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ABSTRACT: Milk lactose content (LC) physiologically decreases with parity order in dairy cows, but also after udder health inflammation(s) and in presence of elevated milk SCC in subclinical cases. Therefore, the progressive decrease in milk LC observed along cows' productive life can be attributed to a combination of factors that altogether impair the epithelial integrity, resulting in weaker tight junctions, e.g., physiological aging of epithelium, mechanical epithelial stress due to milking, and experienced clinical or subclinical mastitis. Mastitis is also known to affect the udder synthesis ability, so our intention through this study was to evaluate if there is a cumulative and lasting effect of mammary gland inflammation(s) on milk yield (MY) and LC. For this purpose, we used diagnoses of clinical mastitis and milk data of Austrian Fleckvieh cows to evaluate the effect of cumulative mastitis events on LC and MY. Only mastitis diagnoses recorded by trained veterinarians were used. Finally, we investigated if cumulative mastitis is a heritable trait and whether it is genetically correlated with either LC or MY. Estimates were obtained using univariate and bivariate linear animal models. A significant reduction in LC and MY was observed in cows that suffered from mastitis compared with those that did not experience udder inflammation. The h2 of cumulative mastitis is promising and much greater (0.09) than the h2 of the binary event itself (≤0.03). The genetic correlations between cumulative mastitis with LC and MY were negative, suggesting that cows with a great genetic merit for MY and LC are expected to be more resistant to repeated inflammations and less recidivist. When we used number of lifetime SCC peaks (≥200,000 or 400,000 cells/mL) to calculate cumulative inflammation events, h2 was even higher (up to 0.38), implying that subclinical mastitis also has a relevant negative impact on both LC and MY. Finally, the present study demonstrated how repeated mastitis events can permanently affect the mammary gland epithelial integrity and synthesis ability, and that the number of cumulative mastitis is a promising phenotype to be used in selection index in combination with other indicator traits toward more resistant and resilient mammary glands.

Lameness is an important health and welfare issue that causes considerable economic losses in dairy herds. The objective of this study was to investigate whether the hind feet position score (HFPS) can be used as an auxiliary trait for genetic evaluation of lameness. The HFPS is evaluated by visual scoring of the position of both the hind-digits to the mid-line of the cow's body. The higher the heel height of the lateral claw, the higher is the HFPS, and the higher is the risk for development of lameness. In total, 3,478 records from 1,064 Fleckvieh cows from 35 farms were obtained between September 1, 2021, and March 5, 2022. Data collection was carried out by the regional milk recording organizations. Hind feet position was scored visually by trained personnel during routine milk performance testing in the milking parlor using a 3-class scoring system: score 1 = 0° to 24°. After all cows had been milked, locomotion scoring was performed for each animal using a 5-class scoring system with locomotion scores ranging between 1 (normal) and 5 (severely lame). Using HFPS, sensitivity and specificity were 69.5% and 66.8%, respectively, for detecting lameness defined by locomotion score ≥2. For genetic analyses, a bivariate linear animal model was fitted with fixed effects of herd, parity, lactation stage, and classifier, and random effects of animal and permanent environment. Heritabilities for HFPS and locomotion score were 0.07 and 0.10, respectively, and the genetic correlation between the 2 traits studied was 0.80. These results suggest that the HFPS could be used for genetic evaluations to reduce lameness incidence in dairy cattle.

ABSTRACT: Bovine mastitis is the most commonly diagnosed disease of dairy cows worldwide and causes extensive economic losses to milk producers. Intramammary infection status before dry-off plays a decisive role with respect to udder health and milk yield in the subsequent lactation. The aim of this study was to compare the effect of antibiotic dry cow therapy (DCT) versus no treatment at dry-off on milk yield, somatic cell count (SCC), inflammation of the mammary gland (IMG), and the incidence of clinical mastitis in the subsequent lactation. Dairy herd data from 251 Austrian dairy farms were recorded over an observation period of 12 mo and subsequently analyzed. The data set included 5,018 dairy cows: 2,078 were treated with antibiotics (abDCT group) and 2,940 were not treated (noDCT group) at dry-off. The abDCT group was subdivided, based on the antimicrobial active substances used for drying off, into 4 different groups (penicillins, cloxacillin, cephalosporins, and rifaximin). Based on bacteriological culture results, infections were grouped into those caused by major, minor, and other pathogens. Additionally, the IMG was defined via SCC from milk recording data using a cutoff of 200,000 cells/mL before drying off and after calving. The incidence of clinical mastitis cases within 30 and 90 d in milk was calculated using veterinary diagnosis data. To investigate the effect of different dry cow therapies on the following parameters: milk yield, SCC, and diagnosed clinical mastitis cases, different linear mixed models were constructed. Overall, the abDCT group was determined to have a significantly higher milk yield over 305 d in milk in the subsequent lactation (increase of 6.18%), compared with the noDCT group (increase of 4.29%). Both groups (abDCT and noDCT) demonstrated a decrease in the first SCC after calving compared with the SCC before dry-off, although the treated cows had a significantly higher reduction. Regarding the different antibiotic groups, with exception of the rifaximin treated cows, all antibiotic groups showed a significant difference from not treated cows with respect to SCC. Additionally, we were able to demonstrate that cows with IMG before dry-off had a 2.073 times higher chance of an increased SCC (>200,000 cells/mL) after calving. With respect to the veterinary diagnosis data, neither the IMG before drying off nor the type of DCT had a significant influence on the probability of developing clinical mastitis within 30 or 90 d in milk. Only a small number of treatments was accompanied with a bacteriological examination before drying off. However, the existing data in this study indicates that the intramammary infection status before dry-off in combination with different dry cow treatments influences udder health and milk yield after calving. Nevertheless, further studies with larger data sets of bacteriological examinations are necessary to enable a more in-depth investigation into the effects of different antibiotic substances used for DCT.

ABSTRACT: In addition to somatic cell count records and clinical mastitis diagnoses, results of bacteriological milk analyses provide valuable information regarding udder health. The pathogen causing an udder infection is currently not considered in Austria as part of the information used for estimation of routine breeding values for mastitis resistance. Therefore the objective of this study was to estimate heritabilities for, and genetic correlations between, udder traits of bacterial infection (bacterial infection, gram-positive and gram-negative bacterial infection) and routinely recorded udder health traits [acute mastitis, chronic mastitis, culling due to udder health problems, and somatic cell score (SCS)] in Austrian Fleckvieh cows. The basis for the genetic analyses was a data set with results from bacteriological milk analyses collected from 237 dairy farms and 6,822 cows over a period of 1 yr. Traits were defined as binary, apart from SCS, for which measures were available continuously. Multivariate analyses using a linear animal model were applied for estimating genetic parameters. The heritabilities for the occurrence of bacterial udder infection traits were 0.01. Heritabilities were 0.04 for acute mastitis, 0.02 for chronic mastitis, 0.02 for culling due to udder health problems, and 0.20 for SCS. Genetic correlations between bacteriological infection and the routinely recorded udder health traits were positive and ranged from 0.62 to 0.96. The genetic correlation between gram-positive and gram-negative bacterial infection was −0.20. The genetic correlation between acute and chronic mastitis was also close to zero. These results show that mastitis caused by different pathogens may be seen as different traits. As analyses were based on a relatively small data set and results were associated with rather high standard errors, further research with a larger data set should be carried out to confirm these results.

The aim of this study was twofold: first, to evaluate the influence of body weight on the efficiency of dairy cows, and second, to analyze the current state of dairy cattle populations as part of the Austrian Cattle Breeding Association's Efficient Cow project. Data of Fleckvieh (FV, dual-purpose Simmental), Fleckvieh×Red Holstein (FV×RH), Holstein (HF) and Brown Swiss (BS) dairy cows (161 farms, 6098 cows) were collected at each performance recording during the year 2014. In addition to routinely recorded data (e.g., milk yield, fertility), body weight, body measurements, body condition score (BCS) and individual feed information were also collected. The following efficiency traits were considered: body weight efficiency as the ratio of energy-corrected milk (ECM) to metabolic body weight, feed efficiency (kilogram ECM per kilogram dry-matter intake) and energy efficiency expressed as the ratio of energy in milk to energy intake. The relationship of milk yield to body weight was shown to be nonlinear. Milk yield decreased in cows above the 750 kg body weight class for HF, BS and FV×RH with 68 % RH genes, but less dramatically and later for FV at 800 kg. This resulted in an optimum body weight for feed and energy efficiency. BS and HF had the highest efficiency in a narrower and lighter body weight range (550–700 kg) due to a stronger curvature of the parabolic curve. Contrary to this, the efficiency of FV did not change as much as it did in the dairy breeds with increasing body weight, meaning that FV had a similar feed and energy efficiency in a range of 500–750 kg. The breed differences disappeared when body weight ranged between 750 and 800 kg. The average body weight of the breeds studied (FV 722 kg, BS 649 and HF 662 kg) was in the optimum range. FV was located at the upper end of the decreasing segment. In conclusion, an optimum body weight range for efficiency does exist, due to the nonlinear relationship of milk yield and body weight. Specialized dairy breeds seem to respond more intensively to body weight range than dual-purpose breeds, due to the stronger curvature. Cows with medium weights within a population are the most efficient. Heavy cows (>750 kg) produce even less milk. A further increase in dairy cows' body weights should therefore be avoided.

The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (ModelHG BG) reduced RMSE to 32.5 kg, and adding HW reduced it further to 30.4 kg (ModelHG BG HW). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, ModelHG BG and ModelHG BG HW, were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0 kg (ModelHG BG HW, ModelHG BG, lactation) and 39.9 and 41.3 kg (ModelHG BG HW, ModelHG BG, dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6 % of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.

For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.

The objective of this study was to compare linear models and survival analysis for genetic evaluation of ovulatory disorders, which included veterinary treatments of silent heat/anestrus and cystic ovaries. Data of 23 450 daughters of 274 Austrian Fleckvieh sires were analyzed. For linear model analyses, ovulatory disorders were defined as a binary response (presence or absence) in the time periods from calving to 150 days after calving and from calving to 300 days after calving. For survival analysis, ovulatory disorders were defined either as the number of days from calving to the day of the first treatment for an ovulatory disorder (uncensored record) or from calving to the day of culling, or the last day of the period under investigation (until 150 or 300 days after calving; censored record). Estimates of heritability were very similar (0.016 to 0.020) across methods and periods. Correlations between sire estimated breeding value from linear model and survival analysis were 0.98, whereas correlations between different time periods were somewhat lower (0.95 and 0.96). The results showed that the length of time period had a larger effect on genetic evaluation than methodology.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high-frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 wk. A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 wk and when using information from the last 3 wk, combined with a balanced accuracy of 79%. Removing sensor data results has a tendency to reduce the precision for predictions, especially when using information from the last 1, 2, or 3 wk. Moving to a larger dataset (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high-resolution data from other systems, such as automated milking systems., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Currently, prevalence and incidence of claw lesions are used as parameters for benchmarking claw health. The aims of this study were to create a benchmarking system for claw health utilizing the claw health indicators Farm-Claw-Score (FCS) for the herd and Cow-Claw-Score (CCS) for the individual animal, and to benchmark claw health of the three predominant dairy cattle breeds in Austria. Claw health data from 17,642 cows from 508 Austrian dairy farms were analyzed. The CCS and FCS were calculated based on recorded claw lesions and their three severity levels using geometrically weighted scoring. The FCS of each of the dairy farms was classified into five percentile thresholds (P10, P25, P50, P75, P90), with the FCS calculated using the median value of CCS in each herd. Furthermore, claw health was benchmarked for three breeds (Fleckvieh, Holstein, Brown Swiss cows), using claw lesion prevalences and CCS values. When the median FCS was calculated, dairy farms in P50 and below had an FCS of 20.0, indicating very good claw health. However, P90 farms showed an FCS-MEDIAN of 67.5. Evaluation of the prevalences of the 14 claw lesions considered and the CCS values revealed that Fleckvieh cows (CCS-MEDIAN: 24.0), followed closely by Holstein cows (CCS-MEDIAN: 22.7) had significantly poorer claw health (P < 0.0001) compared to Brown Swiss cows (CCS-MEDIAN: 12.0). The use of CCS and FCS as primary claw health indicators allowed for a quick assessment of the current state of an individual cow and a dairy herd in a benchmarking system. Detailed information on the claw health of each animal and the dairy herd can be easily reviewed by examining diagnosis lists that display prevalences, particularly those related to lameness, in the respective electronic documentation systems., Competing Interests: Conflict of Interest None of the authors has any financial or personal relationships that could inappropriately influence or bias the content of the paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h 2 ) and genetic correlations (r a ) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h 2  = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (r a  = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (r a  = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h 2  = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health., (© 2024 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

We recently reported the ubiquitous occurrence of mycotoxins and their secondary metabolites in dairy rations and a substantial variation in the feeding management among Austrian dairy farms. The present study aimed to characterize to which extent these factors contribute to the fertility, udder health traits, and performance of dairy herds. During 2019 and 2020, we surveyed 100 dairy farms, visiting each farm 2 times and collecting data and feed samples. Data collection involved information on the main feed ingredients, nutrient composition, and the levels of mycotoxin and other metabolites in the diet. The annual fertility and milk data of the herds were obtained from the national reporting agency. Calving interval was the target criterion for fertility performance, whereas the percentage of primiparous and multiparous cows in the herd with somatic cell counts above 200,000 cells/mL was the criterion for impaired udder health. For each criterion, herds were classified into 3 groups: high/long, mid, and low/short, with the cut-off corresponding to the <25th and >75th percentiles and the rest of the data, respectively. Accordingly, for the calving interval, the cut-offs for the long and short groups were ≥400 and ≤380 d, for the udder health in primiparous cows were ≥20% and ≤8% of the herd, and for the udder health in multiparous cows were ≥35% and ≤20% of the herd, respectively. Quantitative approaches were further performed to define potential risk factors in the herds. The high somatic cell count group had higher dietary exposure to enniatins (2.8 vs. 1.62 mg/cow per d), deoxynivalenol (4.91 vs. 2.3 mg/cow per d), culmorin (9.48 vs. 5.72 mg/cow per d), beauvericin (0.32 vs. 0.18 mg/cow per d), and siccanol (13.3 vs. 5.15 mg/cow per d), and total Fusarium metabolites (42.8 vs. 23.2 mg/cow per d) and used more corn silage in the ration (26.9% vs. 17.3% diet DM) compared with the low counterparts. Beauvericin was the most substantial contributing variable among the Fusarium metabolites, as indicated by logistic regression and modeling analyses. Logistic analysis indicated that herds with high proportions of cows with milk fat-to-protein ratio >1.5 had an increased odds for a longer calving interval, which was found to be significant for primiparous cows (odds ratio = 5.5, 95% confidence interval = 1.65-21.7). As well, herds with high proportions of multiparous cows showing levels of milk urea nitrogen >30 mg/dL had an increased odds for longer calving intervals (odds ratio = 2.96, 95% confidence interval = 1.22-7.87). In conclusion, the present findings suggest that dietary contamination of Fusarium mycotoxins (especially emerging ones), likely due to increased use of corn silage in the diet, seems to be a risk factor for impairing the udder health of primiparous cows. Mismatching dietary energy and protein supply of multiparous cows contributed to reduced herd fertility performance., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The aim of this study was to investigate the prevalence of ESBL/AmpC-producing E. coli and the resistance pattern of commensal E. coli , as well as the link between the use of antibiotics (AMU) and the occurrence of resistance in E. coli on Austrian dairy farms. AMU data from 51 farms were collected over a one-year period in 2020. Fecal samples were collected from cows, pre-weaned and weaned calves in 2020 and 2022. Samples were then analyzed using non-selective and selective agar plates, E. coli isolates were confirmed by MALDI-TOF analysis. Broth microdilution was used for antimicrobial susceptibility testing. The AMU of each farm was quantified as the number of Defined Daily Doses (nDDD vet ) and Defined Course Doses (nDCD vet ) per cow and year. Cephalosporins (mean 1.049; median 0.732 DDD vet /cow/year) and penicillins (mean 0.667; median 0.383 DDD vet /cow/year) were the most frequently used antibiotics on these farms, followed by tetracyclines (mean 0.275; median 0.084 DDD vet /cow/year). In 2020, 26.8% of the E. coli isolated were resistant to at least one antibiotic class and 17.7% of the isolates were classified as multidrug resistant (≥3 antibiotic classes). Out of 198 E. coli isolates, 7.6% were identified as extended-spectrum/AmpC beta-lactamase (ESBL/AmpC) producing E. coli . In 2022, 33.7% of E. coli isolates showed resistance to at least one antibiotic and 20.0% of isolates displayed multidrug resistance. Furthermore, 29.5% of the samples carried ESBL/AmpC-producing E. coli . In 2020 and 2022, the most frequently determined antibiotic resistances among commensal E. coli isolates were to tetracyclines, sulfonamides and penicillins. In addition, pre-weaned calves had the highest resistance rates in both years. Statistical analyses showed a significant association between low and high use AMU classifications for penicillins (in nDDD vet /cow/year) and their respective resistance among commensal E. coli isolates in 2020 ( p  = 0.044), as well as for sulfonamide/trimethoprim ( p  = 0.010) and tetracyclines ( p  = 0.042). A trend was also noted between the total amount of antibiotics used on farm in 2020 (by nDDD vet /cow/year) and multidrug resistances in commensal E. coli isolated on farm that year ( p  = 0.067). In conclusion, the relationship between AMU and antimicrobial resistance (AMR) on dairy farms continues to be complex and difficult to quantify., Competing Interests: CE-D is employed by ZuchtData EDV-Dienstleistungen GmbH; WO owns his own veterinary practice. All authors collaborated on this project as part of the D4Dairy research consortium (www.d4dairy.com), which was made up of both commercial and academic research institutions as required by the funding agency. The funders did not contribute to the study’s design, data collection, analysis or interpretation, manuscript writing, or decision to publish the results. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Werner, Käsbohrer, Wasner, Köberl-Jelovcan, Vetter, Egger-Danner, Fuchs, Obritzhauser and Firth.)

Mid-infrared (MIR) spectroscopy is routinely applied to determine major milk components, such as fat and protein. Moreover, it is used to predict fine milk composition and various traits pertinent to animal health. MIR spectra indicate an absorbance value of infrared light at 1060 specific wavenumbers from 926 to 5010 cm -1 . According to research, certain parts of the spectrum do not contain sufficient information on traits of dairy cows. Hence, the objective of the present study was to identify specific regions of the MIR spectra of particular importance for the prediction of mastitis and ketosis, performing variable selection analysis. Partial least squares discriminant analysis (PLS-DA) along with three other statistical methods, support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and random forest (RF), were compared. Data originated from the Austrian milk recording and associated health monitoring system (GMON). Test-day data and corresponding MIR spectra were linked to respective clinical mastitis and ketosis diagnoses. Certain wavenumbers were identified as particularly relevant for the prediction models of clinical mastitis (23) and ketosis (61). Wavenumbers varied across four distinct statistical methods as well as concerning different traits. The results indicate that variable selection analysis could potentially be beneficial in the process of modeling.

This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30-42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.

Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (-/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health.

While benchmarking is already used for the assessment of performance gaps in cattle herd management and welfare concerns, its application to quantifying claw health performance is relatively new. The goal here was to establish a benchmarking system for claw health in Austrian dairy cattle. We used electronically registered claw health data of cows from 512 dairy herds documented by professional hoof trimmers, culling data from the same herds, and locomotion scores taken at regular milk performance testings in 99 dairy herds during 2020. Mean, median and the 10th, 25th, 75th, and 90th percentiles of the incidences of risk of lameness, 13 common claw lesions, and the annual culling risk directly related to claw and limb disorders were used as key performance indicators. Only validated data sets were used and participating trimmers and locomotion scorers had to pass interobserver reliability tests with weighted Cohen's kappa values ≥ 0.61 indicating substantial interobserver agreement. This claw health benchmarking system is intended to be used henceforth in the transnational cattle data network (RDV) by all participating farmers and is also available for veterinarians and consultants, with the agreement of respective farmers.

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: The impact of lameness on fertility in dairy cows has already been investigated, however predominantely in Holstein cows. The aim of this study was to evaluate the impact of lameness during the dry period, between calving and first service and between calving and conception (days open) on selected fertility traits in Austrian Fleckvieh cows. Locomotion scoring of dairy cows was performed during the course of routine performance testing in 2014 and 2015. Using the observed maximum locomotion score (MLSC) during pre- and postcalving periods, the cows were classified into three groups: cows never lame (MLSC 1), cows that showed MLSC 2, and cows with MLSC ≥3 during these defined periods. Data sets of 3,998 lactations of 3,058 Austrian Fleckvieh cows from 97 dairy herds could be evaluated. In several statistical models the fixed effects of MLSC (1, 2, ≥ 3), farm, year and season of calving, parity*age class at calving, and early fertility disorders were considered for analysis of the traits days from calving to first insemination, interval from first to last insemination, days from calving to conception and calving interval (CI), as well as the non-return-rate90 (NRR90). Mean lameness prevalence during the dry period was 19,43 %, and reached 27,70 % in the period between calving and conception. Lameness (MLSC ≥ 3) during the dry period significantly (P = 0,030) prolonged the period between calving and conception, and lameness (MLSC ≥ 3) during the period from calving to first service had a significantly detrimental effect on the periods calving to first insemination, days open and CI (P < 0,001). Further, highly significant associations (P < 0,001) in cows showing MLSC ≥ 2 during the period between calving and conception on all fertility traits were determined. Apart from lameness, farm, year and season of calving, parity*-age class at calving, early fertility disorders and, partly, the interaction of the latter two effects significantly (P.

In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors., (© 2021. The Author(s).)

Introduction: Introduction The impact of lameness on milk yield in dairy cows has been investigated already in many countries by several authors, taking into consideration almost exclusively locomotion scores ≥ 3. The aim of this study was to evaluate the impact of lameness and of the various lameness scores (2-5) on milk yield and milk solids in cows of the three most important dairy cattle breeds (Fleckvieh, Braunvieh, Holstein-Friesian) in Austria within one lactation period. Material and methods Locomotion scoring of dairy cows was performed by trained personnel every 30-40 days in 2014 during the course of routine performance testing. From the recorded locomotion scores (1-5) and the number of lameness episodes, the cows were classified into five lactation-locomotion-score-groups (LLS-G1-5). In total, data sets of 4005 cows from 144 dairy farms across Austria could be evaluated. Using two statistical models the fixed effects on LLS group, breed (evaluation across all breeds in model 1), farm, year and season of calving, parity and the «continuous effect» number of milking days were included in the analyses of milk, fat and protein yield. Results The mean, cumulative lameness prevalence during the observation period was 51.0%, and 8.1% were cows from the LLS-G5 group showing repeated and severe locomotion scores. During the first 100 days in milk 34.7% of all cows were lame. In model 1, all effects considered had a significant impact (P .

Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the genetic background underlying differences in the incidence of hoof disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. Despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the total number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh were as follows: CBLB on BTA1, which causes arthritis in rats; CAV2 on BTA4, which affects skeletal muscles in mice; PTHLH on BTA5, which causes disease phenotypes related to the skeleton in humans, mice, and zebrafish; and SORCS2 on BTA6, which causes decreased susceptibility to injury in mice. Some of the significant SNPs (BTA1, BTA4, BTA5, BTA13, and BTA16) revealed allelic heterogeneity-i.e., different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions (BTA1, BTA5, BTA13, and BTA16) correlated to inter-breed differences in linkage disequilibrium (LD) structure and may thus represent false-positive heterogeneity. However, positions on BTA6 ( SORCS2 ), BTA14, and BTA24 mark Braunvieh-specific regions. We hypothesize that the observed genetic heterogeneity of hoof disorders is a by-product of different selection goals defined for the analyzed breeds-toward dairy production in Braunvieh and toward beef production in Fleckvieh. Based on the current dataset, it is not possible to unequivocally confirm or exclude the hypothesis of genetic heterogeneity in the susceptibility to hoof disorders between Fleckvieh and Braunvieh. The main reason for the problem is that the potential heterogeneity was explored through SNP-phenotype associations and not through causal mutations, due to a limited SNP density offered by the SNP-chip. The rationale against genetic heterogeneity comprises a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, different selection goals defined for the analyzed breeds accompanied by no systematic, genome-wide differences in LD structure between the breeds favor the heterogeneity hypothesis at some smaller genomic regions., (Copyright © 2020 Kosińska-Selbi, Suchocki, Egger-Danner, Schwarzenbacher, Frąszczak and Szyda.)

Feet and legs disorders influence dairy cattle breeding by their effect on animal welfare, economic losses due to lower production and fertility, costs of treatment, and problems with herd management. In our study, we estimated heritabilities and performed a 2-step GWAS for 3 traits describing hoof health: hoof health status defined by a veterinarian (HSV), hoof health status defined by a claw trimmer (HSC), and the total number of hoof disorders (NHD), scored in 1,998 Fleckvieh and 979 Braunvieh cows. The individuals were genotyped with a high-density (HD) panel consisting of 76,934 SNP. For significant genomic regions, the SNP information was enhanced by SNP imputed from the whole-genome sequence of Fleckvieh and Braunvieh bulls from the 1000 Bulls Genome project. The heritabilities were estimated to be 0.035 for HSV, 0.249 for HSC, and 0.279 for NHD. Based on the first-stage GWAS with SNP from the HD panel, 7 significant genomic regions on 6 chromosomes were defined: (1) 120 SNP spanning 15,522 bp on BTA1, including the TOPBP1 gene; (2) 4,139 SNP spanning 1,426,046 bp on BTA7, including the RIOK2 and RGMB genes; (3) 167 SNP spanning 167,352 bp on BTA13, including the C13H20orf194 gene; (4) 2 regions on BTA14, one harboring 1,071 SNP spanning 380,024 bp, including RRM2B and NCALD, and the other comprising 632 SNP spanning 385,111 bp, including STK3; (5) 328 SNP on BTA15, spanning 235,567 bp between FAM168A and PLEKHB1; and (6) 1,549 SNP on BTA22, spanning 596,101 bp in the neighborhood of PTPRG. Then, we conducted a second-stage GWAS based on SNP from whole-genome sequences within the significant regions obtained in the first stage of the analysis. For HSV, the highest additive effect was estimated for 23 SNP located within a region on BTA15, close to FAM168A, corresponding to a predicted gene sequence. For HSC, the highest additive effect was attributed to 44 SNP located within a region of BTA22 corresponding to 4 predicted gene sequences, with rs135082893 within a sequence encoding a microRNA. Another potential causal mutation for HSC was rs134142607 on BTA13, within the exon of C13H20orf194. For NHD, 33 SNP with the highest estimated effect were located on BTA7 within a region of a predicted gene positioned between RIOK2 and RGMB. On BTA14, all significant SNP were located in introns of STK3, which is responsible for the "abnormal gait" phenotype in mice., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This study reports on the exploration of temporal relationships between milk mid-infrared predicted biomarkers and lameness events. Lameness in dairy cows is an issue that can vary greatly in severity and is of concern for both producers and consumers. Metabolic disorders are often associated with lameness. However, lameness can arise weeks or even months after the metabolic disorder, making the detection of causality difficult. We already use mid-infrared technology to predict major milk components, such as fat or protein, during routine milk recording and for milk payment. It was recently shown that this technology can also be used to predict novel biomarkers linked to metabolic disorders in cows, such as oleic acid (18:1 cis-9), β-hydroxybutyrate, acetone, and citrate in milk. We used these novel biomarkers as proxies for metabolic issues. Other studies have explored the possibility of using mid-infrared spectra to predict metabolic diseases and found it (potentially) usable for indicating classes of metabolic problems. We wanted to explore the possible relationship between mid-infrared-based metabolites and lameness over the course of lactation. In total, data were recorded from 6,292 cows on 161 farms in Austria. Lameness data were recorded between March 2014 and March 2015 and consisted of 37,555 records. Mid-infrared data were recorded between July and December 2014 and consisted of 9,152 records. Our approach consisted of fitting preadjustments to the data using fixed effects, computing pair-wise correlations, and finally applying polynomial smoothing of the correlations for a given biomarker at a certain month in lactation and the lameness events scored on severity scale from sound or non-lame (lameness score of 1) to severely lame (lameness score of 5) throughout the lactation. The final correlations between biomarkers and lameness scores were significant, but not high. However, for the results of the present study, we should not look at the correlations in terms of absolute values, but rather as indicators of a relationship through time. When doing so, we can see that metabolic problems occurring in mo 1 and 3 seem more linked to long-term effects on hoof and leg health than those in mo 2. However, the quantity (only 1 pair-wise correlation exceeded 1,000 observations) and the quality (due to limited data, no separation according to more metabolic-related diseases could be done) of the data should be improved., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Antimicrobial use in livestock production is a controversial subject. While antimicrobials should be used as little as possible, it is still necessary, from both an animal health and welfare point of view, to treat infected animals. The study presented here aimed to analyse antimicrobial use on Austrian dairy farms by calculating the number of Defined Course Doses (DCD vet ) administered per cow and year for dry cow therapy. Antimicrobial use was analysed by production system and whether farmers stated that they used blanket dry cow therapy (i.e., all cows in the herd were treated) or selective dry cow therapy (i.e., only cows with a positive bacteriological culture or current/recent history of udder disease were treated). A statistically significant difference ( p < 0.001) was determined between antimicrobial use for blanket (median DCD vet /cow/year: 0.88) and selective dry cow therapy (median DCD vet /cow/year: 0.41). The difference between antimicrobial use on conventional and organic farms for dry cow therapy as a whole, however, was not statistically significant ( p = 0.22) (median DCD vet /cow/year: 0.68 for conventional; 0.53 for organic farms). This analysis demonstrates that selective dry cow therapy leads to a lower overall use of antimicrobials and can assist in a more prudent use of antimicrobials on dairy farms.

Bovine mastitis is the most frequently reported disease among dairy cows worldwide. Treatment of udder disease often involves the use of antimicrobial substances, which is difficult to justify with respect to their possible effect on the development and spread of antimicrobial resistance. Prevention of udder disease is therefore always preferable to treatment. The study presented here statistically analyzed the probability of mastitis occurring during 3,049 lactation periods on 208 farms and attempted to ascertain which on-farm management factors contributed to the occurrence of this udder disease in Austria. Farm management was assessed via online surveys completed by 211 farmers (211/251; response rate = 84.1%) as well as national milk performance recorders observing milking technique and herd veterinarians evaluating farm hygiene levels. Veterinary treatment records were used as a basis for mastitis reporting. The analysis was carried out using a generalized linear mixed model. The study population was not randomized but was part of a larger observational study. More than three fourths of the study farms were run conventionally, and the remainder were organic. Freestalls (and straw yards) made up 66% of the study population, and 34% of farms had tiestalls. Herd size ranged from 8 to 94 dairy cows (mean = 26.9; median = 21), with the most common breed (74% of all cows) being dual-purpose Simmental (Austrian Fleckvieh). A mastitis risk of 14.4% was reported via veterinary treatment records. The following factors were shown to be associated with a reduction in the risk of mastitis occurring: regular access to pasture (odds ratio, OR = 0.73), automatic milking machine shut-off (OR 0.67), and access to feed immediately after milking (OR = 0.43). Detrimental effects, which were likely to increase the probability of mastitis occurring, included lactation number (OR = 1.18), farming part time (OR = 1.55), and udders on the farm being classed by herd veterinarians as medium to severely soiled (OR = 1.47). The study presented here was able to confirm several management factors recommended to reduce the probability of mastitis occurring during a cow's lactation period, with particular relevance for the small dairy herds common to Austria., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The specific objective of this study was to evaluate the use of lameness scoring to genetically improve claw health in Austrian Fleckvieh, Brown Swiss, and Holstein cows based on data from the "Efficient Cow" project. In 2014, a 1-yr data collection was carried out. Data from 6,519 cows kept on 161 farms were recorded. At each time of milk recording, lameness scores were assessed by trained staff of the milk recording organizations. Hoof trimming on these farms was documented and recorded as well. Veterinarian diagnoses and culling due to foot and leg problems from these farms were available from the routine recording system. As repeated lameness records per cow and lactation were available, an overall lactation lameness score was calculated. Estimated heritabilities for lameness were 0.11, 0.05, and 0.09 for Fleckvieh, Brown Swiss, and Holstein, respectively; however, only heritability estimates for Fleckvieh were significantly different from zero. Breeding values for lameness were obtained, reversed in sign, and cows were ranked according to their breeding value. A low breeding value for lameness resistance (the bottom 10% of the cows) was associated with a significantly higher frequency of trimmed cows, which indicates that the cows selected by the farmer to be trimmed are not completely random. Additionally, a high breeding value for lameness resistance (the top 10% of the cows) was associated with lower frequencies of claw diseases recorded at trimming, claw and leg diagnoses, and culling due to foot and leg problems, which highlights the usefulness of lameness scoring for genetic improvement of claw health. Overall, selecting for a better lameness score has the potential to reduce claw diseases, especially the frequency of severe claw diseases that lead to culling., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Routine recording of claw health status at claw trimming of dairy cattle has been established in several countries, providing valuable data for genetic evaluation. In this review, we examine issues related to genetic evaluation of claw health; discuss data sources, trait definitions, and data validation procedures; and present a review of genetic parameters, possible indicator traits, and status of genetic and genomic evaluations for claw disorders. Different sources of data and traits can be used to describe claw health. Severe cases of claw disorders can be identified by veterinary diagnoses. Data from lameness and locomotion scoring, activity information from sensors, and feet and leg conformation traits are used as auxiliary traits. The most reliable and comprehensive information is data from regular hoof trimming. In genetic evaluation, claw disorders are usually defined as binary traits, based on whether or not the claw disorder was present (recorded) at least once during a defined time period. The traits can be specific disorders, composite traits, or overall claw health. Data validation and editing criteria are needed to ensure reliable data at the trimmer, herd, animal, and record levels. Different strategies have been chosen, reflecting differences in herd sizes, data structures, management practices, and recording systems among countries. Heritabilities of the most commonly analyzed claw disorders based on data from routine claw trimming were generally low, with ranges of linear model estimates from 0.01 to 0.14, and threshold model estimates from 0.06 to 0.39. Estimated genetic correlations among claw disorders varied from -0.40 to 0.98. The strongest genetic correlations were found among sole hemorrhage (SH), sole ulcer (SU), and white line disease (WL), and between digital/interdigital dermatitis (DD/ID) and heel horn erosion (HHE). Genetic correlations between DD/ID and HHE on the one hand and SH, SU, or WL on the other hand were, in most cases, low. Although some of the studies were based on relatively few records and the estimated genetic parameters had large standard errors, there was, with some exceptions, consistency among studies. Various studies evaluate the potential of various data soureces for use in breeding. The use of hoof trimming data is recommended for maximization of genetic gain, although auxiliary traits, such as locomotion score and some conformation traits, may be valuable for increasing the reliability of genetic evaluations. Routine genetic evaluation of direct claw health has been implemented in the Netherlands (2010); Denmark, Finland, and Sweden (joint Nordic evaluation; 2011); and Norway (2014), and other countries plan to implement evaluations in the near future., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

Typically, dairy cows are dried off at the end of lactation. During the dry period, intramammary infections may be cured or persist, and new infections may occur. Traditionally, antibiotics (AB) have been used at dry-off. However, blanket antibiotic dry-off treatment may no longer be justifiable and, recently, selective use of antibiotics at dry off has been proposed and different decision criteria suggested. The objective of the study was to evaluate cows receiving antibiotic treatment at dry off (AB group) compared with cows dried off without antibiotics (non-AB group) using a large data set. Primary outcome parameters were milk yield, somatic cell count (SCC), and frequency of mastitis in the subsequent lactation. Additionally, we aimed to calculate cut-off values to determine, at the cow level, whether antibiotic dry-off treatment is indicated. A data set of 88,534 lactations was used; 27,723 cows were dried off using antibiotics (AB group; 31.3%) and 60,811cows were dried off without antibiotics (non-AB group; 68.7%). Milk yield in previous and subsequent lactations was higher in the AB group. Cows in the AB group produced, on average, 91 kg more milk in the subsequent lactation. The average SCC during the final 90 d of the previous lactation and at dry-off did not differ between the 2 groups. The probability of drying off using antibiotics increased almost linearly with higher milk yield and with higher SCC. The use of antibiotics resulted in an average decrease in SCC of 1,500 cells/mL in the subsequent lactation. The frequency of clinical mastitis during the previous lactation was higher in the AB group than in the non-AB group. Independently of the dry-off treatment, this difference remained in the subsequent lactation within 90 d in milk. The use of antibiotics at dry-off had no significant effect on the frequency of mastitis within 90 d in milk of the subsequent lactation. The study indicates that Austrian farmers and veterinarians are applying selective dry-cow treatment using milk yield and, to a lesser extent, SCC and mastitis frequency, for decision-making. However, even though a large data set was used, cut-off values with sufficient diagnostic value for selective dry-cow treatment could not be identified. It is likely that cut-off values must be identified at the herd level or in combination with additional parameters (e.g., results of bacterial culture)., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This study evaluated the voluntary and compulsory implementation of a bovine viral diarrhoea virus (BVDV) eradication programme in the Austrian Federal State of Styria, Austria, from an economic point of view using ex-post assessment of costs and benefits (disease losses avoided). An economic net benefit (benefit:cost ratio, BCR=1.18) of the programme was demonstrated during the voluntary programme phase (January 1998-July 2004). The break-even point was reached in 2003. If investments in the compulsory programme (August 2004-December 2016) were taken into account, a net economic loss (BCR=0.16) was demonstrated. In contrast to on-going annual testing of all cattle herds, annual testing in accordance with a revised sampling scheme could reduce total surveillance costs by more than 77%. A Bayesian structural time series model was applied to analyse a hypothesised positive impact of the compulsory BVDV programme on the Styrian cattle export market. The average number of exported cows and bulls increased significantly by 42% (P=0.03) and 47% (P=0.01), respectively, and the producer price increased by 14% (P=0.00) and 5% (P=0.16), respectively, during the compulsory programme period compared with the period prior to intervention. This equates to an average revenue increase of €29,754 for cows and €137,563 for bulls per month. These results justify the implementation of eradication programmes, which initially may not appear to be economically viable, particularly if trade effects are not included in the calculations., (Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.)