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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This paper proposes and discusses a methodology to evaluate the performance of automated mastitis-detection systems with respect to their practical value on farm. The protocols are based on 3 on-farm requirements: (1) to detect cows with clinical mastitis promptly and accurately to enable timely and appropriate treatment, (2) to identify cows with high somatic cell count to manage bulk milk SCC levels, and (3) to report the mastitis infection status of cows at the end of lactation to support decisions on individual cow dry-cow therapy. Separate protocols for each requirement are proposed and discussed, including gold standards, evaluation tests, performance indicators, and performance targets. Aspects that require further research or clarification are identified. Actual field data are used as examples. Further debate is invited, the aim being to achieve international agreement on how to evaluate and report performance of different mastitis-detection technologies. Better performance information will allow farmers to compare different mastitis-detection systems sensibly and fairly before investing. Also, the use of evaluation protocols should help technology providers to refine current, or develop new, automated mastitis-detection systems. Such developments are likely to accelerate adoption of these systems, potentially leading to improved animal health, milk quality, and labor productivity.

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Devices used by automated milking systems are similarly accurate in estimting milk yield and in collecting a representative milk sample compared with devices used by farms with conventional milk recording Kamphuis, C.; Bela Rue, B.; Turner, S.-A.; Petch, S.-F This is a "Post-Print" accepted manuscript, which has been published in the "Journal of Dairy Science"This version is distributed under a non-commencial no derivatives Creative Commons (CC-BY-NC-ND) user license, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and not used for commercial purposes. Further, the restriction applies that if you remix, transform, or build upon the material, you may not distribute the modified material.Please cite this publication as follows: In New Zealand, milk recording standards were developed before AMS technology 77 was an option. Therefore, the standards only allow submission of milk recording data into the MATERIALS AND METHODS 103Data used for this study originated from two separate but parallel running studies and, 104 therefore, will be described in two separate sections. 206Before the current study commenced, certified providers were requested to install the 207 milk sampling devices as used in the field without any special preparations. One certified 208 provider, however, did calibrate these devices specifically for this study.

A well performing activity-based ODS can be a valuable tool in identifying cows in oestrus prior to visual confirmation of oestrus status. However the performance of these ODS technologies varies considerably.

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Measurement and monitoring of pasture have been identified as foundations for profitable and sustainable grazing systems. The value that farmers place on pasture assessment in feed management is difficult to ascertain and has seen limited research. The objectives of this study were to test a survey to quantify the perceived value of pasture assessment and identify key criteria for design of pasture assessment technologies. An online survey methodology was piloted with 44 New Zealand farmers to assess perceptions of actual and great grazing management outcomes, good and great pasture assessment, and the value associated with moving from good to great pasture assessment. Results highlighted that many farmers perceive a small potential for improvements in their current pasture performance, whereas industry-level studies suggest that this is not the case. We found limitations with farmers linking better pasture management performance with eventual improvements in milk production. There were anomalies with assessing current and potential improved pasture performance through this type of survey methodology, with many farmers claiming very high levels of current performance, and some rating themselves as performing at more than 100% of potential. This research highlights that pasture assessment technology designers need to be aware of the high expectations of farmers regarding performance, for example measurement accuracy and data timeliness. Over, or under, specification of technology for specific tasks, such as daily allocation of pasture at a herd level, may lead to farmer dissatisfaction around costs of technology, return on investment, and if the technology is fit-for-purpose.