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16 pages, 511 KiB  
Review
Embryonic and Fetal Mortality in Dairy Cows: Incidence, Relevance, and Diagnosis Approach in Field Conditions
by Maria Francisca Andrade and João Simões
Dairy 2024, 5(3), 526-541; https://doi.org/10.3390/dairy5030040 - 31 Aug 2024
Viewed by 964
Abstract
Pregnancy loss (PL) in dairy cattle results in animal health and welfare disruption and has a great economic impact on farms, with decreases in fertility and increased culling. It can occur at any stage of embryonic or fetal development. Abortion occurring from the [...] Read more.
Pregnancy loss (PL) in dairy cattle results in animal health and welfare disruption and has a great economic impact on farms, with decreases in fertility and increased culling. It can occur at any stage of embryonic or fetal development. Abortion occurring from the second half of pregnancy has a more negative impact on dairy farms. There are several infectious and non-infectious factors that can lead to PL and vary according embryonic or fetal stages. As this is a multifactorial or multi-etiological occurrence, it is important to identify the risk factors and the best diagnostic tools to approach these reproductive losses that can occur sporadically or by outbreaks. Reaching a final diagnosis can be challenging, especially when it occurs at a very early stage of pregnancy, where losses may not be detected and neonatal deaths may be related to alterations in the fetus in utero. Also, laboratorial results from animal samples should be interpreted according to the full clinical approach. This review aimed to highlight all these essential aspects, identifying the main infectious and non-infectious causes leading to PL, as well as the best veterinary practices for diagnosing it, mainly through transrectal palpation, ultrasound, and laboratory methods, in bovine dairy farms. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>Representation of the different stages in the gestational interruption. Data adapted from [<a href="#B9-dairy-05-00040" class="html-bibr">9</a>,<a href="#B10-dairy-05-00040" class="html-bibr">10</a>,<a href="#B11-dairy-05-00040" class="html-bibr">11</a>,<a href="#B12-dairy-05-00040" class="html-bibr">12</a>].</p>
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13 pages, 533 KiB  
Article
Good Handling Practices Have Positive Impacts on Dairy Calf Welfare
by Lívia C. M. Silva-Antunes, Maria Camila Ceballos, João A. Negrão and Mateus J. R. Paranhos da Costa
Dairy 2024, 5(2), 295-307; https://doi.org/10.3390/dairy5020024 - 15 May 2024
Viewed by 939
Abstract
The objective was to evaluate the effects of good handling practices on dairy calf welfare. Forty-eight crossbred dairy calves were assigned to two treatments: conventional handling (CH): calves kept in individual pens, fed milk replacer in buckets without nipples and abruptly weaned; or [...] Read more.
The objective was to evaluate the effects of good handling practices on dairy calf welfare. Forty-eight crossbred dairy calves were assigned to two treatments: conventional handling (CH): calves kept in individual pens, fed milk replacer in buckets without nipples and abruptly weaned; or good handling practices (GHP): calves kept in group pens, fed milk replacer in buckets with nipples, given daily tactile stimulation during feeding, and progressive weaning. Calf welfare was assessed from birth to 120 days of age, based on: health (plasma concentrations of glucose and IgG, and occurrences of diarrhea, pneumonia, tick-borne disease, or death); physiology (heart rate [HR], respiratory rate [RR], and rectal temperature [RT]); behavior (flight distance [FD], latencies for first movement [LM] and to hold the calf in a pen corner [LH], and total time a calf allowed touching [TTT]); and performance indicators (body weight, average daily gain, and weaning success at 70 days of age). Calves in the GHP treatment had a lower HR at 30 days of age, shorter FD and LH, longer TTT, and lower RR and RT than CH (p < 0.05). However, health, deaths and performance indicators did not differ (p > 0.05) between treatments. Based on various indicators, GHP improved dairy calf welfare. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>Adjusted means and SEM of plasma IgG in dairy calves according to treatment (GHP = Good handling practices; CH = Conventional handling) and age (1, 30, 60, 90, and 120 days). Means without a common letter differed between treatments and calf age (<span class="html-italic">p</span> &lt; 0.05).</p>
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10 pages, 442 KiB  
Article
Assessing Serum Vaspin Dynamics in Dairy Cows during Late Pregnancy and Early Lactation in Relation to Negative Energy Balance
by Hala Abbas Naji, Atiaf Ghanim Rhyaf, Noora Khadhim Hadi ALyasari and Hassan Al-Karagoly
Dairy 2024, 5(1), 229-238; https://doi.org/10.3390/dairy5010019 - 21 Mar 2024
Viewed by 1195
Abstract
The periparturient period, which spans late pregnancy to early lactation in dairy cows, is a crucial phase characterized by complex metabolic and endocrine adjustments necessary for sustained milk production. This research focused on the relationship between serum vaspin, inflammatory cytokines (IL-1, TNF), and [...] Read more.
The periparturient period, which spans late pregnancy to early lactation in dairy cows, is a crucial phase characterized by complex metabolic and endocrine adjustments necessary for sustained milk production. This research focused on the relationship between serum vaspin, inflammatory cytokines (IL-1, TNF), and markers of negative energy balance (NEB) in 100 primiparous and multiparous Holstein dairy cows. The results demonstrated that one month post-calving, both groups had a significant decrease in serum vaspin levels but increased NEFA levels, indicating possible consequences for lipid metabolism and energy balance. Multiparous cows showed significant elevations in cholesterol, IL-1, and TNF concentrations after calving, indicating increased inflammatory responses. Primiparous cows, on the other hand, responded differently, indicating the role of parity in metabolic adjustments. The study acknowledges limitations such as sample size and its observational nature. Future research should investigate the long-term effects of these metabolic changes on herd health and lactational performance, using advanced technologies to gain a molecular understanding. Despite limitations, this study provides valuable insights into how adipokines, inflammatory markers, and energy balance interact during the periparturient period, offering the potential for improved dairy cow management and productivity while ensuring animal welfare. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>Comparison of (<b>A</b>) milk Yield (Kg/day) and (<b>B</b>) body condition scores in primiparous and multiparous dairy cows one month after calving. (***): statistically significant; (ns): non-significant.</p>
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8 pages, 965 KiB  
Communication
A Comparison between Crossbred (Holstein × Local Cattle) and Bangladeshi Local Cattle for Body and Milk Quality Traits
by Sudeb Saha, Md. Nazmul Hasan, Md. Nazim Uddin, B. M. Masiur Rahman, Mohammad Mehedi Hasan Khan, Syed Sayeem Uddin Ahmed and Haruki Kitazawa
Dairy 2024, 5(1), 153-160; https://doi.org/10.3390/dairy5010012 - 2 Feb 2024
Viewed by 2294
Abstract
Crossbreeding in dairy cattle with exotic breeds continues to be an appealing practice to the dairy farmers of Bangladesh. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk quality traits of crossbred cattle in Bangladesh. [...] Read more.
Crossbreeding in dairy cattle with exotic breeds continues to be an appealing practice to the dairy farmers of Bangladesh. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk quality traits of crossbred cattle in Bangladesh. Therefore, the primary objective of this study was to evaluate the impact of crossbreeding Bangladeshi local cattle with the exotic Holstein breed on their body characteristics and milk quality. To achieve the goal, data pertaining to body traits and milk samples were gathered from a total of 981 cows from 19 dairy farms located in the northwestern region of Bangladesh. A trained evaluator measured body condition score (BCS), udder score, locomotion score, and body conformation traits. Milk yield information was acquired from official records, while milk composition details were determined through milk analysis. Notably, crossbred cows (Holstein × Local cattle) exhibited greater values for wither height (141 vs. 135, cm), body length (157 vs. 153, cm), heart girth (211 vs. 204, cm), BCS (3.69 vs. 3.27), and udder score (3.29 vs. 2.08) than their Bangladeshi local counterparts. Furthermore, crossbred cows produced 42.4% and 35.3% more milk (10.89 vs. 7.65, kg/d) and fat-corrected milk (10.35 vs. 7.54, kg/d) than Bangladeshi local cattle. However, milk from crossbred cows displayed lower fat and protein content, although their somatic cell score (SCS) and energy-corrected milk remained similar. Additionally, milk from crossbred cows exhibited a longer coagulation time when compared to that of Bangladeshi local cattle. In conclusion, crossbred cows (Holstein × Local cattle) had improved body characteristics with greater milk yield than Bangladeshi local cattle; however, lower fat and protein contents in milk with longer coagulation time were noted. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>A typical representative of cattle: (<b>a</b>) crossbred (Holstein × Local Cattle) and (<b>b</b>) Bangladeshi local cattle.</p>
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<p>Scheme of body measurements for body length a, withers height b, and heart girth c of cow.</p>
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12 pages, 2236 KiB  
Article
Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats
by Rocío Jiménez-Granado, Antonio Molina, Manuel Sánchez Rodríguez, Chiraz Ziadi and Alberto Menéndez Buxadera
Dairy 2024, 5(1), 1-12; https://doi.org/10.3390/dairy5010001 - 21 Dec 2023
Viewed by 1048
Abstract
The aim of this study was to estimate genetic parameters of somatic cell score (SCS) and fat plus protein yield (FPY) using repeatability (RM) and random regression (RRM) models in Florida goats. The data consisted of 340,654 test-day controls of the first three [...] Read more.
The aim of this study was to estimate genetic parameters of somatic cell score (SCS) and fat plus protein yield (FPY) using repeatability (RM) and random regression (RRM) models in Florida goats. The data consisted of 340,654 test-day controls of the first three lactations, and the pedigree contained 36,144 animals. Covariance components were estimated with a bivariate RM and RRM using the REML approach. Both models included as fixed effects the combination of herd and control date, litter size, kidding number and lactation length, and as random effects, the additive genetic and permanent environmental effects. A variation in the shape of the genetic parameters along the lactation curve was observed for both traits, and h2 oscillated between 0.272 and 0.279 for SCS and 0.099 and 0.138 for FPY. The genetic correlation between SCS and FPY was negative and medium (−0.304 to −0.477), indicating that a low-SCS EBV is associated with a genetic predisposition to high FPY production. Our results showed that given the magnitude of h2 for SCS and its rg with FPY, the SCS could be used as a selection criterion to increase resistance to mastitis, thus obtaining an improved dairy and cheese aptitude in this breed. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>Least-squares means of somatic cell score (SCS) and fat plus protein yield (FPY) across lactation length (in weeks) and number of kiddings in the Florida breed.</p>
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<p>Evolution of variance components, heritabilities and genetic correlations across lactations and each parity for somatic cell score (SCS) and fat plus protein yield (FPY) using a random regression model in the Florida breed.</p>
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<p>Evolution of the genetic correlations between somatic cell score (SCS) and fat plus protein yield (FPY) throughout lactation and in each parity using a random regression model in the Florida breed.</p>
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<p>Frequency distribution of estimated genetic values (EGVs) for somatic cell score (SCS) and fat plus protein yield (FPY) up to 240 days of lactation and variation in shapes of the lactation curves of the 500 best animals, using a random regression model in the Florida breed.</p>
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<p>Correlations between the estimated genetic values (EGVs) of somatic cell score (SCS) and fat plus protein yield (FPY) estimated by the repeatability and random regression models throughout lactation for each kidding in the Florida breed.</p>
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17 pages, 1547 KiB  
Article
Analysis of Dairy Cow Behavior during Milking Associated with Lameness
by Diana Schönberger, Roxanne Magali Berthel, Pascal Savary and Michèle Bodmer
Dairy 2023, 4(4), 554-570; https://doi.org/10.3390/dairy4040038 - 16 Oct 2023
Cited by 1 | Viewed by 1747
Abstract
The detection of lame cows is a challenging and time-consuming issue for dairy farmers. Many farmers use the milking time to monitor the condition of their animals. Because lame cows often show increased stepping when standing to relieve pressure on aching claws, we [...] Read more.
The detection of lame cows is a challenging and time-consuming issue for dairy farmers. Many farmers use the milking time to monitor the condition of their animals. Because lame cows often show increased stepping when standing to relieve pressure on aching claws, we investigated whether lame cows showed increased activity in the milking parlor. On 20 Swiss dairy farms, 647 cows were scored on lameness with a five-point locomotion score and categorized as clinical lame and non-lame cows in order to see if there are differences in behavior between these two groups (non-lame = scores 1 and 2; lame = scores 3, 4, and 5). During one evening milking, the behavior of the cows was analyzed. A three-dimensional accelerometer, attached to the milking cluster, detected the hind leg activity indirectly via the movements of the milking unit. Additionally, head movements, as well as weight shifting and the number of steps with the front legs, were analyzed from video recordings. Owing to a high percentage of false positive hind leg activities in some milkings measured by the sensor, only 60% of the collected data were evaluated for behavior (356 cows/milkings on 17 farms). Twenty-seven percent of the investigated cows were classified as lame. The lameness prevalence was increasing with increasing parity. Lame cows showed a higher hind leg activity during milking as well as a higher frequency of front steps and weight shifting events during their stay in the milking parlor than non-lame cows. No relation between the status of lameness and the number of head movements could be seen. Observation of increased stepping and weight shifting of individual animals during milking by the farmer could be used as an additional indicator to detect lame cows, but further investigations are required. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>Prevalence of lameness across all 20 farms. Open dots show the individual prevalence per farm. The data basis for the graph was 647 cows in total, of which 174 were scored as lame cows. Lameness includes cows with locomotion scores 3–5.</p>
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<p>Prevalence of lame cows in relation to parity. Solid lines show the estimated means, dashed lines the estimated lower and upper 95% confidence interval. Graph includes the lameness status of 356 cows in total. <span class="html-italic">n</span> = number of lame cows per parity, <span class="html-italic">N</span> = total number of cows per parity.</p>
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<p>HLA per minute between non-lame and lame cows depending on parity. Open dots show the estimated means, error bars the estimated lower and upper 95% confidence interval, and closed dots the measured HLA of each cow.</p>
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<p>FS per minute between non-lame and lame cows depending on parity. Open dots show the estimated means, error bars the estimated lower and upper 95% confidence interval, and closed dots the measured FS of each cow.</p>
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<p>Raw data of HLA in relation to the degree of lameness. Measured HLA per minute at a threshold of 0.25 g plotted against assessed lameness scores of 356 cows on 17 farms. All measurements with high intrinsic motion of the milking unit were excluded beforehand.</p>
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32 pages, 9343 KiB  
Article
Estimation of Oral Exposure of Dairy Cows to the Mycotoxin Deoxynivalenol (DON) through Toxin Residues in Blood and Other Physiological Matrices with a Special Focus on Sampling Size for Future Predictions
by Sven Dänicke, Susanne Kersten, Fabian Billenkamp, Joachim Spilke, Alexander Starke and Janine Saltzmann
Dairy 2023, 4(2), 360-391; https://doi.org/10.3390/dairy4020024 - 31 May 2023
Cited by 1 | Viewed by 1750
Abstract
Evaluation of dairy cow exposure to DON can generally be managed through analyses of feed or physiological specimens for DON residues. The latter enables a diagnosis not only on an individual basis but also on a herd basis. For this purpose, on the [...] Read more.
Evaluation of dairy cow exposure to DON can generally be managed through analyses of feed or physiological specimens for DON residues. The latter enables a diagnosis not only on an individual basis but also on a herd basis. For this purpose, on the basis of published data, linear regression equations were derived for blood, urine, milk, and bile relating DON residue levels as predictor variables to DON exposure. Amongst the matrices evaluated, blood was identified to reflect the inner exposure to DON most reliably on toxicokinetic backgrounds, which was supported by a linear relationship between DON residues in blood and DON exposure. On the basis of this, and because of extended blood data availability, the derived regressions were validated using internal and external data, demonstrating a reasonable concordance. For all matrices evaluated, the ultimately recommended linear regression equations intercepted the origin and enabled the prediction of the DON exposure to be expected within the prediction intervals. DON exposure (µg/kg body weight/d) can be predicted by multiplying the DON residues (ng/mL) in blood by 2.52, in urine by 0.022, and in milk by 2.47. The span of the prediction intervals varied according to the dispersion of the observations and, thus, also considered apparent outliers that were not removed from the datasets. The reasons were extensively discussed and included toxicokinetic aspects. In addition, the suggestions for sample size estimation for future characterization of the mean exposure level of a given herd size were influenced by expectable variation in the data. It was concluded that more data are required for all specimens to further qualify the preliminary prediction equations. Full article
(This article belongs to the Section Dairy Animal Health)
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<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>) using the original dataset pooled over Experiments 1 and 2 (<italic>n</italic> = 237). Red solid lines denote the linear regression, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstrap replications. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and presented as density distributions (solid red vertical lines show the mean value of the regression coefficients, and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
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<p>Influential statistics for the linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON exposure without intercept (black crosses mark the centroid of the scatter; red and blue filled dots represent data from Experiments 1 and 2, respectively): Hat scores (<bold>A</bold>) (scores increase from smallest to largest dot size from 0.004 to 0.11); Studentized residuals (<bold>B</bold>) (residuals increase from smallest to largest dot size from −3.62 to 7.01); Cook’s distance (<bold>C</bold>) (distances increase from smallest to largest dot size from ~0.0 to 0.8); plot of Studentized residuals against hat scores with Cook’s distances indicated by dot size (<bold>D</bold>) (distances increase from smallest to largest dot size from ~0.0 to 0.8); blue vertical lines indicate the mean value of hat score plus the two- and threefold standard deviation of hat score. Blue horizontal lines show −2 and +2 Studentized residuals. Observations that were either greater than 2, less than −2 Studentized residuals, had values larger than the mean value of hat scores plus the two- and threefold standard deviation of hat scores, or had a Cook’s distance greater than 1 are circled in black. Red solid lines denote the linear regression, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs.</p>
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<p>Internal (<bold>A</bold>,<bold>B</bold>) and external (<bold>C</bold>,<bold>D</bold>) validation of prediction equation for DON residues in blood (Equation 6) using the data from which the regression was estimated (combined dataset of Experiments 1 and 2) and data from an independent study (Experiment 3), respectively. Results of the linear regressions of the DON exposure as predicted by Equation 6 on observed DON exposure are shown as regression lines along with the 90° angle bisector (dotted lines) in (<bold>A</bold>,<bold>C</bold>) and as Bland–Altman plots in (<bold>B</bold>,<bold>D</bold>) for internal and external validation, respectively. (<bold>A</bold>) y = 2.12 + 0.77x, n = 237, RSE = 27.3 µg/kg BW/d for solid line; Lin’s concordance correlation coefficient (CCC) = 0.83, Pearson’s correlation coefficient (r) = 0.84. (<bold>B</bold>) Mean difference of 6.4 (red solid line) ± 1.96·30.2 (standard deviation of difference, blue dashed lines) µg/kg BW/d. (<bold>C</bold>) y = −29.06 + 0.87x, n = 267, left-censored n = 80, residual standard error (RSE) = 38.2 µg/kg BW/d for the Tobit regression (dashed line), and y = −6.28 + 0.74x, n = 267, RSE = 31.1 µg/kg BW/d for the ordinary linear regression (solid line); Lin’s CCC = 0.79, Pearson’s r = 0.87. (<bold>D</bold>) Mean difference of −29.4 (red solid line) ± 1.96·36.8 (standard deviation of difference, blue dashed lines) µg/kg BW/d. Red, blue, and green dots represent data from Experiments 1, 2, and 3, respectively, in (<bold>A</bold>,<bold>C</bold>); orange dots indicate the addition of the left-censored data from Experiment 3.</p>
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<p>(<bold>A</bold>) Sample size to be collected dependent on herd size as a fraction of standard deviation (std) for different half widths of the confidence interval (CI; <bold><named-content content-type="color:#385623">green</named-content></bold>: 0.6 × std; <bold><named-content content-type="color:#002060">blue</named-content></bold>: 0.5 × std; <bold><named-content content-type="color:#C00000">red</named-content></bold>: 0.4 × std) (<bold>B</bold>) Sample size to be collected dependent on half width of the confidence interval (CI) as a percent fraction of standard deviation (std) for different herd sizes (N; <bold><named-content content-type="color:#385623">green</named-content></bold>: N = 100; <bold><named-content content-type="color:#002060">blue</named-content></bold>: N = 50; <bold><named-content content-type="color:#C00000">red</named-content></bold>: N = 10).</p>
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<p>Theoretical toxicokinetic profiles of deoxynivalenol (<bold>DON</bold>) residue concentrations in blood for 3 exposure levels (blue—low, green—medium, and red—high) (<bold>A</bold>) as a basis for linear relationships between DON concentration in blood and diet (<bold>B</bold>) or exposure (<bold>C</bold>): (<bold>A</bold>) After feeding the DON-contaminated diets for the first time, the (mean) DON concentration in blood increases until a mean steady state is reached (filled circles, dashed line). This scenario applies for ad libitum fed animals consuming contaminated meals several times per day. The magnitude of oscillation of DON concentrations in blood depends mainly on the half-lives of DON in blood, meal frequency, and on meal size. (<bold>B</bold>) Based on (<bold>A</bold>), plotting of mean steady state DON residues (filled circles) or individual DON residues (unfilled circles) versus the DON concentration of the underlying DON-containing diets results in linear dose–response relationships. Variation of individual values at comparable exposure levels occurs in the direction of the abscissa only and represents variation in time of blood sampling relative to the last meal and is further modified by the meal size. (<bold>C</bold>) Compared with (<bold>B</bold>), variation additionally occurs in the direction of the ordinate, as individual DON exposure varies at similar dietary DON concentrations due to differences in body weight (<bold>BW</bold>) and DON intake as the product of dry matter intake and DON concentration of the diet. This individuality may result in overlapping between different exposure levels and an overall increased dispersion of observations over the entire observation range. Black unfilled circles and squares represent possible scenarios observable at dietary DON background contamination. In addition to non-detection in blood (unfilled squares that intercept the ordinate), the DON exposure might become virtually zero when dietary DON concentrations remain lower than LOD/LOQ. In this situation, DON residues in blood might still be detectable (unfilled black squares that intercepts the abscissa), owing to sensitivity differences of analytical methods for feed and blood.</p>
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<p>Ultrasound-guided localization and puncturing of the gall bladder (photographs by Alexander Starke).</p>
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<p>Exemplarily demonstration of the consequences of setting different half interval widths of the confidence interval (CI) as a fraction of standard deviation (SD = 20 ng/mL blood, see <xref ref-type="table" rid="dairy-04-00024-t001">Table 1</xref>, combined dataset for Exps. 1 and 2) as an indicator of the precision of the estimation of the mean value at the predictor (DON residues in blood) and, consequently, at the response variable (DON exposure).</p>
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<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON exposure with ((<bold>A</bold>,<bold>B</bold>) for zooming in the lower concentration range) and without intercept ((<bold>C</bold>,<bold>D</bold>) for zooming in the lower concentration range) separately for Experiments 1 and 2 (n = 116 and n = 121, respectively). Red and blue solid lines denote the linear regressions, and dashed red and blue lines limit the prediction intervals at a 0.95 confidence level for future predictions for Experiments 1 and 2, respectively. Red and blue filled dots show the corresponding measured data pairs. Blue open circles lying on the red solid line represent the predicted values of Experiment 2 when regression of Experiment 1 is used as prediction equation.</p>
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<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON exposure with ((<bold>A</bold>,<bold>B</bold>) for zooming in the lower concentration range) and without intercept ((<bold>C</bold>,<bold>D</bold>) for zooming in the lower concentration range) separately for Experiments 1 and 2 (n = 116 and n = 121, respectively). Red and blue solid lines denote the linear regressions, and dashed red and blue lines limit the prediction intervals at a 0.95 confidence level for future predictions for Experiments 1 and 2, respectively. Red and blue filled dots show the corresponding measured data pairs. Blue open circles lying on the red solid line represent the predicted values of Experiment 2 when regression of Experiment 1 is used as prediction equation.</p>
Full article ">Figure A2 Cont.
<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON exposure with ((<bold>A</bold>,<bold>B</bold>) for zooming in the lower concentration range) and without intercept ((<bold>C</bold>,<bold>D</bold>) for zooming in the lower concentration range) separately for Experiments 1 and 2 (n = 116 and n = 121, respectively). Red and blue solid lines denote the linear regressions, and dashed red and blue lines limit the prediction intervals at a 0.95 confidence level for future predictions for Experiments 1 and 2, respectively. Red and blue filled dots show the corresponding measured data pairs. Blue open circles lying on the red solid line represent the predicted values of Experiment 2 when regression of Experiment 1 is used as prediction equation.</p>
Full article ">Figure A3
<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>) using the original dataset pooled over Experiments 1 and 2 (n = 237). Red solid lines denote the linear regression, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients, and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
Full article ">Figure A4
<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON concentration in feed with ((<bold>A</bold>,<bold>B</bold>) for zooming in the lower concentration range) and without intercept ((<bold>C</bold>,<bold>D</bold>) for zooming in the lower concentration range) separately for Experiments 1 and 2 (n = 116 and n = 121, respectively). Red and blue solid lines denote the linear regressions, and dashed red and blue lines limit the prediction intervals at a 0.95 confidence level for future predictions for Experiments 1 and 2, respectively. Red and blue filled dots show the corresponding measured data pairs. Blue open circles lying on the red solid line represent the predicted values of Experiment 2 when regression of Experiment 1 is used as prediction equation.</p>
Full article ">Figure A5
<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>blood</bold> on DON concentration in feed with (<bold>A</bold>) and without intercept (<bold>C</bold>) using the original dataset pooled over Experiments 1 and 2 (n = 237). Red solid lines denote the linear regression, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients, and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
Full article ">Figure A6
<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>urine</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 99, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients, and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
Full article ">Figure A6 Cont.
<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>urine</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 99, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients, and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
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<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>urine</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 99, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients, and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
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<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>bile</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 85, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
Full article ">Figure A9
<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>bile</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 85, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
Full article ">Figure A9 Cont.
<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>bile</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 85, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
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<p>Linear regression (method <bold><italic>lm</italic></bold>) of deoxynivalenol (DON) residues in <bold>milk</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 109, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
Full article ">Figure A11
<p>Linear regression (method <bold><italic>rlm</italic></bold>) of deoxynivalenol (DON) residues in <bold>milk</bold> on DON exposure with (<bold>A</bold>) and without intercept (<bold>C</bold>). Red solid lines denote the linear regression using the original dataset of Experiment 1, n = 109, and dashed red lines limit the prediction intervals at a 0.95 confidence level for future predictions. Red dots show the measured data pairs. Blue solid lines represent 200 bootstrap regressions randomly selected from a total of 2000 bootstraps. Intercept and slopes generated by bootstrapping (n = 2000) using the original dataset were used for validation and are presented as density distributions (solid red vertical lines show the mean value of the regression coefficients and dashed blue vertical lines include the 0.95 confidence interval) and qq-plots (with intercept (<bold>B</bold>) and without intercept (<bold>D</bold>)).</p>
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15 pages, 264 KiB  
Article
Effect of Blend-Pelleted Products Based on Carinata Meal or Canola Meal in Combination with Lignosulfonate on Ruminal Degradation and Fermentation Characteristics, Intestinal Digestion, and Feed Milk Value When Fed to Dairy Cows
by Aya Ismael, Basim Refat, Victor Hugo Guevara-Oquendo and Peiqiang Yu
Dairy 2023, 4(2), 345-359; https://doi.org/10.3390/dairy4020023 - 30 May 2023
Viewed by 1542
Abstract
The objectives of this study were to investigate the effect of newly developed blend-pelleted products based on carinata meal (BPPCR) or canola meal (BPPCN) in combination with peas and lignosulfonate on ruminal fermentation characteristics, degradation kinetics, intestinal digestion and feed milk values (FMV) [...] Read more.
The objectives of this study were to investigate the effect of newly developed blend-pelleted products based on carinata meal (BPPCR) or canola meal (BPPCN) in combination with peas and lignosulfonate on ruminal fermentation characteristics, degradation kinetics, intestinal digestion and feed milk values (FMV) when fed to high-producing dairy cows. Three dietary treatments were Control = control diet (common barley-based diet in western Canada); BPPCR = basal diet supplemented with 12.3%DM BPPCR (carinata meal 71.4% + pea 23.8% + lignosulfonate4.8%DM), and BPPCN = basal diet supplemented with 13.3%DM BPPCN (canola meal 71.4% + pea 23.8% + lignosulfonate 4.8%DM). In the whole project, nine mid-lactating Holstein cows (body weight, 679 ± 124 kg; days in milk, 96 ± 22) were used in a triplicated 3 × 3 Latin square study for an animal production performance study. For this fermentation and degradation kinetics study, the experiment was a 3 × 3 Latin square design with three different dietary treatments in three different periods with three available multiparous fistulated Holstein cows. The results showed that the control diet was higher (p < 0.05) in total VFA rumen concentration (138 mmol/L) than BPPCN. There was no dietary effect (p > 0.10) on the concentration of rumen ammonia and ruminal degradation kinetics of dietary nutrients. There was no significant differences (p > 0.10) among diets on the intestinal digestion of nutrients and metabolizable protein. Similarly, the feed milk values (FMV) were not affected (p > 0.10) by diets. In conclusion, the blend-pelleted products based on carinata meal for a new co-product from the bio-fuel processing industry was equal to the pelleted products based on conventional canola meal for high producing dairy cattle. Full article
(This article belongs to the Section Dairy Animal Health)
7 pages, 588 KiB  
Case Report
Associations of Eliminating Free-Stall Head Lock-Up during Transition Period with Milk Yield, Health, and Reproductive Performance in Multiparous Dairy Cows: A Case Report
by Sushil Paudyal, Juan Piñeiro and Logan Papinchak
Dairy 2023, 4(1), 215-221; https://doi.org/10.3390/dairy4010015 - 9 Mar 2023
Viewed by 1833
Abstract
The objective of this retrospective case study was to understand the effects of eliminating free-stall lock-up time during 21 days postpartum on milk yield, reproductive performance, and health events at a large dairy herd. A group of 200 cows were selected as the [...] Read more.
The objective of this retrospective case study was to understand the effects of eliminating free-stall lock-up time during 21 days postpartum on milk yield, reproductive performance, and health events at a large dairy herd. A group of 200 cows were selected as the treatment (TRT) group, which did not receive a lock-up time during early lactation, and a separate group of 200 cows served as the control (CON) group, which received on average 2 h/day of lockup time. The TRT group had greater milk yield (mean ± SE) on the third monthly milk test day (33.1 ± 0.75 vs. 29.9 ± 1.22; p = 0.04) and tended to have greater milk yield on the second test day (38.3 ± 1.55 vs. 39.1 ± 0.79; p = 0.06) compared to the CON cows. Milk fat% (mean ± SE) was greater in the TRT group than in the CON group on the first monthly milk test (3.65 ± 0.06 vs. 3.31 ± 0.12, p = 0.01). The TRT group had lower linear somatic cell scores on the first monthly milk test day compared to the CON group (2.6 ± 0.24 vs. 3.2 ± 0.11; p = 0.01). Cows in the TRT group had lower days in milk at first breeding (DIMFB) (66.2 ± 3.7 vs. 76.7 ± 2.9; p = 0.02) and were confirmed pregnant earlier as indicated by smaller days in milk to pregnancy (DIMPREG) (96.9 ± 12.32 vs. 112.1 ± 5.5; p < 0.01). Cows in the TRT group also had fewer incidences of all health events combined (13% vs. 30.5%; p < 0.001), lameness (3% vs. 9.5%; p = 0.01), and mastitis (3% vs. 16%; p < 0.001). We conclude that eliminating the stall lockup may have contributed to the increased milk yield, health, and reproductive performance of dairy cows in this dairy herd. Future prospective cohort studies are needed to further assess the potential effect of eliminating lock up time on cow performance. Full article
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Figure 1

Figure 1
<p>Distribution of milk production and linear somatic cell score (LSCC) across the monthly milk test days among cows in treatment (TRT; no headlocks) and control (CON; regular headlock) groups. ** denotes a statistically significant difference.</p>
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<p>Distribution of milk fat% and milk protein% across the monthly milk test days among cows in the treatment (TRT; no headlocks) and control (CON; regular headlock) groups. ** denotes a statistically significant difference.</p>
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26 pages, 474 KiB  
Review
Mastitis: Impact of Dry Period, Pathogens, and Immune Responses on Etiopathogenesis of Disease and its Association with Periparturient Diseases
by Ashley F. Egyedy and Burim N. Ametaj
Dairy 2022, 3(4), 881-906; https://doi.org/10.3390/dairy3040061 - 19 Dec 2022
Cited by 9 | Viewed by 4396
Abstract
Mastitis is an inflammation of the mammary gland initiated by pathogenic bacteria. In fact, mastitis is the second most important reason for the culling of cows from dairy herds, after infertility. In this review we focus on various forms of mastitis, including subclinical [...] Read more.
Mastitis is an inflammation of the mammary gland initiated by pathogenic bacteria. In fact, mastitis is the second most important reason for the culling of cows from dairy herds, after infertility. In this review we focus on various forms of mastitis, including subclinical and clinical mastitis. We also stress the importance of the dry-off period as an important time when pathogenic bacteria might start their insult to the mammary gland. An important part of the review is the negative effects of mastitis on milk production and composition, as well as economic consequences for dairy farms. The two most important groups of bacteria that are involved in infection of the udder, Gram-negative and Gram-positive bacteria, are also discussed. Although all cows have both innate and adaptive immunity against most pathogens, some are more susceptible to the disease than others. That is why we summarize the most important components of innate and adaptive immunity so that the reader understands the specific immune responses of the udder to pathogenic bacteria. One of the most important sections of this review is interrelationship of mastitis with other diseases, especially retained placenta, metritis and endometritis, ketosis, and laminitis. Is mastitis the cause or the consequence of this disease? Finally, the review concludes with treatment and preventive approaches to mastitis. Full article
9 pages, 875 KiB  
Article
Assessment of Noninferiority of Delayed Oral Calcium Supplementation on Blood Calcium and Magnesium Concentrations and Rumination Behavior in Dairy Cows
by Cainan C. Florentino, Elise Shepley, Megan Ruch, Joao V. L. Silva, Brian A. Crooker and Luciano S. Caixeta
Dairy 2022, 3(4), 872-880; https://doi.org/10.3390/dairy3040060 - 13 Dec 2022
Cited by 1 | Viewed by 2309
Abstract
We investigated whether delaying oral calcium (Ca) bolus administration to the second day postpartum (DEL) was noninferior to bolus administration within 24 h of calving (CON) in its effects on plasma Ca concentrations during the first five days in milk (DIM). We also [...] Read more.
We investigated whether delaying oral calcium (Ca) bolus administration to the second day postpartum (DEL) was noninferior to bolus administration within 24 h of calving (CON) in its effects on plasma Ca concentrations during the first five days in milk (DIM). We also investigated the effects of DEL vs. CON strategies on magnesium (Mg) concentrations and daily rumination time (RT). Twenty-three multiparous (parity ≥ 3) dairy cows were randomly assigned to the CON (n = 11) or DEL (n = 12) treatment. Blood Ca and Mg were measured at 1–5 DIM and RT was monitored from −7 d to 7 d relative to calving. The noninferiority margin was a difference in Ca concentration of 0.15 mmol/L. Blood Ca and Mg concentrations and RT were analyzed by multivariable linear mixed models accounting for repeated measures. Blood Ca concentrations were 0.07 mmol/L (95% confidence interval: −0.30–0.17) less in DEL cows than CON cows, thus non-inferiority results were inconclusive. The Ca concentration increased across the first 5 DIM but did not differ between treatments while Mg concentrations decreased in both treatments (p < 0.001). There was no treatment difference in RT (CON: 436 ± 21, DEL: 485 ± 19 min/d). While noninferiority results were inconclusive, similar blood Ca dynamics between CON and DEL treatment strategies indicates that delayed Ca administration is a potential management option for commercial dairy farms; however, additional studies using large sample sizes are warranted to confirm these findings. Full article
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Figure 1
<p>Blood plasma calcium (<bold>a</bold>) and magnesium (<bold>b</bold>) concentrations in multiparous cows administered oral Ca boluses. Two Ca boluses were administered simultaneously within 24 h (CON) or between 24 and 48 h (DEL) of calving. Least square means (±SEM) from 1 to 5 days in milk (DIM) are reported for 9 CON and 9 DEL cows. Blood plasma Ca concentration did not differ between treatments (<italic>p</italic> = 0.52), increased with DIM (<italic>p</italic> &lt; 0.001) and was not affected by the interaction of treatment by DIM (<italic>p</italic> = 0.48). Blood plasma Mg concentration was greater in DEL cows (<italic>p</italic> = 0.04), decreased with DIM (<italic>p</italic> &lt; 0.001), and was altered by the interaction of treatment * DIM (<italic>p</italic> = 0.02).</p>
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<p>Total daily rumination time (RT, min/d) in multiparous cows administered oral Ca boluses. Two Ca boluses were administered simultaneously within 24 h (CON) or between 24 and 48 h (DEL) of calving. Least square means (±SEM) from −7 to +7 days in milk (DIM) are reported for 6 CON and 8 DEL cows. The first hour of day 1 coincides with the time of reported calving. Effect of DIM on total Daily RT differed among days (<italic>p</italic> &lt; 0.001) but was not affected by treatment (<italic>p</italic> = 0.07) or the interaction of treatment by day (<italic>p</italic> = 0.65).</p>
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10 pages, 265 KiB  
Article
Effect of Major Diseases on Productivity of a Large Dairy Farm in a Temperate Zone in Japan
by Yuki Fukushima, Erina Kino, Aina Furutani, Tomoya Minamino, Kazuyuki Honkawa, Yoichiro Horii and Yosuke Sasaki
Dairy 2022, 3(4), 789-798; https://doi.org/10.3390/dairy3040054 - 9 Nov 2022
Cited by 3 | Viewed by 1592
Abstract
The objective of the present study was to investigate the associations between major diseases (clinical mastitis, peracute mastitis, metabolic disorders, peripartum disorders) and four parameters related to productivity (305-day milk yield, number of days open, culling rate, death rate) on a large dairy [...] Read more.
The objective of the present study was to investigate the associations between major diseases (clinical mastitis, peracute mastitis, metabolic disorders, peripartum disorders) and four parameters related to productivity (305-day milk yield, number of days open, culling rate, death rate) on a large dairy farm in a temperate zone with approximately 2500 Holstein cows. Data were collected from 2014 to 2018 and involved 9663 calving records for 4256 cows. We found negative effects of clinical mastitis, peracute mastitis, metabolic disorders, and peripartum disorders on the productivity of cows. Clinical-mastitis-suffered cows with multiple diseases had more days open compared with those with clinical mastitis alone and the healthy group, and they had a higher death rate than the healthy group, whereas there was no difference in death rate between the clinical mastitis only and healthy groups. Cows suffering from peracute mastitis, metabolic disorders, and peripartum disorders with either single or multiple diseases exhibited reduced productivity compared with the healthy group. Our findings clearly show that major diseases of cows in a temperate zone have severely negative effects on their productivity. Full article
(This article belongs to the Section Dairy Animal Health)
25 pages, 9559 KiB  
Review
Mastitis: What It Is, Current Diagnostics, and the Potential of Metabolomics to Identify New Predictive Biomarkers
by Klevis Haxhiaj, David S. Wishart and Burim N. Ametaj
Dairy 2022, 3(4), 722-746; https://doi.org/10.3390/dairy3040050 - 17 Oct 2022
Cited by 22 | Viewed by 14742
Abstract
Periparturient diseases continue to be the greatest challenge to both farmers and dairy cows. They are associated with a decrease in productivity, lower profitability, and a negative impact on cows’ health as well as public health. This review article discusses the pathophysiology and [...] Read more.
Periparturient diseases continue to be the greatest challenge to both farmers and dairy cows. They are associated with a decrease in productivity, lower profitability, and a negative impact on cows’ health as well as public health. This review article discusses the pathophysiology and diagnostic opportunities of mastitis, the most common disease of dairy cows. To better understand the disease, we dive deep into the causative agents, traditional paradigms, and the use of new technologies for diagnosis, treatment, and prevention of mastitis. This paper takes a systems biology approach by highlighting the relationship of mastitis with other diseases and introduces the use of omics sciences, specifically metabolomics and its analytical techniques. Concluding, this review is backed up by multiple studies that show how earlier identification of mastitis through predictive biomarkers can benefit the dairy industry and improve the overall animal health. Full article
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Figure 1

Figure 1
<p>Schematic presentation of mastitis pathogenesis. Typically, (1) once bacteria invade the teat end and ascend into the teat canal and alveoli, a local immune reaction starts; (2) bacterial by products, such as lipopolysaccharide (LPS) or outer membrane vesicle (OMV) of Gram-negative pathogens act as pathogen-associated molecular pattern (PAMP), which is recognized by pathogen recognition receptors (PRR), specifically Toll-like receptor 4 (TLR4), on macrophage type 1 (M1). After this contact, (3) humoral elements, such as cytokines (IL1, IL6, and TNF) and chemokines [interleukin(IL)-8] are released that alert other white blood cells, mainly polymorphonuclear (PMN) leuckocytes in the systemic circulation and trigger the release of acute phase proteins; (4) Once PMN have entered in the infected area, through mammary epithelial cells (MEC), (5) they engulf and kill bacteria through phagoctysis. If this inflammation is persistent, (6) then adaptive immunity is activated via the interaction of macrophages with lymphocytes, like T-regulatory cells.</p>
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<p>Presence of somatic cells in healthy and infected quarters of a cow. In the milk of a healthy quarter there are present more macrophages, followed by a small percentage of lymphocytes, neutrophils, and epithelial cells, whereas an infected quarter, with clinical mastitis or subclinical mastitis, is overpopulated with neutrophils and few macrophages, lymphocytes, and epithilail cells.</p>
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<p>Comparison of two of the most used tests to detect somatic cells in milk. CMT kit image courtesy of ImmuCell (USA); Fossomatic 7 image courtesy of FOSS (Denmark).</p>
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23 pages, 319 KiB  
Article
Herd Routines and Veterinary Advice Related to Drying-Off and the Dry Period of Dairy Cows
by Karin Persson Waller, Håkan Landin and Ann-Kristin Nyman
Dairy 2022, 3(2), 377-399; https://doi.org/10.3390/dairy3020029 - 1 Jun 2022
Viewed by 2176
Abstract
Bovine mastitis at calving or early lactation is often associated with intra-mammary bacterial infections (IMI) at drying-off (DO) or during the dry period (DP). The IMI risk is associated with management routines at the herd, but knowledge on how farmers and veterinarians comply [...] Read more.
Bovine mastitis at calving or early lactation is often associated with intra-mammary bacterial infections (IMI) at drying-off (DO) or during the dry period (DP). The IMI risk is associated with management routines at the herd, but knowledge on how farmers and veterinarians comply with national recommendations is scarce, as is their attitudes to the importance of such routines. Therefore, the main aims of this study were to collect information on farmer routines and attitudes, and on veterinary advice and attitudes to DO and DP. Associations between routines and advice, and demographic herd and veterinary variables were also studied. Web-based questionnaires were sent to 2472 dairy farmers and 517 veterinarians. The answers were summarized descriptively, and associations with demographics were evaluated using univariable regression models. The response rate was 14% for farmers and 25% for veterinarians. Routines and advice were in line with recommendations at the time of the study in many, but not all, areas of questioning. Significant associations between herd routines or veterinary advice and demographic variables were also found. Milking system and post-graduate training were the variables associated with the largest number of farmer and veterinary answers, respectively. In conclusion, the results indicate a need for more education on good routines during DO and DP. It was also clear that the national recommendations valid at the time of the study were in need of revision. Full article
15 pages, 1852 KiB  
Article
Influence of Post-Milking Treatment on Microbial Diversity on the Cow Teat Skin and in Milk
by Isabelle Verdier-Metz, Céline Delbès, Matthieu Bouchon, Philippe Pradel, Sébastien Theil, Etienne Rifa, Agnès Corbin and Christophe Chassard
Dairy 2022, 3(2), 262-276; https://doi.org/10.3390/dairy3020021 - 15 Apr 2022
Cited by 5 | Viewed by 3281
Abstract
In dairy cattle, teat disinfection at the end of milking is commonly applied to limit colonization of the milk by pathogenic microorganisms via the teat canal. The post-milking products used can irritate the teat skin and unbalance its microbial population. Our study aimed [...] Read more.
In dairy cattle, teat disinfection at the end of milking is commonly applied to limit colonization of the milk by pathogenic microorganisms via the teat canal. The post-milking products used can irritate the teat skin and unbalance its microbial population. Our study aimed to assess the impact of different milking products on the balance of the microbial communities on the teat skin of cows and in their milk. For 12 weeks at the end of each milking operation, three groups of seven Holstein dairy cows on pasture received either a chlorhexidine gluconate-based product (G) or a hydrocolloidal water-in-oil emulsion (A), or no post-milking product (C). The composition of the bacterial and fungal communities on the teat skin and in the milk were characterized using a culture-dependent method and by high-throughput sequencing of marker genes to obtain amplicon sequence variants (ASVs). The individual microbiota on the cows’ teat skin was compared for the first time to that of a cow pool. In contrast to the milk, the post-milking treatment influenced the microbiota of the teat skin, which revealed a high microbial diversity. The water-in-oil emulsion appeared to slightly favour lactic acid bacteria and yeasts and to limit the development of undesirable bacteria such as Pseudomonas and Staphylococcus. Full article
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Graphical abstract

Graphical abstract
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<p>Bacterial (16S) and fungal (ITS) betadiversity of individual teat suspensions according to (<b>a</b>) the treatment at each period and (<b>b</b>) the period for each treatment.</p>
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