Effect of repeated episodes of generic clinical mastitis on milk yield in dairy cows

Transcriptomic Changes and Regulatory Networks Associated with Resistance to Mastitis in Xinjiang Brown Cattle

Xinjiang brown cattle are highly resistant to disease and tolerant of roughage feeding. The identification of genes regulating mastitis resistance in Xinjiang brown cattle is a novel means of genetic improvement. In this study, the blood levels of IL-1β, IL-6, IL-10, TNF-α, and TGF-β in Xinjiang brown cattle with high and low somatic cell counts (SCCs) were investigated, showing that cytokine levels were higher in cattle with high SCCs. The peripheral blood transcriptomic profiles of healthy and mastitis-affected cattle were constructed by RNA-seq. Differential expression analysis identified 1632 differentially expressed mRNAs (DE-mRNAs), 1757 differentially expressed lncRNAs (DE-lncRNAs), and 23 differentially expressed circRNAs (DE-circRNAs), which were found to be enriched in key pathways such as PI3K/Akt, focal adhesion, and ECM-receptor interactions. Finally, ceRNA interaction networks were constructed using the differentially expressed genes and ceRNAs. It was found that keynote genes or mRNAs were also enriched in pathways such as PI3K-Akt, cholinergic synapses, cell adhesion molecules, ion binding, cytokine receptor activity, and peptide receptor activity, suggesting that the key genes and ncRNAs in the network may play an important role in the regulation of bovine mastitis.

The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms

In commercial dairy farms, mastitis is associated with increased antimicrobial use and associated resistance, which may affect milk production. This study aimed to develop sensor-based prediction models for naturally occurring clinical bovine mastitis using nine machine learning algorithms with data from 447 mastitic and 2146 healthy cows obtained from five commercial farms in Northeast China. The variables were related to daily activity, rumination time, and daily milk yield of cows, as well as milk electrical conductivity. Both Z-standardized and non-standardized datasets pertaining to four specific stages of lactation were used to train and test prediction models. For all four subgroups, the Z-standardized dataset yielded better results than those of the non-standardized one, with the multilayer artificial neural net algorithm showing the best performance. Variables of importance had a similar rank in this algorithm, indicating the consistency of these variables as predictors for bovine mastitis in commercial farms with similar automatic systems. Moreover, the peak milk yield (PMY) of mastitic cows was significantly higher than that of healthy cows (p < 0.005), indicating that high-yielding cattle are more prone to mastitis. Our results show that machine learning algorithms are effective tools for predicting mastitis in dairy cows for immediate intervention and management in commercial farms.

The Economic Evaluation of Mastitis Control Strategies in Holstein-Friesian Dairy Herds

The economic evaluation of mastitis control is challenging. The objective of this study was to perform the economic evaluation of mastitis control, under different intervention scenarios, quantifying the total cost of mastitis caused by S. aureus in Holstein cows in Argentina. A model was set for a dairy herd of Holstein cows endemically infected with S. aureus. A basic mastitis control plan including proper milking procedures, milking machine test, dry cow therapy, and treatment for clinical mastitis, was compared against other more complex and costly interventions, such as segregation and culling of chronically infected cows. Sensitivity analysis was performed by modifying the intramammary infection transition probabilities, economic parameters, and efficacy of treatment strategies. The basic mastitis control plan showed a median total cost of USD88.6/cow per year, which was close to the infected cows culling scenarios outputs. However, the segregation scenario was the most efficient, in which the total cost was reduced by about 50%. Such cost was more sensitive to probabilities and efficacy than the economic parameters. The model is flexible and can be customized by producers and veterinarians according to different control and herd settings.

Multivariable time series classification for clinical mastitis detection and prediction in automated milking systems

In this study, we developed a machine learning framework to detect clinical mastitis (CM) at the current milking (i.e., the same milking) and predict CM at the next milking (i.e., one milking before CM occurrence) at the quarter level. Time series quarter-level milking data were extracted from an automated milking system (AMS). For both CM detection and prediction, the best classification performance was obtained from the decision tree-based ensemble models. Moreover, applying models on a data set containing data from the current milking and past 9 milkings before the current milking showed the best accuracy for detecting CM; modeling with a data set containing data from the current milking and past 7 milkings before the current milking yielded the best results for predicting CM. The models combined with oversampling methods resulted in specificity of 95 and 93% for CM detection and prediction, respectively, with the same sensitivity (82%) for both scenarios; when lowering specificity to 80 to 83%, undersampling techniques facilitated models to increase sensitivity to 95%. We propose a feasible machine learning framework to identify CM in a timely manner using imbalanced data from an AMS, which could provide useful information for farmers to manage the negative effects of CM.

The costs of chronic mastitis: A simulation study of an automatic milking system farm

Mastitis is a production disease in dairy farming that causes economic losses. Especially chronic mastitis (i.e., mastitis cases continuing longer than 28 days) can substantially affect the risk of transmission of intramammary infections (IMI) and total milk production losses. Insights into the impact of chronic mastitis on production and farm economics are needed to guide chronic mastitis decision-making. We aimed to estimate the costs of chronic mastitis with a Monte Carlo simulation model in which the costs of chronic mastitis were estimated as part of the total mastitis costs. The model simulated milk yields, IMI dynamics, somatic cell count (SCC), and pregnancy status on an average Dutch dairy farm with 100 cow places over 9 years. The model was parameterized using information from the literature and actual sensor data from automatic milking system (AMS) farms. The daily subclinical milk production losses were modeled using a generalized additive model and sensor data. Transmission of IMI was modeled as well. The model results indicated median total costs of mastitis of € 230 per generic IMI case (i.e., a weighted average of all pathogens). The most substantial cost factors were the extra mastitis cases due to transmission, culling, and milk production losses. Other significant costs originated from dry cow treatments and diverted milk. The model also indicated median total costs due to chronic mastitis of € 118 (51 % of the total mastitis costs). The share of chronic mastitis relative to the total mastitis costs was substantial. Transmission of contagious bacteria had the largest share among the chronic mastitis costs (51 % of the costs of chronic cases). The large share of chronic mastitis costs in the total mastitis costs indicates the economic importance of these mastitis cases. The results of the study point to the need for future research to focus on chronic mastitis and reducing its presence on the AMS dairy farm.

The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms

We use multidimensional data from automated monitoring systems and milking systems to predict disorders of dairy cows by employing eight machine learning algorithms. The data included the season, days in milking, parity, age at the time of disorders, milk yield (kg/day), activity (unitless), six variables related to rumination time, and two variables related to the electrical conductivity of milk. We analyze 131 sick cows and 149 healthy cows with identical lactation days and parity; all data are collected on the same day, which corresponds to the diagnosis day for disordered cows. For disordered cows, each variable, except the ratio of rumination time from daytime to nighttime, displays a decreasing/increasing trend from d-7 or d-3 to d0 and/or d-1, with the d0, d-1, or d-2 values reaching the minimum or maximum. The test data sensitivity for three algorithms exceeded 80%, and the accuracies of the eight algorithms ranged from 65.08% to 84.21%. The area under the curve (AUC) of the three algorithms was >80%. Overall, Rpart best predicts the disorders with an accuracy, precision, and AUC of 81.58%, 92.86%, and 0.908, respectively. The machine learning algorithms may be an appropriate and powerful decision support and monitoring tool to detect herds with common health disorders.

Genetic and seasonal variations of Trypanosoma theileri and the association of Trypanosoma theileri infection with dairy cattle productivity in Northern Japan

Trypanosoma theileri is considered a non- or low-pathogenic trypanosome that generally causes latent infection in apparently healthy cattle; however, T. theileri propagates in the bloodstream and may cause clinical disease in pregnant animals or co-infection with bovine leukemia virus or Theileria orientalis. In the current study, a monthly survey of T. theileri infection over one year was carried out in a research dairy farm in Hokkaido, Japan to determine the 1) seasonal variations in the prevalence, 2) genetic characterization of T. theileri, and 3) associations of milk and blood parameters in dairy cattle with T. theileri infection, including data of metabolic profile tests and dairy herd performance tests, using linear mixed models. We found that 1) the prevalence of T. theileri infection was significantly higher in summer and winter than in other seasons; 2) T. theileri possibly showed genetic diversity in Eastern Hokkaido; and 3) T. theileri infection was associated with significantly lower levels of blood urea nitrogen, milk protein, and solids-not-fat, which are caused by a low rumen fermentation level. This is the first study to report the negative impact of T. theileri infection in dairy cattle, and our study indicates that control of T. theileri infection can improve the productivity of dairy cattle.

Influence of production, reproduction, morphology, and health traits on true and functional longevity in French Holstein cows

In the long term, resilient animals are able to maintain their normal biological processes when confronted with environmental perturbations, reducing their risk of being culled. Therefore, longevity can be proposed as an indicator of long-term resilience. Decisions to remove a given dairy cow from the herd are mainly related to low milk production (i.e., voluntary culling) or to reasons other than production (i.e., involuntary culling). The aptitude of animals to delay any culling is defined as true longevity (TL), whereas functional longevity (FL) is the ability to avoid involuntary culling. The aim of the study was to investigate the influence of production, reproduction, morphology, and health traits on TL and FL, to identify risk factors for culling. Data included 278,217 lactations from 122,461 Holstein Friesian cows reared in 640 herds. The length of productive life, calculated as the time between first calving and culling, or censoring, was used as the measure of longevity. Survival analysis was performed using proportional hazards models assuming a piecewise Weibull distribution of the baseline hazard function, with or without adjustment for milk production to evaluate FL and TL. Insemination status, calving ease, mastitis, somatic cell count, displaced abomasum, and udder depth had significant relationships with TL and FL. Differences in estimates of relative risk between TL and FL showed that milk production often influenced culling decisions: farmers are more prone to cull animals with low production even when they had good other characteristics. The culling risk factors identified in the present study can be used to study resilience in dairy cattle and to improve genetic evaluations of functional or total longevity.

Dairy farm-workers' knowledge of factors responsible for culling and mortality in the Eastern Cape Province, South Africa

Milk serves as a significant source of protein for many families and aids in combating food insecurity. However, the demand for milk and milk-related products far exceeds the supply. The objective of the study was to evaluate dairy farm-workers' knowledge of factors responsible for culling and mortality of dairy cows in the Eastern Cape Province. Data was collected from 106 dairy farm-workers using a questionnaire. Any correctly answered question by the majority amounted to a point and a zero for incorrectly answered questions. Correct answering by the majority to more than half the questions of a subsection amounted to a pass. A less than 50% pass rate was considered a poor level of knowledge, 51-69% pass rate was considered an average level of knowledge, and anything higher than that was considered a good level of knowledge. Most farm-workers (66.0%) relied on their colleagues for dairy health information. Most dairy farm-workers (49.1%) indicated that lameness, milk fever (56.6%), and mastitis (47.2%) do not lead to culling and mortality of dairy cows. A majority (83%) of farm-workers agreed that reproduction problems, poor milk yield (77.3%), and age (81.1%) are the main reasons for culling dairy cows. The participants had varying perceptions and limited knowledge (28.3%) about the major contributing factors of culling and mortality. The lack of training courses and minimal use of other sources of information such as the internet might contribute to this poor knowledge and perceptions.

Validation of genomic predictions for a lifetime merit selection index for the US dairy industry

Selection indices are a critical component of many breeding programs. A common purpose of a selection index is to predict an animal's genetic potential for total economic merit. The objective of this study was to evaluate retrospectively whether a specific selection index comprising genomically-enhanced predicted transmitting abilities had the ability to predict observed lifetime profit in US Holstein animals. The selection index evaluated was dairy wellness profit (DWP$). In total, 2,185 animals were included in this study. Index values were used to rank and assign animals to quartiles (genetic groups: worst 25%, 26-50%, 51-75%, and best 25%). Generalized linear mixed effects models were applied to estimate the associations between index quartile and defined economic outcomes. Similar analyses were conducted to estimate associations between index quartile and observed phenotype to characterize the extent to which profitability outcomes were driven by economically relevant production and health traits. Differences in lifetime profit and annuity value between the best and worst genetic groups for DWP$ were $811 (±297) and $232 (±88), respectively. Significant differences were also observed between top and bottom quartiles for milk production (8,077 kg), fat production (336 kg), protein production (264 kg), live calves (0.5), time spent in the lactating herd (6.6 mo), and cow mortality (8.4%). Additionally, differences in disease incidence were significant between the best and worst DWP$ quartiles for metritis (5.2%), mastitis (14.9%), and lameness (15.9%). The observed results of this study demonstrated the ability of DWP$ predictions to predict lifetime profitability of Holstein animals and its potential utility as a tool to guide selection and breeding programs. Improving DWP$ through genetic selection, when combined with good management practices, provides an opportunity for dairy producers to improve overall herd profitability.

Prediction of quarter level subclinical mastitis by combining in-line and on-animal sensor data

We investigated the potential for automatic detection of subclinical mastitis (SCM) in pasture-based automatic milking systems. The objective of the study was to determine the ability of electrical conductivity (EC), together with relative changes in daily activity (activity) and daily rumination (rumination) recorded using heat and rumination–long-distance tags, to predict quarter-level SCM. Activity (arbitrary unit/day) and rumination (min/day) data were determined across 21 days using heat and rumination–long-distance tags for 170 cows. Cows were allocated into the following three groups: SCM (n = 32, EC ≥ 7.5 millisiemens/cm (mS/cm) in one or more quarters and a positive bacteriological culture in the corresponding quarter(s)); true-negative (TN, n = 9, EC ≥ 7.5 mS/cm and a negative culture in all four quarters); and apparently healthy (n = 129, no culture test and EC &lt; 7.5 mS/cm). Group mean differences in activity and rumination were compared using Welch’s t-tests. Logistic mixed models were used to predict SCM by EC, activity and rumination changes before mastitis detection, including parity information between SCM and TN groups. Cow- and quarter-specific information were included as random effects, followed by model assessment by producing receiver operating-characteristic curve and area under the curve (AUC) value. In total, 287 quarters were used in the prediction model, including 143 quarters with a positive culture (Gram-positive; n = 131, Gram-negative; n = 6, mixed; n = 6) and 144 quarters with a negative culture. On average, SCM group had 4.65% greater (P &lt; 0.01) activity and 9.89% greater (P &lt; 0.001) rumination than did the TN group and 11.70% greater (P &lt; 0.001) activity than did the apparently healthy group. A combined model with terms for EC, activity changes, rumination changes prior to detect SCM and parity had a better SCM prediction (AUC = 0.92) ability than did any of them separately (all AUC &lt; 0.8). Hence, we conclude that EC in combination with activity and rumination information can improve the accuracy of prediction of quarter-level SCM.

Mastitis risk effect on the economic consequences of paratuberculosis control in dairy cattle: A stochastic modeling study

The benefits and efficacy of control programs for herds infected with Mycobacterium avium subsp. paratuberculosis (MAP) have been investigated under various contexts. However, most previous research investigated paratuberculosis control programs in isolation, without modeling the potential association with other dairy diseases. This paper evaluated the benefits of MAP control programs when the herd is also affected by mastitis, a common disease causing the largest losses in dairy production. The effect of typically suggested MAP controls were estimated under the assumption that MAP infection increased the rate of clinical mastitis. We evaluated one hundred twenty three control strategies comprising various combinations of testing, culling, and hygiene, and found that the association of paratuberculosis with mastitis alters the ranking of specific MAP control programs, but only slightly alters the cost-benefit difference of particular MAP control components, as measured by the distribution of net present value of a representative U.S. dairy operation. In particular, although testing and culling for MAP resulted in a reduction in MAP incidence, that control led to lower net present value (NPV) per cow. When testing was used, ELISA was more economically beneficial than alternative testing regimes, especially if mastitis was explicitly modeled as more likely in MAP-infected animals, but ELISA testing was only significantly associated with higher NPV if mastitis was not included in the model at all. Additional hygiene was associated with a lower NPV per cow, although it lowered MAP prevalence. Overall, the addition of an increased risk of mastitis in MAP-infected animals did not change model recommendations as much as failing to consider.

Use of Rapid Culture Systems to Guide Clinical Mastitis Treatment Decisions

The selective treatment of clinical mastitis based on culture results can reduce antibiotic use by more than 50% without sacrificing treatment efficacy. Local laboratories reporting results in a 24-hour period, or adoption of on-farm milk culture systems, allow producers to make strategic treatment decisions. On-farm culture systems are most reliable when used to classify infections in broad diagnostic categories such as no growth, gram-positive, or gram-negative growth. Diagnostic accuracy is crucial for on-farm culture systems to be efficacious and economically advantageous. Quality assurance is necessary for the success of on-farm culture systems.

Associations of the first occurrence of pathogen-specific clinical mastitis with milk yield and milk composition in dairy cows

The aim of this study was to estimate the associations of the first occurrence of pathogen-specific clinical mastitis (CM) with milk yield and milk composition (somatic cell count (SCC), lactose, fat, protein content in milk and milk urea nitrogen (MUN)). We studied 3149 dairy cows in 31 Hokkaido dairy farms in Japan. Five pathogen groups were studied: Streptococcus spp.; Staphylococcus aureus (S. aureus); coagulase-negative staphylococci (CNS); coliforms; and fungi. Test-day milk data and clinical records were collected from June 2011 until February 2014. Mixed models with an autoregressive correlation structure were fitted to quantify the effects of CM and several other control variables (herd, calving season, parity, week of lactation, and other diseases). Primipara (first lactation) and multipara (second and later lactations) were analysed separately. All pathogens, particularly S. aureus and fungi, were associated with significant milk losses in multipara. In this study, S. aureus and CNS infections were not associated with significant milk loss in primipara. All pathogens, in particular S. aureus and fungi, significantly increased SCC in both parity groups. All pathogens, especially CNS (in primipara) and S. aureus (in multipara), decreased lactose content. All pathogen groups except for fungi were associated with significant changes in fat, protein and MUN. Some pathogens such as Streptococcus spp. and coliforms seemed to be associated with long-term fat, protein and MUN changes. These findings provide estimates that could be used to calculate precise costs of CM, and also provide better indicators of pathogen-specific mastitis.

The farm cost of decreasing antimicrobial use in dairy production

Antimicrobials are used in animal agriculture to cure bacterial infectious diseases. However, antimicrobial use (AMU) inevitably leads to the selection of resistant bacteria, potentially infecting humans. As a global public threat, antimicrobial resistance has led policy makers to implement regulations supervising AMU. The objective of our research was to investigate the farm impact of several potential policies aimed at decreasing AMU. We modeled a dairy herd of 1000 cows with an average level of disease prevalence for the nine most frequent bacterial dairy diseases found in western countries. We calculated the farm net costs of AMU prohibition, as well as cost increases in antimicrobial treatments prices, and an increase in the milk withdrawal period after AMU. Sensitivity analyses were conducted to assess the impact of output and input prices, and disease prevalence. At a mean disease prevalence, the average net costs of not using antimicrobials were $61 per cow per year greater compared to a scenario modeling current farm AMU. The model predicted that the minimum and maximum increased costs associated with AMU prohibition were $46 and $73 per cow per year compared to current AMU. In each scenario, the cost difference increased with disease prevalence. Sensitivity analysis showed that the three stochastic variables which most significantly influenced the cost difference were respectively, cow replacement prices, cow slaughter price, and the milk price. Antimicrobial price increases of a factor of five, or extending the milk withdrawal period by 15 days, resulted in increasing the costs of diseases to a level where the farmer was better off not using antimicrobials. Our results suggest that the farm level costs of AMU prohibition in many cases might be minor, although the consequences of any policy instrument should be carefully evaluated to reach the ultimate goal of decreasing AMU without threatening the sustainability of milk production.

Invited review: Incidence, risk factors, and effects of clinical mastitis recurrence in dairy cows

Clinical mastitis (CM) is one of the most frequent and costly diseases in dairy cows. A frustrating aspect of CM is its recurrent nature. This review was conducted to synthesize knowledge on risk of repeated cases of CM, effects of recurrent CM cases, and risk factors for CM recurrence. A systematic review methodology was used to identify articles for this narrative review. Searches were performed to identify relevant scientific literature published after 1989 in English or French from 2 databases (PubMed and CAB Abstracts) and 1 search platform (Web of Science). Fifty-seven manuscripts were selected for qualitative synthesis according to the inclusion criteria. Among the 57 manuscripts selected in this review, a description of CM recurrence, its risk factors, and effects were investigated and reported in 33, 37, and 19 selected manuscripts, respectively. Meta-analysis and meta-regression analyses were used to compute risk ratio comparing risk of CM in cows that already had 1 CM event in the current lactation with risk of CM in healthy cows. For these analyses, 9 manuscripts that reported the total number of lactations followed and the number of lactations with ≤1 and ≤2 CM cases were used. When summarizing results from studies requiring ≥5 d between CM events to consider a CM event as a new case, we observed no significant change in CM susceptibility following a first CM case (risk ratio: 0.99; 95% confidence interval: 0.86-1.14). However, for studies using a more liberal CM recurrence definition (i.e., only 24 h between CM events to consider new CM cases), we observed a 1.54 times greater CM risk (95% confidence interval: 1.20-1.97) for cows that already had 1 CM event in the current lactation compared with healthy cows. The most important risk factors for CM recurrence were parity (i.e., higher risk in older cows), a higher milk production, pathogen species involved in the preceding case, and whether a bacteriological cure was observed following the preceding case. The most important effects of recurrent CM were the milk yield reduction following a recurrent CM case, which was reported to be similar to that of the first CM case, and the increased risk of culling and mortality, which were reported to surpass those of first CM cases.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.

Efficacy assessment of an intramammary treatment with a new recrystallized enrofloxacin vs ceftiofur and parenteral enrofloxacin in dairy cows with nonsevere clinical mastitis

A recrystallized form of enrofloxacin as dehydrate-HCl (enro-C) was assessed for bacteriological and clinical cure efficacies in Holstein-Friesian cows affected of nonsevere clinical mastitis. Treatments were enro-C_susp (n = 81), treated with a pharmaceutical suspension of enro-C/quarter; group enro-C_pd (n = 80) treated as above, but using enro-C powder suspended in water; group CF (n = 65), treated with ceftiofur HCl/quarter; and group enro_R (n = 66), treated with standard enrofloxacin solution (5 mg/kg, intramuscular). Cows had a mean milk production of 31 L/day and were 2-3 lactational periods old. Treatments were administered every 24 hr for 3 days. Groups treated with enro-C exhibited statistically significant (p > .05) better clinical cure as compared to groups treated with CF or enro_R (95.06%, 96.25%, 67.79%, and 57.55%, for enro-C_susp , enro-C_pd , CF, and enro_R , respectively). In contrast, probability of bacteriological cure was not statistically different among treatments. Yet, the outstanding clinical and bacteriological cure rates obtained for enro-C for nonsevere cases of mastitis is superior to previously reported data for parenteral enrofloxacin and other antibacterial-intramammary treatments. Impact of using enro-C on the rate and pattern of bacterial resistance, somatic cell counts and milk electric conductivity, must be studied. Also, the use of enro-C for complicated cases of mastitis should be studied and milk withdrawal times must be accurately established.

2,4-Thiazolidinedione Treatment Improves the Innate Immune Response in Dairy Goats with Induced Subclinical Mastitis

Mastitis is a major disease in dairy cows resulting in significant economic losses. In vitro works suggest that ruminants peroxisome proliferator-activated receptor gamma (PPARγ) can aid in improving the response to mastitis and can control milk fat synthesis. The objectives of the present experiment were to test if treatment with the putative PPARγ agonist 2,4-thiazolidinedione (TZD) improves (1) the response to subclinical mastitis and (2) milk fat production. Lactating goats received daily injections of 8 mg/kg BW of TZD or saline for 3 weeks. After one week of TZD injection, half of the goats in each group received intramammary infusion of Strep. uberis or saline in both halves for a total of 4 groups (n = 6/group). TZD treatment did not affect milk fat but had positive effect on milk somatic cells count, blood nonesterified fatty acids, inflammatory markers, and liver function. TZD significantly increased myeloperoxidase but did not affect leukocytes phagocytosis or insulin. TZD increased adipocytes size and had minor effect on expression of PPARγ target genes in mammary epithelial cells but not in adipose tissue. Overall, TZD ameliorated the response to intramammary infection but the effect on milk fat synthesis and expression of related transcripts was less than expected.

Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part II. Mastitis

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with mastitis based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of mastitis by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cows (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel.) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. An HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as an HIS of <86 units during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were also determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 58% [95% confidence interval (CI): 49, 67] for all cases of clinical mastitis (n=123), and 55% (95% CI: 46, 64; n=114) and 89% (95% CI: 68, 100; n=9) for cases of mastitis alone or concurrent with other health disorders, respectively. Among clinical cases, sensitivity was 80.7% (95% CI: 67, 97) for cases caused by Escherichia coli (n=31) and ranged from 45 to 48% for cases caused by gram-positive bacteria (n=39; Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Streptococcus spp., Staphylococcus spp., and Trueperella pyogenes), Staphylococcus aureus (n=11), or cases with no bacterial growth (n=25). Days between the first HIS <86 and CD were -0.6 (95% CI: -1.1, -0.2) for all cases of mastitis. Cows diagnosed with mastitis had alterations of their rumination, activity, HIS patterns, and reduced milk production around CD depending on the type of mastitis case. Cows with mastitis also had some alterations of their calcium and haptoglobin concentrations around calving. The AHMS used in this study was effective for identifying cows with clinical cases of mastitis caused by E. coli and cows with another disease occurring during an event of mastitis, but it was less effective in identifying cows with mastitis not caused by E. coli.

Invited review: The economic impact and control of paratuberculosis in cattle

Paratuberculosis (also called Johne's disease) is a chronic disease caused by Mycobacterium avium ssp. paratuberculosis (MAP) that affects ruminants and other animals. The epidemiology of paratuberculosis is complex and the clinical manifestations and economic impact of the disease in cattle can be variable depending on factors such as herd management, age, infection dose, and disease prevalence, among others. Additionally, considerable challenges are faced in the control of paratuberculosis in cattle, such as the lack of accurate and reliable diagnostic tests. Nevertheless, efforts are directed toward the control of this disease because it can cause substantial economic losses to the cattle industry mainly due to increased premature culling, replacement costs, decreased milk yield, reduced feed conversion efficiency, fertility problems, reduced slaughter values, and increased susceptibility to other diseases or conditions. The variability and uncertainty surrounding the estimations of paratuberculosis prevalence and impact influence the design, implementation, and efficiency of control programs in diverse areas of the world. This review covers important aspects of the economic impact and control of paratuberculosis, including challenges related to disease detection, estimations of the prevalence and economic effects of the disease, and the implementation of control programs. The control of paratuberculosis can improve animal health and welfare, increase productivity, reduce potential market problems, and increase overall business profitability. The benefits that can derive from the control of paratuberculosis need to be communicated to all industry stakeholders to promote the implementation of control programs. Moreover, if the suspected link between Johne's disease in ruminants and Crohn's disease in humans was established, significant economic losses could be expected, particularly for the dairy industry, making the control of this disease a priority across dairy industries internationally.

Episodes of clinical mastitis and its relationship with duration of treatment and seasonality in crossbred cows maintained in organized dairy farm

Aim:

Present study aimed to evaluate the different episodes of clinical mastitis (CM) and influence of duration of treatment and seasonality on the occurrence of different episodes of CM in crossbred cows.

Materials and Methods:

A total of 1194 lactation data of crossbred CM cows were collected from mastitis treatment record from 2002 to 2012. Data of CM cows were classified into types of episodes (pattern of repeated or multiple episodes occurrence) and number of episodes (magnitude of multiple cases). Types of episodes were divided as single (clinical cure by a single episode of treatment), relapse (retreatment of the same cow within 21 days), recurrence (new CM at least 21 days after treatment), and both (relapse and recurrence). The season was classified as winter (December to March), summer (April to June), rainy (July to September), and autumn (October to November). The difference between incidences of different types of CM episodes and the association between number or type of CM episodes with duration of treatment and seasons of CM occurrence were analyzed by Chi-square test.

Results:

Among 1194 animals suffered with CM, 53, 16, and 18% had the single episode, relapse, and recurrence, respectively; while 13% suffered by both relapse and recurrence. We estimated the duration of treatment and found 80% of the cows treated 1-8 days, in which 65% treated for 1-4 days, while 35% cows were treated for 5-8 days. Further, 12% cows treated for 9-15 days and 7.5% cows treated >15 days. The relationship between duration of treatment and different episodes of CM revealed that 1-8 days treated cows were mostly cured by the single episode with less relapse and recurrence. In contrast, the incidences of recurrence and relapse episodes were higher in cows treated for more than 9 days. The highest incidence of relapse was noticed in winter (36%) than other seasons (10-28%), while the recurrence was less during autumn (9%) compared to other seasons (20-40%).

Conclusion:

Cows those suffered by both relapse and recurrence were more susceptible to CM, and they need to be culled from farm to control the transmission of infections. Although the influence of seasonality was difficult to understand, the higher magnitude of relapse and recurrence during winter suggested the adverse effects of cold stress on treatment outcome.

Investigation of the association between the test day milk fat-protein ratio and clinical mastitis using a Poisson regression approach for analysis of time-to-event data

The objective of the present study was to investigate the association between the milk fat-protein ratio and the incidence rate of clinical mastitis including repeated cases of clinical mastitis to determine the usefulness of this association to monitor metabolic disorders as risk factors for udder health. Herd records from 10 dairy herds of Holstein cows in Saxony, Germany, from September 2005-2011 (36,827 lactations of 17,657 cows) were used for statistical analysis. A mixed Poisson regression model with the weekly incidence rate of clinical mastitis as outcome variable was fitted. The model included repeated events of the outcome, time-varying covariates and multilevel clustering. Because the recording of clinical mastitis might have been imperfect, a probabilistic bias analysis was conducted to assess the impact of the misclassification of clinical mastitis on the conventional results. The lactational incidence of clinical mastitis was 38.2%. In 36.2% and 34.9% of the lactations, there was at least one dairy herd test day with a fat-protein ratio of <1.0 or >1.5, respectively. Misclassification of clinical mastitis was assumed to have resulted in bias towards the null. A clinical mastitis case increased the incidence rate of following cases of the same cow. Fat-protein ratios of <1.0 and >1.5 were associated with higher incidence rates of clinical mastitis depending on week in milk. The effect of a fat-protein ratio >1.5 on the incidence rate of clinical mastitis increased considerably over the course of lactation, whereas the effect of a fat-protein ratio <1.0 decreased. Fat-protein ratios <1.0 or >1.5 on the precedent test days of all cows irrespective of their time in milk seemed to be better predictors for clinical mastitis than the first test day results per lactation.

Progression to multi-scale models and the application to food system intervention strategies

The aim of this article is to discuss how the systems science approach can be used to optimize intervention strategies in food animal systems. It advocates the idea that the challenges of maintaining a safe food supply are best addressed by integrating modeling and mathematics with biological studies critical to formulation of public policy to address these challenges. Much information on the biology and epidemiology of food animal systems has been characterized through single-discipline methods, but until now this information has not been thoroughly utilized in a fully integrated manner. The examples are drawn from our current research. The first, explained in depth, uses clinical mastitis to introduce the concept of dynamic programming to optimize management decisions in dairy cows (also introducing the curse of dimensionality problem). In the second example, a compartmental epidemic model for Johne's disease with different intervention strategies is optimized. The goal of the optimization strategy depends on whether there is a relationship between Johne's and Crohn's disease. If so, optimization is based on eradication of infection; if not, it is based on the cow's performance only (i.e., economic optimization, similar to the mastitis example). The third example focuses on food safety to introduce risk assessment using Listeria monocytogenes and Salmonella Typhimurium. The last example, practical interventions to effectively manage antibiotic resistance in beef and dairy cattle systems, introduces meta-population modeling that accounts for bacterial growth not only in the host (cow), but also in the cow's feed, drinking water and the housing environment. Each example stresses the need to progress toward multi-scale modeling. The article ends with examples of multi-scale systems, from food supply systems to Johne's disease. Reducing the consequences of foodborne illnesses (i.e., minimizing disease occurrence and associated costs) can only occur through an understanding of the system as a whole, including all its complexities. Thus the goal of future research should be to merge disciplines such as molecular biology, applied mathematics and social sciences to gain a better understanding of complex systems such as the food supply chain.

Estimating the effect of mastitis on the profitability of Irish dairy farms

The objective of this paper was to estimate the effect of the costs of mastitis on the profitability of Irish dairy farms as indicated by various ranges of bulk milk somatic cell count (BMSCC). Data were collected from 4 sources and included milk production losses, cases treated, and on-farm practices around mastitis management. The Moorepark Dairy Systems Model, which simulates dairying systems inside the farm gate, was used to carry out the analysis. The cost components of mastitis that affect farm profitability and that were included in the model were milk losses, culling, diagnostic testing, treatment, veterinary attention, discarded milk, and penalties. Farms were grouped by 5 BMSCC thresholds of ≤ 100,000, 100,001-200,000, 200,001-300,000, 300,001-400,000, and > 400,000 cells/mL. The ≤ 100,000 cells/mL threshold was taken as the baseline and the other 4 thresholds were compared relative to this baseline. For a 40-ha farm, the analysis found that as BMSCC increased, milk receipts decreased from €148,843 at a BMSCC <100,000 cells/mL to €138,573 at a BMSCC > 400,000 cells/mL. In addition, as BMSCC increased, livestock receipts increased by 17%, from €43,304 at a BMSCC <100,000 cells/mL to €50,519 at a BMSCC > 400,000 cells/mL. This reflected the higher replacement rates as BMSCC increased and the associated cull cow value. Total farm receipts decreased from €192,147 at the baseline (< 100,000 cells/mL) to €189,091 at a BMSCC > 400,000 cells/mL. Total farm costs increased as BMSCC increased, reflecting treatment, veterinary, diagnostic testing, and replacement heifer costs. At the baseline, total farm costs were €161,085, increasing to €177,343 at a BMSCC > 400,000 cells/mL. Net farm profit decreased as BMSCC increased, from €31,252/yr at the baseline to €11,748/yr at a BMSCC > 400,000 cells/mL. This analysis highlights the impact that mastitis has on the profitability of Irish dairy farms. The analysis presented here can be used to develop a "cost of mastitis" tool for use on Irish dairy farms to motivate farmers to acknowledge the scale of the problem, realize the value of improving mastitis control, and implement effective mastitis control practices.

Using mastitis records and somatic cell count data

Developing a records plan useful for managing the complex relationships between environment, personal application of SOP, and protocols that integrates clinical and subclinical udder requires planning the data entry and data reports that interpret the herd without abandoning the individual cow. Then use the data regularly.

The effect of recurrent episodes of clinical mastitis caused by gram-positive and gram-negative bacteria and other organisms on mortality and culling in Holstein dairy cows

The objective of this study was to estimate the effects of recurrent episodes of different types of clinical mastitis (CM) caused by gram-positive (Streptococcus spp., Staphylococcus aureus, Staphylococcus spp.) and gram-negative (Escherichia coli, Klebsiella, Citrobacter, Enterobacter, Pseudomonas) bacteria, and other organisms (Arcanobacterium pyogenes, Mycoplasma, Corynebacterium bovis, yeast, miscellaneous) on the probability of mortality and culling in Holstein dairy cows. Data from 30,233 lactations in cows of 7 dairy farms in New York State were analyzed. Cows were followed for the first 10 mo in lactation, or until death or culling occurred, or until the end of our study period. Generalized linear mixed models with a Poisson error distribution were used to study the effects of recurrent cases of the different types of CM and several other factors (herd, parity, month of lactation, current year and season, profitability, net replacement cost, other diseases) on cows' probability of death (model 1) or being culled (model 2). Primiparous and multiparous cows were modeled separately because they had different risks of mortality and culling and potentially different CM effects on mortality and culling. Approximately 30% of multiparous cows had at least one case of CM in lactation compared with 16.6% of primiparous cows. Multipara also had higher lactational incidence risks of second (10.7%) and third (4.4%) cases than primipara (3.7% and 1.1%, respectively). For primipara, CM increased the probability of death, with each successive case occurring in a month being increasingly lethal. In multipara, gram-negative CM increased the probability of death, especially when the gram-negative case was the first or second CM case in lactation. Primiparous cows with CM were more likely to be culled after CM than if they did not have CM, particularly after a second or third case. In multipara, any type of CM increased the probability of being culled. Gram-negative CM cases were associated with the numerically highest risk of culling.

Milk yield and somatic cell count during the following lactation after selective treatment of cows at dry-off

Selective dry cow therapy (SDCT) has received increasing attention in recent years owing to global concerns over agricultural use of antimicrobial drugs and development of antimicrobial resistance. The objective of this study was to evaluate the effect of SDCT on milk yield and somatic cell count (SCC) in dairy herds in the USA. Cows in four Ohio dairy herds were categorized into two groups (low-SCC and high-SCC) at dry-off based on their SCC and clinical mastitis (CM) history during the lactation preceding the dry-off. Low-SCC cows were randomly assigned to receive or not to receive intramammary antibiotics at dry-off. Milk yield and SCC of these cows during the following lactation were compared using linear mixed effects models, adjusting for parity, calving season, stage of lactation, previous lactation milk yield and herd. Milk yield of untreated and treated low-SCC cows at dry-off did not differ significantly during the following lactation. Overall, treated low-SCC cows had 16% lower SCC (approximately 35 000 cells/ml, P=0·0267) than the untreated cows during the following lactation; however, the effect was variable in different herds. Moreover the impact of treatment, or the lack thereof, on milk yield varied considerably between herds. The results suggested that in some herds treating all cows at dry-off may be beneficial while in other herds leaving healthy cows without antibiotic dry cow treatment has no negative impact on milk yield or milk quality (SCC), and in fact, may be beneficial. Further studies are needed to identify characteristics of herds where treating all cows routinely at dry-off may be needed for maintaining good udder health and where switching to selective treatment of cows at dry-off would be the optimal approach to achieve best results.

Association between interleukin-8 receptor-α (CXCR1) polymorphism and disease incidence, production, reproduction, and survival in Holstein cows

The objective was to evaluate the association between the single nucleotide polymorphism at position +735 in the interleukin-8 receptor-α (CXCR1) gene (CXCR1c.735) and disease incidence, milk production, reproductive performance, and survival in Holstein cows. Three-hundred fifty Holstein cows were enrolled. No association was found between CXCR1c.735 genotype and retained fetal membranes, metritis, or endometritis. Incidence rate of clinical mastitis was associated with CXCR1c.735 genotype; cows with genotypes CC and GC had a decreased incidence rate of clinical mastitis compared with GG cows. Milk yield was associated with CXCR1c.735 genotype; cows with genotype GC had greater milk yield than GG cows. Hazard of pregnancy was not associated with CXCR1c.735 genotype. Cows that had clinical mastitis had decreased hazard of pregnancy, and cows that had endometritis tended to have a decreased hazard of pregnancy. Hazard of death or culling was not associated with CXCR1c.735 genotype. Multiparous cows and cows that had mastitis had increased hazard of death or culling. In contrast to what we expected, cows with the genotype GG had an increased incidence rate of clinical mastitis and decreased milk yield.

Mastitis and the shape of the lactation curve in Norwegian dairy cows

An investigation of the shape of the lactation curve and the mastitis incidence was conducted to identify whether management interventions of the lactation curve constitute a potential for reducing incidence of mastitis at herd level. Lactation curves were estimated to describe the variation of daily milk yield during the 305-d lactation period in Norwegian Red cows. Associations between mastitis incidence at herd level and lactation curve characteristics such as production level at onset of lactation, magnitude and time of peak milk yield, and increase and decrease of milk yield rates were studied. Data from 250 303 lactations occurring during 2005 and 2006 from 14 766 herds were obtained from the Norwegian Dairy Herd Recording System. Besides veterinary treatments, the records included information on monthly test-day milk yields. The shapes of the lactation curves at herd level were parameterized using a modified Wilmink model in two separate mixed model analyses. In the first analysis a subset of lactations with no records of veterinary treatments was used. Lactation curves from herds with high (>0·31 cases/305-d lactation) and low (<0·07 cases/305-d lactation) herd mastitis incidence rate were parameterized and compared for three separate strata of parity. The result showed that high herd mastitis incidence rate was associated with a low intercept (P<0·05), a steep slope before peak milk yield (P<0·01) and a rapid decline after peak milk yield (P<0·01). In the second analysis a subset of high-yielding lactations with veterinary treatments of mastitis only and lactations with no records of veterinary treatment were compared. This was done to investigate whether the findings at herd level were also reflected at cow level. These results showed that lactation curves from lactations with mastitis cases were associated with a steep slope before peak milk yield (P<0·05) in second and later parities and a rapid decline after peak milk yield (P<0·01) in all three parity groups.

Effects of repeated gram-positive and gram-negative clinical mastitis episodes on milk yield loss in Holstein dairy cows

The objective of this study was to estimate the effects of recurrent episodes of gram-positive and gram-negative cases of clinical mastitis (CM) on milk production in Holstein dairy cows. We were interested in the severity of repeated cases in general, but also in the severity of the host response as judged by milk production loss when a previous case was caused by a similar or different microorganism. The results were based on data from 7,721 primiparous lactations and 13,566 multiparous lactations in 7 large dairy herds in New York State. The distribution of organisms in the CM cases showed 28.5% gram-positive cases, 31.8% gram-negative cases, 15.0% others, and 24.8% with no organism identified. Mixed models, with a random herd effect and an autoregressive covariance structure to account for repeated measurements, were used to quantify the effect of repeated CM and several other control variables (parity, week of lactation, other diseases) on milk yield. Our data indicated that repeated CM cases showed a very similar milk loss compared with the first case. No reduction of severity was present with increasing count of the CM case. Gram-negative cases had more severe milk loss compared with gram-positive and other cases irrespective of the count of the case in lactation. Milk loss in multipara (primipara) due to gram-negative CM was approximately 304 kg (228 kg) in the 50 d following CM. This loss was approximately 128 kg (133 kg) for gram-positive cases and 92 kg (112 kg) for other cases. The severity of a second case of gram-negative CM was not reduced by previous cases of gram-negative CM in multipara and only slightly less severe in a similar scenario in primipara cows. Similarly, a previous gram-positive case did not reduce severity of a second or third gram-positive case. Hence, our data do not support that immunological memory of previous exposure to an organism in the same generic class provides protection for a next case of CM with an organism in the same class.

Changing trends in mastitis

The global dairy industry, the predominant pathogens causing mastitis, our understanding of mastitis pathogens and the host response to intramammary infection are changing rapidly. This paper aims to discuss changes in each of these aspects. Globalisation, energy demands, human population growth and climate change all affect the dairy industry. In many western countries, control programs for contagious mastitis have been in place for decades, resulting in a decrease in occurrence of Streptococcus agalactiae and Staphylococcus aureus mastitis and an increase in the relative impact of Streptococcus uberis and Escherichia coli mastitis. In some countries, Klebsiella spp. or Streptococcus dysgalactiae are appearing as important causes of mastitis. Differences between countries in legislation, veterinary and laboratory services and farmers' management practices affect the distribution and impact of mastitis pathogens. For pathogens that have traditionally been categorised as contagious, strain adaptation to human and bovine hosts has been recognised. For pathogens that are often categorised as environmental, strains causing transient and chronic infections are distinguished. The genetic basis underlying host adaptation and mechanisms of infection is being unravelled. Genomic information on pathogens and their hosts and improved knowledge of the host's innate and acquired immune responses to intramammary infections provide opportunities to expand our understanding of bovine mastitis. These developments will undoubtedly contribute to novel approaches to mastitis diagnostics and control.

Costs of mastitis: facts and perception

A model to calculate the economic losses of mastitis on an average Dutch dairy farm was developed and used as base for a tool for farmers and advisors to calculate farm-specific economic losses of mastitis. The economic losses of a clinical case in a default situation were calculated as euro210, varying from euro164 to euro235 depending on the month of lactation. The total economic losses of mastitis (subclinical and clinical) per cow present in a default situation varied between euro65 and euro182/cow per year depending on the bulk tank somatic cell count. The tool was used to measure perception of the total economic losses of mastitis on the farm and the farmers' assessment of the cost factors of mastitis on 78 dairy farms, of which 64 were used for further analyses. Most farmers (72%) expected their economic losses to be lower than those revealed by our calculation made with their farm information. Underestimating the economic losses of mastitis can be regarded as a general problem in the dairy sector. The average economic losses assessed by the farmers were euro78/cow per year, but a large variation was given, euro17-198/cow per year. Although the average assessment of the farmers of the different cost factors is close to the default value, there is much variation. To improve the adoption rate of advice and lower the incidence of mastitis, it is important to show the farmers the economic losses of mastitis on their farm. The tool described in this paper can play a role in that process.