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Context. The aim of farm-systems research is to test concepts in a holistic context that enables results to be as applicable as possible to commercial farmers. A downside of such research has been that it can take several seasons to be confident in the consistency of the outcomes and interpretation of the results. Scientists are often reluctant to communicate outcomes until they have clear conclusions, which conflicts with farmers' desire to see rapid answers and engage with research. Aims. This paper reviews a 3-year farm-systems research project investigating flexible milking strategies for New Zealand dairy farmers. The aims of this paper are to (1) examine the impact of real-time science communication in achieving farm system change, and (2) develop a framework for science communication and engagement in adaptive farm-systems research. Methods. The project involved farmer interviews, a farmlet experiment, a component experiment, partner farms, modelling, and a farmer reference group. We use data from sources such as e-newsletter engagement, webinar participation, web-page engagement, presentation attendance, and farmer feedback to collect insights on the impact of the project. Key results. The analysis highlighted that farmers do not always seek complete information and will follow a project as it evolves, particularly when it is highly topical. We propose a framework for delivering adaptive farm systems research and communication. The framework includes the critical aspects for real-time farm-systems research: credible evidence, rapid results, inclusion of farmer voices, meaningful outcomes, flexible communication channels, iterative feedback loops, and adaptable research design. Conclusion. Farm-systems research can be designed to communicate results to farmers as a project develops, while simultaneously using the engagement with farmers to refine the direction of the research. Implications. This framework can guide scientists leading multi-year farm systems projects to design, implement, and communicate the project outcomes to improve farmer engagement and adoption. [ABSTRACT FROM AUTHOR]

Innovation and technology are a feature of New Zealand's dairy sector. To overcome current challenges, dairy farmers require agile and multi-dimensional innovation, supported by forward-looking and integrated policy from both the sector and government. In this paper, we outline some of the current dairy sector challenges, and potential technologies to address these challenges. We focus on the future for digital agriculture innovation and discuss policy approaches to enable the sector to leverage digitalisation. These approaches include co-innovation, responsible innovation, multi-scale approaches, micro-innovation and poly-innovation and mission-oriented innovation. Digital agriculture and policy may interact in two ways: (1) policy may be used to enhance digital agriculture innovation and, (2) digitalisation itself may act to enhance agricultural policy design and delivery. Overall, innovation policy requires greater directionality, use of policy bundles and a focus on technology as a mediator of new dairy farming practices and institutional configurations. [ABSTRACT FROM AUTHOR]

Globally, dairy farmers face issues with attracting and retaining high-quality staff. In the present study, a qualitative research method was used to explore the current challenges in relation to people on farm, the approaches currently used by farmers to make dairying more attractive and productive for people, and perspectives on the challenges for attracting and retaining people on future farms. Current challenges were in the areas of recruitment, productivity, skills and learning, farm and industry structural issues, and impact of farm profitability on ability to implement new people practices. Participants' vision of the future dairy workplace was one that is highly dynamic, more open to consumers and the community, and largely data-driven. We suggest that dairy workplace research priorities focus on the design and testing of new systems to provide people with meaningful work and a good lifestyle, without compromising profit. Specific priorities include using new ways of connecting and communicating to create engaged and effective teams, developing flexible farm teams who deeply understand their role in the value chain and the consumer connection, defining the opportunity for technology to make the job easier and more enjoyable, developing farm systems that are safe, innovative, and provide a good career, and helping farming businesses demonstrate their people performance to consumers. Globally, dairy farmers face issues with attracting and retaining high-quality staff. Future management of dairy farms will demand farm teams with flexibility, understanding of consumer and community expectations, and the ability to effectively use technology, only exacerbating the challenge of finding the right staff. The present study used workshops and interviews with experts to examine people-related challenges associated with future dairy farms, and we suggest research and development priorities for the next decade. [ABSTRACT FROM AUTHOR]

Dairy farmers are adopting precision technologies to assist with milking and managing their cows due to increased herd sizes and a desire to improve labour efficiency, productivity and sustainability. In the present study, we evaluated the adoption of technologies installed at or near the dairy, and milking practices, on New Zealand dairy farms. These data quantify current use of technology for milking and labour efficiency, and decision-making, and provide insight into future technology adoption. A telephone survey of 500 farmers, randomly selected from a database of New Zealand dairy farms, was conducted in 2018. Adoption for all farms is indicated for six automation technologies, including automatic cup removers (39%), automatic drafting (24%), automatic teat spraying (29%), computer-controlled in-shed feeding (29%), automatic plant wash (18%) and automatic yard wash systems (27%). Five data-capture technologies also included in the survey were electronic milk meters (8%), automatic animal weighing (7%), in-line mastitis detection (7%), automatic heat detection (3%) and electronic animal-identification readers (23%). Analysis by dairy type indicated an adoption level for the automation technologies in rotary dairies of 36–77%, and 7–49% for data-capture technologies, with 10% having none of these 11 technologies installed. This compares with herringbone dairies at 4–21% and 2–11% for automation and data-capture technologies respectively, with 56% having none of these technologies. Rotary dairies, with a combination of automatic cup removers, automatic teat spraying, and automatic drafting, were associated with 43% higher labour efficiency (cows milked/h.person) and 14% higher milking efficiency (cows milked/h) than were rotary dairies without all three technologies. Dairy farmers will increasingly use technologies that deliver value, and the present study has provided information to guide investment decisions, product development and research in areas such as applying technology in new workplaces. Precision-farming technologies are receiving greater attention by researchers and policy makers for potential improvements in farm management. New Zealand dairy farmers have focussed on investment in automation technology in the dairy, some of which provide greater labour efficiency, rather than investing in data-capture technology for decision support. Further improvements in labour efficiency from technology in high-performing conventional dairies may be limited, and adoption of data-capture technologies may need to accelerate to support an increasingly diverse and less experienced workforce. [ABSTRACT FROM AUTHOR]

Context: Advances in automated oestrus detection have made this an attractive technology to help reduce manual oestrus detection labour on dairy farms. Aims: A decision-support tool was created to help farmers estimate the investment outcome of adopting automated oestrus detection technologies in a seasonal dairy production system. Methods: A decision-support tool was created using Excel 2011 (Microsoft Inc., Redmond, WA, USA). The tool allows farmers to input both current herd reproductive management costs and performance and automated oestrus detection technology system costs and performance to receive herd-specific estimates of investment benefit. The investment analysis outputs include the net present value (NPV), internal rate of return (IRR), and payback period associated with automated oestrus detection adoption. Two different automated oestrus detection technologies were compared with visual oestrus detection aided by tail paint with a 72.0% oestrus detection rate (sensitivity) to demonstrate the value of the investment analysis tool. The alternative scenarios, technology one and technology two, were compared over an eight-year investment period. Key results: Technology one, with a 62.4% oestrus detection rate, resulted in a negative NPV and IRR (–NZ$182 567 and –100% respectively), indicating a poor investment. Technology two, with an oestrus detection rate of 91.0%, provided a positive NPV and IRR (NZ$177 890 and 38.7% respectively), indicating a beneficial investment. The payback period for technology one was estimated as >10 years, whereas technology two's payback period was <1 year. Conclusions: The investment tool results are dependent on farm-specific and automated oestrus detection inputs. Implications: Farmers can use farm-specific inputs in the tool to aid them when considering adoption of new automated oestrus detection technologies. Maintaining high oestrus detection rates in seasonal dairy herds is imperative to optimising production during the peak grazing period. A decision-support tool was created to estimate the investment outcome of adopting automated oestrus detection technologies as alternatives to labour-intensive visual oestrus detection. Farmers may use farm-specific inputs in the decision-support tool to visualise financial implications of adopting automated oestrus detection technologies. [ABSTRACT FROM AUTHOR]

The use of pasture measurement tools and decision-support systems (DSS) for grazing management remains limited on New Zealand dairy farms. However, effective use of such tools provides opportunities to optimise pasture grown and pasture harvested. The present study used a mixed-method qualitative research approach to investigate pasture data and technology use for grazing decision making, through interviews and workshops with farmers, rural professionals, commercial software developers and a panel of farming-system specialists. Results suggest that different drivers for use of pasture data and DSS exist between farm owner-operators and corporate farming operations. Larger multi-farm businesses are collecting pasture data for use at a governance level as well as for operational decision making. Understanding the seasonal influences on decision making, and incorporating major regional differences such as pasture growth rates and impact of irrigation use, provides guidance on how to better match DSS to farmer practice. Study participants identified a need for greater integration of software tools to connect in-paddock data capture with real-time feedback. Also, data integration is needed to enable the transfer of information across different platforms for corporate farming operations. Rural professionals used commercial grazing DSS products, but also constructed their own spreadsheets to enable functionality and reporting not available in the DSS products. The research highlighted a need for farmer-orientated tools that are flexible to incorporate differences in user goals, decision making, mobility and desired outputs. Key attributes identified were seasonality, simplicity, ability to trial before purchase, flexibility in application, scalability to match farm systems, and integration with other tools. Future research and design of DSS tools requires a focus on co-creation with farmers, to merge scientific and practical knowledge. [ABSTRACT FROM AUTHOR]

Approximately one-third of New Zealand dairy farms have in-shed feeding systems, and quarter of these systems have individual-cow feeding capability. There is little information about the factors motivating farmers to install individualised feeding technology in New Zealand and their management practices. A survey was conducted of 42 farmers using individualised feeding, followed by interviews of 32 farmers and five farm consultants. The aim of the study was to understand why farmers were installing individualised feeding, how they were using it, and what challenges they were facing. Results indicated that these farmers were commonly motivated by improving productivity and most farmers strongly believed that they were gaining value with this approach compared with flat-rate feeding. However, there was also uncertainty about the most profitable feeding strategies. The farmers were most commonly feeding cows in distinct groups rather than as individuals, and were typically adjusting feed allocation at three to four stages of the lactation. Consultants identified a lack of a value proposition for investment, limited justification for feeding-decision rules, and the absence of information to support the perceived productivity (and other) benefits. The perceived productivity benefit of individualised feeding held by farmers in the present study is contrary to recent research that has indicated no marginal milk-production benefit compared with flat-rate feeding. Although, few of these studies relate to individualised feeding in grazing systems where pasture allowance is restricted as is typical of New Zealand farm systems, indicating a need for further experimental and modelling research to quantify the potential value in these systems. Farmers were seeking reliable information on the response rate and cost effectiveness of various feeds and feeding rules, how to distribute a fixed amount of feed to the herd most profitably, and technical expertise, support and back-up for specific feeding systems. Findings from the present study suggest a lack of advisor expertise related to in-shed feeding of concentrate supplement in pasture-grazed systems, signalling opportunities for more training of rural professionals to provide informed advice and improved communication channels with farmers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper reports on a field validation of previously developed protocols for evaluating the performance of in-line mastitis-detection systems. The protocols outlined 2 requirements of these systems: (1) to detect cows with clinical mastitis (CM) promptly and accurately to enable timely and appropriate treatment and (2) to identify cows with high somatic cell count (SCC) to manage bulk milk SCC levels. Gold standard measures, evaluation tests, performance measures, and performance targets were proposed. The current study validated the protocols on commercial dairy farms with automated in-line mastitis-detection systems using both electrical conductivity (EC) and SCC sensor systems that both monitor at whole-udder level. The protocol for requirement 1 was applied on 3 commercial farms. For requirement 2, the protocol was applied on 6 farms; 3 of them had low bulk milk SCC (128 x 10³cells/mL) and were the same farms as used for field evaluation of requirement 1. Three farms with high bulk milk SCC (270 x 10³ cells/mL) were additionally enrolled. The field evaluation methodology and results were presented at a workshop including representation from 7 international suppliers of in-line mastitis-detection systems. Feedback was sought on the acceptance of standardized performance evaluation protocols and recommended refinements to the protocols. Although the methodology for requirement 1 was relatively labor intensive and required organizational skills over an extended period, no major issues were encountered during the field validation of both protocols. The validation, thus, proved the protocols to be practical. Also, no changes to the data collection process were recommended by the technology supplier representatives. However, 4 recommendations were made to refine the protocols: inclusion of an additional analysis that ignores small (low-density) clot observations in the definition of CM, extension of the time window from 4 to 5 milkings for timely alerts for CM, setting a maximum number of 10 milkings for the time window to detect a CM episode, and presentation of sensitivity for a larger range of false alerts per 1,000 milkings replacing minimum performance targets. The recommended refinements are discussed with suggested changes to the original protocols. The information presented is intended to inform further debate toward achieving international agreement on standard protocols to evaluate performance of in-line mastitisdetection systems. [ABSTRACT FROM AUTHOR]

The benefits of sufficient and high-quality sleep for people are well documented. Insufficient sleep increases the risk of accidents, injuries, and negative health implications for people. This is especially relevant for farmers, as they work with large animals and machinery. Dairy farming often requires early start times and long days, particularly over the high workload calving period in block calving, pasture-based systems. However, there is little published data quantifying the sleep quantity and quality of farmers over this period. In this study, the sleep patterns of workers (n = 33) on 10 New Zealand dairy farms was measured for 90 d over the spring calving period using a sleep measuring device (Oura TM ring, Oura Health Ltd., Oulu, Finland). Total sleep time (TST) averaged 6 h 15 min, lower than the required 7 to 9 h for optimal wellbeing and cognitive functioning. TST decreased over the calving period and was significantly correlated with both sleep start and wake times. Factors such as work start time, farm location, and role on farm influenced sleep quantity and quality; indicating adjusting these on-farm factors could positively impact TST. Further research is required to better understand sleep and its effect on dairy farmers, over both the calving period and the remaining months of the year., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Application of robotics and automation in pasture-grazed agriculture is in an emergent phase. Technology developers face significant challenges due to aspects such as the complex and dynamic nature of biological systems, relative cost of technology versus farm labor costs, and specific market characteristics in agriculture. Overlaying this are socio-ethical issues around technology development, and aspects of responsible research and innovation. There are numerous examples of technology being developed but not adopted in pasture-grazed farming, despite the potential benefits to farmers and/or society, highlighting a disconnect in the innovation system. In this perspective paper, we propose a "responsibility by design" approach to robotics and automation innovation, using development of batch robotic milking in pasture-grazed dairy farming as a case study. The framework we develop is used to highlight the wider considerations that technology developers and policy makers need to consider when envisaging future innovation trajectories for robotics in smart farming. These considerations include the impact on work design, worker well-being and safety, changes to farming systems, and the influences of market and regulatory constraints., Competing Interests: All authors are employed by DairyNZ Ltd, a levy‐funded industry-good organization representing all New Zealand dairy farmers., (Copyright © 2022 Eastwood, Dela Rue, Edwards and Jago.)

Technological change has been a constant feature of livestock systems leading to the third agricultural 'green' revolution of the mid-20th century. Digital technologies are now leading us into the fourth agricultural revolution, where sustainable food production is supported by technologies that collect data useful for farm and supply chain performance improvement, along with task automation and compliance. However, the potential benefits of digital agricultural futures are uncertain and plagued by unrealized expectations of previous innovations. The aims of this paper are to articulate current trends in technology and livestock systems and anticipate future trajectories for Agriculture 4.0 in relation to meeting sustainability and animal welfare outcomes for livestock systems. We use a 'Futures Triangle' approach to review the role of technology in livestock systems. The main findings are that previous work envisioning technological livestock futures have favoured pull of the future factors (techno-optimists) or weight of the past (techno-pessimists), rather than a balance of pull, push and weighting factors. Responsible Agriculture 4.0 innovation requires public-private collaboration of innovation system stakeholders, including policy makers, farmers, consumers, as well as technology developers, to enable development of transition pathways from a systems perspective. The use of responsible innovation processes, including anticipation on alternative futures, should also be built into innovation processes to support critical reflection on technological trajectories and related innovation system consequences, both desirable and undesirable., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

The use of precision technology is increasingly seen as an option to improve productivity, animal welfare, resource use efficiency, and workplace features on dairy farms. There is limited research related to longitudinal adoption patterns of precision dairy technologies and reasons for any patterns. The aim of this analysis was to investigate trends in technology adoption regarding both the amount (number of farms with a technology) and intensity (number of technologies per farm) of adoption. Surveys of parlor technology adoption were conducted on New Zealand dairy farms in 2008, 2013, and 2018, with 532, 500, and 500 respondents, respectively. Technologies were grouped into labor-saving (LS, such as automatic cluster removers) or data-capture (DC, such as in-line milk meters) categories. Trends were examined for farms that had only LS, only DC, or LS+DC technologies. Technology adoption increased over time; the likelihood of technology adoption in 2018 (and 2013 in parentheses) increased by 21 (22), 7 (68), and 378% (165) for LS, DC, and LS+DC technology groups, respectively, compared to 2008. Farms with LS+DC technologies also had a greater proportion of LS technologies compared to non-LS+DC farms, although this relationship declined over the 10-yr period. The use of a rotary versus herringbone parlor was estimated to be associated with 356 and 470% increase in the likelihood of adopting LS technologies and LS+DC, respectively, from 2008 to 2018. Regional differences in adoption were also found, with the likelihood of adopting DC and LS+DC technologies found to be 46 and 59% greater, respectively, in the South Island of New Zealand, compared to the base region of Waikato. The results highlight the importance of understanding spatial and temporal farm characteristics when considering future effect and adoption of precision dairy technologies. For example, the analysis indicates the occurrence of 2 trajectories to technology investment on farms, where larger farms are able to take advantage of technology opportunities, but smaller farms may be constrained by factors such as lack of economies of scale, limited capital to invest, and inability to retrofit technology into aging parlor infrastructure., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The assessment of grazing behavior is important for research and practice in pasture-grazed dairy farm systems. However, few devices are available that enable assessment of cow grazing behavior at an individual animal level. This study investigated whether commercially available Smarttag "eating time" sensors (Nedap Livestock Management, Groenlo, the Netherlands) were suitable for recording the grazing time of cows. Smarttag sensors were mounted on the neck collars of multiparous Holstein-Friesian cows in a herd in Taranaki, New Zealand. Cows were randomly selected each observation day from the milking herd for 8 separate days across a 1-mo period. Trained observers conducted 90-min observation periods to evaluate the relationship between the sensor eating time measure and grazing time. A set of 5 defined cow behaviors (2 "head up" and 3 "head down" behaviors) were assessed. In total, observations of 37 cows were recorded in 14 sessions over 8 d in the study period, providing 55.5 total hours of observations. Observation data were aligned with sensor data according to the sensor time stamps and grouped into matching 15-min intervals. Interobserver reliability was assessed both before and after the main trial period, and the mean percentage eating time per observer had a coefficient of variation of 0.46% [mean 93.2, standard deviation (SD) 0.425] before and 0.07% (mean 96.3, SD 0.074) after. In the main trial, the relationship between observed (mean 70.8%) and sensor-derived (mean 69.3%) percentage eating time over the observation period gave a Pearson correlation coefficient of 0.971, concordance correlation coefficient 0.968, mean difference 1.50% points, and SD 5.8% points. Therefore, sensor-identified percentage "eating time" and observed percentage active grazing time were shown to be both very well correlated and concordant (in agreement, with high correlation and little bias). Therefore, the relationship between observed and sensor-derived data had a high degree of agreement for identifying cow grazing activity. In conclusion, Smarttag sensors are a valid and useful tool for estimating grazing activity at time periods of 1 h or more., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

To attract and retain quality employees, dairy farms must be competitive with other workplaces offering more conventional hours of work. Milking requires significant labor input and influences the start and end times of the working day, affecting flexibility to suit employee needs or availability. The use of labor-saving technology and milking management strategies could help with this challenge. Previous studies have used scenario modeling in attempt to quantify the value of in-parlor technologies, however, they have relied on assumptions about the effect of the technologies on labor in the dairy. Similarly, the effect of management strategies on work patterns, such as flexible milking intervals (changing the timing of milking), has not been evaluated. The aims of this study were to (1) quantify the milking labor requirements in a range of pasture-based dairy farm systems and (2) identify practices or technologies that facilitate efficient milking. A telephone survey of 500 dairy farmers in New Zealand was conducted during April and May 2018, with questions asked about milking practices and technology use. Predictive analysis showed that at peak lactation, milking required between 17 and 24 h/wk per worker for farms milking twice a day, representing 43 to 58% of a conventional 40-h work week, depending on parlor type (herringbone or rotary), the number of clusters, and herd size. Using milking intervals of 8 and 16 h (intervals between milkings), compared with the more usual 10 and 14 h, largely avoided starting milking before 0500 h. Eight percent of herds were milked once a day, which required between 7 and 14 h/wk per worker (18-35% of a 40-h week). ANOVA showed that for metrics that related to people (labor efficiency and work routine), using automatic teat spraying had a positive effect on efficiency. Having both automatic cluster removers and drafting were associated with longer milking times in terms of throughput and row/rotation time compared with using drafting only. The results highlight considerable opportunity to reduce the number of hours those milking (employers and employees) spend in the parlor and increase staff time flexibility through milking (e.g., intervals between milkings) and parlor management (e.g., row/rotation time) and use of specific technologies. This study provides useful data for those wishing to analyze the likely value of an in-parlor automation technology or management practice for an individual situation., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)