Short communication: Temporal changes in the skin morphology of dairy cows during the periparturient period

Management of dairy cow productivity requires monitoring of their nutritional status by visual observation. It has been suggested that changes in hair coat appearance are among the indicators of nutritional state in dairy cows. Temporal changes in the skin morphology in cows, however, have not been reported. In this study, we examined the changes in the skin of dairy cows that occur during the peripartum period. Seven pluriparous cows were used. Skin samples were collected at 28 d before the due date and 28 d and 56 d after calving for morphological examination. Hair follicle width was 108.8 ± 5.9 µm (±SD) in the dry period, 95.5 ± 5.5 µm at 28 d after calving, and 104.2 ± 5.3 µm at 56 d postpartum. The percentages of anagen hair follicles during these 3 periods were 41.4 ± 3.4, 18.5 ± 3.4, and 32.3 ± 3.3%, respectively. The corresponding sebaceous gland sizes were 8,362.0 ± 707.6, 7,800.0 ± 831.4, and 9,186.8 ± 962.6 µm², respectively. Hair follicle width was positively correlated with percentage of anagen hair follicles. The thickness of epidermal and proliferation rate of epidermal cell were also correlated. However, the hair follicle width, sebaceous gland size and cell proliferation rate, and thickness and proliferation rate of epidermal cells did not show any marked changes.     

Effect of manipulating progesterone before timed artificial insemination on reproductive and endocrine outcomes in high-producing multiparous Holstein cows

Our objective was to evaluate the effect of manipulating progesterone (P4) concentrations before timed artificial insemination (TAI) on reproductive and endocrine outcomes in high-producing Holstein cows. Multiparous lactating Holstein cows (n = 80) were synchronized for first TAI using a Double-Ovsynch protocol and were randomly assigned to receive 25 mg of PGF_2α 1 d after the first GnRH treatment of the Breeding-Ovsynch protocol that included a once-used P4 insert (low-P4 group) or to receive 2 new P4 inserts during the Breeding-Ovsynch protocol (high-P4 group). Blood samples were collected thrice weekly from ?10 to 32 d relative to TAI for all cows and from 32 to 67 d after TAI for pregnant cows and were analyzed for P4 and pregnancy-specific protein B (PSPB) concentrations. Expression of IFNτ-stimulated gene 15 (ISG15) was assessed in blood leukocytes 18 and 20 d after TAI. As expected, P4 concentrations were greater for high-P4 cows than for low-P4 cows from 3 to 8 d before TAI. Incidence of double ovulation was 3-fold greater for low-P4 cows than for high-P4 cows (33 vs. 10%), which resulted in more twin pregnancies 32 d after TAI for low-P4 cows than for high-P4 cows (29 vs. 0%). Low-P4 cows had larger preovulatory follicles at the last GnRH treatment of the Double-Ovsynch protocol and greater P4 concentrations than high-P4 cows after TAI. Relative expression of ISG15 mRNA 18 and 20 d after TAI was greater for low-P4 cows than for high-P4 cows and for pregnant cows than for nonpregnant cows. Overall, PSPB concentrations tended to be greater for low-P4 cows than for high-P4 cows, and pregnant cows had greater P4 concentrations than nonpregnant cows. In summary, cows with low P4 before TAI had increased preovulatory follicle diameter, PSPB concentrations, relative expression of ISG15 mRNA 18 and 20 d after TAI, double ovulations, and twinning compared with cows with high P4 before TAI. Increasing P4 before TAI may effectively decrease double ovulation and twinning in high-producing multiparous Holstein cows.     

Genetic parameters of colostrum traits in Holstein dairy cows

The main objective of this study was to assess the genetic background of colostrum yield and quality traits after calving in Holstein dairy cows. The secondary objective was to investigate genetic and phenotypic correlations among laboratory-based and on-farm–measured colostrum traits. The study was conducted in 10 commercial dairy herds located in northern Greece. A total of 1,074 healthy Holstein cows with detailed pedigree information were examined from February 2015 to September 2016. All cows were clinically examined on the day of calving and scored for body condition. All 4 quarters were machine-milked, and a representative and composite colostrum sample was collected and examined. Colostrum total solids (TS) content was determined on-farm using a digital Brix refractometer. Colostrum fat, protein, and lactose contents were determined using an infrared milk analyzer, and energy content was calculated using National Research Council (2001) equations. Dry period length (for cows of parity ≥2), milk yield of previous 305-d lactation (for cows of parity ≥2), age at calving, parity number, season of calving, time interval between calving and first colostrum milking, and milk yield were recorded. Each trait (colostrum yield and quality traits) was analyzed with a univariate mixed model, including fixed effects of previously mentioned factors and the random animal additive genetic effect. All available pedigrees were included in the analysis, bringing the total animal number to 5,662. Estimates of (co)variance components were used to calculate heritability for each trait. Correlations among colostrum traits were estimated with bivariate analysis using the same model. Mean percentage (±SD) colostrum TS, fat, protein, and lactose contents were 25.8 ± 4.7, 6.4 ± 3.3, 17.8 ± 4.0, and 2.2 ± 0.7%, respectively; mean energy content was 1.35 ± 0.3 Mcal/kg and mean colostrum yield was 6.18 ± 3.77 kg. Heritability estimates for the above colostrum traits were 0.27, 0.21, 0.19, 0.15, 0.22, and 0.04, respectively. Several significant genetic and phenotypic correlations were derived. The genetic correlation of TS content measured on-farm with colostrum protein was practically unity, whereas the correlation with energy content was moderate (0.61). Fat content had no genetic correlation with TS content; their phenotypic correlation was positive and low. Colostrum yield was not correlated genetically with any of the other traits. In conclusion, colostrum quality traits are heritable and can be amended with genetic selection.     

Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study

About 30% of producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (AI) in Canada. Days from calving to first service (CTFS) and first service to conception (FSTC) become masked phenotypes leading to biased genetic evaluations of cows for these fertility traits. The objectives of this study were to (1) demonstrate and quantify the potential amount of bias in genetic evaluations, and (2) find a procedure that could remove the bias. Simulation was used for both objectives. The proposed solution was to identify cows that have been treated by hormone protocols, make their CTFS and FSTC missing, and perform a multiple trait analysis including traits that have high genetic correlations with CTFS and FSTC, and which are not affected by the hormone protocols themselves. A total of 12 scenarios (S1–S12) were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. Cows that were given hormone protocols had CTFS of 86 d and FSTC of 0, which were used in genetic evaluation. Four criteria were used to indirectly measure the presence of bias: (1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); (2) the differences in the mean EBV of top 25, 50, and 75 sires; (3) changes in correlation between TBV and EBV rankings; and (4) the changes in mean EBV over the simulated generations. All criteria changed unfavorably and proportionally to the increased use of timed AI. The accuracy within each class of animals (cows, dams, or sires) decreased proportionally with increased use of timed AI, varying from 0.32 (S12) to 0.52 (S1) for bull EBV for CTFS. The average EBV of the top sires (best 25, 50, 75, or 100 sires) approached population average EBV values when increasing the number of treated animals. The sire rank correlation between EBV and TBV within simulated scenarios was smaller for scenarios with more synchronized animals, going from 0.38 (S12) to 0.67 (S1). The long-term use of hormonal synchronized cows clearly decreased the mean EBV over generations in the population for CTFS and FSTC. The inclusion of genetically correlated traits in a multiple trait model was effective in removing the bias due to the presence of hormonal synchronized cows. However, given the constraints within the simulation, it is important that further investigation with real data is conducted to determine the true effect of including timed AI records within genetic evaluations of fertility traits in dairy cattle.     

Between-herd variation in resilience and relations to herd performance

Resilient cows are minimally affected in their functioning by infections and other disturbances, and recover quickly. Herd management is expected to have an effect on disturbances and the resilience of cows, and this effect was investigated in this study. Two resilience indicators were first recorded on individual cows. The effect of herd-year on these resilience indicators was then estimated and corrected for genetic and year-season effects. The 2 resilience indicators were the variance and the lag-1 autocorrelation of daily milk yield deviations from an expected lactation curve. Low variance and autocorrelation indicate that a cow does not fluctuate much around her expected milk yield and is, thus, subject to few disturbances, or little affected by disturbances (resilient). The herd-year estimates of the resilience indicators were estimated for 9,917 herd-year classes based on records of 227,655 primiparous cows from 2,644 herds. The herd-year estimates of the resilience indicators were then related to herd performance variables. Large differences in the herd-year estimates of the 2 resilience indicators (variance and autocorrelation) were observed between herd-years, indicating an effect of management on these traits. Furthermore, herd-year classes with a high variance tended to have a high proportion of cows with a rumen acidosis indication (r = 0.31), high SCS (r = 0.19), low fat content (r = ?0.18), long calving interval (r = 0.14), low survival to second lactation (r = ?0.13), large herd size (r = 0.12), low lactose content (r = ?0.12), and high production (r = 0.10). These correlations support that herds with high variance are not resilient. The correlation between the variance and the proportion of cows with a rumen acidosis indication suggests that feed management may have an important effect on the variance. Herd-year classes with a high autocorrelation tended to have a high proportion of cows with a ketosis indication (r = 0.14) and a high production (r = 0.13), but a low somatic cell score (r = ?0.17) and a low proportion of cows with a rumen acidosis indication (r = ?0.12). These correlations suggest that high autocorrelation at herd level indicates either good or poor resilience, and is thus a poor resilience indicator. However, the combination of a high variance and a high autocorrelation is expected to indicate many fluctuations with slow recovery. In conclusion, herd management, in particular feed management, seems to affect herd resilience.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Risk-based approach to developing a national residue sampling plan for testing under European Union regulation for veterinary medicinal products and coccidiostat feed additives in domestic animal production

A ranking system for veterinary medicinal products and coccidiostat feed additives has been developed as a tool to be applied in a risk-based approach to the residue testing programme for foods of animal origin in the Irish National Residue Control Plan (NRCP). Three characteristics of substances that may occur as residues in food are included in the developed risk ranking system: Potency, as measured by the acceptable daily intake assigned by the European Medicines Agency Committee for Medicinal Products for Veterinary Use, to each substance; Usage, as measured by the three factors of Number of Doses, use on Individual animals or for Group treatment, and Withdrawal Period; and Residue Occurrence, as measured by the number of Non-Compliant Samples in the NRCP. For both Number of Doses and Non-Compliant Samples, data for the 5-year period 2008–12 have been used. The risk ranking system for substances was developed for beef cattle, sheep and goats, pigs, chickens and dairy cattle using a scoring system applied to the various parameters described above to give an overall score based on the following equation: Potency × Usage (Number of Doses + Individual/Group Use + Withdrawal Period) × Residue Occurrence. Applying this risk ranking system, the following substances are ranked very highly: antimicrobials such as amoxicillin (for all species except pigs), marbofloxacillin (for beef cattle), oxytetracycline (for all species except chickens), sulfadiazine with trimethoprim (for pigs and chickens) and tilmicosin (for chickens); antiparasitic drugs, such as the benzimidazoles triclabendazole (for beef and dairy cattle), fenbendazole/oxfendazole (for sheep/goats and dairy cattle) and albendazole (for dairy cattle), the avermectin ivermectin (for beef cattle), and anti-fluke drugs closantel and rafoxanide (for sheep/goats); the anticoccidials monensin, narasin, nicarbazin and toltrazuril (for chickens). The risk ranking system described is a relatively simple system designed to provide a reliable basis for selecting the veterinary medicinal products and coccidiostat feed additives that might be prioritised for residue testing.     

A screening sampling plan to detect Mycobacterium avium subspecies paratuberculosis-positive dairy herds

Mycobacterium avium ssp. paratuberculosis (MAP) is the etiological agent of paratuberculosis, a chronic contagious bacterial disease primarily affecting dairy cattle. Paratuberculosis represents a dual problem for the milk production chain: in addition to economic losses to affected herds, MAP may have zoonotic potential. Infected herds must be identified in order to implement programs designed to reduce the incidence of disease within and between herds and to prevent MAP from entering the food chain. The objective of this study was to evaluate the sensitivity and specificity of a screening sampling plan (SSP) to detect MAP-positive dairy herds by repetitive analysis of bulk tank milk (BTM) samples by ELISA and in-line milk filter (ILMF) samples by PCR. Samples from BTM and ILMF were collected twice from 569 dairy herds in southern Italy. Additionally, 12,016 individual milk samples were collected: 9,509 from 102 SSP-positive herds (SSP MAP-positive) and 2,507 from 21 randomly selected SSP-negative herds (SSP MAP-negative). There was a total of 126 SSP MAP-positive herds (i.e., 21.3% SSP MAP-positive herds; 95% confidence interval = 18.0–24.9); the within-herd apparent prevalence (AP) ranged between 0.00 and 22.73% (mean 6.07%). A significant difference in within-herd AP was shown between SSP MAP-positive herds and SSP MAP-negative herds. A highly significant association was shown between the median AP herd status (>5%) and positivity to at least one ILMF or BTM sample. The SSP detected a minimum of 56.25% of low AP herds (AP ≤2.0%) up to a maximum of 100% of herds with a within-herd AP ≥8.0%. Overall, the SSP detected 85.57% of herds in which at least one individual milk sample was positive by ELISA. The proposed SSP was an inexpensive and useful tool to detect MAP-positive herds with a higher risk of infection diffusion and milk contamination. Although the SSP cannot be used for MAP-free certification of herds, it could be useful to prioritize appropriate control measures aimed at reducing the prevalence of infection in dairy herds and milk contamination.     

Study of progesterone and cortisol concentrations in the Italian Friesian claw

The present research was conducted to study progesterone and cortisol concentrations in the claw of cattle and to verify whether the cattle claw could be considered an efficient matrix to provide retrospective information regarding progesterone and cortisol concentrations related to pregnancy and peripartum periods. These 2 steroids are involved in hoof growth. The study was performed on 32 calves and 24 pregnant milking cows of the Holstein breed, which were clinically healthy and lacking any claw disorders. Samples of at least 0.5 cm in thickness were taken from the sole. Progesterone and cortisol concentrations were determined by RIA. The cortisol concentration in the horny shoe of calves from 0 to 30 d of age was significantly higher than the concentration at 31 to 60 and 61 to 120 d of age. Conversely, the progesterone concentration showed no statistically significant difference in relation to age. The horn progesterone concentrations recorded in the milking dairy cows at 7 mo of pregnancy showed high variability in the horizontal sections of the sole (the individual coefficient of variation ranged between 0.09 and 1.11). In 6 cows, genuine extreme values (genuine outliers) of the progesterone level were found. Moreover, significant differences existed among the progesterone concentrations of the sole's transverse sections. We detected a significant positive correlation between the weight of the horn samples after freeze-drying and their weight after hydration. The cortisol and progesterone levels in soaked horn samples were found to be significantly lower than in the same dry samples. These results show that cortisol and progesterone can be measured in the cattle claw horn. The claws of mature dairy cows could not be used as a matrix to provide a retrospective measure of cumulative hormone secretion, whereas the analysis of the calves’ claw horns showed retrospective hormonal information similar to hair samples.