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.

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.

Identification of discriminating behavioural and movement variables in lameness scores of dairy cows at pasture from accelerometer and GPS sensors using a Partial Least Squares Discriminant Analysis.

The behaviour and movement of lame dairy cows at pasture have been studied little, yet they could be relevant to improve the automatic detection of lameness in cows in pasture-based systems. Our aim in this study is to identify behavioural and movement variables of dairy cows at pasture that could discriminate lameness scores. Individual cow behaviours were predicted from accelerometer data and movements measured using GPS data. Sixty-eight dairy cows from three pasture-based commercial farms were equipped with a 3-D accelerometer and a GPS sensor fixed on a neck collar for 1-5 weeks, depending on the farm, in spring and summer 2018. A lameness score was assigned to each cow by a trained observer twice a week. Behaviours were predicted every 10 s based on accelerometer data, and then combined with the GPS position. Segmentation on behavioural time series was used to delineate each behavioural bout within each outdoor period. Thirty-seven behavioural and movement variables were then calculated from the behavioural bouts for each cow. A partial least square discriminant analysis was performed to identify the variables that best discriminate lameness scores. Time spent grazing, grazing bout duration, duration before lying down in the pasture, time spent resting, number of resting bouts, distance travelled during grazing, and dispersion were the most discriminant variables in the PLS-DA (VIP > 1). Severely lame cows spent 4.5 times less time grazing and almost twice as much time resting as their sound congeners, especially in the lying position. Exploratory behaviour was also reduced for both moderately and severely lame cows, resulting in 1.2 and 1.7 times less distance travelled respectively, especially during grazing. These variables could be used as additional variables to improve the performance of existing lameness detection devices in pasture-based systems.

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.

Does feeding extruded linseed to dairy cows improve reproductive performance in dairy herds? An observational study.

Feeding n-3 fatty acids (FA) is often cited as a promising strategy to tackle impaired reproduction in dairy cows. However, the scientific literature shows conflicting results that may be explained by the nature of n-3 FA used, the amount supplemented and the timing of supplementation. In addition, designing a proper experimental design to study n-3 FA and reproduction is subjected to other difficulties such as the choice of the control diet or gaining enough statistical power. The objective of this retrospective observational study was to quantify the average effects of supplementing extruded linseed (EL), a feed rich in α-linolenic acid, to dairy cows on reproductive performances under field conditions in French commercial farms. Exposure measurement to EL feeding was particularly challenging as exact cow diets are not traced in farms. Therefore, to investigate the potential dose-effect relationship, we defined a proxy of EL intake per day by using deliveries of EL based feeds from 22 companies in the study period 2008-2015 in France. An artificial insemination (AI) was considered exposed only if the cow was supplemented with EL from the calving until 17 days after AI. Based on recommendations for EL use on the field, 4 exposures classes were created: [1-50] (n = 14,126 AIs), [50-300] (n = 88,261 AIs), [300-600] (n = 66,136 AIs), and [600-1500] (n = 28,287 AIs) g/cow/d. The reference population was composed of cows that did not receive any EL between calving until 17 days after AI within herds that were supplied, but not continuously during the study period (n = 226,795 AIs). Mean daily EL intake in exposed population was 337 g/cow/d (±239.4). Reproductive performance was studied on 423,605 AIs from 1096 herds and 158,125 cows using Cox models for days to first AI and days to conception, and logistic regression models for risk of return-to-service, adjusted for factors likely to influence the reproductive performance and for a herd random effect. Risk of return-to-service between 18 and 78 days after first and second AI did not differ between exposed and reference populations, Nevertheless, the effect on the days to first AI was higher with the lowest EL intake (HR: 1.14; 95% CI: 1.11, 1.17) than with higher EL intake levels (HR ranging from 1.06 to 1.07; 95% CI: 1.04, 1.09). Similarly, for the effect on the time from calving to conception from the lowest EL intake (HR: 1.19; 95% CI: 1.15, 1.23) compared to the higher EL intake levels (HR ranging from 1.08 to 1.11; 95% CI: 1.06, 1.14). This original large-scale epidemiological study provides new insights into the effects of feeding EL at a commercially sustainable level to dairy cows.

Evaluation of the impact of a Herd Health and Production Management programme in organic dairy cattle farms: a process evaluation approach.

Animal health planning activities are not always providing a satisfactory positive impact on herd health and welfare. Moreover, evaluating the impact of advisory programmes is complex due to multiple interacting elements that influence its outcome. Therefore, measuring solely health outcomes is not sufficient: the whole process of the implementation and use of such programmes should be evaluated. In order to evaluate the impact of an intervention with a Herd Health and Production Management (HHPM) programme a process evaluation framework was designed and used. The intervention involved 20 organic dairy cattle farmers and their advisors, in both France and Sweden. In both countries 20 organic dairy farms were selected as control herds. The evaluation of the HHPM programme was based on: (a) the compliance to the programme; (b) the programme's functions influencing herd health management practices and stimulating dialogue between farmers and advisors; (c) its effectiveness in terms of improving herd health compared with control farms. Complete compliance to the programme was fulfilled by 21 out of 40 farmers-advisors. Results from a questionnaire showed that the programme functioned as intended (e.g. by allowing early identification of herd health problems), stimulated change in farmers' herd health management practices and farmer-advisor dialogue. Even though the majority of the users perceived that the programme contributed to herd health improvements, no significant differences in health outcomes were found when compared with control farms 12 months after the start of the intervention. The programme allowed creating an environment promoting the exchange of information between farmers and advisors, necessary to define pertinent advice in a farm-specific situation. Future research should aim at improving methods for the evaluation of the effect of advisory programmes, by identifying early indicators for effective advice and developing methods to evaluate the quality of advisory situations without interfering with them.

Mid-season targeted selective anthelmintic treatment based on flexible weight gain threshold for nematode infection control in dairy calves.

The suitability of a single mid-season targeted selective treatment (TST) for gastrointestinal nematodes control, based on flexible average daily weight gain (ADWG) thresholds, was investigated in 23 groups of first grazing season calves. In each group, animals were weighed three times: before turnout, at mid-season and at housing. Just after the first weighing, each group was divided in two homogenous sub-groups in terms of age, breed and weight, and randomly allocated to one of two sub-groups intented for two different mid-season anthelmintic treatment strategies: (1) a treatment of all calves composing the sub-group (whole-group treatment (WT)) or (2) a targeted selective weight gain-based treatment (TST) of the animals showing an individual pre-treatment ADWG inferior to the mean pre-treatment ADWG of the corresponding WT sub-group. Anthelmintic treatment (levamisole 7.5 mg/kg BW) was performed 3 to 4 months after turnout. At housing, two parasitological parameters (the anti-Ostertagia ostertagi antibody level-Ostertagia optical density ratio (ODR) and the pepsinogen level) and a clinical parameter (the breech soiling score) were assessed at individual level in each group. Then, the high exposed groups to gastrointestinal nematode (GIN) were defined as groups for which untreated animals exhibited a mean Ostertagia ODR ⩾0.7 and among these groups, the ones characterized by high abomasal damage due to Ostertagia for which untreated animals exhibited a mean pepsinogen level ⩾2.5 U Tyr were also identified. Among TST sub-groups, the treatment ADWG thresholds varied from 338 to 941 g/day and the percentage of treated animals from 28% to 75%. Pre- and post-treatment ADWG as well as parasitological and clinical parameters measured at housing were similar between TST and WT sub-groups including the 17 high exposed groups to GIN. Within these 17 groups, the treatment allowed to significantly improve post-treatment ADWG compared with untreated animals. In the six high exposed groups showing mean pepsinogen level ⩾2.5 U Tyr, the average effect of treatment on post-treatment ADWG was the highest and estimated up to 14 kg after a grazing duration of 4 months. In contrast, in six other groups showing mean Ostertagia ODR<0.7 in untreated animals, no effect of treatment was seen suggesting an absence of production losses related to a low level of GIN infection. This study highlighted the suitability of a convenient mid-season TST strategy for first grazing season calves, based on the use of flexible thresholds of ADWG, allowing similar growth compared with a whole-group treatment while keeping a GIN population in refugia.

How can veterinarians be interesting partners for organic dairy farmers? French farmers' point of views.

Organic dairy farmers must live up to the organic goal of 'good health' in respect the organic principles and regulation. Veterinarians could be the organic dairy farmers' expected sparring partners in reaching this goal but have found difficulties to establish advisory relationships with them. The objectives of this study are -from organic dairy farmers' points of view- (i) to describe farmers' objectives and strategies regarding herd health, (ii) to describe private veterinarians' roles in farmers' animal health promotion strategies and (iii) to identify farmers' reasons for accepting veterinarians in an advisory role. Fourteen organic dairy farmers were interviewed using qualitative research interviews. Data collection and analysis was performed using a modified approach to Grounded Theory. Organic dairy farmers had animal health management strategies focusing on animal health promotion. Veterinarians had most often solely the role of therapist in farmers' animal health management strategies. Reasons explaining that veterinarians were not able to establish advisory roles were found in the differences between veterinarians and farmers regarding their animal health strategies and solutions to disease problems. Furthermore, veterinarians did not always share farmers' (organic) objectives, values and priorities and this could lead to disagreement on the best choice in animal health management practices. This might be further amplified in situations where there exists a lack of dialogue and mutual interest in other.

Perceptions of French private veterinary practitioners' on their role in organic dairy farms and opportunities to improve their advisory services for organic dairy farmers.

Veterinarians could be the expected sparring partners of organic dairy farmers in promoting animal health which is one of the main organic principles. However, in the past organic dairy farmers did not always consider veterinarians to be pertinent advisors for them. The objectives of this study are - from private veterinary practitioners' point of views- i) to describe the roles of veterinarians today in organic dairy farmers' animal health promotion strategies, ii) to identify factors related to organic farming which determine their role on organic dairy farms, and, iii) to identify opportunities for improvement of veterinarians' advisory services for organic dairy herds. Fourteen veterinarians, providing herd health advisory services to dairy farmers, were interviewed using qualitative semi-structured research interviews. A modified approach to Grounded Theory was used for data collection and analysis. Most often veterinarians had only contact with the organic dairy farmers in cases of individual ill animals or acute herd health problems. Even though certain veterinarians experienced situations and approaches of animal health and welfare on organic dairy farms not meeting their standards, they were not always able to establish themselves an advisory role supporting farmers in improving this. Indeed, organic production principles, regulations and farmers' health approaches challenged veterinarians' values on animal health and welfare and their perceptions of 'good veterinary practices'. Also, some veterinarians considered that there was no direct economic interest for them in the organic dairy sector and that could diminish their willingness to invest in this sector. Possible opportunities for improvement were identified; for example proposing more proactively advice via existing organisations, by making adaptations to advisory services for the organic sector and/or by dissociating veterinarians' curative role from their advisory role in disease prevention.

A participatory approach to design monitoring indicators of production diseases in organic dairy farms.

Production diseases have an important negative effect on the health and welfare of dairy cows. Although organic animal production systems aim for high animal health levels, compliance with European organic farming regulations does not guarantee that this is achieved. Herd health and production management (HHPM) programs aim at optimizing herd health by preventing disease and production problems, but as yet they have not been consistently implemented by farmers. We hypothesize that one reason is the mismatch between what scientists propose as indicators for herd health monitoring and what farmers would like to use. Herd health monitoring is a key element in HHPM programs as it permits a regular assessment of the functioning of the different components of the production process. Planned observations or measurements of these components are indispensable for this monitoring. In this study, a participatory approach was used to create an environment in which farmers could adapt the indicators proposed by scientists for monitoring the five main production diseases on dairy cattle farms. The adaptations of the indicators were characterized and the farmers' explanations for the changes made were described. The study was conducted in France and Sweden, which differ in terms of their national organic regulations and existing advisory services. In both countries, twenty certified organic dairy farmers and their animal health management advisors participated in the study. All of the farmers adapted the initial monitoring plan proposed by scientists to specific production and animal health situation on their farm. This resulted in forty unique and farm-specific combinations of indicators for herd health monitoring. All but three farmers intended to monitor five health topics simultaneously using the constructed indicators. The qualitative analysis of the explanations given by farmers for their choices enabled an understanding of farmers' reasons for selecting and adapting indicators. This is valuable information for scientists involved in the design of HHPM programs. Advisors in the field also can benefit from this participatory approach because it transforms monitoring tools provided by scientists into farm-specific tools.

A semi-parametric model for lactation curves: development and application.

We propose a semi-parametric model for lactation curves that, along with stage of lactation, accounts for day of the year at milk recording and stage of gestation. Lactation is described as having 3 different phases defined by 2 change points of which the second is a function of gestation stage. Season of milk recording is modelled using cosine and sine functions. As an application, the model is used to estimate the association between intramammary infections (IMI) dynamics as measured by somatic cell count (SCC) over the dry period and the shape of the lactation curve. Milk recording data collected in 2128 herds from England and Wales between 2004 and 2007 were used in the analysis. From a random sample of 1000 of these herds, smoothed milk production was used to test the behaviour of the model and estimate model parameters. The first change point was set at 60 days in milk. The second change point was set at 100 days of gestation or 200 days in milk when the latter was not available. Using data from the 1128 remaining herds, multilevel models were then used to model individual test-day milk production within lactations within herds. Average milk production at 60 days in milk for cows of parities 1, 2, 3 and greater than 3 were 26.9 kg, 31.6 kg, 34.4 kg and 34.7 kg respectively and, after this stage, decreases in milk production per 100 days milk of lactation were 3.1 kg, 5.1 kg, 6.3 kg and 6.7 kg respectively. Compared to cows that had an SCC below 200,000 cells/mL on both the last milk recording in a lactation and the first milk recording in the following lactation, cows that had an SCC greater than 200,000 cells/mL on their first milk recording after calving had an estimated loss of milk production of between 216 and 518 kg depending on parity. These estimates demonstrate the impact of the dynamics of SCC during the dry period on milk production during the following lactation.

Risk factors for a high somatic cell count at the first milk recording in a large sample of UK dairy herds.

The dry period is important for the cure of existing intramammary infection (IMI) and the acquisition of new IMI. Somatic cell count (SCC) at both the last milk recording before drying off and at the first milk recording following calving can be used on farm to describe the dynamics of IMI during the dry period. The aims were to quantify the association between the main risk factors collected from milk recording data and the occurrence of a high SCC in early lactation as well as to partition the observed variation into the prevalence of high SCC in within-herd and between-herd variation. Milk recording data collected between 2004 and 2006 from 2,000 herds in England and Wales were used. Cows with an SCC ≥200,000 cells/mL were classified as high, and other cows as low. The median prevalences of the high classification were 42 and 21% at the last milk recording before drying off and the first milk recording following calving, respectively. Cows classified high or producing more milk before drying off as well as cows of greater parity or recorded in early lactation were more likely classified high at the first recording following calving. Cows from herds in which the prevalences of the high classification or the probability of remaining or becoming high over the dry period were elevated during the previous year were more likely classified as high at the first recording following calving. Half of the variability in the proportion of cows with a high SCC after calving originated at the herd level. The other half was unexplained within herd, but by between-year variability. Most cow-level predictors were important in explaining individual cow performance, but accounted for little of the overall between-herd variability. Of the predictors identified as important at the cow level, only milk yield at drying off was important in explaining the between-herd variability.

Association between somatic cell count and serial locomotion score assessments in UK dairy cows.

This research investigated the effect of lameness, measured by locomotion score (LS) on the somatic cell count (SCC) of UK dairy cows. The data set consisted of 11,141 records of SCC and LS collected monthly on 12 occasions from 1,397 cows kept on 7 farms. The data were analyzed to account for the correlation of repeated measures of SCC within cow. Results were controlled for farm of origin, stage of lactation, parity, season, and test-day milk yield. Compared with the geometric mean SCC for cows with LS 1 on each farm, cows on farm 3 with LS 2 produced milk with 28,000 fewer somatic cells/mL, and cows with LS 2 on farm 6 produced milk with 30,000 fewer somatic cells/mL at a test day within 10 d. Cows that would have LS 3 six months later produced milk with 16,000 fewer somatic cells/mL compared with the geometric mean SCC for cows that would have LS 1 in 6 mo time. These results illustrate differences in disease dynamics between farms, highlight potential conflict between lameness and mastitis control measures, and emphasize the importance of developing farm-specific estimates of disease costs, and hence, health management plans in clinical practice.

Use of individual cow milk recording data at the start of lactation to predict the calving to conception interval.

Milk recording data collected in 2,128 dairy herds in England and Wales between 2004 and 2006 were used to predict the calving to conception intervals. The average cumulative milk production was 8,200 kg. Conception (or not) within 5 intervals measured in days (20 to 60 d, 61 to 81 d, 82 to 102 d, 103 to 123 d, 124 to 144 d) was modeled as a function of milk yields and milk constituents at the start of lactation using multilevel discrete-time survival models. Milk yield, weight and percentage of fat, protein, and lactose, and somatic cell counts on the first 2 test-days of lactation were corrected for either stage of lactation alone or stage of lactation and time of year. Five hundred and 1,628 herds, respectively, were used for parameter estimation and cross-validation. Covariates were retained in the final model if their coefficient was at least twice its standard error and their inclusion resulted in a decrease in the deviance. Overall, 73% of cows recalved. The percentage of cows that had conceived by d 20, 61, 82, 103, 124, and 145 were 0.5, 7.3, 17.9, 29.3, 38.7, and 46, respectively. The probability of conception before 145 d in milk increased with lower milk production on the second test-day, higher percentage of protein on the second test-day, and higher percentage of lactose on the first test-day. Positive associations were of a limited magnitude but nonetheless significant with the percentage of protein on the first test-day, the percentage of butterfat on the first test-day, and somatic cell count on both test-days. The model predicted the probability of conception in the cross validation data set very well. Despite the common use of fat to protein ratio as a measure of energy balance, this parameter exhibited wide variation with stage of lactation and time of the year and had a much-reduced ability to predict an early conception compared with other combinations of milk quantity and constituents.

Somatic cell count dynamics in a large sample of dairy herds in England and Wales.

An essential reason to record and evaluate patterns of cow somatic cell count (SCC) within a dairy herd is to help in making clinical decisions on the control of mastitis. An understanding of when new infections occur and how patterns of infection influence herd bulk milk somatic cell count (BMSCC) are critical when implementing mastitis control because it enables advisors to target specific problem areas. The objective of this research was to evaluate individual cow SCC patterns in terms of their contribution to BMSCC. Data collected in 2128 herds from England and Wales between 2004 and 2006 were used. Cows were categorised as having a low, medium or high SCC based on thresholds of 100,000 cells/mL and 200,000 cells/mL. Movements between these categories in consecutive months, before or after 30 days in milk, in primiparous (heifers) and multiparous cows (cows) were used to predict BMSCC. From these categories, new variables representing different SCC patterns, were calculated and included in different models: the medium SCC category was grouped with either the low or the high category, and the denominator was either the total number of cows recorded during the herd-year or the number of cows eligible for a particular transition. Model fitting and predictions were carried out in a Bayesian framework. A random sample of 1500 herds was used for parameter estimation and the remaining 628 herds for model validation. Heifers were more likely to remain at, or to move to, a low SCC than cows. A transition threshold of 100,000 cells/mL for heifers resulted in a poorer model fit and predictive ability than a threshold of 200,000 cells/mL. A model using a single threshold of 200,000 cells/mL regardless of parity was the best to predict BMSCC. The sensitivity and specificity of this final model to correctly predict a BMSCC > 200, 000 cells/mL in the validation dataset were 86.5% and 86.8%, respectively. Important SCC patterns that influenced BMSCC were cows and heifers staying above 200,000 cells/mL for two consecutive recordings during lactation, cows moving from below to above 200,000 cells/mL across the dry period, cows remaining above 200,000 cells/mL across the dry period and heifers calving with an SCC above 200,000 cells/mL in the first month of lactation. The variation between herds in SCC transitions was evaluated and it was concluded that the performance of the top 10% of herds would be useful to provide benchmarks to evaluate dairy herd mastitis.