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.     

牦牛绒过氧化氢/过硫酸铵脱色体系工艺优化及其机制

针对现有商业化牦牛绒脱色过程复杂、脱色时间长等问题,提出新型过氧化氢/过硫酸铵体系牦牛绒脱色工艺。在尽可能提高纤维白度的前提下尽量减少纤维损伤,通过单因素试验确定了脱色时间、过硫酸铵用量和试剂pH值,对脱色工艺进行探讨并与商业化脱色牦牛绒进行比较。结果表明:采用过硫酸铵质量浓度为400 g/L,试剂pH值为9,在室温下脱色为180 min可达到商业脱色牦牛绒的效果,脱色时间节省至原来的1/8;脱色机制分析发现,脱色过程中硫酸根自由基起主要作用,羟基自由基起次要作用。该脱色工艺简单,为牦牛绒高值化利用提供了新的解决方案。     

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.     

Active dry Saccharomyces cerevisiae can alleviate the effect of subacute ruminal acidosis in lactating dairy cows

The objective of the study was to determine the effect of active dry Saccharomyces cerevisiae (ADSC) supplementation on dry matter intake, milk yield, milk components, ruminal pH, and microbial community during a dietary regimen that leads to subacute ruminal acidosis (SARA). Sixteen multiparous, rumen-cannulated lactating Holstein cows were randomly assigned to 1 of 2 dietary treatments that included ADSC (Biomate; AB Vista, Marlborough, UK; 8 × 10¹⁰ cfu/head per day) or control. During wk 1 to 6, all cows received a high-forage (HF) diet (77:23, forage:concentrate). Cows were then abruptly switched during wk 7 to a high-grain (HG) diet (49:51, forage:concentrate) and remained on the HG until the end of wk 10. Feed intake and milk yields were recorded daily. Ruminal pH was recorded continuously using an indwelling system for 1 to 2 d per week during the pre-experimental phase, and wk 6, 7, and 10. Ruminal digesta samples were collected at the end of the experiment and analyzed for relative change in microbial communities using real-time quantitative PCR. Cows were considered to have SARA if the duration below pH 5.6 was ≥300 min/d. Ruminal pH during wk 6 (HF plateau) was not different across treatments (15 ± 46 min/d at pH <5.6). The dietary regimen successfully induced SARA during wk 7 (transition from HF to HG diet), and ruminal pH (551 ± 46 min/d at pH <5.6) was not different across treatments. However, cows receiving ADSC had an improved ruminal pH (122 ± 57 vs. 321 ± 53 min/d at pH <5.6) during wk 10 (HG plateau) compared with control. Additionally, cows receiving ADSC had a better dry matter intake (23.3 ± 0.66 vs. 21.6 ± 0.61 kg/d) and 4% fat-corrected milk yield (29.6 ± 1.2 vs. 26.5 ± 1.2 kg/d) than control cows during the HG phase (wk 8 to 10). During HG feeding, cows receiving ADSC had greater total volatile fatty acid and propionate concentrations (175 ± 7.5 vs. 154 ± 7.5 and 117 ± 6.1 vs. 94 ± 5.7 mM for ADSC and control, respectively) and lower acetate:propionate ratio (0.26 ± 0.5 vs. 0.36 ± 0.05 for ADSC and control, respectively). Microbial analyses conducted on samples collected during wk 10 showed that cows supplemented with S. cerevisiae had a 9-fold, 2-fold, 6-fold, 1.3-fold, and 8-fold increase in S. cerevisiae, Fibrobacter succinogenes, Anaerovibrio lipolytica, Ruminococcus albus, and anaerobic fungi, respectively, which suggested an increase in cellulolytic microbes within the rumen. Cows supplemented with ADSC had 2.2-fold reduction in Prevotella albensis, which is a gram-negative bacterium predominant during SARA. Prevotella spp. are suggested to be an important source of lipopolysaccharide responsible for inflammation within the rumen. Cows supplemented with ADSC had a 2.3-fold increase in Streptococcus bovis and a 12-fold reduction in Megasphaera elsdenii. The reduction in M. elsdenii may reflect lower concentration of lactic acid within the rumen for ADSC cows. In conclusion, ADSC supplementation to dairy cows was demonstrated to alleviate the condition of SARA caused by abrupt dietary changes from HF to HG, and can potentially improve rumen function, as indicated by greater numbers of cellulolytic microorganisms within the rumen.     

Evaluation of the peptide profile with a view to authenticating buffalo mozzarella cheese

Electrophoretic and chromatographic techniques were used to determine water‐soluble peptide profiles aiming to identify the adulteration of buffalo milk mozzarella cheese by the addition of cow's milk. Thus, cheeses were produced with contents of cow's milk varying from 0% to 100%, and the peptides extracted after production and after 20 days of refrigeration. Polyacrylamide gel electrophoresis under denaturing conditions in the presence of sodium dodecyl sulphate (SDS‐PAGE) identified a potential peptide marker of exclusively bovine origin with a size of about 21 kDa for the addition of cow's milk above 30%. Reverse‐phase high‐performance liquid chromatography (RP‐HPLC) indicated the existence of two potential peptides present in higher concentrations in buffalo milk and one exclusive for cow's milk, the latter making it possible to estimate the addition of cow's milk to buffalo milk. Six commercial brands of buffalo mozzarella cheese were evaluated, and indications of adulteration found in four of them.     

Potential risk indicators of retained placenta and other diseases in multiparous cows

Retained placenta (RP), defined as fetal membranes not being expelled within 24 h after calving, is a costly disease in multiparous dairy cows that has been linked to immune suppression, infections, elevated lipid mobilization, and depleted status of antioxidants including α-tocopherol, and that increases the risk of other diseases (OD) in early lactation. Early detection of cows at increased risk of developing RP, OD, or both in early lactation could improve treatment success and result in improved milk production and reproductive performance. To identify risk indicators of RP, OD, or both, we used a nested case-control design and compared multiparous dairy cows that developed RP (n = 32) with cows that remained healthy (H; n = 32) or cows that developed OD (n = 32) in early lactation. We compared peripartal body condition score (BCS) as well as serum concentrations of α-tocopherol, metabolites [β-hydroxybutyrate (BHBA), cholesterol, glucose, nonesterified fatty acids (NEFA), and urea N], haptoglobin, and macrominerals (i.e., calcium, magnesium, and phosphorus) on d −21, −14, −7, −3, −1, 0, 1, 3, 7, 14, 21, 28, 35, 42, and 49 postpartum. In addition, average serum concentrations were calculated for each cow for the last 3 wk prepartum, for 3 and 2 wk prepartum combined, for the last week prepartum, and for the morning after calving and compared between groups. The RP cows had lower BCS than the H or OD cows until 2 wk postpartum. During the prepartal periods, RP and OD cows had lower α-tocopherol concentrations (corrected or not for cholesterol concentration) and higher NEFA and BHBA concentrations than H cows. Thus, lower prepartal BCS could be an early predictor for RP risk, and lower α-tocopherol concentrations and higher NEFA and BHBA concentrations could be early predictors for disease.