Robotic milking: Technology, farm design, and effects on work flow

Comparative Analysis of Milking and Behavior Characteristics of Multiparous and Primiparous Cows in Robotic Systems

Robotic milking systems are successful innovations in the development of dairy cattle. The objective of this study was to analyse the milking characteristics and behavior of dairy cows of different calving orders in "milk first" robotic milking systems. The data were collected from a commercial herd located in the Midwest region of Minas Gerais (Brazil), which uses an automatic milking system (AMS TM, DeLaval). Were analysed 26,574 observations of 235 Holstein cows were available. Data were evaluated by multivariate analysis of variance and the Tukey test. - Tthe characteristics milk flow and milking efficiency were more favourable for multiparous cows (p <0.01), while the time in the stall was more favourable for primiparous females (p <0.01). The values of handling time were better in the primiparous cows (p <0.01). Primiparous cows had higher amounts of kick-off (p <0.001), and multiparous cows had higher incomplete milkings (p <0.001). The number of incomplete milkings showed a higher ratio in terms of reduction in milk production in 26.6% in primiparous cows and 26.7% in multiparous cows (p <0.01). Regarding the behavioral characteristics, primiparous cows had higher amounts of kickbacks, while multiparous cows had greater quantities of incomplete milkings.

Human-animal interactions and machine-animal interactions in animals under human care: A summary of stakeholder and researcher perceptions and future directions

Animals under human care are exposed to a potentially large range of both familiar and unfamiliar humans. Human-animal interactions vary across settings, and individuals, with the nature of the interaction being affected by a suite of different intrinsic and extrinsic factors. These interactions can be described as positive, negative or neutral. Across some industries, there has been a move towards the development of technologies to support or replace human interactions with animals. Whilst this has many benefits, there can also be challenges associated with increased technology use. A day-long Animal Welfare Research Network workshop was hosted at Harper Adams University, UK, with the aim of bringing together stakeholders and researchers (n = 38) from the companion, farm and zoo animal fields, to discuss benefits, challenges and limitations of human-animal interactions and machine-animal interactions for animals under human care and create a list of future research priorities. The workshop consisted of four talks from experts within these areas, followed by break-out room discussions. This work is the outcome of that workshop. The key recommendations are that approaches to advancing the scientific discipline of machine-animal interactions in animals under human care should focus on: (1) interdisciplinary collaboration; (2) development of validated methods; (3) incorporation of an animal-centred perspective; (4) a focus on promotion of positive animal welfare states (not just avoidance of negative states); and (5) an exploration of ways that machines can support a reduction in the exposure of animals to negative human-animal interactions to reduce negative, and increase positive, experiences for animals.

Exploring inclusion in UK agricultural robotics development: who, how, and why?

The global agricultural sector faces a significant number of challenges for a sustainable future, and one of the tools proposed to address these challenges is the use of automation in agriculture. In particular, robotic systems for agricultural tasks are being designed, tested, and increasingly commercialised in many countries. Much touted as an environmentally beneficial technology with the ability to improve data management and reduce the use of chemical inputs while improving yields and addressing labour shortages, agricultural robotics also presents a number of potential ethical challenges - including rural unemployment, the amplification of economic and digital inequalities, and entrenching unsustainable farming practices. As such, development is not uncontroversial, and there have been calls for a responsible approach to their innovation that integrates more substantive inclusion into development processes. This study investigates current approaches to participation and inclusion amongst United Kingdom (UK) agricultural robotics developers. Through semi-structured interviews with key members of the UK agricultural robotics sector, we analyse the stakeholder engagement currently integrated into development processes. We explore who is included, how inclusion is done, and what the inclusion is done for. We reflect on how these findings align with the current literature on stakeholder inclusion in agricultural technology development, and suggest what they could mean for the development of more substantive responsible innovation in agricultural robotics.

Soft robotics for farm to fork: applications in agriculture & farming

Agricultural tasks and environments range from harsh field conditions with semi-structured produce or animals, through to post-processing tasks in food-processing environments. From farm to fork, the development and application of soft robotics offers a plethora of potential uses. Robust yet compliant interactions between farm produce and machines will enable new capabilities and optimize existing processes. There is also an opportunity to explore how modeling tools used in soft robotics can be applied to improve our representation and understanding of the soft and compliant structures common in agriculture. In this review, we seek to highlight the potential for soft robotics technologies within the food system, and also the unique challenges that must be addressed when developing soft robotics systems for this problem domain. We conclude with an outlook on potential directions for meaningful and sustainable impact, and also how our outlook on both soft robotics and agriculture must evolve in order to achieve the required paradigm shift.

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

The traditional point of view regarding dairy cattle selection has been challenged by recent genomic studies indicating that livestock productivity prediction can be redefined based on the evaluation of genomic and phenotypic data. Several studies that included different genomic-derived traits only indicated that interactions among them or even with conventional phenotypic evaluation criteria require further elucidation. Unfortunately, certain genomic and phenotypic-derived traits have been shown to be secondary factors influencing dairy production. Thus, these factors, as well as evaluation criteria, need to be defined. Owing to the variety of genomic and phenotypic udder-derived traits which may affect the modern dairy cow functionality and conformation, a definition of currently important traits in the broad sense is indicated. This is essential for cattle productivity and dairy sustainability. The main objective of the present review is to elucidate the possible relationships among genomic and phenotypic udder evaluation characteristics to define the most relevant traits related to selection for function and conformation in dairy cattle. This review aims to examine the potential impact of various udder-related evaluation criteria on dairy cattle productivity and explore how to mitigate the adverse effects of compromised udder conformation and functionality. Specifically, we will consider the implications for udder health, welfare, longevity, and production-derived traits. Subsequently, we will address several concerns covering the application of genomic and phenotypic evaluation criteria with emphasis on udder-related traits in dairy cattle selection as well as its evolution from origins to the present and future prospects.

The Aspects of Artificial Intelligence in Different Phases of the Food Value and Supply Chain

The types of artificial intelligence, artificial intelligence integration to the food value and supply chain, other technologies embedded with artificial intelligence, artificial intelligence adoption barriers in the food value and supply chain, and solutions to overcome these barriers were analyzed by the authors. It was demonstrated by the analysis that artificial intelligence can be integrated vertically into the entire food supply and value chain, owing to its wide range of functions. Different phases of the chain are affected by developed technologies such as robotics, drones, and smart machines. Different capabilities are provided for different phases by the interaction of artificial intelligence with other technologies such as big data mining, machine learning, the Internet of services, agribots, industrial robots, sensors and drones, digital platforms, driverless vehicles and machinery, and nanotechnology, as revealed by a systematic literature analysis. However, the application of artificial intelligence is hindered by social, technological, and economic barriers. These barriers can be overcome by developing the financial and digital literacy of farmers and by disseminating good practices among the participants of the food supply and value chain.

Occupational Safety and Health with Technological Developments in Livestock Farms: A Literature Review

In recent decades, there have been considerable technological developments in the agriculture sector to automate manual processes for many factors, including increased production demand and in response to labor shortages/costs. We conducted a review of the literature to summarize the key advances from installing emerging technology and studies on robotics and automation to improve agricultural practices. The main objective of this review was to survey the scientific literature to identify the uses of these new technologies in agricultural practices focusing on new or reduced occupational safety risks affecting agriculture workers. We screened 3248 articles with the following criteria: (1) relevance of the title and abstract with occupational safety and health; (2) agriculture technologies/applications that were available in the United States; (3) written in English; and (4) published 2015-2020. We found 624 articles on crops and harvesting and 80 articles on livestock farming related to robotics and automated systems. Within livestock farming, most (78%) articles identified were related to dairy farms, and 56% of the articles indicated these farms were using robotics routinely. However, our review revealed gaps in how the technology has been evaluated to show the benefits or potential hazards to the safety and well-being of livestock owners/operators and workers.

Optimising profitability and productivity of pasture-based dairy farms with automatic milking systems

There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.

Assessment of technical-productive aspects in Italian dairy farms equipped with automatic milking systems: A multivariate statistical analysis approach

The aim of this study was to assess technical-productive aspects of dairy farms equipped with automatic milking system (AMS) in Northern and Central Italy. A survey was carried out on 62 dairy farms selected through convenience sampling with the following inclusion criteria: adoption of robotic milking for at least 1 yr and ability to provide farm data. Data were collected using a structured questionnaire to obtain a general description of farm characteristics and overall management practices. Through the combination of principal component analysis and k-means cluster analysis, the farms were allocated in 3 clusters. The identified clusters were described and afterward compared using one-way ANOVA or a chi-squared test. The main observed differences between clusters were the average number of lactating cows and AMS installed, average annual milk production, average AMS loading, average annual milk yield per full-time employee, average daily milk yield per cow and AMS, and the average annual veterinary costs per cow. cluster 1 (n = 24) included small-to-medium-sized semi-intensive farms with low AMS loading and low average daily milk yield per cow. In this farm typology, the AMS is not fully used and is likely perceived as a means to improve quality of life rather than profitability. Clusters 2 (n = 31) and 3 (n = 7) included, respectively, small-medium-sized and large intensive farms. These 2 farm typologies are characterized by an intensive approach to dairy cattle breeding, with average higher AMS loading, labor efficiency, and milk yield compared with the farms of cluster 1, likely due to better farm management. This classification could help dairy technicians give farmers customized management advice for the function of the cluster they belong to, and farmers falling in a specific cluster could evaluate whether they are reaching their objectives.

Responsible robotics design-A systems approach to developing design guides for robotics in pasture-grazed dairy farming

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.

Does Small-Scale Livestock Production Use a High Technological Level to Survive? Evidence from Dairy Production in Northeast-ern Michoacán, Mexico

The objective of this study was to identify the technological level used by dairy farmers in the northeastern region of Michoacán, Mexico, through a characterisation of small-scale dairy production units, as well as to learn about the socioeconomic conditions that have enabled them to survive in the current context. A semi-structured interview was applied to 114 production units, chosen by stratified random sampling. The interview included technological, production and socioeconomic aspects. Twenty-eight variables were initially explored and 12 were used for multivariate analysis, which included Principal Component Analysis, Hierarchical Cluster Analysis and K-means Cluster. The characterisation carried out showed that the production units that predominate in northeastern Michoacán have survived with a low technological level, having as strengths the diversification of their activities and the use of family labour. On the contrary, production units with a high technological level and high productivity are few and less diversified. This shows the need to generate differentiated public policies for each cluster, aimed at strengthening the aspects that have allowed them to survive and guaranteeing a market for their production, before promoting the use of technologies.

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

Precision Agriculture for Crop and Livestock Farming-Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.

Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

Background  Workflow automation, which involves identifying sequences of tasks that can be streamlined by using technology and modern computing, offers opportunities to address the United States health care system's challenges with quality, safety, and efficiency. Other industries have successfully implemented workflow automation to address these concerns, and lessons learned from those experiences may inform its application in health care.

Objective  Our aim was to identify and synthesize (1) current approaches in workflow automation across industries, (2) opportunities for applying workflow automation in health care, and (3) considerations for designing and implementing workflow automation that may be relevant to health care.

Methods  We conducted a targeted review of peer-reviewed and gray literature on automation approaches. We identified relevant databases and terms to conduct the searches across sources and reviewed abstracts to identify 123 relevant articles across 11 disciplines.

Results  Workflow automation is used across industries such as finance, manufacturing, and travel to increase efficiency, productivity, and quality. We found automation ranged from low to full automation, and this variation was associated with task and technology characteristics. The level of automation is linked to how well a task is defined, whether a task is repetitive, the degree of human intervention and decision-making required, and the sophistication of available technology. We found that identifying automation goals and assessing whether those goals were reached was critical, and ongoing monitoring and improvement would help to ensure successful automation.

Conclusion  Use of workflow automation in other industries can inform automating health care workflows by considering the critical role of people, process, and technology in design, testing, implementation, use, and ongoing monitoring of automated workflows. Insights gained from other industries will inform an interdisciplinary effort by the Office of the National Coordinator for Health Information Technology to outline priorities for advancing health care workflow automation.

Got milk? How AI, lab techniques and automation could help you get more

Revolutionary techniques to improve dairy herd health and making them globally accessible could improve the sustainability of food production in the dairy farming industry.

Occupation-level automation probability is associated with psychosocial work conditions and workers' health: A multilevel study

OBJECTIVE

Work automation is increasing worldwide, and the probability of job automation has been associated with workers' adverse health outcomes. This study aimed to examine the association of occupation-level automation probability with work stress and workers' health.

METHODS

We used data from a national survey of 14,948 randomly selected general workers conducted in 2016. Job control and job demand were assessed by the Job Content Questionnaire, and working hours and job insecurity were self-reported. Health outcomes were measured according to burnout and work-related injury or disease. We derived automation probabilities for 38 occupational groups and conducted multilevel analyses to examine the associations between occupation-level automation probability and workers' safety and health after adjusting for psychosocial work conditions.

RESULTS

Participants working in jobs with a high probability of automation were more likely to have low job control, higher job insecurity, and work-related injury and disease prevalence; whereas workers in jobs with a low automation probability had higher psychological and physical demands and burnout prevalence. Furthermore, automation probability significantly predicted workers' health after adjustment for demographic characteristics and psychosocial work conditions.

CONCLUSIONS

Workers with low automation probability jobs may experience work stress other than that captured by traditional measures of job strain. Organizational approaches to improve employment security and psychosocial conditions are essential for workers' safety and health in the context of increasing job automation.

Relationships between Selected Physiological Factors and Milking Parameters for Cows Using a Milking Robot

Simple Summary

Modern milking robots create the possibility for better organization of work. In recent years, in selection indexes constructed for Holstein Friesian cattle, milking capacity features have become of great importance. This study showed a negative correlation between the milk yield and the content of its components. The quantity of milk released from the hind quarters was found to be higher than that determined for the fore quarters. At the same time, the milk flow rate turned out to be statistically significantly higher in the front quarters as compared to the back quarters. The speed of milking is an individual feature; therefore, it is important that the milking machines have the ability to control the negative pressure during the milking of individual quarters, and thus react to the rate of milk flow from individual teats.

Abstract

The aim of the study was to determine the effect of the number and stage of lactations, time of day and calving season of cows on milk yield from a single milking, average milking time, average milking per minute, daily milking frequency and the relationship between the tested parameters of quarter milking. The study included a herd of 65 Polish Holstein Friesian black and white cows used in a free-range barn located in south-west Poland. The animals were kept in proper welfare conditions, fed using the partly mixed ration (PMR) method on the feeding table. The milk was obtained using the Lely-Astronaut A4 Automatic Milking System (AMS). The animals on the dairy cattle farm were used in the range from the first to the seventh lactation, i.e., at the age of 2.0 to approximately 10 years. In this study, the amount of milk yielded from the hind quarters was statistically significantly higher (p < 0.05) than the trait determined for the front quarters. At the same time, the milk flow rate was statistically significantly higher (p < 0.05) in the front quarters compared to the rear quarters. The daily milk yield in right rear (RR) and left rear (LR) hind quarters was higher by 1.0 kg of milk, respectively, than in right front (RF) and left front (LF) fore quarters. The milking time of the RR and LR hind quarters during the day was longer by 104.9 and 128.8 s, respectively, than the RF and LF fore quarters. The milking speed of the RR and LR hind quarters during the day was lower by 0.2 and 1.12 g/s, respectively, than in the RF and LF fore quarters. The values of the correlation between the yields of milk and its components obtained in this study were high and positive. Correlations between the milk yield and the content of its components were negative. The obtained results confirmed that the natural physiological variability of the udder and teats structure, as well as the course of lactation, significantly affects the individual composition and milk flow during milking. The ability to regulate the milk flow by adjusting the appropriate negative pressure during the robot’s operation, in the observed variability of individual lobes of the mammary gland, increases the efficiency of milking and, as a result, reduces the risk of mastitis in cows.

Genetic, farm, and lactation effects on behavior and performance of US Holsteins in automated milking systems

Selecting for favorable behavior and performance could enhance the efficiency of production in automated milking systems (AMS). The objectives of this study were to describe AMS behavior and performance in Holsteins, estimate genetic parameters among AMS traits, and determine genetic relationships of AMS traits with other routinely recorded traits. The edited data included 1,101,651 individual milking records and 394,636 daily records from 2,531 lactations and 1,714 cows that resided on 3 farms; data were obtained from the Dairy Data Warehouse (Assen, Netherlands) cloud. Traits considered were individual milking and daily totals for milk yield, milking time, milk harvest rate (the ratio of milk yield to milking time), milk flow rate, electrical conductivity, machine kickoffs, incomplete milkings, and blood in milk; the number of milkings per day and 305-d mature-equivalent milk yield (305ME) were also evaluated. Individual milkings were evaluated with mixed models that included fixed effects of week of lactation, lactation group (1, 2, ≥3), hour of day, and farm; random effects included cow within lactation, lactation group by week of lactation, and interactions of farm with date, hour, week of lactation, and year-season of calving. Daily records were evaluated with 3-trait animal models that included 305ME and 2 AMS traits with random additive genetic and permanent environment effects. Estimated breeding values were extracted and correlated with yield, conformation, and udder health genetic evaluations. Farm specific robot access policies had notable effects on week of lactation patterns for milk yield and number of milkings. Mature cows had higher milk harvest rates (2.05 kg/min) than first-lactation cows (1.73 kg/min) with larger differences in early lactation. First-lactation cows were more likely to kick off the machine (15.04%) than mature cows (8.62%), particularly in early lactation. Heritability estimates were generally lower for behavior traits (0.03 for incomplete milkings and 0.08 for kickoffs) than for milk harvest rate (0.30) and flow rate (0.55). Udder conformation traits did not have favorable genetic correlations with AMS traits, with the exception that longer teats were correlated with fewer kickoffs (-0.34) and incomplete milkings (-0.49); increased milk harvest rate and flow rate were unfavorably associated with genetic merit for udder health. There is genetic variation for milking efficiency and behavioral traits, suggesting genetic selection to enhance efficiency in AMS systems is possible. Genetic associations with udder conformation indicate that selection for udder morphology is unlikely to substantially improve milking efficiency. This calls for more direct selection of traits related to AMS efficiency.

Association between teat skin colonization and intramammary infection with Staphylococcus aureus and Streptococcus agalactiae in herds with automatic milking systems

The objective of this study was to investigate the association between teat skin colonization and intramammary infection (IMI) with Staphylococcus aureus or Streptococcus agalactiae at the quarter level in herds with automatic milking systems. Milk and teat skin samples from 1,142 quarters were collected from 300 cows with somatic cell count >200,000 cells/mL from 8 herds positive for Strep. agalactiae. All milk and teat skin samples were cultured on calf blood agar and selective media. A subset of samples from 287 quarters was further analyzed using a PCR assay (Mastit4 PCR; DNA Diagnostic A/S, Risskov, Denmark). Bacterial culture detected Staph. aureus in 93 (8.1%) of the milk samples and 75 (6.6%) of the teat skin samples. Of these, 15 (1.3%) quarters were positive in both the teat skin and milk samples. Streptococcus agalactiae was cultured in 84 (7.4%) of the milk samples and 4 (0.35%) of the teat skin samples. Of these, 3 (0.26%) quarters were positive in both the teat skin and milk samples. The PCR detected Staph. aureus in 29 (10%) of the milk samples and 45 (16%) of the teat skin samples. Of these, 2 (0.7%) quarters were positive in both the teat skin and milk samples. Streptococcus agalactiae was detected in 40 (14%) of the milk samples and 51 (18%) of the teat skin samples. Of these, 16 (5.6%) quarters were positive in both the teat skin and milk samples. Logistic regression was used to investigate the association between teat skin colonization and IMI at the quarter level. Based on bacterial culture results, teat skin colonization with Staph. aureus resulted in 7.8 (95% confidence interval: 2.9; 20.6) times higher odds of Staph. aureus IMI, whereas herd was observed as a major confounder. However, results from the PCR analyses did not support this association. Streptococcus agalactiae was isolated from the teat skin with both PCR and bacterial culture, but the number of positive teat skin samples detected by culture was too low to proceed with further analysis. Based on the PCR results, Strep. agalactiae on teat skin resulted in 3.8 (1.4; 10.1) times higher odds of Strep. agalactiae IMI. Our results suggest that Staph. aureus and Strep. agalactiae on teat skin may be a risk factor for IMI with the same pathogens. Focus on proper teat skin hygiene is therefore recommended also in AMS.

Accuracy of qPCR and bacterial culture for the diagnosis of bovine intramammary infections and teat skin colonisation with Streptococcus agalactiae and Staphylococcus aureus using Bayesian analysis

Streptococcus agalactiae (Strep. agalactiae) and Staphylococcus aureus (Staph. aureus) are originally regarded as contagious mastitis pathogens, however, both pathogens have recently been isolated from extramammary and environmental sites, indicating that other sites than the udder might contribute to the spread of these pathogens potentially causing intramammary infections. Diagnostic tools to identify pathogens at extramammary sites are available but still needs to be validated. The objective of this cross-sectional field study was to estimate the diagnostic sensitivity (Se) and specificity (Sp) of the commercially available Mastit4 qPCR assay and bacterial culture (BC) in identifying Strep. agalactiae and Staph. aureus from milk and teat skin samples. We randomly selected 30-40 cows with high somatic cell counts from eight Danish Strep. agalactiae-positive dairy herds with automatic milking systems. Teat skin samples and aseptic milk samples were collected from right rear quarters (n = 287) for BC and PCR analysis. Se and Sp were estimated in a Bayesian latent class analysis. For milk samples, the Se and Sp of qPCR for Strep. agalactiae were estimated to 0.97 and 0.99, respectively, whereas the Se and Sp of BC were 0.41 and 1.00, respectively. The Se and Sp of qPCR for Staph. aureus were estimated to 0.95 and 0.99, respectively, whereas the Se and Sp of BC were 0.54 and 0.77, respectively. For teat skin samples, the Se and Sp of qPCR for Strep. agalactiae were estimated to be 0.97 and 0.96, respectively, whereas the Se and Sp of BC were 0.33 and 1.00, respectively. The Se and Sp of qPCR for Staph. aureus were estimated to 0.94 and 0.98, respectively, whereas the Se and Sp of BC were 0.44 and 0.74, respectively. In conclusion, the Se for diagnosing Strep. agalactiae and Staph. aureus IMI was higher for qPCR than BC, suggesting that qPCR is a valuable method for detecting both pathogens from quarter-level milk samples. The performance of BC in the detection of Strep. agalactiae and Staph. aureus on teat skin was poor compared to qPCR, indicating that differences in the target condition of the two methods should be considered when implementing them as routine diagnostic tests for detecting teat skin colonisers. The low Se of BC may preclude the use of BC for skin testing, and qPCR is better for this task.

Considering milk price volatility for investment decisions on the farm level after European milk quota abolition

After the abolition of the milk quota in the European Union, milk price volatility is expected to increase because of the liberalized market conditions. At the same time, investment appraisal methods have not been updated to capture the increased uncertainty. Therefore, the objective of this paper is to assess the effect of changing price volatility due to quota abolition on investment decisions at the dairy farm level. To contribute to the objective and to approximate milk price volatility after the European milk quota abolition, the risk-adjusted discount rate for risk-averse dairy farmers is derived based on the milk price volatility of a milk price series from New Zealand. New Zealand dairy farmers have faced liberalized market conditions for more than 3 decades. Afterward, the risk-adjusted discount rate is applied to appraise milking technology investments for an average German dairy farmer. The results show that it is still more reasonable to invest in a parlor system than an automated milking system, although the net present value of the parlor system investment varies between €191,723 for risk-neutral dairy farmers and €100,094 for modestly risk-averse dairy farmers. For the automated milking system investment, the same calculations lead to €132,702 for risk-neutral dairy farmers and €31,635 for risk-averse dairy farmers. According to higher levels of milk price volatility after milk quota abolition, the reduction of the expected utility of the underlying investment decision for modest risk-averse dairy farmers is almost similar to a milk price decrease of 5% for risk-neutral dairy farmers. Therefore, the findings urge finance providers and extension services to consider the change of increasing milk price volatility after dairy quota abolition when giving dairy farmers financial advice. The risk-adjusted discount rate is a flexible tool to do so.