productivity and efficiency aims, but there is an increasing awareness of potential socio-ethical challenges. The responsible research and innovation (RRI) approach aims to address such challenges but has had limited application in smart farming contexts. Using smart dairying research and development (R&D) in New Zealand (NZ) as a case study, we examine the extent to which principles of RRI have been applied in NZ smart dairying development and assess the broader lessons for RRI application in smart farming. We draw on insights from: a review of research on dairy technology use in NZ; interviews with smart dairying stakeholders; and the application of an analytical framework based on RRI dimensions. We conclude that smart dairying R&D and innovation activities have focused on technology development and on-farm use without considering socio-ethical implications and have excluded certain actors such as citizens and consumers. This indicates that readiness to enact RRI in this context is not yet optimal, and future RRI efforts require leadership by government or dairy sector organisations to fully embed RRI principles in the guidelines for large R&D project design (what has also been referred to as 'RRI maturity'). More broadly, enacting RRI in smart farming requires initial identification of RRI readiness in a given sector or country and devising a roadmap and coherent project portfolio to support capacity building for enacting RRI.

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An increase in the average herd size on Australian dairy farms has also increased the labor and animal management pressure on farmers, thus potentially encouraging the adoption of precision technologies for enhanced management control. A survey was undertaken in 2015 in Australia to identify the relationship between herd size, current precision technology adoption, and perception of the future of precision technologies. Additionally, differences between farmers and service providers in relation to perception of future precision technology adoption were also investigated. Responses from 199 dairy farmers, and 102 service providers, were collected between May and August 2015 via an anonymous Internet-based questionnaire. Of the 199 dairy farmer responses, 10.4% corresponded to farms that had fewer than 150 cows, 37.7% had 151 to 300 cows, 35.5% had 301 to 500 cows; 6.0% had 501 to 700 cows, and 10.4% had more than 701 cows. The results showed that farmers with more than 500 cows adopted between 2 and 5 times more specific precision technologies, such as automatic cup removers, automatic milk plant wash systems, electronic cow identification systems and herd management software, when compared with smaller farms. Only minor differences were detected in perception of the future of precision technologies between either herd size or farmers and service providers. In particular, service providers expected a higher adoption of automatic milking and walk over weighing systems than farmers. Currently, the adoption of precision technology has mostly been of the type that reduces labor needs; however, respondents indicated that by 2025 adoption of data capturing technology for monitoring farm system parameters would be increased.

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Three surveys of a pastoral–cropping farming system were carried out over a period of 1 year, using an electromagnetic sensor and real-time-kinematic (RTK)-GPS. The maps produced delineated areas of different apparent soil electrical conductivity (ECa). These delineated areas were compared with soil units of a conventional soil map and results showed the ECa map related well to soil-particle-size classes. In addition ECa could be used to predict groupings of soil phases accurately within one soil type.Soil coring to depths of 1 m, to determine soil physical and chemical properties, showed ECa values were moderately well correlated (R2 = 0.72) to soil clay percentage, weighted for the soil profile. Soil fertility indicators, Olsen P (R2 = 0.61), cation exchange capacity (R2 = 0.59), and exchangeable magnesium (R2 = 0.76) also related well. The linear regression (R2 = 0.76) of ECa with exchangeable magnesium is thought to reflect the dominant clay mineralogy of the study area, i.e. chlorites weathering to illites and releasing magnesium to the soil solution. Discriminant statistical analysis of results showed point ECa values could be used to predict 2 major groupings of the mapped soil phases with 100% accuracy. More precise prediction of these mapped soil units is constrained by localised management effects. Elevated ECa values occur at areas of soil compaction, which have been deduced from measurements of soil strength, aggregate size distribution and visual soil assessment.

The use of advanced management technologies is increasing in pasture-based dairy systems, an evolution that has been termed precision dairying. This change has been driven, at least in part, by a continual increase in the scale of dairy farming and an associated drive for efficiencies, and technological advances in the area of sensors and automated devices for animal and resource management. In this paper, a survey of New Zealand precision dairy farmers is presented, highlighting lessons from farmers for technology developers and industry. Respondents indicated that they invested in technologies such as electronic identification, milk meters, automated cup removers, and automated drafting for labour saving and to make herd management easier. Most were positive about their investments, with perceived benefits from saved time during milking, decreased farm workforce requirement, and increased farm profitability. Farmers also felt there was unused functionality in their herd management systems and that they could benefit from increased support and training to get more from their technology. Technology suppliers need to refocus on after-sales service and tailor their support programs to stages of learning development, while creating a value proposition for farmers to pay for such services. Dairy industry organisations need to take the lead in building awareness of the opportunities such technologies offer, while facilitating access to independent information about technology capability and investment.

Dairy farm management has historically been based on the experiential learning and intuitive decision-making skills of the owner-operator. Larger herds and increasingly complex farming systems, combined with the availability of new information technologies, are prompting an evolution to an increasingly data-driven 'precision dairy' (PD) management approach. Automation and the collection of fine-scale data on animals and farm resources via precision technologies can facilitate enhanced efficiency and decision making on dairy farms. The proportion of dairy farmers using this approach is relatively small (between 10 and 20% of farmers); however, industry trends suggest a continual increase in the use of precision technologies. Australasian PD farms have reported both positive and negative stories regarding the approach but to date there has been little industry attention or co-ordination in Australia or New Zealand. A series of workshops was held in late 2011 between industry-good representatives, researchers and farmers, from Australia and New Zealand, to discuss the opportunities and risks associated with PD. To take advantage of the emerging PD opportunity the trans-Tasman workshop group suggested five focus areas including: industry-good co-ordination and leadership in precision dairy; working to define the on-and off-farm value of PD; improving the technology available to farmers; integration of PD within farming systems for improved management; and developing learning and training initiatives for farmers and service providers. Action in these focus areas will enable future dairy farmers to implement the PD approach with enhanced confidence and effectiveness.