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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]

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]

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]