When counting cattle is not enough: multiple perspectives in agricultural and veterinary research

A traditional approach in agricultural and veterinary research is focussing on the biological perspective where large cattle-databases are used to analyse the dairy herd. This approach has yielded valuable insights. However, recent research indicates that this knowledge-base can be further increased by examining agricultural and veterinary challenges from other perspectives. In this paper we suggest three perspectives that may supplement the biological perspective in agricultural and veterinary research; the economic-, the managerial-, and the social perspective. We review recent studies applying or combining these perspectives and discuss how multiple perspectives may improve our understanding and ability to handle cattle-health challenges.     

Forecasting clinical disease in pigs: comparing a naive and a Bayesian approach

In veterinary practice the clinician often evaluates and predicts herd health status over time according to clinical criteria. In this paper, we modeled three different clinical signs among pigs based on longitudinal clinical observations in 15 pig herds. We compared and discussed the outputs from two different approaches for making clinical forecasts in a herd: a naive approach using a simple time series model with previous disease observations as predictors and a Bayesian state space models approach, in which the time lag variable entered into the random component of the model. We used the Markov chain Monte Carlo technique to calculate posterior distributions of the forecasts. For the herd specific forecasts the results showed that there were only minor differences between the forecasts from the simple time series model and the median forecasts from the Bayesian model. However, the credibility intervals from the Bayesian model were wider than the forecasts from the simple model and, therefore the Bayesian model encompassed the variability in the forecasts better. Compared to the statistical model, the simple time series would be easier to implement in a practical setting. However, the latter lacks the inherent “generality” from the statistical model that allows the user to make statements about the distribution of the herds and to predict disease status based on the “average” correlation among the herds. The applicability of the Bayesian approach within a clinical decision-making framework was discussed, with special emphasis on the use of prior information and clinical forecasting.     

The role of spatial factors in the success of an Aujeszky's disease eradication programme in a high pig density area (Northeast Spain, 2003–2007)

We tested the role of several spatial variables on the risk of a sow herd being Aujeszky's disease virus (ADV) seropositive in certain areas of North Eastern Spain and during different periods of the eradication programme. Distance to the nearest slaughterhouse, distance to the nearest conventional road and number of ADV serologically positive sows and ADV serologically positive fattening pigs within different distances (1000, 1500 and 2000 m) of each sow herd, were included in a hierarchical Bayesian binomial model. A variable without spatial characteristics, type of herd (farrow to weaning and farrow to finish), was also included. Presence of positive fattening pigs or positive sows up to a distance of 1500 m of a sow herd increased its risk of being seropositive, although this variable had no effect on the risk when located at distances up to 1000 or 2000 m. The number of seropositive sows increased the risk of a sow herd being ADV seropositive only in the first period of study, when the proportion of serologically positive sow herds was nearly 60%. The spatial pattern of the residuals of the hierarchical Bayesian binomial model (observed versus predicted) was very similar to the observed infection in sow herds in all of the eradication periods, showing that spatial factors might not be the main factors related to the eradication of Aujeszky's disease from sow herds. Other herd-specific risk factors might be much more strongly related to the risk of a sow herd being ADV seropositive.     

An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds

The implementation of an effective control strategy against disease in a finisher herd requires knowledge regarding the disease level in the herd. A Bayesian network was constructed that can estimate risk indexes for three cause-categories of leg disorders in a finisher herd. The cause-categories of leg disorders were divided into infectious causes (arthritis caused by infectious pathogens), physical causes (e.g. fracture and claw lesions), and inherited causes (osteochondrosis). Information about the herd (e.g. the herd size, floor type and number of suppliers) and information about individual pigs (e.g. results from diagnostic tests) were used to estimate the most likely cause of leg disorders at herd level. As information to the model originated from two different levels, we used an object-oriented structure in order to ease the specification of the Bayesian network. Hence, a Herd class and a Pig class comprised the basic components of the object-oriented structure. The causal structure of the model was based on evidence from published literature. The conditional probabilities used in the model were elicited from experts within the field and from the published literature. To illustrate the behaviour of the model, we investigated the value of different levels of evidence in two fictitious herds with different herd characteristics related to the risk of leg disorders (e.g. purchase policy, production type and the stocking density in pens). The model enabled us to demonstrate the value of performing systematic collection of additional information (i.e. clinical, pathological and bacteriological examination) when identifying causes of leg disorders at herd level.     

Bayesian analysis of animal movements related to factors at herd and between herd levels: Implications for disease spread modeling

A method to assess the influence of between herd distances, production types and herd sizes on patterns of between herd contacts is presented. It was applied on pig movement data from a central database of the Swedish Board of Agriculture. To determine the influence of these factors on the contact between holdings we used a Bayesian model and Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters. The analysis showed that the contact pattern via animal movements is highly heterogeneous and influenced by all three factors, production type, herd size, and distance between holdings. Most production types showed a positive relationship between maximum capacity and the probability of both incoming and outgoing movements. In agreement with previous studies, holdings also differed in both the number of contacts as well as with what holding types contact occurred with. Also, the scale and shape of distance dependence in contact probability was shown to differ depending on the production types of holdings.To demonstrate how the methodology may be used for risk assessment, disease transmissions via animal movements were simulated with the model used for analysis of contacts, and parameterized by the analyzed posterior distribution. A Generalized Linear Model showed that herds with production types Sow pool center, Multiplying herd and Nucleus herd have higher risk of generating a large number of new infections. Multiplying herds are also expected to generate many long distance transmissions, while transmissions generated by Sow pool centers are confined to more local areas. We argue that the methodology presented may be a useful tool for improvement of risk assessment based on data found in central databases.     

Characterisation of the repeat breeding syndrome in Swedish dairy cattle

Repeat breeding (RB), defined as cows failure to conceive from 3 or more regularly spaced services in the absence of detectable abnormalities, is a costly problem for the dairy producer. To elucidate the occurrence of RB in Swedish dairy herds and to identify risk factors of the syndrome totally 57,616 dairy cows in 1,541 herds were investigated based on data from the official Swedish production-, AI- and disease-recording schemes. The characteristics of the RB syndrome were studied on both herd and individual cow level. The effects of risk factors on the herd frequency of RB were studied by logistic regression. A generalised linear mixed model with logit link, and accounting for herd-level variation by including a random effect of herd, was used to study the individual animal risk for RB. The total percentage of RB animals was 10.1% and the median proportion of RB animals in the herds studied was 7.5%. The proportion of RB cows in herds increased with decreased herd sizes with decreased average days from calving to first AI, with increased herd incidence of clinical mastitis, with decreased reproductive disorders, and increased other diseases treated by a veterinarian. On animal level, the risk factors were milk yield, lactation number, difficult calving or dystocia, season at first service, days in milk at first service and veterinary treatment for reproductive disorders before the first service. Cows being an RB animal in the previous lactation had a higher risk of becoming an RB animal also in the present lactation. In conclusion our results show that the repeat breeding syndrome is a multifactorial problem involving a number of extrinsic factors as well as intrinsic factors coupled to the individual animal.     

Direct production losses and treatment costs from bovine viral diarrhoea virus,bovine leukosis virus,Mycobacterium avium subspecies paratuberculosis,and Neospora caninum

Our purpose was to determine direct production losses (milk loss, premature voluntary culling and reduced slaughter value, mortaliy loss, and abortion and reproductive loss) and treatmetn costs (veterinary services, medication cost, and extra farm labour cost) due to four infectious diseases in the maritime provinces of Canada: bovine viral diarrhoea (BVD), enzootic bovine leukosis (EBL), Johne's Disease (JD), and neosporosis. We used a partial-budget model, and incorporated risk and sensitivity analyses to identify the effects of uncertainty on costs. Total annual costs for an average, infected, 50 cow herd were: JD$ 2472; BVD$ 2421; neosporosis $ 2304; EBL$ 806. The stochastic nature of the proportion of infected herds and prevalence of infection within a herd were used to estimate probability distributions for these ex post costs. For all diseases, these distributions were right skewed. A sensitivity analysis showed the largest effect on costs was due to milk yield effects. For example, changing milk production loss from 0 to 5% for BVD increased the costs for the disease by 266%.     

An individual modelling tool for consecutive clinical mastitis during the same lactation in dairy cows: a method based on a survival model

The high number of clinical mastitis recurring within the same lactation in dairy cows constitutes one of the factors of overdispersion in standard Poisson models. Our method, based on biological parameters, i.e., recurrence hazard in relation to udder exogenous infection (Rex) or recurrence hazard and rate in relation to endogenous infection (Ren), produced a model capable of integrating a possible change of state in the udder after clinical mastitis. This model was based on a study of the time intervals between successive clinical episodes, both types of risk being considered in the form of a distribution mixture in the survival model. The modelling tool allowed to determine the factors that specifically act on either one of the potential risks and estimated the distribution of the number of clinical mastitis per lactation, as well as the distribution of when mastitis occurs. Estimation results obtained by this method in an experimental herd were compared with those from more classical models with or without random individual effects. The distribution of the number of mastitis per lactation estimated by our method was well-fitted to the data and the method identified variation factors which were relatively standard in this type of study: lactation number, lactation stage and calving month. Prediction results obtained in another experimental herd with more recent data without parameter re-estimation demonstrated the adequacy of the model in fitting observed data. This modelling method based on biological parameters in a mixture of survival distributions was interesting to model clinical mastitis recurring within the same lactation. However in the future it will also be important to integrate the possible relationship between successive lactations and to apply this model to other types of farming systems.     

Bayesian zero-inflated predictive modelling of herd-level Salmonella prevalence for risk-based surveillance

The national control programme for Salmonella in Danish swine herds introduced in 1993 has led to a large decrease in pork-associated human cases of salmonellosis. The pork industry is increasingly focused on the cost-effectiveness of surveillance while maintaining consumer confidence in the pork food supply. Using national control programme data from 2003 and 2004, we developed a zero-inflated binomial model to predict which farms were most at risk of Salmonella. We preferentially sampled these high-risk farms using two sampling schemes based on model predictions resulting from a farm's covariate pattern and its random effect. Zero-inflated binomial modelling allows assessment of similarities and differences between factors that affect herd infection status (introduction), and those that affect the seroprevalence in infected herds (persistence and spread). Both large (producing greater than 5000 pigs per annum), and small herds (producing less than 2000 pigs per annum) were at significantly higher risk for infection and subsequent seroprevalence, when compared with medium sized herds (producing between 2000 and 5000 pigs per annum). When compared with herds being located elsewhere, being located in the south of Jutland significantly decreased the risk of herd infection, but increased the risk of a pig from an infected herd being seropositive. The model suggested that many of the herds where Salmonella was not detected were infected, but at a low prevalence. Using cost and sensitivity, we compared the results of our model based sampling schemes with those under the standard sampling scheme, based on herd size, and the recently introduced risk-based approach. Model-based results were less sensitive but show significant cost savings. Further model refinements, sampling schemes and the methods to evaluate their performance are important areas for future work, and these should continue to occur in direct consultation with Danish authorities.     

Identification,occurrence and clinical findings of canine hemoplasmas in southern Brazil

Hemoplasmas are ubiquitous pleomorphic and epicellular bacteria detected in erythrocytes in several species. In Brazil, studies on hemoplasmas have not included information on occurrence, clinical signs, and risk factors in dogs. This paper investigates the occurrence of hemoplasmas in dogs, focusing on risk factors and clinical status. Conventional PCR for the four types of canine hemoplasmas was performed in 331 blood samples collected from dogs clinically treated at a teaching veterinary hospital. Of all samples, 17/331 (5.1%) were positive for Mycoplasma haemocanis and 6/331 (1.8%) were positive for a ‘Candidatus Mycoplasma haemominutum-like’ organism. Risk factors included the presence of vectors, old age, dog bite wounds, and neoplastic diseases. In the multivariate analysis, a 4.40 odds ratio in dogs with vector-borne diseases indicated risk for hemoplasmosis. There was correlation between hemoplasma infection and neoplastic disease, suggesting that neoplastic conditions are a risk factor for hemoplasma infection in dogs.     

Bayesian evaluation of budgets for endemic disease control: An example using management changes to reduce milk somatic cell count early in the first lactation of Irish dairy cows

The aim of this research was to determine budgets for specific management interventions to control heifer mastitis in Irish dairy herds as an example of evidence synthesis and 1-step Bayesian micro-simulation in a veterinary context. Budgets were determined for different decision makers based on their willingness to pay. Reducing the prevalence of heifers with a high milk somatic cell count (SCC) early in the first lactation could be achieved through herd level management interventions for pre- and peri-partum heifers, however the cost effectiveness of these interventions is unknown. A synthesis of multiple sources of evidence, accounting for variability and uncertainty in the available data is invaluable to inform decision makers around likely economic outcomes of investing in disease control measures. One analytical approach to this is Bayesian micro-simulation, where the trajectory of different individuals undergoing specific interventions is simulated. The classic micro-simulation framework was extended to encompass synthesis of evidence from 2 separate statistical models and previous research, with the outcome for an individual cow or herd assessed in terms of changes in lifetime milk yield, disposal risk, and likely financial returns conditional on the interventions being simultaneously applied. The 3 interventions tested were storage of bedding inside, decreasing transition yard stocking density, and spreading of bedding evenly in the calving area. Budgets for the interventions were determined based on the minimum expected return on investment, and the probability of the desired outcome. Budgets for interventions to control heifer mastitis were highly dependent on the decision maker's willingness to pay, and hence minimum expected return on investment. Understanding the requirements of decision makers and their rational spending limits would be useful for the development of specific interventions for particular farms to control heifer mastitis, and other endemic diseases.     

Mycoplasma bovis and otitis in dairy calves in the United Kingdom

Signs of severe otitis media in 20% of dairy calves on one farm were associated with Mycoplasma bovis infection, based on isolation from the external ear canal and nares. Affected calves seroconverted to M. bovis and no other significant bacteria were isolated. Infection was considered likely to have originated from cows in the milking herd based on evidence of seroconversion and detection of infection in a milk sample. M. bovis infection should be considered when investigating otitis problems in calves.     

Stochastic modelling as a tool for planning animal-health surveys and interpreting screening-test results

Traditionally, the planning of surveys (in particular, sample-size calculations) has relied on assumptions including the assumption of perfect screening tests. This paper presents a novel approach that can be used for planning animal-health surveys and interpreting screening-test results in the context of these surveys. A stochastic simulation model developed to assess the properties of herd-level sampling schemes and surveys has been adapted for large surveys aimed at substantiating freedom from infection at a national or regional level. We use a Bayesian approach to derive the post-survey probability of freedom from infection from the pre-survey probability of freedom and the likelihood ratio that is associated with screening-test results. We applied the model to two consecutive surveys conducted in 1998 and 1999 in Switzerland to substantiate freedom from infectious bovine rhinotracheitis (IBR) in the cattle population of about 56000 herds (median herd size of 15 cattle > 2 yr of age in 1999). In 1998, serum samples were taken from five cattle > 2 yr in 4672 herds, and in 1999 from all cattle > 2 yr old in 648 herds; samples were analysed by ELISA. The survey of 1999 provided less evidence than that of 1998 to support a status of freedom from infection; also, the characteristics of both herd-level sampling schemes were similar. We argue that the rationale for survey planning depends on the pre-survey probability of freedom from infection (i.e. our level of confidence that the infection does not occur in the targeted animal population). In consequence, surveys should be tailored to individual populations in the respective countries or regions. The model has been developed in an Excel spreadsheet to allow flexibility of use, and adaptation to many other animal-health issues.     

Risk factors for epidemic respiratory disease in Norwegian cattle herds

An epidemic of acute respiratory disease associated with bovine respiratory syncytial virus (BRSV) occurred during the winter and spring of 1995 in two neighbouring veterinary districts in the south-eastern part of Norway.The objective of this study was to describe the time course of the outbreak associated with BRSV in the cattle herds, and to determine the association between selected herd factors and the risk of experiencing a herd outbreak of acute respiratory disease.Data from 431 cattle herds on the dates of disease occurrence, location of the farms, herd size, age profile and production type were collected retrospectively for 1995. The risk of acute respiratory disease occurring in a cattle herd was related to the herd size as well as the type of production, with an expressed interaction between these two variables. From the Cox proportional-hazards model, the risk of a herd outbreak in a mixed herd of 20 animals was estimated to be 1.7-times greater than in a dairy herd and 3.3-times greater than a beef herd (reference category) of a comparable size. On increasing the herd size to 50 animals, the risk increased 1.3-fold for a mixed herd, 3.3-fold for a dairy herd, and 2.1-fold for a beef herd, compared to the risk for a corresponding type of herd of 20 animals.     

Modelling BVD at herd level compared with individual animal level

We assess two closely related stochastic models of BVD in a beef herd for their utility. One is an individual (animal) based model and the other a herd based model.     

Seroprevalence and risk factors for leptospirosis in cattle,sheep, and goats at consorted rearing from the State of Piauí, northeastern Brazil

Leptospirosis is an endemic disease in Latin America, caused by pathogenic bacteria of the genus Leptospira. It is considered one of the main causes responsible for the negative economic impact on global livestock by causing reproductive problems. The research aimed to determine the prevalence of leptospirosis in cattle, sheep, and goats at consorted rearing in the micro-region of Teresina, Piauí state, northeastern Brazil, as well as to identify prevalent serovars and risk factors associated with seroprevalence. Serum samples were analyzed in 336 sheep, 292 goats, and 253 cattle using microscopic agglutination test (MAT). Overall, 378 samples were positive to MAT, with seroprevalence of 42.9%. The prevalences in cattle, sheep, and goats were 50.5, 40.5, and 34.6%, respectively. All herds presented at least one seropositive animal; the Hardjo/Wolffi serovar association was the most common in cattle and Icterohaemorrhagiae in goats and sheep. Beef production (OR?=?4.9), cattle herd over 35 animals (OR?=?4.0), feeding on pasture (OR?=?6.4), weir and/or stream as water source (OR?=?2.1), and no veterinary services (OR?=?2.9) were risk factors for cattle infection. For sheep, intensive management system (OR?=?5.3), suspended slatted facilities (OR?=?2.2), more than 20 sheep in reproductive age (OR?=?1.9), and absence of deworming (OR?=?3.5) were the risk factors, while for goats, the identified risk factors were sheep herd over 52 animals (OR?=?1.9) and no veterinary services (OR?=?1.8). We conclude that the infection was spreading in consorted herds in this region. Thus, it would be interesting and important to conduct educative activities to farmers on the economic impacts of this disease and the need of preventive and control strategies mainly focused on sanitary measures and animal handling.     

Bayesian mixture models for within-herd prevalence estimates of bovine paratuberculosis based on a continuous ELISA response

Diagnostic inference by use of assays such as ELISA is usually done by dichotomizing the optical density (OD)-values based on a predetermined cut-off. For paratuberculosis, a slowly developing infection in cattle and other ruminants, it is known that laboratory factors as well as animal specific covariates influence the OD-value, but while laboratory factors are adjusted for, the animal specific covariates are seldom utilized when establishing cut-offs. Furthermore, when dichotomizing an OD-value, information is lost. Considering the poor diagnostic performance of ELISAs for diagnosis of paratuberculosis, a framework for utilizing the continuous OD-values as well as known coavariates could be useful in addition to the traditional approaches, e.g. for estimating within-herd prevalences.

The objective of this study was to develop a Bayesian mixture model with two components describing the continuous OD response of infected and non-infected cows, while adjusting for known covariates. Based on this model, four different within-herd prevalence indicators were considered: the mean prevalence in the herd; the age adjusted prevalence of the herd for better between-herd comparisons; the rank of the age adjusted prevalence to better compare across time; and a threshold-based prevalence to describe differences between herds. For comparison, the within-herd prevalence and associated rank using a traditional dichotomization approach based on a single cut-off for an OD corrected for laboratory variation was estimated in a Bayesian model with priors for sensitivity and specificity.

The models were applied to the OD-values of a milk ELISA using samples from all lactating cows in 100 Danish dairy herds in three sampling rounds 13 months apart. The results of the comparison showed that including covariates in the mixture model reduced the uncertainty of the prevalence estimates compared to the cut-off based estimates. This allowed a more informative ranking of the herds where low ranking and high ranking herds were easier to identify.     

Estimating herd prevalence of bovine brucellosis in 46 USA states using slaughter surveillance

Making valid inferences about herd prevalence from data collected at slaughter is difficult because the observed sample is dependent on the number of animals sampled from each herd, which varies with herd size and culling practices, and the probability of a positive test result, which depends on variable within-herd prevalence levels as well as test sensitivity and specificity. In this study, brucellosis herd prevalence among beef cow-calf operations is estimated from slaughter surveillance data using a method that combines process modeling with Bayesian inference. Inferences are made for two populations; the first population comprises cow-calf beef herds in a typical U.S. state. The second population represents all beef herds in a collection of 46 low-risk states. The Bayesian Monte Carlo method used in this study links process model inputs to observed surveillance results via Bayes Theorem. The surveillance evidence across multiple years is accumulated at a discounted rate based on the probability of introducing new infection into an area. The process model's inputs include herd size, culling rate per herd, within-herd prevalence, serologic test performance, and the probability of successfully investigating positive results. The surveillance results comprise the number of cows and bulls tested at slaughter and the number of affected herds detected each year. The results find at least 95% confidence that brucellosis herd prevalence among beef cow-calf herds is less than 0.014% (3 per 21,500 herds) and 0.00081% (5 per 6,15,770) after 5 years of slaughter surveillance (with no detections of affected herds) in a typical U.S. state and across 46 low-risk U.S. states, respectively. These results were based on conservative modeling assumptions, but sensitivity analysis suggests only slight changes in the results from changing the assumed process model input values. The most influential analytic input was the probability of introducing new infection into a putatively brucellosis-free state or group of states.     

Investigations of the motivation underlying Norwegian dairy farmers' use of homoeopathy

Eighteen Norwegian dairy farmers were interviewed to examine their reasons for using homoeopathic treatments in managing their herds' health. Overall, they chose the treatments on the basis of factors related to their personal experience, considerations of individual animals and the framework for dairy production. For individual animals homoeopathy was used as an alternative to conventional veterinary treatment, but at the herd level it was used to complement it. The farmers' use of homoeopathic treatment for personal health problems and the experience of their colleagues with its use in dairy production were important factors motivating their initial use of homoeopathy. Other factors included a desire to decrease the use of antibacterial drugs, reduce costs and find alternatives when conventional veterinary medicine provided no effective treatment. In individual cases, the severity of disease, previous experience and the farmers' personal knowledge and resources were important. These factors parallel those found to influence the use of complementary and alternative therapies in human medicine. The lack of understanding and documentation of the effects of homoeopathic remedies was not important to the farmers, and they valued personal experience more highly than scientific evidence or the opposition to homoeopathy encountered within the veterinary profession.     

Control of Mycoplasma hyopneumoniae infections in pigs

Mycoplasma hyopneumoniae, the primary pathogen of enzootic pneumonia, occurs worldwide and causes major economic losses to the pig industry. The organism adheres to and damages the ciliated epithelium of the respiratory tract. Affected pigs show chronic coughing, are more susceptible to other respiratory infections and have a reduced performance. Control of the disease can be accomplished in a number of ways. First, management practices and housing conditions in the herd should be optimized. These include all-in/all-out production, limiting factors that may destabilize herd immunity, maintaining optimal stocking densities, prevention of other respiratory diseases, and optimal housing and climatic conditions. Strategic medication with antimicrobials active against M. hyopneumoniae and, preferably, also against major secondary bacteria may be useful during periods when the pigs are at risk for respiratory disease. Finally, commercial bacterins are widely used to control M. hyopneumoniae infections. The main effects of vaccination include less clinical symptoms, lung lesions and medication use, and improved performance. However, bacterins provide only partial protection and do not prevent colonization of the organism. Different vaccination strategies (timing of vaccination, vaccination of sows, vaccination combined with antimicrobial medication) can be used, depending on the type of herd, the production system and management practices, the infection pattern and the preferences of the pig producer. Research on new vaccines is actively occurring, including aerosol and feed-based vaccines as well as subunit and DNA vaccines. Eradication of the infection at herd level based on age-segregation and medication is possible, but there is a permanent risk for re-infections.