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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]

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]

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]

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.)