Neonatal calves are relatively susceptible to heat loss, and previous research suggests that reduced environmental temperatures are associated with reduced average daily gain (ADG) during the preweaning phase. Current methods of mitigating negative effects of colder environmental conditions include the use of calf jackets and the provision of supplementary heat sources; however, previous research is limited. The aim of this study was to evaluate the effect of calf jackets and 1-kW heat lamps on the growth rates of preweaning calves and evaluate associations between environmental temperature and ADG using a Bayesian approach to incorporate both current and previous data. Seventy-nine calves from a single British dairy farm were randomly allocated at birth to 1 of the following 4 groups: no jacket and no heat lamp, heat lamp but no jacket, jacket but no heat lamp, or both heat lamp and jacket between January and April of 2021. Calves were weighed at both birth and at approximately 21 d of age. Temperature was recorded both inside and outside of the calf building, and in pens both with and without heat lamps using data loggers. To explore the effect of treatment group and environmental temperature on ADG, a fixed effects model was fitted over 1,000 bootstrap samples. The effect of environmental temperature on ADG was further explored within a Bayesian framework that used temperature and ADG data for 484 calves from 16 farms available from a previous trial as prior information. Calves housed under a 1-kW heat lamp had an increased ADG of 0.09 kg/d (95% bootstrap confidence interval: -0.02 to 0.20 kg/d), and no effect of jacket or interactions between jacket and heat lamp were found. A significant positive association was identified between the mean environmental temperature of the calf building and ADG, with a 1°C increase in temperature being associated with a 0.03 kg/d increase in ADG (95% bootstrap confidence interval: 0.01 to 0.04 kg/d). Associations between environmental temperature and ADG were further evaluated within a Bayesian framework, and posterior estimates were 0.014 kg/d of ADG per 1°C increase (95% credible interval: 0.009 to 0.021 kg/d). This study demonstrated that a 1-kW heat lamp was effective in increasing ADG in calves, and no significant effect of calf jacket on ADG was found. A significant, positive effect of increased pen temperature on calf ADG was identified in this study and was reinforced when including prior information from previous research within a Bayesian framework.

Previous research has identified key factors associated with improved average daily gain (ADG) in preweaning dairy calves and these factors have been combined to create a web app-based calf health plan (www.nottingham.ac.uk/herdhealthtoolkit). A randomized controlled trial was conducted to determine the effect of implementing this evidence-based calf health plan on both productivity and health outcomes for calves reared on British dairy farms. Sixty dairy farms were randomized by location (North, South, and Midlands) to either receive the plan at the beginning (INT) or after the end of the trial (CON) and recorded birth and weaning weights by weigh tape, and cases of morbidity and mortality. Calf records were returned for 3,593 calves from 45 farms (21 CON, 24 INT), with 1,760 calves from 43 farms having 2 weights recorded >40 d apart for ADG calculations, with 1,871 calves from 43 farms born >90 d before the end of the trial for morbidity and mortality calculations. Associations between both intervention group and the number of interventions in place with ADG were analyzed using linear regression models. Morbidity and mortality rates were analyzed using beta regression models. Mean ADG was 0.78 kg/d, ranging from 0.33 to 1.13 kg/d, with mean rates of 20.12% (0-96.55%), 16.40% (0-95.24%), and 4.28% (0-18.75%) for diarrhea, pneumonia, and mortality. The INT farms were undertaking a greater number of interventions (9.9) by the end of the trial than CON farms (7.6). Mean farm ADG was higher for calves on INT farms than CON farms for both male beef (MB, +0.22 kg/d) and dairy heifer (DH, +0.03 kg/d) calves. The MB calves on INT farms had significantly increased mean ADG (0.12 kg/d, 95% confidence interval: 0.02-0.22) compared with CON farms. No significant differences were observed between intervention groups for morbidity or mortality. Implementing one additional intervention from the plan, regardless of intervention group, was associated with improvements in mean ADG for DH calves of 0.01 kg/d (0.01, 0-0.03) and MB calves of 0.02 kg/d (0.00-0.04). Model predictions suggest that a farm with the highest number of interventions in place (15) compared with farms with the lowest number of interventions in place (4) would expect an improvement in growth rates from 0.65 to 0.81 kg/d for MB, from 0.73 to 0.88 kg/d for DH, a decrease in mortality rates from 10.9% to 2.8% in MB, and a decrease in diarrhea rates from 42.1% to 15.1% in DH. The calf health plan tested in this study represents a useful tool to aid veterinarians and farmers in the implementation of effective management interventions likely to improve the growth rates, health, and welfare of preweaning calves on dairy farms.

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management.

Abstract The control of mastitis remains a focus of attention for dairy farmers, veterinary surgeons and advisors due to its impact on cow health and welfare, milk quality, sustainable production, and the financial costs associated with treatment, prevention and ongoing control. In addition, the focus on the unnecessary use of antibiotics in agriculture has meant that mastitis control in dairy herds has received renewed interest, particularly around prevention of new infection and alternative treatment strategies. The latter includes the selective use of intra-mammary antibiotic for infected cows at drying-off, reserving parenteral antibiotic for clinical cases where the cow is ill, and selecting intra-mammary antibiotic treatment for clinical mastitis based on culture results. Treating clinical mastitis caused by Gram-positive pathogens such as Streptococcus spp. with intra-mammary antibiotic remains important to optimise chance of cure and reduce risk of transmission of infection, although antibiotics may not be required for clinical mastitis infections caused by other pathogens, particularly E. coli . The long-term reduction and rationalisation of antibiotic use in mastitis control is achieved through improved management to prevent new infections and avoid the need to treat mastitis. This comes through understanding the predominant epidemiological ‘pattern’ of infection in the herd and targeted implementation of well-specified interventions to reduce the rate of new infection, either in lactation or during the dry period. For most dairy herds, environmental mastitis pathogens predominate and therefore management and hygiene of housed and pastured environments is a key component of mastitis control plans designed to reduce the need to use antibiotics in mastitis control.

The preweaning period is vital in the development of calves on dairy farms and improving daily liveweight gain (DLWG) is important to both financial and carbon efficiency; minimising rearing costs and improving first lactation milk yields. In order to improve DLWG, veterinary advisors should provide advice that has both a large effect size as well as being consistently important on the majority of farms. Whilst a variety of factors have previously been identified as influencing the DLWG of preweaned calves, it can be challenging to determine their relative importance, which is essential for optimal on-farm management decisions. Regularised regression methods such as ridge or lasso regression provide a solution by penalising variable coefficients unless there is a proportional improvement in model performance. Elastic net regression incorporates both lasso and ridge penalties and was used in this research to provide a sparse model to accommodate strongly correlated predictors and provide robust coefficient estimates. Sixty randomly selected British dairy farms were enrolled to collect weigh tape data from preweaned calves at birth and weaning, resulting in data being available for 1014 calves from 30 farms after filtering to remove poor quality data, with a mean DLWG of 0.79 kg/d (range 0.49-1.06 kg/d, SD 0.13). Farm management practices (e.g. colostrum, feeding, hygiene protocols), building dimensions, temperature/humidity and colostrum quality/bacteriology data were collected, resulting in 293 potential variables affecting farm level DLWG. Bootstrapped elastic net regression models identified 17 variables as having both a large effect size and high stability. Increasing the maximum preweaned age within the first housing group (0.001 kg/d per 1d increase, 90 % bootstrap confidence interval (BCI): 0.000-0.002), increased mean environmental temperature within the first month of life (0.012 kg/d per 1 °C increase, 90 % BCI: 0.002-0.037) and increased mean volume of milk feeding (0.012 kg/d per 1 L increase, 90 % BCI: 0.001-0.024) were associated with increased DLWG. An increase in the number of days between the cleaning out of calving pen (-0.001 kg/d per 1d increase, 90 % BCI: -0.001-0.000) and group housing pens (-0.001 kg/d per 1d increase, 90 % BCI: -0.002-0.000) were both associated with decreased DLWG. Through bootstrapped elastic net regression, a small number of stable variables have been identified as most likely to have the largest effect size on DLWG in preweaned calves. Many of these variables represent practical aspects of management with a focus around stocking demographics, milk/colostrum feeding, environmental hygiene and environmental temperature; these variables should now be tested in a randomised controlled trial to elucidate causality.

Dairy herd health management benefits dairy farmers, the environment, dairy cows and citizens. It is an important part of modern dairy farm veterinary care. Dairy herd health management is assessing, monitoring and improving the health of dairy cows at a population level. Good herd health management takes a holistic approach and is ongoing and cyclical. All members of the dairy farm team and their advisors are involved, decisions are informed by data generated by the herd. These data may come from numerous sources. The data are processed and analysed to monitor cow health, target investigations and evaluate progress. To make lasting change on farms, advisors must communicate appropriately with farm managers to understand behaviour and motivate change. This chapter reviews these aspects of dairy herd health management, giving practical suggestions on how to get started, how to incorporate herd health management into business models and how to maintain momentum.

Abstract Background Heart girth tapes (HGTs) are often used as an alternative to weight scales for calves. This study investigated the accuracy of HGT in estimating bodyweight and daily liveweight gain (DWLG) of pre‐weaned calves, and the impact of inter‐observer variation. Method In Study 1, 119 calves were weighed using HGT and electronic scales on multiple occasions. Mixed‐effects models for both bodyweight and DLWG were used to determine the accuracy of HGT compared to the electronic scales. Simulation data were used to further analyse the accuracy of DLWG estimation including for factors such as the effect of group size on group DLWG estimates. In Study 2, 10 observers weighed 20 pre‐weaned calves, using HGT and electronic scales. Mixed‐effect model was used to investigate the impact of different observers on the accuracy of HGT on measuring bodyweights. Results Mixed‐effects model results suggest HGT provides a relatively accurate estimation of weight (MAE: 2.66 kg) and relatively inaccurate estimation of DLWG (MAE 0.10 kg/d). Simulated data identified associations between time between weight dates and error in DLWG estimation, with MAE of individual DLWG estimation decreasing from 0.43 kg/d when 14 days apart to 0.08 kg/d when 70 days apart. Increased calf numbers reduced error rates of group DLWG estimation, with <0.05 kg/d error achieved in >90% of simulations when 12 calves were weighed 70 days apart. Conclusions HGTs are relatively accurate at estimating individual bodyweights but are unreliable methods for measuring DLWG in individual calves, particularly weighed within a short‐time period. Estimates at group level however are relatively accurate, providing there is a suitable period of time between weigh dates and an appropriate number of calves per group.

Total bacterial counts (TBC) and coliform counts (CC) were estimated for 328 colostrum samples from 56 British dairy farms. Samples collected directly from cows' teats had lower mean TBC (32,079) and CC (21) than those collected from both colostrum collection buckets (TBC: 327,879, CC: 13,294) and feeding equipment (TBC: 439,438, CC: 17,859). Mixed effects models were built using an automated backwards stepwise process in conjunction with repeated bootstrap sampling to provide robust estimates of both effect size and 95% bootstrap confidence intervals (BCI) as well as an estimate of the reproducibility of a variable effect within a target population (stability). Colostrum collected using parlor (2.06 log cfu/ml, 95% BCI: 0.35–3.71) or robot (3.38 log cfu/ml, 95% BCI: 1.29–5.80) milking systems, and samples collected from feeding equipment (2.36 log cfu/ml, 95% BCI: 0.77–5.45) were associated with higher TBC than those collected from the teat, suggesting interventions to reduce bacterial contamination should focus on the hygiene of collection and feeding equipment. The use of hot water to clean feeding equipment (−2.54 log cfu/ml, 95% BCI: −3.76 to −1.74) was associated with reductions in TBC, and the use of peracetic acid (−2.04 log cfu/ml, 95% BCI: −3.49 to −0.56) or hypochlorite (−1.60 log cfu/ml, 95% BCI: −3.01 to 0.27) to clean collection equipment was associated with reductions in TBC compared with water. Cleaning collection equipment less frequently than every use (1.75 log cfu/ml, 95% BCI: 1.30–2.49) was associated with increased TBC, the use of pre-milking teat disinfection prior to colostrum collection (−1.85 log cfu/ml, 95% BCI: −3.39 to 2.23) and the pasteurization of colostrum (−3.79 log cfu/ml, 95% BCI: −5.87 to −2.93) were associated with reduced TBC. Colostrum collection protocols should include the cleaning of colostrum collection and feeding equipment after every use with hot water as opposed to cold water, and hypochlorite or peracetic acid as opposed to water or parlor wash. Cows' teats should be prepared with a pre-milking teat disinfectant and wiped with a clean, dry paper towel prior to colostrum collection, and colostrum should be pasteurized where possible.

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating "dry" period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a "positive" diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a "positive" diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use.

National bodies in Great Britain (GB) have expressed concern over young stock health and welfare and identified calf survival as a priority; however, no national data have been available to quantify mortality rates. The aim of this study was to quantify the temporal incidence rate, distributional features, and factors affecting variation in mortality rates in calves in GB since 2011. The purpose was to provide information to national stakeholder groups to inform resource allocation both for knowledge exchange and future research. Cattle birth and death registrations from the national British Cattle Movement Service were analyzed to determine rates of both slaughter and on-farm mortality. The number of births and deaths registered between 2011 and 2018 within GB were 21.2 and 21.6 million, respectively. Of the 3.3 million on-farm deaths, 1.8 million occurred before 24 mo of age (54%) and 818,845 (25%) happened within the first 3 mo of age. The on-farm mortality rate was 3.87% by 3 mo of age, remained relatively stable over time, and was higher for male calves (4.32%) than female calves (3.45%). Dairy calves experience higher on farm mortality rates than nondairy (beef) calves in the first 3 mo of life, with 6.00 and 2.86% mortality rates, respectively. The 0- to 3-mo death rate at slaughterhouse for male dairy calves has increased from 17.40% in 2011 to 26.16% in 2018, and has remained low (<0.5%) for female dairy calves and beef calves of both sexes. Multivariate adaptive regression spline models were able to explain a large degree of the variation in mortality rates (coefficient of determination = 96%). Mean monthly environmental temperature and month of birth appeared to play an important role in neonatal on-farm mortality rates, with increased temperatures significantly reducing mortality rates. Taking the optimal month of birth and environmental temperature as indicators of the best possible environmental conditions, maintaining these conditions throughout the year would be expected to result in a reduction in annual 0- to 3-mo mortality of 37,571 deaths per year, with an estimated economic saving of around £11.6 million (USD $15.3 million) per annum. National cattle registers have great potential for monitoring trends in calf mortality and can provide valuable insights to the cattle industry. Environmental conditions play a significant role in calf mortality rates and further research is needed to explore how to optimize conditions to reduce calf mortality rates in GB.

The risk of mortality and morbidity in calves is highest during the first few weeks of life. The main causes of mortality change throughout the preweaning period: septicaemia is most likely to occur in neonatal calves (up to 28 days of age); diarrhoea in calves less than 30 days old, and bovine respiratory disease in dairy calves more than 30 days old (McGuirk 2008). During this critical period, many producers look to vaccination and other preventive interventions to minimise the risk of disease. This article discusses the role of vaccinations in the rearing of dairy calves, alongside other preventive measures, and considers the immunological changes that occur during the first few weeks of a calf's life that are intrinsically linked with risk of morbidity and subsequent mortality in calves.

The concept of big data, associated data sources and analytics is becoming increasingly talked about both in society as a whole and within the livestock industry. This article provides a clinician-focused review of what big data means, how it is already influencing cattle farm and veterinary businesses, and where this may lead in the future. Cattle clinicians have a major role to play in making the best use of big data to improve animal health and efficiency.

The objective of this study was to use probabilistic sensitivity analysis to evaluate the cost-effectiveness of using an on-farm culture (OFC) approach to the treatment of clinical mastitis in dairy cows and compare this to a 'standard' treatment approach. A specific aim was to identify the herd circumstances under which an OFC approach would be most likely to be cost-effective. A stochastic Monte Carlo model was developed to simulate 5000 cases of clinical mastitis at the cow level and to calculate the associated costs simultaneously when treated according to 2 different treatment protocols; i) a 'conventional' approach (3 tubes of intramammary antibiotic) and ii) an OFC programme, whereby cows are treated according to the results of OFC. Model parameters were taken from recent peer reviewed literature on the use of OFC prior to treatment of clinical mastitis. Spearman rank correlation coefficients were used to evaluate the relationships between model input values and the estimated difference in cost between the standard and OFC treatment protocols. The simulation analyses revealed that both the difference in the bacteriological cure rate due to a delay in treatment when using OFC and the proportion of Gram-positive cases that occur on a dairy unit would have a fundamental impact on whether OFC would be cost-effective. The results of this study illustrated that an OFC approach for the treatment of clinical mastitis would probably not be cost-effective in many circumstances, in particular, not those in which Gram-positive pathogens were responsible for more than 20% of all clinical cases. The results highlight an ethical dilemma surrounding reduced use of antimicrobials for clinical mastitis since it may be associated with financial losses and poorer cow welfare in many instances.

Veterinary RecordVolume 180, Issue 7 p. 183-183 Letter Tool to measure antimicrobial use on farms Robert Hyde, Corresponding Author Robert Hyde svxrh1@exmail.nottingham.ac.uk Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorMartin Green, Martin Green Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorJohn Remnant, John Remnant Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorPeter Down, Peter Down Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorJon Huxley, Jon Huxley Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorPeers Davies, Peers Davies Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorChris Hudson, Chris Hudson Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorJames Breen, James Breen Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this author Robert Hyde, Corresponding Author Robert Hyde svxrh1@exmail.nottingham.ac.uk Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorMartin Green, Martin Green Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorJohn Remnant, John Remnant Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorPeter Down, Peter Down Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorJon Huxley, Jon Huxley Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorPeers Davies, Peers Davies Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorChris Hudson, Chris Hudson Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this authorJames Breen, James Breen Nottingham Dairy Herd Health Group, School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, LE12 5RDSearch for more papers by this author First published: 18 February 2017 https://doi.org/10.1136/vr.j823Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume180, Issue7February 2017Pages 183-183 RelatedInformation

The use of antimicrobials in food producing animals receives much attention and is highlighted as a focus in the efforts against antimicrobial resistance documented in the O'Neil report (2015) , with DEFRA committing to a <50 mg/kg antimicrobial usage across farmed species. Veterinary surgeons must work with their clients to achieve this aim in such a way that does not compromise animal health or productivity. The control and treatment of mastitis is one of the most common reasons for antimicrobial use on UK dairy farms and offers an opportunity for veterinary surgeons to engage in both reducing and rationalising use of antimicrobials in dairy cattle.

Mastitis in dairy herds continues to be important and relevant to veterinary advisors for many reasons, including cow welfare, cost of disease and sustainability of farming and food supply, environmental impact, wastage and use of antimicrobials. When implementing control measures to reduce mastitis infection rates on farm, the importance of a herd level analysis of patterns to inform decision making has been shown to be a crucial step. Although the AHDB Dairy Mastitis Control Plan has performed well since its inception, feedback from participants and businesses indicate that the first stage of the Plan, analysing patterns and making a herd 'diagnosis' is often a difficult stage for Plan Deliverers. Analysis of data using software and understanding of mastitis epidemiology remain barriers to more widespread control of mastitis in the UK. This project aims to provide a fully automated method of assessing the predominant mastitis infection patterns present on farm, using somatic cell count and clinical mastitis records. Importantly, this automated tool will enable mastitis control Plan Deliverers and farmers to immediately highlight the most important areas on their unit for mastitis control and direct efforts accordingly.

Although antibiotic use in the dairy sector is relatively low when compared with other species such as pigs and poultry, dairy herds are likely to come under increasing scrutiny regarding the use of antimicrobials to prevent endemic diseases, particularly in the context of mastitis control. While the treatment of existing infections remains an important part of mastitis herd health, it is prudent to consider the use of antibiotics in mastitis control within the framework of the British Veterinary Association's recent guide to the responsible use of antimicrobial drugs in veterinary practice. Working with clients to avoid the need for antimicrobials using disease control programmes is the first of eight points in the guide and the most important; the recent roll-out of the national DairyCo Mastitis Control Plan has prompted renewed interest in the prevention and control of new intramammary infections and therefore minimising the use of antibiotics in mastitis control.