Temporal trends in bulk milk antibodies to Salmonella, Neospora caninum, and Leptospira interrogans serovar hardjo in Irish dairy herds

peer-reviewedBulk milk samples were collected from 309 Irish dairy herds at four time points during 2009 and tested for antibodies to Salmonella spp., N. caninum, and L. hardjo, three abortifacient agents in Irish dairy herds. Of the 312 study herds, 49% vaccinated against Salmonella and 76% vaccinated against L. hardjo. In unvaccinated herds, the overall prevalence of antibody positive herds was 49% for Salmonella, 19% for N. caninum and 86% for L. hardjo. There was no association between both testing positive for and incidence of Salmonella or L. hardjo on sample date and calving season. A significant association was found between sample date and both testing positive for [p = <0.0001 OR = 2.41 (95% CI 1.54–3.80)] and incidence [p = 0.001 OR = 3.10 (95% CI 1.72–5.57)] of N. caninum. No association with region of Ireland was found for either testing positive for or incidence of N. caninum, or L. hardjo. There was however a tendency towards a higher incidence of Salmonella in regions of Ireland with higher cattle densities

Effect of exposure to Neospora caninum, Salmonella, and Leptospira interrogans serovar Hardjo on the economic performance of Irish dairy herds

peer-reviewedThe objective of the current study was to quantify the effects of exposure to Salmonella, Neospora caninum, and Leptospira interrogans serovar Hardjo (L. hardjo) on dairy farm profitability and to simulate the effect of vaccination for Salmonella and L. hardjo on dairy farm profitability. The production effects associated with exposure to each of these pathogens in study herds were defined under 3 categories: (1) milk production effects, (2) reproduction effects (including culling), and (3) mortality effects. The production effects associated with exposure to Salmonella, N. caninum, and L. hardjo were incorporated into the Moorepark Dairy Systems Model. In the analysis, herds negative for exposure to Salmonella, N. caninum, and L. hardjo were assumed baseline herds, with all results presented relative to this base. In simulations examining the effect of vaccination for Salmonella and L. hardjo on farm profitability, vaccinated herds (vaccination costs included) were considered as baseline herds and results were presented relative to this base. Total annual profits in unvaccinated herds were reduced by €77.31, €94.71, and €112.11 per cow at milk prices of €0.24, €0.29, and €0.34/L, respectively, as a result of exposure to Salmonella. In the current study, herds positive for exposure to Salmonella recorded a 316-kg reduction in milk yield, whereas no association was detected between exposure to N. caninum or L. hardjo and milk production. Exposure to both N. caninum and L. hardjo was associated with compromised reproductive performance. Herds positive for exposure to N. caninum and Salmonella had greater rates of adult cow mortality and calf mortality, respectively. Vaccination for both Salmonella and L. hardjo was associated with improved performance in study herds. Exposure to N. caninum resulted in a reduction in annual farm profits of €11.55, €12, and €12.44 per cow at each milk price, whereas exposure to L. hardjo resulted in a reduction in annual farm profits of €13.83, €13.78, and €13.72 per cow at each milk price. Herds that tested positive for exposure to Salmonella and L. hardjo were compared with herds vaccinated for the respective pathogens. Herds vaccinated for Salmonella generated €67.09, €84.48, and €101.89 per cow more profit at each milk price compared with herds positive for exposure. Similarly, herds vaccinated for L. hardjo generated €9.74, €9.69, and €9.63 per cow more profit compared with unvaccinated exposed herds. However, herds that tested negative for exposure to Salmonella and L. hardjo generated additional profits of €10.22 and €4.09 per cow, respectively, compared with vaccinated baseline herds

An investigation into reduced milk production following dietary alteration on an Irish dairy farm

A nutritional evaluation of an Irish dairy herd indicated gross overfeeding of late lactation cows, over-conditioning of cows at parturition and a high rate of body condition loss in early lactation. Metabolisable-energy based nutritional modelling software was used to guide recommended dietary changes to prevent excessive condition gain in late lactation. Immediately following the implementation of the changes there was an unexpected reduction in performance affecting both milk yield and protein concentration. An investigation into the poor performance revealed underestimation of peak milk yield; over-estimation of maize silage quality; a large difference in the concentrate being fed compared to the concentrate recommended, and failure of the blend of concentrate ingredients to maintain the intended proportions in the in-parlour feeding system. The estimated maximum cumulative effect of these errors was to cause undersupply of energy and protein in the recommended diet of 16% and 3% respectively to cows in early lactation. Use of a net-energy nutritional model would have indicated a requirement for a higher energy supply in this case. This report highlights the challenges in obtaining accurate on-farm data for use in dairy cow nutritional models

Made from Mud: Functional Categorization and Analyses of Bronze Age Earthen Materials from Western Turkey

This contribution presents the results of a pilot study of earthen materials excavated at the Middle to Late Bronze Age site of Kaymakçı, located in western Anatolia. It argues that systematic collection and analysis of fragmentary and difficult‑to‑identify earthen materials is challenging, yet crucial. These materials inform on activities of which traces are preserved in the archaeological record but which have been largely under‑ ‑researched. Flourishing studies on earthen findings foreground architectural materials, such as mudbrick, and well‑preserved features and objects. However, earthen objects and architectural features were utilized more widely than in building architecture and only a small portion of excavated sites has good preservation. We, therefore, present the different categories of earthen materials discovered at Kaymakçı, specifically ar‑ chitecture, installations, and portable items. Our work demonstrates that by incorporating new knowledge of archaeological remains at the site and re‑studying the earthen assemblage it is possible to gain a better understanding of the morphological, functional, and social aspects of this dataset

Stability selection for mixed effect models with large numbers of predictor variables: A simulation study

Covariate selection when the number of available variables is large relative to the number of observations is problematic in epidemiology and remains the focus of continued research. Whilst a variety of statistical methods have been developed to attempt to overcome this issue, at present very few methods are available for wide data that include a clustered outcome. The purpose of this research was to make an empirical evaluation of a new method for covariate selection in wide data settings when the dependent variable is clustered. We used 3300 simulated datasets with a variety of defined structures and known sets of true predictor variables to conduct an empirical evaluation of a mixed model stability selection procedure. Comparison was made with an alternative method based on regularisation using the least absolute shrinkage and selection operator (Lasso) penalty. Model performance was assessed using several metrics including the true positive rate (proportion of true covariates selected in a final model) and false discovery rate (proportion of variables selected in a final model that were non-true (false) variables). For stability selection, the false discovery rate was consistently low, generally remaining ≤ 0.02 indicating that on average fewer than 1 in 50 of the variables selected in a final model were false variables. This was in contrast to the Lasso-based method in which the false discovery rate was between 0.59 and 0.72, indicating that generally more than 60% of variables selected in a final model were false variables. In contrast however, the Lasso method attained higher true positive rates than stability selection, although both methods achieved good results. For the Lasso method, true positive rates remained ≥ 0.93 whereas for stability selection the true positive rate was 0.73–0.97. Our results suggest both methods may be of value for covariate selection with high dimensional data with a clustered outcome. When high specificity is needed for identification of true covariates, stability selection appeared to offer the better solution, although with a slight loss of sensitivity. Conversely when high sensitivity is needed, the Lasso approach may be useful, even if accompanied by a substantial loss of specificity. Overall, the results indicated the loss of sensitivity when employing stability selection is relatively small compared to the loss of specificity when using the Lasso and therefore stability selection may provide the better option for the analyst when evaluating data of this type

A HACCP-based approach to mastitis control in dairy herds. Part 1: Development

Hazard Analysis and Critical Control Points (HACCP) systems are a risk based preventive approach developed to increase levels of food safety assurance. This is part 1 of a pilot study on the development, implementation and evaluation of a HACCP-based approach for the control of good udder health in dairy cows. The paper describes the use of a novel approach based on a deconstruction of the infectious process in mastitis to identify Critical Control Points (CCPs) and develop a HACCP-based system to prevent and control mastitis in dairy herds. The approach involved the creation of an Infectious Process Flow Diagram, which was then cross-referenced to two production process flow diagrams of the milking process and cow management cycle. The HACCP plan developed, may be suitable for customisation and implementation on dairy farms. This is a logical, systematic approach to the development of a mastitis control programme that could be used as a template for the development of control programmes for other infectious diseases in the dairy herd

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

peer-reviewedThe aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling

Relative importance of herd-level risk factors for probability of infection with paratuberculosis in Irish dairy herds

Control of paratuberculosis is challenging due to the relatively poor performance of diagnostic tests, a prolonged incubation period, and protracted environmental survival. Prioritization of herd-level interventions is not possible because putative risk factors are often not supported by risk factor studies. The objective for this study was to investigate the relative importance of risk factors for an increased probability of herd paratuberculosis infection. Risk assessment data, comprehensive animal purchase history, and diagnostic test data were available for 936 Irish dairy herds. Both logistic regression and a Bayesian β regression on the outcome of a latent class analysis were conducted. Population attributable fractions and proportional reduction in variance explained were calculated for each variable in the logistic and Bayesian models, respectively. Routine use of the calving area for sick or lame cows was found to be a significant explanatory covariate in both models. Purchasing behavior for the previous 10 yr was not found to be significant. For the logistic model, length of time calves spend in the calving pen (25%) and routine use of the calving pen for sick or lame animals (14%) had the highest attributable fractions. For the Bayesian model, the overall R2 was 16%. Dry cow cleanliness (7%) and routine use of the calving area for sick or lame cows (6%) and had the highest proportional reduction in variance explained. These findings provide support for several management practices commonly recommended as part of paratuberculosis control programs; however, a large proportion of the observed variation in probability of infection remained unexplained, suggesting other important risks factors may exist

A comparison of machine learning techniques for predicting insemination outcome in Irish dairy cows

peer-reviewedReproductive performance has an important effect on economic efficiency in dairy farms with short yearly periods of breeding. The individual factors affecting the outcome of an artificial insemination have been extensively researched in many univariate models. In this study, these factors are analysed in combination to create a comprehensive multivariate model of conception in Irish dairy cows. Logistic regression, Naive Bayes, Decision Tree learning and Random Forests are trained using 2,723 artificial insemination records from Irish research farms. An additional 4,205 breeding events from commercial dairy farms are used to evaluate and compare the performance of each data mining technique. The models are assessed in terms of both discrimination and calibration ability. The logistic regression model was found to be the most useful model for predicting insemination outcome. This model is proposed as being appropriate for use in decision support and in general simulation of Irish dairy cows

The interplay between extrinsic and intrinsic factors in determining migration decisions in brown trout (Salmo trutta): An experimental study

Many species are capable of facultative migration, but the relative roles of extrinsic versus intrinsic factors in generating diverse migratory tactics remain unclear. Here we explore the proximate drivers of facultative migration in brown trout in an experimental laboratory setting. The effects of reduced food, as a putative environmental cue, were examined in two populations: one that exhibits high rates of anadromy (sea-migration) in nature, and one that does not exhibit anadromy in nature. Juveniles derived from wild-caught parents were reared for two years under four environmental treatments: low food in years 1 and 2 (Low-Low); high food in years 1 and 2 (High-High), low food in year 1 and high in year 2 (Low-High), and vice versa (High-Low). Food restriction had a significant effect on migratory tactics, with the frequency of smolts (juveniles choosing migration) highest in the Low-Low treatment in both populations. No individuals became smolts in the High-High treatment, and intermediate smolting rates were observed in the Low-High and High-Low treatments. Higher overall smolting rates in the naturally anadromous population suggested an inherited component to anadromy/migration decisions, but both populations showed variability in migratory tactics. Importantly, some fish from the naturally non-anadromous population became smolts in the experiment, implying the capacity for migration was lying ‘dormant’, but they exhibited lower hypo-osmoregulatory function than smolts from the naturally anadromous population. Tactic frequencies in the naturally anadromous population were more affected by food in the 2nd year, while food in the 1st year appeared more important for the naturally non-anadromous population. Migratory tactics were also related to sex, but underpinned in both sexes by growth in key periods, size and energetic state. Collectively these results reveal how migration decisions are shaped by a complex interplay between extrinsic and intrinsic factors, informing our ability to predict how facultatively migratory populations will respond to environmental change