Effect of rumen-protected choline on dairy cow metabolism, immunity, lactation performance, and vaginal discharge microbiome

Rumen-protected choline (RPC) promotes benefits in milk production, immunity, and health in dairy cows by optimizing lipid metabolism during transition period management and early lactation. However, the RPC success in dairy cows depends on choline bioavailability, which is affected by the type of protection used in rumen-protected choline. Therefore, our objectives were to determine the effects of a novel RPC on dry matter intake (DMI), identify markers of metabolism and immunity, and evaluate lactation performance. Dry Holstein (n = 48) cows at 245 ± 3 d of gestation were blocked by parity and assigned to control or RPC treatment within each block. Cows enrolled in the RPC treatment received 15 g/d of CholiGEM (Kemin Industries, Cavriago RE, Italy) from 21 d prepartum and 30 g/d of CholiGEM from calving to 21 d postpartum. During the transition period, DMI was measured daily, and blood was sampled weekly for energy-related metabolites such as β-hydroxybutyrate (BHB), glucose, and nonesterified fatty acids (NEFA), as well as immune function markers such as haptoglobin (Hp) and lipopolysaccharide-binding protein (LPB). Vaginal discharge samples were collected at the calving and 7 d postpartum and stored in microcentrifuge tubes at -80°C until 16S rRNA sequencing. The main responses of body condition score, body weight, DMI, milk yield, milk components, and immune function markers were analyzed using the GLIMMIX procedure of SAS with the effects of treatment, time, parity, and relevant covariates added to the models. The relative abundance of microbiome α-diversity was evaluated by 3 indexes (Chao1, Shannon, and Simpson) and β-diversity by principal coordinate analysis and permutational multivariate ANOVA. We found no differences in DMI in the pre- and postpartum periods. Cows fed RPC increased the yields of energy- and 3.5% fat-corrected milk and fat yield in primiparous and multiparous cows, with an interaction between treatment and parity for these lactation variables. However, we found no differences in milk protein and lactose up to 150 DIM between treatments. Glucose, NEFA, and BHB had no differences between the treatments. However, RPC decreased BHB numerically (control = 1.07 ± 0.13 vs. RPC = 0.63 ± 0.13) in multiparous on the third week postpartum and tended to reduce the incidence of subclinical ketosis (12.7% vs. 4.2%). No effects for Hp and LPB were found in cows fed RPC. Chao1, Shannon, and Simpson indexes were lower at calving in the RPC treatment than in the Control. However, no differences were found 7 d later for Chao1, Shannon, and Simpson indexes. The vaginal discharge microbiome was altered in cows fed RPC at 7 d postpartum. Fusobacterium, a common pathogen associated with metritis, was reduced in cows fed RPC. Rumen-protected choline enhanced lactation performance and health and altered the vaginal discharge microbiome which is a potential proxy for uterine healthy in dairy cows. The current study's findings corroborate that RPC is a tool to support adaptation to lactation and shed light on opportunities for further research in reproductive health.

Genome-Wide Association Study on Reproduction-Related Body-Shape Traits of Chinese Holstein Cows

Reproduction is an important production activity for dairy cows, and their reproductive performance can directly affect the level of farmers' income. To better understand the genomic regions and biological pathways of reproduction-related traits of dairy cows, in the present study, three body shape traits-Loin Strength (LS), Rump Angle (RA), and Pin Width (PW)-were selected as indicators of the reproductive ability of cows, and we conducted genome-wide association analyses on them. The heritability of these three traits was medium, ranging from 0.20 to 0.38. A total of 11 significant single-nucleotide polymorphisms (SNPs) were detected associated with these three traits. Bioinformatics analysis was performed on genes close to the significant SNPs (within 200 Kb) of LS, RA, and PW, and we found that these genes were totally enriched in 20 gene ontology terms and six KEGG signaling pathways. Finally, the five genes CDH12, TARP, PCDH9, DTHD1, and ARAP2 were selected as candidate genes that might affect LS. The six genes LOC781835, FSTL4, ATG4C, SH3BP4, DMP1, and DSPP were selected as candidate genes that might affect RA. The five genes USP6NL, CNTN3, LOC101907665, UPF2, and ECHDC3 were selected as candidate genes that might affect the PW of Chinese Holstein cows. Our results could provide useful biological information for the improvement of body shape traits and contribute to the genomic selection of Chinese Holstein cows.

Of rodents and ruminants: a comparison of small noncoding RNA requirements in mouse and bovine reproduction

Ruminants are major producers of meat and milk, thus managing their reproductive potential is a key element in cost-effective, safe, and efficient food production. Of particular concern, defects in male germ cells and female germ cells may lead to significantly reduced live births relative to fertilization. However, the underlying molecular drivers of these defects are unclear. Small noncoding RNAs, such as piRNAs and miRNAs, are known to be important regulators of germ-cell physiology in mouse (the best-studied mammalian model organism) and emerging evidence suggests that this is also the case in a range of ruminant species, in particular bovine. Similarities exist between mouse and bovids, especially in the case of meiotic and postmeiotic male germ cells. However, fundamental differences in small RNA abundance and metabolism between these species have been observed in the female germ cell, differences that likely have profound impacts on their physiology. Further, parentally derived small noncoding RNAs are known to influence early embryos and significant species-specific differences in germ-cell born small noncoding RNAs have been observed. These findings demonstrate the mouse to be an imperfect model for understanding germ-cell small noncoding RNA biology in ruminants and highlight the need to increase research efforts in this underappreciated aspect of animal reproduction.

Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

Influence of blood metabolites and body condition score at parturition on fertility and milk yield in Holstein cows

Background: Variables associated with body tissue mobilization place dairy cows at greater risk of reproductive failure. Objective: To investigate the association between blood metabolites and body condition score (BCS) at the beginning of lactation and the reproductive efficiency and milk yield of Holstein cows in a hot environment. Methods: In total, 165 Holstein cows were selected for the study from which blood samples were taken to determine the concentration of various blood metabolites and their association with the reproductive efficiency and milk yield. Results: Cows with serum β-hydroxybutyrate (BHBA) ≤0.8 mmol/L one week postpartum were 3.3 times more likely to become pregnant at first service, and 2.2 times more likely to become pregnant before 80 d postpartum than cows with higher serum BHBA levels. The odds (OR 2.7; 95% CI 1.3–5.4; p<0.01) of a cow getting pregnant at first service were higher in cows with serum creatinine levels higher than 2.0 mg/dL one week postpartum than cows with lower blood levels of this metabolite. The BCS at 30 and 60 d postpartum that predicted pregnancy at first service and pregnancy to all services was 3.0. Blood urea nitrogen >15 mg/dL, creatinine <1.8 mg/dL, total protein ≤5.0 mg/dL one week postpartum, and >0.40 units of BCS loss during the first 30 d postpartum were critical threshold that predicted the likelihood of 305-d milk yield higher than 10,500 kg. Conclusions: Serum BHBA and creatinine one wk after calving as well as BCS 30 and 60 d post-calving provided reasonably accurate cut-off screening values to discriminate cows with better reproductive performance and higher 305-d milk yield.

Genomic Evaluation of Primiparous High-Producing Dairy Cows: Inbreeding Effects on Genotypic and Phenotypic Production-Reproductive Traits

Simple Summary

Improving the genomic prediction methodologies in high-producing dairy cattle is a key factor for the selection of suitable individuals to ensure better productivity. However, the most advanced prediction tools based on genotyping show ~75% reliability. Nowadays, the incorporation of new indices to genomic prediction methods, such as the Inbreeding Index (II), can significantly facilitate the selection of reliable production and reproductive traits for progeny selection. Thus, the objective of this study was to determine the impact of II (low: LI and high: HI), based on genomic analysis, and its effect on production and reproductive phenotypic traits in high-producing primiparous dairy cows. Individuals with II between ≥2.5 and ≤5.0 have shown up to a two-fold increase in negative correlations comparing LI versus HI genomic production and reproductive parameters, severely affecting important traits such as Milk Production at 305 d, Protein Production at 305 d, Fertility Index, and Daughter Pregnancy Rate. Therefore, high-producing dairy cows face an increased risk of negative II-derived effects in their selection programs, particularly at II ≥ 2.5.

Abstract

The main objective of this study was to analyze the effects of the inbreeding degree in high-producing primiparous dairy cows genotypically and phenotypically evaluated and its impacts on production and reproductive parameters. Eighty Holstein–Friesian primiparous cows (age: ~26 months; ~450 kg body weight) were previously genomically analyzed to determine the Inbreeding Index (II) and were divided into two groups: low inbreeding group (LI: <2.5; n = 40) and high inbreeding group (HI: ≥2.5 and ≤5.0; n = 40). Genomic determinations of production and reproductive parameters (14 in total), together with analyses of production (12) and reproductive (11) phenotypic parameters (23 in total) were carried out. Statistically significant differences were obtained between groups concerning the genomic parameters of Milk Production at 305 d and Protein Production at 305 d and the reproductive parameter Daughter Calving Ease, the first two being higher in cows of the HI group and the third lower in the LI group (p < 0.05). For the production phenotypic parameters, statistically significant differences were observed between both groups in the Total Fat, Total Protein, and Urea parameters, the first two being higher in the LI group (p < 0.05). Also, significant differences were observed in several reproductive phenotypic parameters, such as Number of Services per Conception, Calving to Conception Interval, Days Open Post Service, and Current Inter-Partum Period, all of which negatively influenced the HI group (p < 0.05). In addition, correlation analyses were performed between production and reproductive genomic parameters separately and in each consanguinity group. The results showed multiple positive and negative correlations between the production and reproductive parameters independently of the group analyzed, being these correlations more remarkable for the reproductive parameters in the LI group and the production parameters in the HI group (p < 0.05). In conclusion, the degree of inbreeding significantly influenced the results, affecting different genomic and phenotypic production and reproductive parameters in high-producing primiparous cows. The determination of the II in first-calf heifers is crucial to evaluate the negative effects associated with homozygosity avoiding an increase in inbreeding depression on production and reproductive traits.

Genetic correlations between energy status indicator traits and female fertility in primiparous Nordic Red Dairy cattle

Inclusion of feed efficiency traits into the dairy cattle breeding programmes will require considering early lactation energy status to avoid deterioration in health and fertility of dairy cows. In this regard, energy status indicator (ESI) traits, for example, blood metabolites or milk fatty acids (FAs), are of interest. These indicators can be predicted from routine milk samples by mid-IR reflectance spectroscopy (MIR). In this study, we estimated genetic variation in ESI traits and their genetic correlation with female fertility in early lactation. The data consisted of 37 424 primiparous Nordic Red Dairy cows with milk test-day records between 8 and 91 days in milk (DIM). Routine test-day milk samples were analysed by MIR using previously developed calibration equations for blood plasma non-esterified FA (NEFA), milk FAs, milk beta-hydroxybutyrate (BHB) and milk acetone concentrations. Six ESI traits were considered and included: plasma NEFA concentration (mmol/l) either predicted by multiple linear regression including DIM, milk fat to protein ratio (FPR) and FAs C10:0, C14:0, C18:1 cis-9, C14:0 * C18:1 cis-9 (NEFAFA) or directly from milk MIR spectra (NEFAMIR), C18:1 cis-9 (g/100 ml milk), FPR, BHB (mmol/l milk) and acetone (mmol/l milk). The interval from calving to first insemination (ICF) was considered as the fertility trait. Data were analysed using linear mixed models. Heritability estimates varied during the first three lactation months from 0.13 to 0.19, 0.10 to 0.17, 0.09 to 0.14, 0.07 to 0.10, 0.13 to 0.17 and 0.13 to 0.18 for NEFAMIR, NEFAFA, C18:1 cis-9, FPR, milk BHB and acetone, respectively. Genetic correlations between all ESI traits and ICF were from 0.18 to 0.40 in the first lactation period (8 to 35 DIM), in general somewhat lower (0.03 to 0.43) in the second period (36 to 63 DIM) and decreased clearly (-0.02 to 0.19) in the third period (64 to 91 DIM). Our results indicate that genetic variation in energy status of cows in early lactation can be determined using MIR-predicted indicators. In addition, the markedly lower genetic correlation between ESI traits and fertility in the third lactation month indicated that energy status should be determined from the first test-day milk samples during the first 2 months of lactation.

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.

Genetic parameters of subclinical macromineral disorders and major clinical diseases in postparturient Holstein cows

The main objective of this study was to assess the genetic parameters of subclinical disorders associated with subclinical hypocalcemia, hypophosphatemia, subclinical hypomagnesemia, hypokalemia, and hyperphosphatemia, as well as major clinical diseases after calving in Holstein cows. The secondary objective was to estimate the associated genetic and phenotypic correlations among these subclinical and clinical conditions after calving in Holstein cows. The study was conducted in 9dairy herds located in Northern Greece. None of the herds used any kind of preventive measures for milk fever (MF). A total of 1,021 Holstein cows with pedigree information were examined from November 2010 until November 2012. The distribution across parities was 466 (parity 1), 242 (parity 2), 165 (parity 3), and 148 (parity 4 and above) cows. All cows were subjected to a detailed clinical examination and blood was sampled on d 1, 2, 4, and 8 after calving. Serum concentrations of Ca, P, Mg, and K were measured in all samples, whereas β-hydroxybutyrate (BHB) was measured only for d 8. The final data set included 4,064 clinical and 16,848 biochemical records (4,020 Ca, 4,019 P, 4,020Mg, 3,792K, and 997 BHB). Data of 1,988 observations of body condition score at d 1 and 8 were also available. All health traits were analyzed with a univariate random regression model. The genetic analysis for macromineral-related disorders included 986 cows with no obvious signs of MF (35 cows with MF were excluded). Analysis for other health traits included all 1,021 cows. A similar single record model was used for the analysis of BHB. Genetic correlations among traits were estimated with a series of bivariate analyses. Statistically significant daily heritabilities of subclinical hypocalcemia (0.13-0.25), hypophosphatemia (0.18-0.33), subclinical hypomagnesemia (0.11-0.38), and hyperphosphatemia (0.14-0.22) were low to moderate, whereas that of hypokalemia was low (0.08-0.10). The heritability of body condition score was 0.20±0.10. Statistically significant daily heritabilities of clinical diseases were those of MF (0.07-0.11), left displaced abomasum (0.19-0.31), and mastitis (0.15-0.41). Results suggest that these health disorders are heritable traits and could be minimized with proper genetic selection. Statistically significant phenotypic correlations were estimated for the first time between macromineral concentrations and almost all transition cow metabolic and infectious health disorders.

Glucose concentration in capillary blood of dairy cows obtained by a minimally invasive lancet technique and determined with three different hand-held devices

BACKGROUND

Dairy cows have a massive demand for glucose at the onset of lactation. A poor adaption to this period leads to an excessive negative energy balance with an increased risk for ketosis and impaired animal health and production. Besides the measurement of ketones, analysing the glucose concentration in blood is reported as helpful instrument for diagnosis and differentiation of ketosis. Monitoring metabolic parameters requires multiple blood sampling. In other species, new blood sampling techniques have been introduced in which small amounts of blood are rapidly analysed using electronic hand-held devices. The objective of this study was to evaluate the suitability of capillary blood for blood glucose measurement in dairy cows using the hand-held devices FreeStyle Precision (FSP, Abbott), GlucoMen LX Plus (GLX, A. Menarini) and the WellionVet GLUCO CALEA, (WGC, MED TRUST). In total, 240 capillary blood samples were obtained from dry and fresh lactating Holstein-Friesian cows. Blood was collected from the skin of the exterior vulva by using a lancet. For method comparison, additional blood samples were taken from a coccygeal vessel and analyzed in a laboratory. Glucose concentrations measured by a standard laboratory method were defined as the criterion standard.

RESULTS

The Pearson correlation coefficients between the glucose concentrations analyzed in capillary blood with the devices and the reference were 73% for the FSP, 81% for the GLX and 41% for the WGC. Bland-Altman plots showed biases of -18.8 mg/dL for the FSP, -11.2 mg/dL for the GLX and +20.82 mg/dL for the WGC. The optimized threshold determined by a Receiver Operating Characteristics analysis to detect hyperglycemia using the FSP was 43 mg/dL with a sensitivity (Se) and specificity (Sp) of 76 and 80%. Using the GLX and WGC optimized thresholds were 49 mg/dL (Se = 92%, Sp = 85%) and 95 mg/dL (Se = 39%, Sp = 92%).

CONCLUSIONS

The results of this study demonstrate good performance characteristics for the GLX and moderate for the FSP to detect hyperglycemia in dairy cows using capillary blood. With the study settings, the WGC was not suitable for determination of glucose concentrations.

Glucose concentration in capillary blood of dairy cows obtained by a minimally invasive lancet technique and determined with three different hand-held devices

Background

Dairy cows have a massive demand for glucose at the onset of lactation. A poor adaption to this period leads to an excessive negative energy balance with an increased risk for ketosis and impaired animal health and production.

Besides the measurement of ketones, analysing the glucose concentration in blood is reported as helpful instrument for diagnosis and differentiation of ketosis. Monitoring metabolic parameters requires multiple blood sampling. In other species, new blood sampling techniques have been introduced in which small amounts of blood are rapidly analysed using electronic hand-held devices.

The objective of this study was to evaluate the suitability of capillary blood for blood glucose measurement in dairy cows using the hand-held devices FreeStyle Precision (FSP, Abbott), GlucoMen LX Plus (GLX, A. Menarini) and the WellionVet GLUCO CALEA, (WGC, MED TRUST). In total, 240 capillary blood samples were obtained from dry and fresh lactating Holstein-Friesian cows. Blood was collected from the skin of the exterior vulva by using a lancet. For method comparison, additional blood samples were taken from a coccygeal vessel and analyzed in a laboratory. Glucose concentrations measured by a standard laboratory method were defined as the criterion standard.

Results

The Pearson correlation coefficients between the glucose concentrations analyzed in capillary blood with the devices and the reference were 73 % for the FSP, 81 % for the GLX and 41 % for the WGC. Bland-Altman plots showed biases of −18.8 mg/dL for the FSP, -11.2 mg/dL for the GLX and +20.82 mg/dL for the WGC. The optimized threshold determined by a Receiver Operating Characteristics analysis to detect hyperglycemia using the FSP was 43 mg/dL with a sensitivity (Se) and specificity (Sp) of 76 and 80 %. Using the GLX and WGC optimized thresholds were 49 mg/dL (Se = 92 %, Sp = 85 %) and 95 mg/dL (Se = 39 %, Sp = 92 %).

Conclusions

The results of this study demonstrate good performance characteristics for the GLX and moderate for the FSP to detect hyperglycemia in dairy cows using capillary blood. With the study settings, the WGC was not suitable for determination of glucose concentrations.

Short communication: Genetic characterization of digital cushion thickness

Dairy cow lameness is a serious animal welfare issue. It is also a significant cause of economic losses, reducing reproductive efficiency and milk production and increasing culling rates. The digital cushion is a complex structure composed mostly of adipose tissue located underneath the distal phalanx and has recently been phenotypically associated with incidence of claw horn disruption lesions (CHDL); namely, sole ulcers and white line disease. The objective of this study was to characterize digital cushion thickness genetically and to investigate its association with body condition score (BCS), locomotion score (LOCO), CHDL, and milk production. Data were collected from 1 large closely monitored commercial dairy farm located in upstate New York; 923 dairy cows were used. Before trimming, the following data were collected by a member of the research team: BCS, cow height measurement, and LOCO. Presence or not of CHDL (sole ulcer or white line disease, or both) was recorded at trimming. Immediately after the cows were hoof trimmed, they underwent digital sonographic B-mode examination for the measurement of digital cushion thickness. Factors such as parity number, stage of lactation, calving date, mature-equivalent 305-d milk yield (ME305MY), and pedigree information were obtained from the farm's dairy management software (DairyCOMP 305; Valley Agricultural Software, Tulare, CA). Univariate animal models were used to obtain variance component estimations for each studied trait (CHDL, BCS, digital cushion thickness average, LOCO, height, and ME305MY) and a 6-variate analysis was conducted to estimate the genetic, residual, and phenotypic correlations between the studied traits. The heritability estimate of DCTA was 0.33±0.09, whereas a statistically significant genetic correlation was estimated between DCTA and CHDL (-0.60±0.29). Of the other genetic correlations, significant estimates were derived for BCS with LOCO (-0.49±0.19) and ME305MY (-0.48±0.20). Digital cushion thickness is moderately heritable and genetically strongly correlated with CHDL.

Genetic parameters for milk urea concentration and milk traits in Polish Holstein-Friesian cows

Milk urea concentration (MU) used by dairy producers for management purposes can be affected by selection for milk traits. To assess this problem, genetic parameters for MU in Polish Holstein-Friesian cattle were estimated for the first three lactations. The genetic correlation of MU with milk production traits, lactose percentage, fat to protein ratio (FPR) and somatic cell score (SCS) were computed with two 5-trait random regression test-day models, separately for each lactation. Data used for estimation (159,044 daily observations) came from 50 randomly sampled herds. (Co)variance components were estimated with the Bayesian Gibbs sampling method. The coefficient of variation for MU in all three parities was high (40-41 %). Average daily heritabilities of MU were 0.22 for the first parity and 0.21 for the second and third lactations. Average genetic correlations for different days in milk in the first three lactations between MU and other traits varied. They were small and negative for protein percentage (from -0.24 to -0.11) and for SCS (from -0.14 to -0.09). The weakest genetic correlation between MU and fat percentage, and between MU and lactose percentage were observed (from -0.10 to 0.10). Negative average genetic correlation with the fat to protein ratio was observed only in the first lactation (-0.14). Genetic correlations with yield traits were positive and ranged from low to moderate for protein (from 0.09 to 0.33), fat (from 0.16 to 0.35) and milk yield (from 0.20 to 0.42). These results suggest that the selection on yield traits and SCS tends to increase MU slightly.

Genetic parameters for female fertility, locomotion, body condition score, and linear type traits in Czech Holstein cattle

The aim of this study was to estimate genetic parameters for fertility traits and linear type traits in the Czech Holstein dairy cattle population. Phenotypic data regarding 12 linear type traits, measured in first lactation, and 3 fertility traits, measured in each of first and second lactation, were collected from 2005 to 2009 in the progeny testing program of the Czech-Moravian Breeders Corporation. The number of animals for each linear type trait was 59,467, except for locomotion, where 53,436 animals were recorded. The 3-generation pedigree file included 164,125 animals. (Co)variance components were estimated using AI-REML in a series of bivariate analyses, which were implemented via the DMU package. Fertility traits included days from calving to first service (CF1), days open (DO1), and days from first to last service (FL1) in first lactation, and days from calving to first service (CF2), days open (DO2), and days from first to last service (FL2) in second lactation. The number of animals with fertility data varied between traits and ranged from 18,915 to 58,686. All heritability estimates for reproduction traits were low, ranging from 0.02 to 0.04. Heritability estimates for linear type traits ranged from 0.03 for locomotion to 0.39 for stature. Estimated genetic correlations between fertility traits and linear type traits were generally neutral or positive, whereas genetic correlations between body condition score and CF1, DO1, FL1, CF2 and DO2 were mostly negative, with the greatest correlation between BCS and CF2 (-0.51). Genetic correlations with locomotion were greatest for CF1 and CF2 (-0.34 for both). Results of this study show that cows that are genetically extreme for angularity, stature, and body depth tend to perform poorly for fertility traits. At the same time, cows that are genetically predisposed for low body condition score or high locomotion score are generally inferior in fertility.