A qualitative survey approach to investigating beef and dairy veterinarians' needs in relation to technologies on farms.

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries.

A living lab approach to understanding dairy farmers' needs of technologies and data to improve herd health: Focus groups from 6 European countries.

For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' needs of technologies has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' needs of data and technologies to improve herd health and inform innovation development. Eighteen focus groups were conducted with, in total, 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the UK. Data were analyzed using Template Analysis and 6 themes were generated which represented the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility, and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, was a particular concern in relation to youngstock management. In conclusion, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.

Exploring the data divide through a social practice lens: A qualitative study of UK cattle farmers.

Appropriate management decisions are key for sustainable and profitable beef and dairy farming. Data-driven technologies aim to provide information which can improve farmers' decision-making practices. However, data-driven technologies have resulted in the emergence of a "data divide", in which there is a gap between the generation and use of data. Our study aims to further understand the data divide by drawing on social practice theory to recognise the emergence, linkages, and reproduction of youngstock data practices on cattle farms in the UK. Eight focus groups with fifteen beef and nineteen dairy farmers were completed. The topics of discussion included data use, technology use, disease management in youngstock, and future goals for their farm. The transcribed data were analysed using reflexive thematic analysis with a social practice lens. Social practice theory uses practices as the unit of analysis, rather than focusing on individual behaviours. Practices are formed of three elements: meaning (e.g., beliefs), materials (e.g., objects), and competencies (e.g., skills) and are connected in time and space. We conceptualised the data divide as a disconnection of data collection practices and data use and interpretation practices. Consequently, we were able to generate five themes that represent these breaks in connection.Our findings suggest that a data divide exists because of meanings that de-stabilise practices, tensions in farmers' competencies to perform practices, spatial and temporal disconnects, and lack of forms of feedback on data practices. The data preparation practice, where farmers had to merge different data sources or type up handwritten data, had negative meanings attached to it and was therefore sometimes not performed. Farmers tended to associate data and technology practices with larger dairy farms, which could restrict beef and small-scale dairy farms from performing these practices. Some farmers suggested that they lacked the skills to use technologies and struggled to transform their data into meaningful outputs. Data preparation and data use and interpretation practices were often tied to an office space because of the required infrastructure, but farmers preferred to spend time outdoors and with their animals. There appeared to be no normalisation of what data should be collected or what data should be analysed, which made it difficult for farmers to benchmark their progress. Some farmers did not have access to discussion groups or veterinarians who were interested in data and therefore could not get feedback on their data practices.These results suggest that the data divide exists because of three types of disconnect: a disconnect between elements within a practice because of tensions in competencies or negative meanings to perform a practice; a disconnect between practices because of temporal or spatial differences; and a break in the reproduction of practices because of lack of feedback on their practices. Data use on farms can be improved through transformation of practices by ensuring farmers have input in the design of technologies so that they align with their values and competencies.

A framework for assessing confidence in freedom from infection in animal disease control programmes.

In the Surveillance Tool for Outcome-based Comparison of FREEdom from infection (STOC free) project (https://www.stocfree.eu), a data collection tool was constructed to facilitate standardised collection of input data, and a model was developed to allow a standardised and harmonised comparison of the outputs of different control programmes (CPs) for cattle diseases. The STOC free model can be used to evaluate the probability of freedom from infection for herds in CPs and to determine whether these CPs comply with the European Union's pre-defined output-based standards. Bovine viral diarrhoea virus (BVDV) was chosen as the case disease for this project because of the diversity in CPs in the six participating countries. Detailed BVDV CP and risk factor information was collected using the data collection tool. For inclusion of the data in the STOC free model, key aspects and default values were quantified. A Bayesian hidden Markov model was deemed appropriate, and a model was developed for BVDV CPs. The model was tested and validated using real BVDV CP data from partner countries, and corresponding computer code was made publicly available. The STOC free model focuses on herd-level data, although that animal-level data can be included after aggregation to herd level. The STOC free model is applicable to diseases that are endemic, given that it needs the presence of some infection to estimate parameters and enable convergence. In countries where infection-free status has been achieved, a scenario tree model could be a better suited tool. Further work is recommended to generalise the STOC free model to other diseases.

Dans le cadre du projet européen STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, outil de surveillance permettant de comparer les probabilités d'absence d'infection sur la base des résultats, https://www.stocfree.eu), un outil de recueil des données a été construit pour faciliter une collecte normalisée des données d'entrée ; un modèle a également été élaboré pour permettre une comparaison normalisée et harmonisée des données sur les résultats des différents programmes de contrôle des maladies des bovins. Le modèle STOC free peut être utilisé pour évaluer la probabilité d'absence d'infection au sein des troupeaux dans le cadre des programmes de contrôle et déterminer si ces programmes sont conformes aux normes définies par l'Union européenne en termes de résultats attendus. L'infection par le virus de la diarrhée virale bovine a été choisie comme maladie d'étude pour ce projet en raison de la diversité des programmes de contrôle dans les six pays participants. Les informations relatives aux programmes de contrôle et aux facteurs de risque d'infection ont été recueillies à l'aide de l'outil de collecte des données. Les aspects clés et valeurs par défaut ont été quantifiés en vue d'être inclus dans le modèle STOC free. Un modèle de Markov caché dont les paramètres sont estimés par inférence bayésienne a été considéré comme le plus adapté et développé pour une application aux données issues des programmes de contrôle de la diarrhée virale bovine. Ce modèle a été testé et validé en utilisant des données réelles des programmes de contrôle du virus de la diarrhée virale bovine des pays participants ; le code informatique correspondant a été rendu public. Le modèle STOC free utilise des données au niveau des troupeaux, même si des données au niveau des animaux individuels peuvent être incluses une fois agrégées au niveau du troupeau. Le modèle STOC free s'applique aux maladies endémiques, puisqu'un certain niveau de présence de l'infection est nécessaire pour estimer les paramètres et permettre la convergence. Dans les pays ayant obtenu le statut indemne d'infection, un modèle du type arbre de scénario pourrait être un outil plus adapté. Des travaux supplémentaires sont recommandés pour généraliser le modèle STOC free à d'autres maladies.

Como parte del proyecto europeo STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, herramienta de vigilancia para comparaciones por resultados respecto a la ausencia de infecciones, https://www.stocfree.eu), se confeccionó una herramienta de obtención de datos para facilitar la recogida normalizada de datos entrantes y se elaboró un modelo que posibilitara una comparación normalizada y armonizada de los resultados (datos salientes) de distintos programas de control de enfermedades bovinas. El modelo STOC free puede servir para calcular la probabilidad de ausencia de infección en los rebaños como parte de los programas de control y para determinar si estos programas se ajustan a las normas predefinidas de resultados de la Unión Europea. Como ejemplo de estudio para el proyecto se eligió el virus de la diarrea viral bovina (virus DVB) por la diversidad que presentaban los correspondientes programas de control de los seis países participantes. Empleando la herramienta de obtención de datos, se reunió información pormenorizada de los programas de control del virus DVB y los factores de riesgo. Para incluir los datos en el modelo STOC free, se cifraron unos aspectos clave y valores predeterminados Juzgando conveniente el empleo de un modelo oculto de Markov cuyos parámetros se estiman por inferencia bayesiana, se elaboró un modelo de esta índole aplicable a los programas de control del virus DVB. Para ensayar y validar el modelo se utilizaron datos reales de los programas de control del virus DVB de los países participantes, tras lo cual se hizo público el correspondiente código informático. El modelo STOC free trabaja con los datos por rebaño, aunque tras la agregación por rebaños pueden incluirse también datos por individuo. Para que este modelo sea aplicable a una enfermedad es preciso que esta sea endémica, pues el modelo requiere la presencia de cierto nivel de infección para calcular los parámetros y determinar convergencias. En aquellos países donde ya esté reconocida la ausencia de infección, sería más apropiado utilizar como herramienta un modelo de árbol de hipótesis. Los autores recomiendan ahondar en esta línea de trabajo para poder extender a otras enfermedades el uso del modelo STOC free.

Prevalence of extended-spectrum and AmpC ß-lactamase-producing Escherichia coli in young calves on Dutch dairy farms.

In young calves on dairy farms the animal prevalence of extended-spectrum and AmpC ß-lactamase-producing Escherichia coli (ESBL/AmpC-EC) is significantly higher compared with the animal prevalence in young stock and dairy cows. Hitherto it was unknown at what age antimicrobial resistant bacteria appear for the first time in the gut of calves on dairy farms, and how long these infections persist. The aim of this study was to examine the prevalence of ESBL/AmpC-EC, the number of excreted ESBL/AmpC-EC (in cfu/g of feces), as well as the ESBL/AmpC genotypes in young dairy calves (0-21 d of age) and the variation of these parameters between calves of different ages. Next to this, the course of shedding ESBL/AmpC-EC during the first year in dairy calves was studied. In a cross-sectional study, fecal samples from 748 calves, from 0 to 88 d of age, on 188 Dutch dairy farms were collected. The prevalence of calves testing positive for ESBL/AmpC-EC in a phenotypic assay was determined for different age categories (per 2 d of age). Positive samples were subjected to a semiquantitative test to determine the numbers of ESBL/AmpC-EC per gram of feces and for a selection of ESBL/AmpC-EC isolates the ESBL/AmpC genotype was determined. Ten of the 188 farms were selected for a longitudinal study based on the presence of at least 1 female calf with ESBL/Amp-EC in the cross-sectional study. These farms were additionally visited 3 times with a 4-mo interval. All calves that were sampled in the cross-sectional study were, if still present, resampled during the follow-up visits. Results show that from the day of birth ESBL/AmpC-EC can be present in the gut of calves. The phenotypic prevalence of ESBL/AmpC-EC was 33.3% in 0- to 21-d-old calves and 28.4% in 22- to 88-d-old calves. The prevalence of ESBL/AmpC-EC positive calves varied per age category among calves up to 21 d of age: significant increases and decreases at an early age were shown. Results of the longitudinal study show that after 4, 8, and 12 mo the prevalence of ESBL/AmpC-EC positive calves dropped to 3.8% (2/53), 5.8% (3/52), and 2.0% (1/49), respectively. This indicates that early gut colonization in young calves with ESBL/AmpC-EC is transient and does not lead to long-term shedding of these bacteria.

Associations of carcass weight and trimming loss with cull dairy cow health observations collected at slaughter.

Cull dairy cows account for around 27 percent of total head EU beef and veal production. For the Netherlands specific, even 42 percent (European Commission, 2022). As they are primarily kept to produce milk, red meat production is an additional source of revenue for dairy farmers. Insights in postmortem health observations that are not always visible on the living animal such as heart or liver issues, bruises, adhesions and injuries on the locomotor system, may contain valuable information for farmers to increase revenue and reduce losses in red meat production from cull dairy cows. Our goal was to obtain insights in the association of postmortem health observations with carcass weight and trimming losses. Data of 592,268 slaughter cows were available for analysis and models were built to explain carcass and trimming loss by the postmortem health observations. Carcass weight is lower for younger cows (-3.2 to -84.9 kg), cows with multiple health observations (-7.4 to -34.3 kg) and specific observations for the locomotor system (-16.7 to -22.7 kg), back (-17.9 kg), hindquarter (-21.6 kg) and chest and ribs (-15.5 to -27.6 kg). Total number of health observations (+2.0 to +6.5 kg), observations on the locomotor system (+3.3 to +5.4 kg) and on the chest and ribs (+2.2 to +9.8 kg) were the main predictors for trimming loss. Carcass weight is more affected by systemic health issues and diseases prior to slaughter leading to a negative energy balance and consequently reduced carcass weight. Trimming loss is more a consequence of the focus on meat quality and food safety in the slaughter process. Better understanding of the effect of on-farm management, on health, carcass weight and trimming loss will provide new insights for farmers and veterinarians but will also give them more action perspective to improve dairy farm preventive management and reduce losses at slaughter.

Longevity and the association with cattle health in Dutch dairy farms.

Longevity of a herd is defined as the average age of all cattle over two years old at the moment of death (either natural, by euthanasia or by slaughter), and is increasing since 2018. The aim of this study was to evaluate the association between longevity and cattle health indicators in Dutch dairy herds. Anonymized census data were available for 16,200 Dutch dairy herds (∼98 % of the dairy herds) between 2016 and 2020. All herds were categorized into one of six longevity groups: herds with a high longevity (>seven years old), increasing longevity (mean increase of one year and two months between 2017 and 2020), median longevity (∼five years and eight months, without major fluctuations in longevity), decreasing longevity (mean decrease eight months), low longevity (

Output-based assessment of herd-level freedom from infection in endemic situations: Application of a Bayesian Hidden Markov model.

Countries have implemented control programmes (CPs) for cattle diseases such as bovine viral diarrhoea virus (BVDV) that are tailored to each country-specific situation. Practical methods are needed to assess the output of these CPs in terms of the confidence of freedom from infection that is achieved. As part of the STOC free project, a Bayesian Hidden Markov model was developed, called STOC free model, to estimate the probability of infection at herd-level. In the current study, the STOC free model was applied to BVDV field data in four study regions, from CPs based on ear notch samples. The aim of this study was to estimate the probability of herd-level freedom from BVDV in regions that are not (yet) free. We additionally evaluated the sensitivity of the parameter estimates and predicted probabilities of freedom to the prior distributions for the different model parameters. First, default priors were used in the model to enable comparison of model outputs between study regions. Thereafter, country-specific priors based on expert opinion or historical data were used in the model, to study the influence of the priors on the results and to obtain country-specific estimates. The STOC free model calculates a posterior value for the model parameters (e.g. herd-level test sensitivity and specificity, probability of introduction of infection) and a predicted probability of infection. The probability of freedom from infection was computed as one minus the probability of infection. For dairy herds that were considered free from infection within their own CP, the predicted probabilities of freedom were very high for all study regions ranging from 0.98 to 1.00, regardless of the use of default or country-specific priors. The priors did have more influence on two of the model parameters, herd-level sensitivity and the probability of remaining infected, due to the low prevalence and incidence of BVDV in the study regions. The advantage of STOC free model compared to scenario tree modelling, the reference method, is that actual data from the CP can be used and estimates are easily updated when new data becomes available.

Capacity of a Bayesian model to detect infected herds using disease dynamics and risk factor information from surveillance programmes: A simulation study.

Control programmes against non-regulated infectious diseases of farm animals are widely implemented. Different control programmes have different definitions of "freedom from infection" which can lead to difficulties when trading animals between countries. When a disease is still present, in order to identify herds that are safe to trade with, estimating herd-level probabilities of being infected when classified "free from infection" using field data is of major interest. Our objective was to evaluate the capacity of a Bayesian Hidden Markov Model, which computes a herd-level probability of being infected, to detect infected herds compared to using test results only. Herd-level risk factors, infection dynamics and associated test results were simulated in a population of herds, for a wide range of realistic infection contexts and test characteristics. The model was used to predict the infection status of each herd from longitudinal data: a simulated risk factor and a simulated test result. Two different indexes were used to categorize herds from the probability of being infected into a herd predicted status. The model predictive performances were evaluated using the simulated herd status as the gold standard. The model detected more infected herds than a single final test in 85 % of the scenarios which converged. The proportion of infected herds additionally detected by the model, compared to test results alone, varied depending on the context. It was higher in a context of a low herd test sensitivity. On average, around 20 %, for high test sensitivity scenarios, and 40 %, for low test sensitivity scenarios, of infected herds that were undetected by the test were accurately classified as infected by the model. Model convergence did not occur for 39 % of the scenarios, mainly in association with low herd test sensitivity. Detection of additional newly infected herds was always associated with an increased number of false positive herds (except for one scenario). The number of false positive herds was lower for scenarios with low herd test sensitivity and moderate to high incidence and prevalence. These results highlight the benefit of the model, in particular for control programmes with infection present at an endemic level in a population and reliance on test(s) of low sensitivity.

Corrigendum: Control and Eradication Programs for Six Cattle Diseases in the Netherlands.

[This corrects the article DOI: 10.3389/fvets.2021.670419.].

Control and Eradication Programs for Six Cattle Diseases in the Netherlands.

Within the European Union, infectious cattle diseases are categorized in the Animal Health Law. No strict EU regulations exist for control, evidence of disease freedom, and surveillance of diseases listed other than categories A and B. Consequently, EU member states follow their own varying strategies for disease control. The aim of this study was to provide an overview of the control and eradication programs (CPs) for six cattle diseases in the Netherlands between 2009 and 2019 and to highlight characteristics specific to the Dutch situation. All of these diseases were listed as C,D or E in the New Animal Health Law. In the Netherlands, CPs are in place for six endemic cattle diseases: bovine viral diarrhea, infectious bovine rhinotracheitis, salmonellosis, paratuberculosis, leptospirosis, and neosporosis. These CPs have been tailored to the specific situation in the Netherlands: a country with a high cattle density, a high rate of animal movements, a strong dependence on export of dairy products, and a high-quality data-infrastructure. The latter specifically applies to the dairy sector, which is the leading cattle sector in the Netherlands. When a herd enters a CP, generally the within-herd prevalence of infection is estimated in an initial assessment. The outcome creates awareness of the infection status of a herd and also provides an indication of the costs and time to achieve the preferred herd status. Subsequently, the herd enrolls in the control phase of the CP to, if present, eliminate the infection from a herd and a surveillance phase to substantiate the free or low prevalence status over time. The high-quality data infrastructure that results in complete and centrally registered census data on cattle movements provides the opportunity to design CPs while minimizing administrative efforts for the farmer. In the CPs, mostly routinely collected samples are used for surveillance. Where possible, requests for proof of the herd status are sent automatically. Automated detection of risk factors for introduction of new animals originating from a herd without the preferred herd status i.e., free or unsuspected, is in place using centrally registered data. The presented overview may inspire countries that want to develop cost-effective CPs for endemic diseases that are not (yet) regulated at EU level.

Possible causes of aberrations in adverse grouping behavior of dairy cows: A field study.

In the Dutch national surveillance system, an increasing number of reports were received in the summer of 2017 from farmers about unusual behavior of their cows. The cows were grouping during the day in summer in one part of the barn and did not move for several hours, which, according to the farmers, led to reduced food and water intake and lying time and resulted in decreased milk production and increased risk of lameness. Many farmers perceived magnetic fields from, for instance, high-voltage lines, automated milking systems, or solar panels as possible causes for the behavior of their cows. Our aim for the study was to study potential factors such as magnetic fields and other factors such as barn climate and insect burden for adverse grouping behavior of dairy cows in the barn. For each case herd, 2 control herds were selected in the same postal area code. A case was a herd in which cattle grouped at least on 7 occasions in a month for several hours. In a control herd, the cows were in the barn during the same time period as in the matching case herd but did not show adverse grouping behavior. A questionnaire was administered by telephone in 31 case herds and 62 control herds. The questionnaire gathered information on behavior of the cows and potential risk factors. In addition, data on the distance of the herd to high-voltage lines was obtained. From a total of 74 variables, all variables with a P-value ≤0.10 were included in full multivariable logistic regression model. Backward selection was carried out at P ≤ 0.10. The grouping behavior of the cows started in most herds in June, was seen only during the day, and lasted mostly 6 to 8 h, with cows often grouped in the northern part of the barn. Identified risk factors appeared to be recently constructed barns, measured stray voltage in barns, and presence of fans in barns. Given the cross-sectional design of the case-control study, causality for these risk factors leading to adverse behavior of the cows could not be proven. Dissemination of the results to farmers hopefully results in measures that can prevent the unusual grouping behavior of cows.

Evaluation of the association between the introduction of data-driven tools to support calf rearing and reduced calf mortality in dairy herds in the Netherlands.

Between 2009 and 2017, calf mortality in the Dutch dairy sector showed a slight but steady increase. The Dutch dairy industry decided to act and supported the development of several data-driven tools that were implemented from 2018 on. The tools informed farmers about their calf mortality rates and stimulated them to improve. The Trend Analysis Surveillance Component of the Dutch cattle Health Surveillance System provided the possibility to evaluate the calf mortality in Dutch dairy herds before and after implementation of these tools. The aim of this study was to evaluate the association between calf mortality and i) all actions that were taken by the Dutch dairy industry to improve the quality of calf rearing and ii) other potential management or environmental factors associated with calf mortality in Dutch dairy herds. Census data from approximately 98 % of all Dutch dairy herds were available from July 2014 until June 2019. Four different calf mortality indicators were defined: perinatal calf mortality risk (i.e., mortality before, during, or shortly after the moment of birth up to the moment of ear-tagging), postnatal calf mortality risk (ear-tagging till 14 d), preweaned calf mortality rate (15 d-55 d) and weaned calf mortality rate (56 d-1 yr.). All data were aggregated to herd and monthly level and were analysed using Population-Averaged Generalized Estimating Equations (PA GEE models) with a Poisson distribution and log link function. When the period before implementation of the tools (2016-2017) was compared to the period thereafter (2018-2019), all four calf mortality indicators decreased. The relative decrease varied from 3 % (postnatal calves) and 10 % (perinatal calves) up to 18 % and 30 % in preweaned and weaned calves, respectively. Registrations of veterinary treatments such as antimicrobial use, vaccinations (calf or cow) and antiparasitic treatments were associated with calf mortality. Additionally, herds with a higher level of metabolic problems in transition cows had a higher calf mortality and also extreme outside temperatures were associated with higher calf mortality. Given that the different tools were implemented nation-wide and a control group was lacking, we could not prove that implementing the different tools caused the reduction in calf mortality. We do however, believe that all the actions and communication towards improvement of calf rearing in dairy herds led to an increased awareness among farmers towards the importance of calf rearing management and therefore a reduction in calf mortality on national level.

Emergence of fatal Mannheimia haemolytica infections in cattle in the Netherlands.

In the Dutch national surveillance system, outbreaks of fatal infections by Mannheimia haemolytica (M. haemolytica) in dairy cows and veal calves have become apparent in recent years. These observations prompted an in-depth analysis of available pathology data over the period 2004-2018 to investigate changes in the occurrence and/or expression of M. haemolytica-associated cattle disease. With multilevel logistic regression models, time trends were identified and corrected for farm, season, pathologist and region. Deaths associated with M. haemolytica infection increased over time with dairy cows and veal calves diagnosed with fatal M. haemolytica infections 1.5 and 1.4 times more frequently every following 3-year period between 2004 and 2018, respectively. M. haemolytica-associated disease showed two distinct disease presentations: acute pleuropneumonia in dairy cows and polyserositis in veal calves. The prevalence of both disease presentations with M. haemolytica confirmed increased in each 3-year time period between 2004 and 2018, with an odds ratio (OR) of 1.5 for acute pleuropneumonia in dairy cows and an OR of 1.7 for polyserositis in veal calves. No change was found for M. haemolytica-associated disease in dairy calves. Although M. haemolytica is considered an opportunist bovine pathogen, and the presence of primary pathogens such as BHV-1, BVDV and Mycoplasma species was not completely ruled out in our study, substantial evidence is provided to indicate infections with M. haemolytica were the most likely cause of death. M. haemolytica-associated diseases occurred more often in October-June than July-September, and were detected more often in necropsied animals from the North, South and East Netherlands than the West Netherlands.

Monitoring udder health on routinely collected census data: Evaluating the short- to mid-term consequences of implementing selective dry cow treatment.

In 2013, the preventive use of antimicrobials in Dutch livestock was prohibited, including a ban on the blanket application of antimicrobial dry cow treatment (BDCT). Since then, selective dry cow treatment (SDCT) has become the standard approach. In this study, we aimed to determine the effect of the ban on BDCT and the extent of the subsequent adoption of SDCT on antimicrobial usage (AMU) and udder health on Dutch dairy farms. In the Dutch cattle health surveillance system, AMU for dry cow treatment (AMUDCT), AMU for intramammary treatment at any point in time (AMUIMM), and udder health indicators are routinely and continuously monitored. This provided the opportunity to study associations among SDCT, udder health, and AMU on census data of approximately 17,000 dairy herds, with about 1.67 million cows in total (>2 yr old) at one moment in time in the period from 2013 until 2017. Six udder health parameters were evaluated using multivariable population-averaged generalized estimating equation models. The year in which the ban on BDCT was introduced (2013) was compared with the period thereafter (2014-2017). Additionally, AMUIMM and AMUDCT were included as independent variables to evaluate whether the extent to which SDCT was implemented on the herd level was associated with udder health. Demographic parameters were included as potential confounders. Since the ban on BDCT, overall declines of 63% in AMUDCT and 15% in AMUIMM were observed. The raw data show an improvement in 5 out of 6 evaluated udder health parameters between 2013 and 2017. Nevertheless, the multivariable model results showed that the period since the ban on BDCT was associated with a small but significant increase in the percentage of cows with high somatic cell count (HSCC) and new HSCC (+0.41% and +0.06%, respectively). Additionally, the probability of belonging to the group of herds with more than 25% of primiparous cows having HSCC during the start of lactation increased slightly, associated with the period after which BDCT was banned (odds ratio = 1.08). The probability of belonging to the group of herds with more than 25% cows having a persistent HSCC during the dry period was not affected and bulk milk somatic cell count showed a slight but significant reduction. The only udder health parameter that notably worsened during the study period was the probability of belonging to the group of herds with more than 25% of multiparous cows with a new HSCC after the dry period, during the start of lactation (odds ratio = 1.23). In herds where the farmer decided not to apply any dry cow therapy (≈20% of all herds), all udder health parameters were poorer compared with herds in which dry cow therapy was applied to some extent. The ban on BDCT and implementation of SDCT in the Netherlands was associated with a considerable reduction in AMU without a major impairment in udder health at the national level. Although negative effects of changed dry cow management were observed in some herds, we conclude that SDCT can be introduced without substantial negative effects on udder health.

Quantification of risk factors for bovine viral diarrhea virus in cattle herds: A systematic search and meta-analysis of observational studies.

Bovine viral diarrhea virus (BVDV) is endemic in many parts of the world, and multiple countries have implemented surveillance activities for disease control or eradication. In such control programs, the disease-free status can be compromised by factors that pose risks for introduction or persistence of the virus. The aim of the present study was to gain a comprehensive overview of possible risk factors for BVDV infection in cattle herds in Europe and to assess their importance. Papers that considered risk factors for BVDV infection in cattle were identified through a systematic search. Further selection of papers eligible for quantitative analysis was performed using a predefined checklist, including (1) appropriate region (i.e., studies performed in Europe), (2) representativeness of the study population, (3) quality of statistical analysis, and (4) availability of sufficient quantitative data. In total, 18 observational studies were selected. Data were analyzed by a random-effects meta-analysis to obtain pooled estimates of the odds of BVDV infection. Meta-analyses were performed on 6 risk factors: herd type, herd size, participation in shows or markets, introduction of cattle, grazing, and contact with other cattle herds on pasture. Significant higher odds were found for dairy herds (odds ratio, OR = 1.63, 95% confidence interval, CI: 1.06-2.50) compared with beef herds, for larger herds (OR = 1.04 for every 10 extra animals in the herd, 95% CI: 1.02-1.06), for herds that participate in shows or markets (OR = 1.45, 95% CI: 1.10-1.91), for herds that introduced cattle into the herd (OR = 1.41, 95% CI: 1.18-1.69), and for herds that share pasture or have direct contact with cattle of other herds at pasture (OR = 1.32, 95% CI: 1.07-1.63). These pooled values must be interpreted with care, as there was a high level of heterogeneity between studies. However, they do give an indication of the importance of the most frequently studied risk factors and can therefore assist in the development, evaluation, and optimization of BVDV control programs.

A description and qualitative comparison of the elements of heterogeneous bovine viral diarrhea control programs that influence confidence of freedom.

For endemic infections in cattle that are not regulated at the European Union level, such as bovine viral diarrhea virus (BVDV), European Member States have implemented control or eradication programs (CEP) tailored to their specific situations. Different methods are used to assign infection-free status in CEP; therefore, the confidence of freedom associated with the "free" status generated by different CEP are difficult to compare, creating problems for the safe trade of cattle between territories. Safe trade would be facilitated with an output-based framework that enables a transparent and standardized comparison of confidence of freedom for CEP across herds, regions, or countries. The current paper represents the first step toward development of such a framework by seeking to describe and qualitatively compare elements of CEP that contribute to confidence of freedom. For this work, BVDV was used as a case study. We qualitatively compared heterogeneous BVDV CEP in 6 European countries: Germany, France, Ireland, the Netherlands, Sweden, and Scotland. Information about BVDV CEP that were in place in 2017 and factors influencing the risk of introduction and transmission of BVDV (the context) were collected using an existing tool, with modifications to collect information about aspects of control and context. For the 6 participating countries, we ranked all individual elements of the CEP and their contexts that could influence the probability that cattle from a herd categorized as BVDV-free are truly free from infection. Many differences in the context and design of BVDV CEP were found. As examples, CEP were either mandatory or voluntary, resulting in variation in risks from neighboring herds, and risk factors such as cattle density and the number of imported cattle varied greatly between territories. Differences were also found in both testing protocols and definitions of freedom from disease. The observed heterogeneity in both the context and CEP design will create difficulties when comparing different CEP in terms of confidence of freedom from infection. These results highlight the need for a standardized practical methodology to objectively and quantitatively determine confidence of freedom resulting from different CEP around the world.

Bovine digital dermatitis in Victoria, Australia.

AIMS: The objectives of this study were to estimate the prevalence of digital dermatitis (DD) in Victoria, Australia, and to investigate which organisms are consistent with typical DD lesions. The prevalence and causative pathogens of DD are not clear yet in Australia and this paper is one of the first to explore these questions in this country. METHODS: Examination and sampling of limbs was undertaken at three knackeries in Victoria, Australia. Limbs were classified as normal (N), active DD-lesion (A), dried or chronic DD-lesion (D) or suspected case of DD (S). A total of 823 cows were examined. Six skin biopsies were taken at each knackery, from which DNA was extracted for diversity profiling. Histochemical staining of samples was performed on eight of the skin biopsies. RESULTS: DD was detected in 29.8% of all cows. The prevalence of DD was significantly higher in dairy cows (32.2%) than in beef cows (10.8%). The differential abundance of Treponema-species was significantly increased in dried lesions, compared with the normal skin biopsies. Actinobacteria, Proteobacteria, Firmicutes and Tenericutes were found to be significantly different in abundance in the DD lesions compared with normal skin biopsies. Silver staining of samples showed only mild inflammation and in two samples organisms with morphology consistent with Spirochaetes were detected. CONCLUSIONS: The calculated prevalence indicates that DD is present in Victoria, Australia. The results of diversity profiling showed that the presence of Treponema-species was significantly different between the samples of DD lesions and normal skin.

Quantifying calf mortality on dairy farms: Challenges and solutions.

In the Netherlands, the mortality rate of ear-tagged calves <1 yr is one of the indicators that is continuously monitored in census data and is defined as the number of deceased calves relative to the number of calf-days-at-risk. In 2017, yearly calf mortality rates were published in the lay press and resulted in discussions about the calculation of this parameter among stakeholders because the same parameter appeared to be calculated in many different ways by different organizations. These diverse definitions of calf mortality answered different aims such as early detection of deviations, monitoring trends, or providing insight into herd-specific results, but were difficult to understand by stakeholders. The aim of this study was to evaluate several definitions of calf mortality for scientific validity, usefulness for policymakers, and comprehensibility by farmers. Based on expert consultations, 10 definitions for calf mortality were evaluated that assessed different age categories, time periods, and denominators. Differences in definitions appeared to have a large effect on the magnitude of mortality. For example, with the original mortality parameter, the mortality rate was 16.5% per year. When the first year of life was subdivided into 3 age categories, the mortality rate was 3.3, 4.5, and 3.1% for postnatal calves (≤14 d), preweaned calves (15-55 d), and weaned calves (56 d-1 yr), respectively. Although it was logical that these mortality rates were lower than the original, the sum of the 3 separate mortality rates was also lower than the original mortality rate. The reason was that the number of calves present in a herd and the risk of mortality are not randomly distributed over a calf's first year of life and the conditional nature of mortality rates when calculated for different age categories. Ultimately, 4 parameters to monitor calf mortality in Dutch dairy herds were chosen based on scientific value, usefulness for monitoring of trends, and comprehensibility by farmers: perinatal calf mortality risk (i.e., mortality before, during, or shortly after the moment of birth up to the moment of ear-tagging), postnatal calf mortality risk (≤14 d), preweaned calf mortality rate (15-55 d), and weaned calf mortality rate (56 d-1 yr). Slight differences in definitions of parameters can have a major effect on results, and many factors have to be taken into account when defining an important health indicator such as mortality. Our evaluation resulted in a more thorough understanding of the definitions of the selected parameters and agreement by the stakeholders to use these key indicators to monitor calf mortality.

Short-term effects of air pollution and temperature on cattle mortality in the Netherlands.

BACKGROUND: Extreme temperatures and air pollution are both associated with increased mortality risk in humans. However, the effects of temperature and air pollution on cattle have not been investigated much before. OBJECTIVES: Short-term effects of temperature (heat and cold) and air pollution on cattle mortality were investigated and quantified in the Netherlands during 2012-2017. METHODS: Daily data on cattle mortality, weather conditions and mean levels of particulate matter (PM10), ozone (O3), ammonia (NH3) and nitrogen dioxide (NO2) of the Netherlands during 2012-2017 were collected. Associations were investigated with time-series regression using distributed lag non-linear models including lags of up to 25 days. Effects of temperature were expressed as those associated with extreme and moderate heat or cold, defined as Temperature Humidity Index (THI) values below the 1st and 5th percentile, and above the 95th and 99th percentile of the national THI distribution. Effects of air pollutants were expressed per 10 µg/m3 change in daily mean concentrations. RESULTS: Both high and low temperatures were associated with increased mortality amongst different age groups. For instance, the newborn calves of at most 14 days showed a cumulative relative risk (RR) of 2.13 (95%CI: 1.99-2.28) for extreme heat and the pre-weaned calves (15-55 days) showed a cumulative RR of 1.50 (95%CI: 1.37-1.64) for extreme cold. Associations of air pollution with mortality were not consistent, except for the effect of ozone of lag 0-7 and lag 0-25. Exposure to O3 in the newborn calves resulted in a cumulative RR of 1.09 (95%CI: 1.04-1.4) for lag 0-7 and 1.09 (95%CI: 1.03-1.16) for lag 0-25. CONCLUSIONS: Both high and low temperatures were associated with increased mortality amongst pre-weaned calves of 15-55 days, whereas associations in weaned calves (56 days - 1 year) were only observed for low temperatures and in newborn calves of at most 14 days and lactating cattle >2 years only for high temperatures. Associations of air pollution with mortality in all age groups were not consistent, except for the effect of ozone of lag 0-7 and lag 0-25.