Improve animal health to reduce livestock emissions: quantifying an open goal.

Greenhouse gas (GHG) emissions from livestock production must be urgently tackled to substantially reduce their contribution to global warming. Simply reducing livestock numbers to this end risks impacting negatively on food security, rural livelihoods and climate change adaptation. We argue that significant mitigation of livestock emissions can be delivered immediately by improving animal health and hence production efficiency, but this route is not prioritized because its benefits, although intuitive, are poorly quantified. Rigorous methodology must be developed to estimate emissions from animal disease and hence achievable benefits from improved health through interventions. If, as expected, climate change is to affect the distribution and severity of health conditions, such quantification becomes of even greater importance. We have therefore developed a framework and identified data sources for robust quantification of the relationship between animal health and greenhouse gas emissions, which could be applied to drive and account for positive action. This will not only help mitigate climate change but at the same time promote cost-effective food production and enhanced animal welfare, a rare win-win in the search for a sustainable planetary future.

Comparison and interobserver reliability between a visual analog scale and the Wisconsin Calf Health Scoring Chart for detection of respiratory disease in dairy calves.

Respiratory disease is an ongoing challenge for calves in the dairy sector with a relatively high prevalence and impact on welfare and economics. Applying scoring protocols for detecting respiratory disease requires that they are easily implemented, consistent between observers and fast to use in daily management. This study was conducted in one Danish dairy farm from September 2020 through January 2021. The study included 126 heifer calves enrolled in the age of 17 to 24 d. All calves were observed every second day for a period of 46 d. At each visit all calves were scored with a new visual analog scale (VAS) and the Wisconsin Calf Health Scoring Chart (WCHSC). We calculated agreement between the 2 scoring systems based on conditional probability to score higher or lower than a cutoff in the VAS compared with a specified cutoff in WCHSC used as reference test. A generalized mixed effects regression model was developed to estimate the prevalence of respiratory disease and the overall agreement between the 2 scoring systems. The overall agreement between the VAS and WCHSC was 89.6%. The second part of the study assessed interobserver reliability between 2 experienced observers and between an experienced observer and veterinary students. The interobserver reliability was calculated by intraclass correlation coefficient and was 0.58 between experienced observers and was 0.34 between an experienced observer and veterinary students indicating a moderate to poor reliability between the observers. It was possible to use VAS as an alternative clinical scoring method, which primarily focuses on the general condition of the individual calf rather than specific categories of clinical signs. Our study set up lacked a comparison to other diagnostic tools i.e., thoracic ultrasound to confirm the findings which should be considered in future studies when exploring VAS as a screening tool for detection of respiratory disease in dairy calves.

Retrospective cohort study of management procedures associated with dairy herd-level eradication of Streptococcus agalactiae in the Danish surveillance program.

The aim of this observational retrospective cohort study was to identify management procedures that are associated with herd-level eradication of Streptococcus agalactiae in dairy herds. The objective was to compare herds that recovered from Strep. agalactiae with herds that remained infected with Strep. agalactiae on the basis of specific management procedures. Data from the Danish surveillance program for Strep. agalactiae, where all milk delivering dairy herds are tested yearly, were used to identify study herds. One hundred ninety-six herds that were classified in the program as infected with Strep. agalactiae, in both January 2013 and January 2014, were identified as study herds. These were followed until January 2017. One hundred forty-four herds remained infected every year until January 2017. Forty-six herds recovered from Strep. agalactiae after January 2014 (were tested negative continuously after January 2015, January 2016, or January 2017 and remained noninfected in the program from recovery until January 2017). Herd characteristics and management procedures were obtained through the Danish Cattle Database. Herd characteristics included herd size, yield, milking system, and bulk milk somatic cell count (SCC). Management procedures included the proportion of cows culled within 100 d after calving due to mastitis, the extent of diagnoses relative to the extent of mastitis treatments, the proportion of cows treated for mastitis during lactation, the proportion of cows treated for mastitis early in lactation, the proportion of cows treated at dry-off, and the median length of the dry period for cows receiving dry cow treatment. All variables were calculated on herd level. Multivariable logistic regression was used to analyze the association between herd infection status and management procedures. A higher proportion of culling due to mastitis within 100 d from calving was associated with a higher probability of herd-level recovery from Strep. agalactiae in herds with conventional milking system. For example, herds with conventional milking, a bulk milk SCC of 260,000 cells/mL, and 10% early culling due to mastitis had a recovery probability of 0.13, whereas similar herds with 20% early culling due to mastitis had a recovery probability of 0.15. A higher proportion of mastitis treatments within 250 d postcalving was associated with a higher probability of herd-level recovery for herds with a relatively high bulk milk SCC. For example, herds with conventional milking, a bulk milk SCC of 260,000 cells/mL, and 10% lactational mastitis treatments had a recovery probability of 0.12, whereas similar herds with 20% lactational mastitis treatments had a recovery probability of 0.15. Herds with a low bulk milk SCC (<220,000 cells/mL) combined with a low proportion of lactational treatments (<0.2) had a relatively high probability of herd-level recovery (>0.2). Additional variables, including the proportion of dry cow treatments, were not associated with herd-level recovery from Strep. agalactiae.

Effect of automatic cluster flushing on the concentration of Staphylococcus aureus in teat cup liners.

Automatic flushing of milking clusters between milking events is a control measure aimed at reducing transmission of mastitis pathogens from infectious milk to a subsequently milked cow. We evaluated the effect of flushing with cold water and flushing with water containing peracetic acid (PAA) on the concentration of Staphylococcus aureus in teat cup liners. Thirty-two clusters in a swing-over milking parlor (Dairymaster, Causeway, Ireland) were subjected to a simulated milking with S. aureus-contaminated milk. Sixteen clusters were not flushed (controls), whereas 8 clusters were flushed with cold water (966 ± 32 mL) and 8 clusters were flushed with water containing PAA (200 mL/mL). A random teat cup in each cluster was sampled by rinsing with a phosphate buffer. Teat cup samples were cultured on the day following collection on Baird-Parker plates to determine the concentration of S. aureus. In teat cup samples from control clusters, the mean concentration of S. aureus was 2.8 × 105 cfu/mL. The concentration of S. aureus was zero in teat cup samples from clusters flushed with cold water. In teat cup samples from clusters flushed with water containing PAA, the concentration of S. aureus was in general reduced compared with control clusters, but S. aureus was not removed completely. However, the automatic cluster flushing did not function properly when clusters were flushed with water containing PAA; thus, results reflected the effect of inadequate function rather than the effect of adding disinfectant to the flushing water. Before the main study, we conducted a pilot study to evaluate whether teat cup sampling with swabs and sample analysis with quantitative PCR were appropriate methods for the main study. Specifically, we evaluated the effect of swab sample mass on detection of S. aureus by quantitative PCR in the laboratory, Further, we compared PCR and bacterial culture on detection of S. aureus in a suspension following disinfection of the suspension with PAA. We sampled 20 identical S. aureus suspensions for culture and PCR by swabs before and after disinfection with PAA. Swab sample mass was determined by differential weighing and contributed to 46% of the variation observed in detection of S. aureus by PCR. Following disinfection with PAA, S. aureus remained detectable by PCR, although culturability ceased. Based on these results, we sampled teat cups in the main study with a buffer rinse and quantified S. aureus in the samples by bacterial culture. We concluded that automatic cluster flushing with cold water was effective in removing S. aureus from teat cup liners and that addition of PAA was therefore not necessary.

Multivariate dynamic linear models for estimating the effect of experimental interventions in an evolutionary operations setup in dairy herds.

Evolutionary operations is a method to exploit the association of often small changes in process variables, planned during systematic experimentation and occurring during the normal production flow, to production characteristics to find a way to alter the production process to be more efficient. The objective of this study was to construct a tool to assess the intervention effect on milk production in an evolutionary operations setup. The method used for this purpose was a dynamic linear model (DLM) with Kalman filtering. The DLM consisted of parameters describing milk yield in a herd, individual cows from a herd, and an intervention effect on a given day. The model was constructed to handle any number of cows, experimental interventions, different data sources, or presence of control groups. In this study, data from 2 commercial Danish herds were used. In herd 1, data on 98,046 and 12,133 milkings registered from an automatic milking system (AMS) were used for model building and testing, respectively. In herd 2, data on 3,689 milkings on test days were used for estimating the initial model parameters. For model testing, data from both bulk tank milk yield (85 observations) and test-day milkings (1,471) were used. In herd 1, the manager wanted to explore the possibility of reducing the amount of concentrate provided to the cows in an AMS. In herd 2, the manager wanted to know if the milk yield could be increased by elevating the energy level provided to the cows in a total mixed ration. The experiment conducted in herd 1 was designed with a treatment and a control group, whereas in herd 2 we used a pretest/posttest design. The constructed tool provided estimates (mean and confidence intervals) for each of 3 interventions carried out in both herds. In herd 1, we concluded that the reduction in concentrate amount provided in the AMS had no negative influence on milk yield. For herd 2, the increased level of energy had a significant positive effect on milk yield but only for the first intervention. In this herd, the effect of intervention was also evaluated for cows in the first lactation and without bulk tank records. The presented model proved to be a flexible and dynamic tool, and it was successfully applied for systematic experimentation in dairy herds. The model can serve as a decision support tool for on-farm process optimization exploiting planned changes in process variables and the response of production characteristics.

Monitoring bovine dairy calf health and related risk factors in the first three months of rearing.

BACKGROUND: Rearing replacement heifers is pivotal for the dairy industry and is associated with high input costs for the preweaned calves, due to their higher susceptibility to diseases. Ensuring calf health and viability calls for systematic approaches in order to mitigate the costs induced by managing sick calves and to ensure animal welfare. The objective of this study was to develop a systematic and feasible health-monitoring tool for bovine dairy calves based on repeated clinical observations and diagnostic results of calves at three time points; the 1st (T0), the 3rd (T1) and the 12th (T3) week of age. The study included observations from 77 dairy heifer calves in nine Danish commercial dairy herds. Immunisation status was assessed by serum Brix% at T0. Clinical scoring included gastrointestinal disease (GD) and respiratory disease (RD). The average daily weight gain (ADWG) was estimated from heart-girth measurements. Pathogen detection from nasal swabs and faecal samples were analysed for 16 respiratory and enteric pathogens by means of high-throughput real time-PCR. All measures obtained in each herd were visualised in a panel to follow the health status of each calf over time. RESULTS: The individual clinical observations combined with diagnostic information from immunisation and pathogen detection form each enrolled calf are presented in a herd dashboard illustrating the health status over the study period. This monitoring revealed failure of passive transfer (Brix% < 8.1) in 31% of the 77 enrolled calves, signs of severe GD peaked at T0 with 20% affected calves, while signs of severe RD peaked at T2 with 42% affected calves. ADWG over the first eight weeks was estimated to be 760 g (± 190 g). Pathogen profiles varied between herds. CONCLUSIONS: The large variation in both clinical disease and pathogen occurrence across herds emphasizes the need for herd specific monitoring. Combining the results of the present study from measures of immunisation, health and growth from individual calves in one visualisation panel allowed for the detection of patterns across age groups in the specific herds, showing promising potential for early detection and interventions that can lead to enhanced calf health and welfare.

A field trial on the effect of cross-fostering on performance, clinical health and antibiotic usage during the suckling period of pigs.

Cross-fostering is a common nursing strategy in pig production, but there is sparse evidence on its effect on antibiotic usage and disease occurrence in piglets. The objective of this study was to compare the effect of two nursing strategies on antibiotic usage, disease occurrence, weight gain and mortality in piglets. A 2×2 randomized factorial experiment was conducted in three Danish commercial pig production herds. The factors were nursing strategy (cross-fostering allowed (CF) vs. cross-fostering not allowed after initial litter equalisation (non-CF)) and weaning age (four vs. five weeks). In CF litters, the herd's usual cross-fostering strategy was applied. Piglets were followed individually from birth until weaning. Data was collected on antibiotic usage, mortality, weight gain and clinical disease. Only individual antibiotic treatments were allowed. At litter level, the effect of nursing strategy (CF vs. non-CF) on average daily gain, mortality, antibiotic treatment, clinical disease, face wounds and carpal wounds was analysed. In total, 241 litters were used for the data analysis. Approximately 30% of the CF litters were cross-fostered (either given a nurse sow, mingled with non-siblings or both) during the nursing period. The odds for antibiotic treatment during the suckling period were 1.58 times higher for CF litters compared to non-CF litters (P < 0.001). Across experimental groups, 60.8% of antibiotic treatments were administered for leg diseases. In CF litters, 15.7-21.3% of the antibiotic treatments were directed against diarrhoea, whereas in non-CF litters this was the case for less than 1%. In CF litters, the odds for carpal wounds were 1.40 times higher than in non-CF litters (P = 0.005). There was a tendency towards a higher occurrence of face wounds (OR = 1.30, P = 0.095) and clinical disease (OR = 1.25, P = 0.059) at weaning in CF litters compared to non-CF litters. There was no difference in average daily gain and mortality from birth to weaning between CF and non-CF litters. The results show that cross-fostering increases antibiotic usage in piglets during the nursing period and tends to affect the clinical health at weaning negatively.

A field trial on the effect of cross-fostering and weaning age on daily gain and disease resilience in weaned pigs.

Alternative management strategies that can increase disease resilience and reduce antibiotics in weaned pigs are needed. Our objective was to compare the effect of two nursing strategies and weaning ages on weight gain, clinical health and antibiotic treatments in weaned pigs not provided with medical zinc oxide in feed. A 2×2 factorial trial was conducted in three Danish commercial herds. Nursing strategies tested were "cross-fostering allowed" (CF) vs. "cross-fostering not allowed" (non-CF). Weaning ages tested were four (24-29 days) vs. five weeks (31-35 days). Pigs were followed from weaning until 33-35 days post-weaning. Herd staff made decisions on and registered antibiotic treatment, removal of pigs to sick pens and mortality. Pigs were weighed at weaning (N = 3139) and on day 33-35 post-weaning (N = 2898). Clinical examinations were carried out on day 4, 7 and 33-35 post-weaning. The effect of nursing strategy and weaning age on weight gain, clinical health and antibiotic treatments was analysed by mixed linear and logistic models. We found that pigs weaned at five weeks of age gained 103.6 g more daily compared to pigs weaned at four weeks during the 33-35 days post-weaning (<0.001). Weaning age affected diarrhoea prevalence, but the effect differed between herds. For pigs weaned at five weeks compared to four weeks of age, the odds for diarrhoea one week post-weaning was 0.7 times lower in one herd whereas the odds for diarrhoea were 2.0 and 1.4 times higher in the two other herds, respectively (P < 0.05). In all herds, we found fewer runted (OR=0.28, P < 0.001) and thin (OR=0.23, P < 0.001) pigs 33-35 days post-weaning in pigs weaned at five weeks of age compared to four weeks. Furthermore, in all herds, CF pigs were more likely to be removed to a sick pen or to die. In total, 5.9%, 13.6% and 64.9% of the studied weaned pigs were treated with antibiotics in the three herds, respectively. Treatment prevalence did not associate to weaning age or nursing strategy and did not in all cases appear to be linked with diarrhoea prevalence. The results indicate that a higher weaning age and less cross-fostering to some extend increase disease resilience post-weaning but herd specific factors interacted with the effects. The prevalence of pigs treated with antibiotics were herd dependent and may relate more to management decisions than to disease level.

Antibiotic and medical zinc oxide usage in Danish conventional and welfare-label pig herds in 2016-2018.

This register-based study compared the usage of antibiotics and medical zinc oxide (ZnOmed) in three different pig production systems: organic, conventional free-range and conventional indoor in 2016-2018. ZnOmed is used to prevent weaning diarrhoea with a dosage of max 2500 mg zinc/kg feed for 14 days post weaning. The treatment incidence (TI) of antibiotics (injectable and oral) and ZnOmed was calculated as the total number of animal daily doses (ADD) per 100 animals per day at herd level over a calendar year. Zero-inflated negative binomial models were used to compare the antibiotic and ZnOmed usage across the three production systems. The correlation between antibiotic and ZnOmed usage was evaluated using Spearman's correlation coefficient. In all age groups, the antibiotic TI was highest in the conventional indoor system, lower in the conventional free-range system and lowest in the organic system. In weaners, which is the age group with the highest antibiotic usage, conventional indoor, conventional free-range and organic herds had an average TI of 7.20, 3.37 and 0,48 ADD/100 animals/day, respectively. Another important finding in the study was that non-use of antibiotics is more common (>30% of herds) in welfare-label production systems (organic and conventional free-range) than in conventional indoor production (16% of herds). The overall differences in usage of ZnOmed between the three production systems were not statistically significant, but the probability of not using ZnOmed, was significantly higher for organic (36%) and conventional free-range herds (61%) compared to conventional indoor herds (19%). There was a weak to moderate positive correlation between antibiotic and ZnOmed usage at herd level with the weakest correlation in conventional indoor herds (0.31). The results indicate that ZnOmed does not appear to replace or prevent the use of antibiotics and suggest that ZnOmed is used differently in different systems. A weak correlation between the usage of antibiotics and ZnOmed in conventional indoor herds could reflect a frequent use of routine treatments within this system. The lower level of antibiotic usage in welfare-label systems indicates that a significant reduction in antibiotic use in pig production would require housing and management changes or regulatory changes in the conventional indoor system. The large variation in both antibiotic and ZnOmed usage between herds in similar production systems indicates that a further reduction in use of antibiotics and ZnOmed is achievable.

Economic value of information from an alert system on physiological imbalance in fresh cows.

Physiological imbalance is an abnormal physiological condition that cannot be directly observed but is assumed to precede subclinical and clinical diseases in the beginning of lactation. Alert systems to detect the physiological imbalance in a cow using Fourier transform mid-infrared spectroscopy in milk have been developed. The objective of this study was to estimate the value of information provided from such system with different indicator accuracies, herd prevalence and prices. A decision tree was created to model the probabilities of detection and associated costs of test outcome, intervention and occurrence of disease. We assumed that the negative effect of physiological imbalance was the development of subclinical ketosis and that this negative effect was prevented by drenching the cows with propylene glycol for 5 days. We simulated the economic impact of subclinical ketosis mediated through physiological imbalance to be $194 per case. The results showed that if the alert system was highly accurate (Se = 0.99/Sp = 0.99), and the prevalence of physiological imbalance was 30 %, the value of information provided from the system is $19 per cow-year. In case the prevalence is 5 % or 50 %, the value of information is $3 and $13, respectively. These estimates for the value do not cover the capital costs and operational costs of the alert system. This study furthermore clearly demonstrated that in order to estimate the value of information correctly, it is important to consider that drenching all cows and not drenching any of the cows are the two relevant alternative options in the absence of the alert system. In conclusion, the decision tree and sensitivity analysis developed in this study show that final economic results are highly variable to the prevalence of physiological imbalance and highest at an intermediate prevalence. Other relevant factors are the costs associated with drenching and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indication-based intervention system.

Expert-based development of a generic HACCP-based risk management system to prevent critical negative energy balance in dairy herds.

The objective of this study was to develop a generic risk management system based on the Hazard Analysis and Critical Control Point (HACCP) principles for the prevention of critical negative energy balance (NEB) in dairy herds using an expert panel approach. In addition, we discuss the advantages and limitations of the system in terms of implementation in the individual dairy herd. For the expert panel, we invited 30 researchers and advisors with expertise in the field of dairy cow feeding and/or health management from eight European regions. They were invited to a Delphi-based set-up that included three inter-correlated questionnaires in which they were asked to suggest risk factors for critical NEB and to score these based on 'effect' and 'probability'. Finally, the experts were asked to suggest critical control points (CCPs) specified by alarm values, monitoring frequency and corrective actions related to the most relevant risk factors in an operational farm setting. A total of 12 experts (40 %) completed all three questionnaires. Of these 12 experts, seven were researchers and five were advisors and in total they represented seven out of the eight European regions addressed in the questionnaire study. When asking for suggestions on risk factors and CCPs, these were formulated as 'open questions', and the experts' suggestions were numerous and overlapping. The suggestions were merged via a process of linguistic editing in order to eliminate doublets. The editing process revealed that the experts provided a total of 34 CCPs for the 11 risk factors they scored as most important. The consensus among experts was relatively high when scoring the most important risk factors, while there were more diverse suggestions of CCPs with specification of alarm values and corrective actions. We therefore concluded that the expert panel approach only partly succeeded in developing a generic HACCP for critical NEB in dairy cows. We recommend that the output of this paper is used to inform key areas for implementation on the individual dairy farm by local farm teams including farmers and their advisors, who together can conduct herd-specific risk factor profiling, organise the ongoing monitoring of herd-specific CCPs, as well as implement corrective actions when CCP alarm values are exceeded.

Evaluation of Two Fecal Occult Blood Tests for Detecting Non-Perforating Abomasal Lesions in Cattle.

Non-perforating abomasal lesions occur with a high prevalence in slaughtered dairy cattle. Ante mortem diagnosis is a challenge, but the presence of occult blood in feces is suggested as a diagnostic criterion. The lower detection limit of Hemo-Fec® (Med-Kjemi, Asker, Norway) and Hemoccult II® SENSA® (Beckman Coulter, Brea, California, USA) for fecal occult blood were estimated. The Hemo-Fec® and Hemoccult II® SENSA® could detect 1-2 mL and 2-4.5 mL of blood in 1000 g of feces, respectively. Therefore, the Hemo-Fec® test was selected to access hemoglobin degradation in the rumen to establish if blood from outside the gastrointestinal tract could result in false-positive tests and an observational study to estimate the diagnostic sensitivity and specificity. Rumen microbiota did not degrade hemoglobin in a 1% blood concentration in vitro during 48 h of fermentation. The Hemo-Fec® test was only able to detect cattle with ≥4 acute lesions (diagnostic sensitivity: 0.40 [95% confidence interval (95% CI): 0.32-0.48] and ≥4 chronic lesions (sensitivity: 0.44 [95% CI: 0.35-0.52]). The Hemo-Fec® test had no diagnostic potential to detect superficial erosions or scar tissue in abomasa. Furthermore, the specificity was 0.71 [95% CI: 0.68-0.75%], and a positive test is thus not equivalent with abomasal lesions in cattle.

Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers.

Blood biomarkers may be used to detect physiological imbalance and potential disease. However, blood sampling is difficult and expensive, and not applicable in commercial settings. Instead, individual milk samples are readily available at low cost, can be sampled easily and analysed instantly. The present observational study sampled blood and milk from 234 Holstein dairy cows from experimental herds in six European countries. The objective was to compare the use of three different sets of milk biomarkers for identification of cows in physiological imbalance and thus at risk of developing metabolic or infectious diseases. Random forests was used to predict body energy balance (EBAL), index for physiological imbalance (PI-index) and three clusters differentiating the metabolic status of cows created on basis of concentrations of plasma glucose, ß-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA) and serum IGF-1. These three metabolic clusters were interpreted as cows in balance, physiological imbalance and "intermediate cows" with physiological status in between. The three sets of milk biomarkers used for prediction were: milk Fourier transform mid-IR (FT-MIR) spectra, 19 immunoglobulin G (IgG) N-glycans and 8 milk metabolites and enzymes (MME). Blood biomarkers were sampled twice; around 14 days after calving (days in milk (DIM)) and around 35 DIM. MME and FT-MIR were sampled twice weekly 1-50 DIM whereas IgG N-glycan were measured only four times. Performances of EBAL and PI-index predictions were measured by coefficient of determination (R2cv) and root mean squared error (RMSEcv) from leave-one-cow-out cross-validation (cv). For metabolic clusters, performance was measured by sensitivity, specificity and global accuracy from this cross-validation. Best prediction of PI-index was obtained by MME (R2cv = 0.40 (95 % CI: 0.29-0.50) at 14 DIM and 0.35 (0.23-0.44) at 35 DIM) while FT-MIR showed a better performance than MME for prediction of EBAL (R2cv = 0.28 (0.24-0.33) vs 0.21 (0.18-0.25)). Global accuracies of predicting metabolic clusters from MME and FT-MIR were at the same level ranging from 0.54 (95 % CI: 0.39-0.68) to 0.65 (0.55-0.75) for MME and 0.51 (0.37-0.65) to 0.68 (0.53-0.81) for FT-MIR. R2cv and accuracies were lower for IgG N-glycans. In conclusion, neither EBAL nor PI-index were sufficiently well predicted to be used as a management tool for identification of risk cows. MME and FT-MIR may be used to predict the physiological status of the cows, while the use of IgG N-glycans for prediction still needs development. Nevertheless, accuracies need to be improved and a larger training data set is warranted.

Effect of non-perforating abomasal lesions on reproductive performance, milk yield and carcass weight at slaughter in Danish Holstein cows.

Non-perforating abomasal lesions are common in Danish Holstein cows, but the impact on production has not been studied. Our objective was to compare milk yield, carcass weight, days to first service and initiation of breeding among cows with different non- perforating abomasal lesion types and locations to cows with no such lesions. The occurrence, type and localisation of non- perforating abomasal lesions were characterised at slaughter in Danish Holstein cows, and first insemination dates (n = 592), milk yield (n = 999) and carcass information (n = 1,317) was also available. Production and reproduction data were retrieved from all other cows from the herds of origin. This information was used to evaluate the expected production performance in the herds. Of the 1,317 cows investigated at slaughter, 572, 428, 677, and 91 cows had at least one lesion Subtype 1a (mucosal erosion), 1b (acute deeper lesion), 1c (chronic deeper lesion) or 1d (scarring following a lesion), respectively. Cows with lesion Subtype 1c and lesions in the pyloric part of the abomasum were generally found to be culled later in lactation than their herd mates. Milk yield and days to first service were not associated with any lesion types. The occurrence of lesion Subtype 1c and lesions in the pylorus were associated with a higher weight at slaughter compared to cows without lesions of this subtype or in this location. Most Subtype 1c lesions was found in the pylorus. Initiation of breeding before 150 days in milk was positively associated with the number of lesions in the corpus of the abomasum. We discovered two sources of selection bias. First, cows with lesions had a higher carcass weight but also remained in the herds for longer after calving than cows without lesions. This would give them more time to overcome the period of negative energy balance in early lactation. Second, although the causes of culling were not known, cows with no lesions generally had a relatively low milk yield and could have been culled for this reason.". While non- perforating abomasal ulcers may have an effect on the production and reproduction data, the observational data presented show no evidence of such a relationship. It may be necessary to employ other study designs to identify any effects that might exist, but if the relationship cannot be demonstrated in a dataset of this magnitude, it is not deemed to be of practical relevance.

A quantitative screening method to detect rater-introduced bias in clinical ratings.

BACKGROUND: We suggest a 'screening test' to examine large data files with clinical ratings for the occurrence of rater-introduced bias prior to using the data for quantitative analyses. The test is based on a statistical model in which a well-standardized interval-scale outcome (for example, milk yield) is related to clinical ratings (for example, body condition scores) obtained from multiple contexts (for example, dairy herds). FINDINGS: 84,968 calvings from 279 herds, with subsequent body condition scores performed by 117 veterinarians within the first 21 days postpartum were analyzed with a multilevel random coefficient regression model. The model included an independent variable, where body condition score was centered within veterinarian. This is a so-called comparison effect to describe possible rater-introduced bias in the body condition scores. A highly significant comparison effect was found for second and older parities, indicating occurrence of possible rater-introduced bias in this large multi-herd data file. CONCLUSIONS: A within-group centering technique (the comparison effect) appeared to be useful for discriminating between biased and unbiased clinical scores. In some cases, this test for bias should prevent further analysis of the data and divert the focus of study to the calibration of raters or alternative study designs.