Search

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]

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]

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]

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]