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The use of selective dry cow antimicrobial therapy requires precisely differentiating cows with an intramammary infection (IMI) from uninfected cows close to drying-off to enable treatment allocation. Milk somatic cell count (SCC) is an indicator of an inflammatory response in the mammary gland and is usually associated with IMI. However, SCC can also be influenced by cow-level variables such as milk yield, lactation number, and stage of lactation. In recent years, predictive algorithms have been developed to differentiate cows with IMI from cows without IMI based on SCC data. The objective of this observational study was to explore the association between SCC and subclinical IMI, taking cognizance of cow-level predictors on Irish seasonal spring calving, pasture-based systems. Additionally, the optimal test-day SCC cut-point (maximized sensitivity and specificity) for IMI diagnosis was determined. A total of 2,074 cows across 21 spring calving dairy herds with an average monthly milk weighted bulk tank SCC of ≤200,000 cells/mL were enrolled in the study. Quarter-level milk sampling was carried out on all cows in late lactation (interquartile range = 240–261 d in milk) for bacteriological culturing. Bacteriological results were used to define cows with IMI when ≥1 quarter sample resulted in bacterial growth. Cow-level test-day SCC records were provided by the herd owners. The ability of the average, maximum, and last test-day SCC to predict infection were compared using receiver operator curves. Predictive logistic regression models tested included parity (primiparous or multiparous), yield at last test-day, and a standardized count of high SCC test-days. In total, 18.7% of cows were classified as having an IMI, with first-parity cows having a higher proportion of IMI (29.3%) compared with multiparous cows (16.1%). Staphylococcus aureus accounted for the majority of these infections. The last test-day SCC was the best predictor of infection with the highest area under the curve. The inclusions of parity, yield at last test-day, and a standardized count of high SCC test-days as predictors did not significantly improve the ability of last test-day SCC to predict IMI. The cut-point for last test-day SCC that maximized sensitivity and specificity was 64,975 cells/mL. This study indicates that in Irish seasonal pasture-based dairy herds with low bulk tank SCC, the last test-day SCC (interquartile range days in milk = 221–240) is the best predictor of IMI in late lactation.

Milk protein fractions and free amino acids (FAA) are important traits for the dairy industry specialized in cheese and milk powder production. Despite this, they are not included in any breeding objective due to the lack of quick and cost-effective routine determination of these compounds. In the present study, mid-infrared spectroscopy prediction models previously developed on bovine milk samples in Ireland were applied to stored spectral data to predict both protein fractions and FAA concentrations. After edits, 134,546 test-day records from 16,166 lactations and 9,572 cows were available. Variance components were estimated using repeatability animal models including the fixed effects of contemporary group, milking time, cow breed proportion, general heterosis, recombination loss, and parity-by-stage of lactation. Random terms were additive genetic effect of the animal, and within- and across lactation permanent environmental effects. Means of αs1-casein, αs2-casein, β-casein, and κ-casein were 13.70 g/l, 3.66 g/l, 12.96 g/l, and 6.03 g/l, respectively. About 83% of total whey proteins (6.13 g/l) were represented by β-lactoglobulin. Concentrations of FAA exhibited average values that ranged from 8.09 μg/ml (glycine) to 1.52 μg/ ml (valine), except for glutamic acid (30.93 μg/ml). Heritability estimates for protein fractions ranged from 0.36 (αs2-casein and κ-casein) to 0.46 (β-lactoglobulin A), except for α-lactalbumin (0.19). The range of heritability estimates for FAA was smaller (0.15 for glycine to 0.36 for aspartic acid). Genetic correlations between casein fractions and whey proteins were all positive. Genetic correlations between FAA ranged from -0.44 (aspartic acid and lysine) to 0.97 (glutamic acid and total free amino acids). Breeding strategies for genetically improving protein fractions and FAA may represent a viable solution to meet the requirements of specialized dairy industry.

The aim of this study was to determine the associations of parity, stage of lactation and milking time with protein composition and colour of bovine milk. Mid-infrared spectral (MIRS) data from 2013 to 2015, inclusive were obtained from the milk samples of 3 research herds. Spectral outliers were detected using mahalanobis distance and removed. Data were further edited to remove records from cows which were greater than 305 days in milk or were greater than tenth parity. Equations developed recently to predict individual protein fractions and milk colour directly from the MIR spectrum were applied to the data yielding 64,606 records from 1,102 Holstein-Friesian cow lactations of alpha s1, beta, and kappa casein as well as the yellowness of milk. Factors associated with each of the milk quality traits were quantified using linear mixed models. All models were adjusted for milking time, parity (1, 2, 3, 4 and 5+) and stage of lactation (5 equal groups of 60); and the random effects of within and across lactation permanent environmental effects and herd_year_season. Older animals had yellower milk with a higher level of kappa casein and lower level of beta casein (P

Milk color and processing traits are important factors informing the potential and ease to manufacture milk into different dairy products. The objective of the present study was to estimate (co)variance components of milk lightness (L*), redness-greenness (a*), yellowness-blueness (b*), rennet coagulation time (RCT), curd-firming time (k20), curd firmness (a30 and a60), heat coagulation time (HCT), casein micelle size (CMS), and pH, measured by mid-infrared spectroscopy on 136,807 test-day records from 9,824 Irish dairy cows between 5 and 305 d-in-milk (DIM) from parities ≤ 10. Cow breed was defined as the proportion of Holstein, Friesian, Jersey, Norwegian Red, Montbéliarde, and “Other.” Random regression models using Legendre polynomials were performed to describe the change of both additive genetic and within-lactation permanent environmental variances across different DIM. Heritability estimates averaged across all DIM for milk color were 0.31 (L*), 0.11 (a*), and 0.42 (b*); average heritability estimates for processing traits ranged from 0.31 (pH) to 0.49 (k20), except for HCT (0.17). Within-trait genetic correlations approached unity between adjacent DIM, and were

Climate change is a growing area of international concern,and it is well established that the release of greenhouse gases(GHG) is a contributing factor. Of the various GHG producedby ruminants, enteric methane (CH4) is the most importantcontributor. One mitigation strategy is to reduce methane emissionthrough genetic selection. Our first attempt used beef cattleand a GWAS to identify genes associated with several CH4traits in Angus beef cattle. The Angus population consisted of1020 animals with phenotypes on methane production (MeP),dry matter intake (DMI), and weight (WT). Additionally, twonew methane traits: residual genetic methane (RGM) and residualphenotypic methane (RPM) were calculated by adjustingCH4 for DMI and WT. Animals were genotyped using the800k Illumina Bovine HD Array. Estimated heritabilities were0.30, 0.19 and 0.15 for MeP, RGM and RPM respectively,and estimated genetic correlations of MeP with DMI and WTwere 0.83 and 0.80, respectively. Strong associations with MePwere found on chromosomes 4, 12, 14, 19, and 30. We haverecently tried another approach in dairy cattle, where we aimedto enlarge the reference population for genomic selection bycombining data on methane emissions in dairy cattle usingdata from 5 countries (Australia, Denmark, Ireland, the Netherlandsand UK). The total dataset consists of 3060 dairy cows,of which most were genotyped, but with various kinds of SNPchips. We ended up with a uniform set of SNPs for each cow.Even though three different types of measurement equipment(laser, sniffer and SF6) and protocols (measuring for 3 d, 1 wk,multiple weeks) were used, these data will be analyzed jointlyto establish genetic and genomic parameters for enteric methane.The average methane production was 448 g/d in Australia(354 cows); 554 g/d in Denmark (1769 cows); 381 g/d in IRL(260 cows); 549 g/d in NL (457 cows); and 325 g/d in UK (216cows). This clearly shows that the populations and diets aredifferent in addition to the equipment and protocol. Therefore,a multi-trait approach will be used in the analysis. Followingthe experiences of a similar project (gDMI), it is expected thateach country will benefit for contributing to an international reference set with increased accuracies of the estimates.Key Words: esnteric methane, genomic selection,international collaborationdoi: 10.2527/jam2016-0407

The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy (MIRS) in predicting individual milk proteins and free amino acids in bovine milk and estimating their correlations with milk processing characteristics. A total of 730 milk samples were collected from seven Irish research herds and represented cows from a range of breeds, parities and stages of lactation. Gold-standard methods were used to quantify protein fractions and free amino acids (FAA) of these samples. Separate prediction equations were developed for each trait using partial least squares regression and accuracy of prediction was assessed using both cross validation and external validation. The greatest coefficient of correlation in external validation obtained for protein fractions was for total casein (0.74), while weak to moderate prediction accuracies were observed for FAA and among these glycine had the greatest coefficient of correlation (0.75 in both cross validation and external validation). Near unity correlations existed between total casein and beta-casein irrespective of whether the traits were based on the gold-standard (0.92) or MIRS predictions (0.95). Pearson correlations between gold-standard protein fractions and gold-standard milk processing characteristics ranged from -0.48 (pH and protein) to 0.50 (total casein and curd firmness). The lactation profile of total FAA indicated that the greatest concentration of FAA in milk was during early and late lactation. Results from this study demonstrate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk.

Rapid, cost-effective monitoring of milk technological traits is an important challenge for dairy industries specialized in cheese manufacturing. For example, moderate to high rennet susceptibility and long heat stability of milk could significantly concur on the profitability of dairy industries. Therefore, the present study aimed to investigate the ability of mid-infrared (MIR) spectroscopy to predict rennet coagulation time (RCT), curd firming time (k20), curd firmness at 30 (a30) and 60 (a60) min after rennet addition, heat coagulation time (HCT), casein micelle size (CMS), and pH in cow milk samples, and to quantify associations between these milk technological traits and milk composition. Samples were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, while MIR spectra were available for all samples. Prediction models were developed using partial least square regression, and prediction accuracy was based on both cross and external validation. Proportion of variance explained by prediction models in external validation was greatest for pH (71%), followed by RCT (55%), and HCT (46%). Prediction models for a60 and CMS were not satisfactory. On average, all prediction models tended to be unbiased (P > 0.05). Such a conclusion is quite important because significant bias could have implication on milk pricing, if a milk pricing was based on technological traits. Linear regression coefficient of reference values on predicted values varied from 0.17 (CMS regression model) to 0.83 (pH regression model) but were all different (P < 0.05) from one. This can potentially have implications for breeding programs, where the true variance of technological traits may be underestimated using predicted rather than reference values. Milk composition and in particular nitrogen fraction affected milk technological traits. Protein concentration was negatively correlated with RCT (-0.46) and k20 (-0.54) and positively correlated with a30 (0.52). Urea concentration had the strongest correlation with HCT (0.48), whereas no correlation was calculated between CMS and milk composition. Results suggest that the developed prediction models for RCT, k20, a30, HCT, and pH can be used as a screening method for milk segregation at the industry level for different dairy productions. For example, milk with short MIR-predicted RCT, as well as with moderate to low MIR-predicted pH, can be used for cheese-making, whereas milk with high MIR-predicted HCT can be processed for milk powder. Moreover, further investigations are needed to quantify sources of variation of milk technological traits at the population level, and to estimate genetic parameters of these traits.