Use of repeated measures analysis for evaluation of genetic background of dairy cattle behavior in automatic milking systems

Milking frequencies measured at official test days were used with repeated measurement analysis to reveal the environmental and genetic impact on the milking frequency of cows in automatic milking systems. Repeated measurements were 3 test-day observations per cow within days in milk (DIM) classes, with 1,216 cows in DIM class 1 (d 0 to 99), from 1,112 cows in DIM class 2 (d 100 to 199), and from 1,004 cows in DIM class 3 (d 200 to 299) kept in 15 farms. Selection criteria for models analyzing repeated measurements were Akaike and Schwarz Bayesian values, which favored the autoregressive [AR(1)] covariance structure over the compound symmetry model. Results from the AR(1) model indicated a significant impact of fixed herd and parity effects. Milking frequencies decreased with increasing parities and were greatest for first-parity cows. High daily milk yield was associated with higher milking frequencies. Heritabilities for milking frequency were 0.16, 0.19, and 0.22 in DIM classes 1, 2, and 3, respectively, from the AR(1) model. Higher heritabilities in the later stage of lactation were due to a substantial reduction of the residual variance. Genetic correlations between test-day milk yield and daily milking frequency were in the range of 0.46 to 0.57 for all DIM classes and between milking frequency and somatic cell score were near zero. For verification of results, milking frequencies of the same cows obtained from herd management programs were averaged within DIM classes. Heritabilities were slightly above the values from the AR(1) model. In conclusion, heritabilities for milking frequency in automatic milking systems are moderate enough to incorporate this behavioral trait in a combined breeding goal. The inevitable improvement of labor efficiency in dairy cattle farming demands such cows going easily and voluntarily in automatic milking systems.     

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from −0.48 to −0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (−0.58 ± 0.02) and MFAIL and FRM (−0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.     

Genetic parameters of milking frequency and milk production traits in Canadian Holsteins milked by an automated milking system

Twice-a-day milking is currently the most frequently used milking schedule in Canadian dairy cattle. However, with an automated milking system (AMS), dairy cows can be milked more frequently. The objective of this study was to estimate genetic parameters for milking frequency and for production traits of cows milked within an AMS. Data were 141,927 daily records of 953 primiparous Holstein cows from 14 farms in Ontario and Quebec. Most cows visited the AMS 2 (46%) or 3 (37%) times a day. A 2-trait [daily (24-h) milking frequency and daily (24-h) milk yield] random regression daily animal model and a multiple-trait (milk, fat, protein yields, somatic cell score, and milking frequency) random regression test-day animal model were used for the estimation of (co)variance components. Both models included fixed effect of herd × test-date, fixed regressions on days in milk (DIM) nested within age at calving by season of calving, and random regressions for additive genetic and permanent environmental effects. Both fixed and random regressions were fitted with fourth-order Legendre polynomials on DIM. The number of cows in the multiple-trait test-day model was smaller compared with the daily animal model. Heritabilities from the daily model for daily (24-h) milking frequency and daily (24-h) milk yield ranged between 0.02 and 0.08 and 0.14 and 0.20, respectively. Genetic correlations between daily (24-h) milk yield and daily (24-h) milking frequency were largest at the end of lactation (0.80) and smallest in mid-lactation (0.27). Heritabilities from the test-day model for test-day milking frequency, milk, fat and protein yield, and somatic cell score were 0.14, 0.26, 0.20, 0.21, and 0.20, respectively. The genetic correlation was positive between test-day milking frequency and official test-day milk, fat, and protein yields, and negative between official test-day somatic cell score and test-day milking frequency.     

Joint international evaluation of Milking Shorthorn dairy cattle for production traits

Pedigree information and test-day records for the first 3 parities of Milking Shorthorn dairy cattle from 5 countries were analyzed. After editing, the data included 1,018,528 test-day records from 68,653 cows. A multiple-lactation random regression test-day model with Legendre polynomials of order 4 and a Bayesian method were used to estimate variance components for both single and multiple-countries. Fixed effects included herd-test-day class and regressions on DIM within age at calving-parity-season of calving. Random effects included animal genetic, permanent environmental, and residual effects. Average daily heritabilities from single country analyses ranged from 0.33 to 0.47 for milk yield and from 0.37 to 0.45 for protein yield across lactations and countries. Common sires (66) and their daughters were identified for creating a connected data set for simultaneous (co)variance component estimation of milk yield across all 5 countries. Between-country genetic correlations were low, with values from 0.08 to 0.46 and standard deviations from 0.08 to 0.12. Estimated breeding values for milk were generated for each animal using the same test-day animal model. Correlations among country estimated breeding values were higher than genetic correlations. Top 100 bull lists were generated on the scale of each country, and genetic progress was assessed. Future evaluation with increased genetic ties among countries may facilitate international comparison of Milking Shorthorns.     

Test-day genetic analysis of condition score and heart girth in Holstein Friesian cows.

This study aimed to estimate heritability for condition score and heart girth using a test-day model, to investigate the genetic relationships between condition score, heart girth, and milk yield traits and to analyze the genetic relationships of condition score and heart girth measured at different stages of lactation. Cows from 25 dairy herds were scored for body condition and measured for heart girth at 3-mo intervals for 2 yr. Approximately 5000 test-day observations on condition score, heart girth, and milk fat and protein yield from 1344 Italian Friesian cows were analyzed using two approaches: 1) repeated observations for a trait were considered repeated measurements of the same trait; 2) observations for a trait collected in different stages of lactation (dry period, 1 to 75, 76 to 130, 131 to 210, and 211 to 300 DIM) were treated as different traits. (Co)variance components and related parameters were estimated using REML multiple-trait procedures and animal models with unequal design for different traits. Heritability estimates for fat and protein test-day yield and for test-day condition score and heart girth were 0.22, 0.18, 0.29, and 0.33, respectively. Condition score was negatively correlated with yield traits and positively correlated with heart girth, whereas genetic relationships between heart girth and milk yield traits were negligible. Heritability estimates were 0.27 for condition score recorded in the first half of lactation (1 to 75 and 76 to 130 DIM), 0.36 for condition score in the second half of lactation (131 to 210 and 211 to 300 DIM) and 0.32 for condition score recorded on dry cows. Genetic correlations between condition scores measured in different lactation stages were generally high (0.85 or more), with the exception of the relationships between the first and the last stage of lactation (0.74) and between the first half of lactation and the dry period (0.7). Heritability estimates for heart girth in different lactation stages ranged from 0.31 to 0.40, and genetic correlations between high girth measured in different lactation stages were higher than 0.80.     

Genetic analysis of skinfold thickness and its association with body condition score and milk production traits in Chinese Holstein population

The skin has many important roles in dairy cattle, and skinfold thickness could be used as an indicator of body fat deposition. The objectives of this study were to estimate genetic parameters of skinfold thickness and to explore its association with body condition score (BCS) and milk production traits in a Chinese Holstein population. Skinfold thicknesses over the neck (STN) and the last rib (STR), BCS, and test-day records of milk production traits were available for 6,416 lactating Holstein cows in the summers of 2015 and 2016 in Beijing, China. Multi-trait animal models were used to estimate variance and covariance components using the DMU software. The average STN was 7.15 ± 1.28 mm, and the average STR was 11.76 ± 1.95 mm (mean ± standard deviation). Estimated heritability was 0.13 ± 0.03 for STN and 0.26 ± 0.04 for STR. We detected a high genetic correlation (0.79 ± 0.08; heritability ± standard error) between STN and STR. Genetic correlations between skinfold thickness and BCS were low to moderate: 0.18 between STR and BCS, and 0.33 between STN and BCS. Genetic correlations between skinfold thickness and milk yield, milk fat percentage, and milk protein percentage were negligible, ranging from ?0.02 to 0.15. Collectively, skinfold thickness is characterized as a trait with moderate heritability. Skinfold thickness is sensitive to changes in body condition or fat deposition across parities and lactation stages in milking cows, and we confirmed the complementary nature of skinfold thickness and BCS genetically as well as phenotypically by comparing their changing trends throughout lactation and across lactations. The use of skinfold thickness, together with BCS, can assist in the monitoring of changes in body fat deposition to achieve higher management precision.     

Responses to metabolic challenges in dairy cows with high or low milk yield during an extended lactation

Responses of dairy cows with high or low milk yield (MY) beyond 450 d in milk (DIM) to 3 metabolic challenges were investigated. Twelve multiparous Holstein-Friesian cows that calved in late winter in a pasture-based system were managed for a 670-d lactation by delaying re-breeding. Cows were selected for either high MY (18.9 ± 1.69 L/cow per d; n = 6) or low MY (12.3 ± 3.85 L/cow per d; n = 6) at 450 DIM. Cows were housed indoors for 2 periods of 12 d at approximately 460 and 580 DIM. Each cow was fed freshly cut pasture (460 DIM) or pasture silage (580 DIM) plus 6.0 kg of DM barley grain daily (approximately 200 MJ of total metabolizable energy/cow per day). At all other times, cows were managed as a single herd and grazed pasture supplemented with cereal grain to an estimated intake of 180 MJ of metabolizable energy/cow per d. Cows were fitted with a jugular catheter during the final week of each experimental period. Over a period of 3 d, each cow underwent an intravenous glucose tolerance test (0.3 g/kg of body weight), an insulin tolerance test (0.12 IU of insulin/kg of body weight), and a 2-dose epinephrine challenge (0.1 and 1.6 µg/kg of body weight). Cows selected for high MY had greater milk and milk solids yields between 450 and 580 DIM than low MY cows (17.3 vs. 10.8 ± 1.49 kg of milk/d and 2.4 vs. 1.5 ± 0.23 kg of milk solids/d). The results indicated that whole body and peripheral tissue responsiveness to insulin may vary between cows of high and low MY. Following the glucose tolerance test, high MY cows had a lower plasma insulin response with a greater glucose area under the curve than low MY cows. Further, high MY cows had slower plasma glucose clearance compared with low MY cows during an insulin tolerance test. The plasma nonesterified fatty acid (NEFA) responses to the IVGTT and the ITT were similar between cows of high and low MY, but the clearance of NEFA from the plasma following both the IVGTT and ITT were slower at 580 compared with 460 DIM. The sensitivity to epinephrine was greater in high MY cows compared with low MY cows as the glucose and NEFA area under the curve and the percentage change in NEFA were greater in high MY after the low dose epinephrine challenge. However, the lipolytic but not the glucose appearance in response to epinephrine was greater in high MY cows than low MY cows. Following the high dose of epinephrine, the glucose response was lower, but the NEFA response was greater in high MY compared with low MY cows. Cows able to sustain greater MY to 580 DIM had a greater propensity for lipid mobilization, possibly enhancing nutrient partitioning to the mammary gland during the late stages of an extended lactation.     

Genetic association of clinical mastitis with test-day somatic cell score and milk yield during first lactation of Finnish Ayrshire cows

In this study the genetic association during lactation of 2 clinical mastitis (CM) traits: CM1 (7 d before to 30 d after calving) and CM2 (31 to 300 d after calving) with test-day somatic cell score (SCS) and milk yield (MY) was assessed using multitrait random regression sire models. The data analyzed were from 27,557 first-lactation Finnish Ayrshire cows. Random regressions on second- and third-order Legendre polynomials were used to model the daily genetic and permanent environmental variances of test-day SCS and MY, respectively, while only the intercept term was fitted for CM. Results showed that genetic correlations between CM and the test-day traits varied during lactation. Genetic correlations between CM1 and CM2 and test-day SCS during lactation varied from 0.41 to 0.77 and from 0.34 to 0.71, respectively. Genetic correlations of test-day MY with CM1 and CM2 ranged from 0.13 to 0.51 and from 0.49 to 0.66, respectively. Correlations between CM1 and SCS were strongest during early lactation, whereas correlations between CM2 and SCS were strongest in late lactation. Genetic correlations lower than unity indicate that CM and SCS measure different aspects of the trait mastitis. Milk yield in early lactation was more strongly correlated with both CM1 and CM2 than milk yield in later lactation. This suggests that selection for higher lactation MY through selection on increased milk yield in early lactation will have a more deleterious effect on genetic resistance to mastitis than selection for higher yield in late lactation. The approach used in this study for the estimation of the genetic associations between test-day and CM traits could be used to combine information from traits with different data structures, such as test-day SCS and CM traits in a multitrait random regression model for the genetic evaluation of udder health.     

Estimation of heritabilities and correlations associated with milk color traits

The objective of this study was to estimate the genetic parameters associated with milk color traits of dairy cattle. The data consisted of test day records of 9516 first lactation dairy cows and the records of 6358 of these cows that went on to produce a second lactation. Friesians, Jerseys, and crossbred cows were included in the data. Test day records included measures of milk, fat, and protein as well as milk color measured as absorbance at 450 nm. From these measurements, fat color and beta-carotene yield were calculated. Analyses were performed both within and across breeds. Jerseys produced more beta-carotene than did Friesians, and milk and fat from Jerseys had more intense color. Lactation model estimates for the heritabilities of milk color traits ranged from 0.33 to 0.44 (across breed), 0.40 to 0.49 (Friesians), and 0.17 to 0.31 (Jerseys). In all analyses, the heritability estimates associated with beta-carotene yield were lower than the estimates associated with the color of milk or fat. Genetic correlations between beta-carotene yield and the production traits were positive, but genetic correlations between fat color and production traits were generally negative. Genetic correlations between milk color and milk and protein yields were negative, and the correlations with fat yield were close to zero.     

Prediction accuracies and genetic parameters for test-day traits from genomic and pedigree-based random regression models with or without heat stress interactions

The aim of this study was to compare genetic (co)variance components and prediction accuracies of breeding values from genomic random regression models (gRRM) and pedigree-based random regression models (pRRM), both defined with or without an additional environmental gradient. The used gradient was a temperature-humidity index (THI), considered in statistical models to investigate possible genotype by environment (G×E) interactions. Data included 106,505 test-day records for milk yield (MY) and 106,274 test-day records for somatic cell score (SCS) from 12,331 genotyped Holstein Friesian daughters of 522 genotyped sires. After single nucleotide polymorphism quality control, all genotyped animals had 40,468 single nucleotide polymorphism markers. Test-day traits from recording years 2010 to 2015 were merged with temperature and humidity data from the nearest weather station. In this regard, 58 large-scale farms from the German federal states of Berlin-Brandenburg and Mecklenburg-West Pomerania were allocated to 31 weather stations. For models with a THI gradient, additive genetic variances and heritabilities for MY showed larger fluctuations in dependency of DIM and THI than for SCS. For both traits, heritabilities were smaller from the gRRM compared with estimates from the pRRM. Milk yield showed considerably larger G×E interactions than SCS. In genomic models including a THI gradient, genetic correlations between different DIM × THI combinations ranged from 0.26 to 0.94 for MY. For SCS, the lowest genetic correlation was 0.78, estimated between SCS from the last DIM class and the highest THI class. In addition, for THI × THI combinations, genetic correlations were smaller for MY compared with SCS. A 5-fold cross-validation was used to assess prediction accuracies from 4 different models. The 4 different models were gRRM and pRRM, both modeled with or without G×E interactions. Prediction accuracy was the correlation between breeding values for the prediction data set (i.e., excluding the phenotypic records from this data set) with respective breeding values considering all phenotypic information. Prediction accuracies for sires and for their daughters were largest for the gRRM considering G×E interactions. Such modeling with 2 covariates, DIM and THI, also allowed accurate predictions of genetic values at specific DIM. In comparison with a pRRM, the effect of a gRRM with G×E interactions on gain in prediction accuracies was stronger for daughters than for sires. In conclusion, we found stronger effect of THI alterations on genetic parameter estimates for MY than for SCS. Hence, gRRM considering THI especially contributed to gain in prediction accuracies for MY.     

Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.     

Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due to its large carbon footprint related to CH₄ emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH₄ proxies [predicted daily CH₄ emission (PME, g/d), and log-transformed predicted CH₄ intensity (LMI)] and (2) their genetic correlations with milk production traits [milk (MY), fat (FY), and protein (PY) yields] from first- and second-parity Holstein cows. A total of 336,126 and 231,400 mid-infrared CH₄ phenotypes were collected from 56,957 and 34,992 first- and second-parity cows, respectively. The PME increased from the first to the second lactation (433 vs. 453 g/d) and the LMI decreased (2.93 vs. 2.86). We used 20 bivariate random regression test-day models to estimate the variance components. Moderate heritability values were observed for both CH₄ traits, and those values decreased slightly from the first to the second lactation (0.25 ± 0.01 and 0.22 ± 0.01 for PME; 0.18 ± 0.01 and 0.17 ± 0.02 for LMI). Lactation phenotypic and genetic correlations were negative between PME and MY in both first and second lactations (?0.07 vs. ?0.07 and ?0.19 vs. ?0.24, respectively). More close scrutiny revealed that relative increase of PME was lower with high MY levels even reverting to decrease, and therefore explaining the negative correlations, indicating that higher producing cows could be a mitigation option for CH₄ emission. The PME phenotypic correlations were almost equal to 0 with FY and PY for both lactations. However, the genetic correlations between PME and FY were slightly positive (0.11 and 0.12), whereas with PY the correlations were slightly negative (?0.05 and ?0.04). Both phenotypic and genetic correlations between LMI and MY or PY or FY were always relatively highly negative (from ?0.21 to ?0.88). As the genetic correlations between PME and LMI were strong (0.71 and 0.72 in first and second lactation), the selection of one trait would also strongly influence the other trait. However, in animal breeding context, PME, as a direct quantity CH₄ proxy, would be preferred to LMI, which is a ratio trait of PME with a trait already in the index. The range of PME sire estimated breeding values were 22.1 and 29.41 kg per lactation in first and second parity, respectively. Further studies must be conducted to evaluate the effect of the introduction of PME in a selection index on the other traits already included in this index, such as, for instance, fertility or longevity.     

Genetic analyses of mastitis data using animal threshold models and genetic correlation with production traits

In the present study, 6 different mastitis data sets of 3 dairy herds with an overall herd size of 3200 German Holstein cows were analyzed. Data collection periods included the first 50, 100, or 300 d of lactation. The 3 data collection periods were analyzed with a lactation model and a test-day model. All models were animal threshold models. Mastitis frequencies in the lactation model data sets varied between 29 and 45%, and varied between 3 and 6% in the test-day model data sets. Depending on the period of data collection, heritabilities of liability to mastitis in the lactation models were 0.05 (50 d), 0.06 (100 d), and 0.07 (300 d). In the test-day models, heritabilities were slightly higher with values of 0.09 (50 and 100 d), and 0.06 (300 d). Between lactation models, the rank correlations between the relative breeding values were high and varied between 0.86 and 0.94. Rank correlations between the relative breeding values of the test-day models ranged from 0.68 to 0.87. The rank correlations between the relative breeding values of lactation models and test-day models varied from 0.51 and 0.80. Genetic correlations between mastitis and milk production traits were estimated with a linear animal test-day model. The correlations with mastitis were 0.29 (milk yield), 0.30 (fat yield), 0.20 (fat content), 0.34 (protein yield), and 0.20 (protein content). The estimated genetic correlation between mastitis and somatic cell score was 0.84.     

Genetic parameters for body weight from birth to calving and associations between weights with test-day,health, and female fertility traits

A data set including 57,868 records for calf birth weight (CABW) and 9,462 records for weight at first insemination (IBW) were used for the estimation of direct and maternal genetic effects in Holstein Friesian dairy cattle. Furthermore, CABW and IBW were correlated with test-day production records and health traits in first-lactation cows, and with nonreturn rates in heifers. Health traits considered overall disease categories from the International Committee for Animal Recording diagnosis key, including the general disease status, diarrhea, respiratory diseases, mastitis, claw disorders, female fertility disorders, and metabolic disorders. For single-trait measurements of CABW and IBW, animal models with maternal genetic effects were applied. The direct heritability was 0.47 for CABW and 0.20 for IBW, and the direct genetic correlation between CABW and IBW was 0.31. A moderate maternal heritability (0.19) was identified for CABW, but the maternal genetic effect was close to zero for IBW. The correlation between direct and maternal genetic effects was antagonistic for CABW (?0.39) and for IBW (?0.24). In bivariate animal models, only weak genetic and phenotypic correlations were identified between CABW and IBW with either test-day production or health traits in early lactation. Apart from metabolic disorders, there was a general tendency for increasing disease susceptibilities with increasing CABW. The genetic correlation between IBW and nonreturn rates in heifers after 56 d and after 90 d was slightly positive (0.18), but close to zero when correlating nonreturn rates with CABW. For the longitudinal BW structure from birth to the age of 24 mo, random regression models with the time-dependent covariate “age in months” were applied. Evaluation criteria (Bayesian information criterion and residual variances) suggested Legendre polynomials of order 3 to modeling the longitudinal body weight (BW) structure. Direct heritabilities around birth and insemination dates were slightly larger than estimates for CABW and IBW from the single-trait models, but maternal heritabilities were exactly the same from both models. Genetic correlations between BW were close to 1 for neighboring age classes, but decreased with increasing time spans. The genetic correlation between BW at d 0 (birth date) and at 24 mo was even negative (?0.20). Random regression model estimates confirmed the antagonistic relationship between direct and maternal genetic effects, especially during calfhood. This study based on a large data set in dairy cattle confirmed genetic parameters and (co)variance components for BW as identified in beef cattle populations. However, BW records from an early stage of life were inappropriate early predictors for dairy cow health and productivity.     

Genetic analysis of milk urea nitrogen and relationships with yield and fertility across lactation

The aim of this project was to investigate the relationship of milk urea nitrogen (MUN) with 3 milk production traits [milk yield (MY), fat yield (FY), protein yield (PY)] and 6 fertility measures (number of inseminations, calving interval, interval from calving to first insemination, interval from calving to last insemination, interval from first to last insemination, and pregnancy at first insemination). Data consisted of 635,289 test-day records of MY, FY, PY, and MUN on 76,959 first-lactation Swedish Holstein cows calving from 2001 to 2003, and corresponding lactation records for the fertility traits. Yields and MUN were analyzed with a random regression model followed by a multi-trait model in which the lactation was broken into 10 monthly periods. Heritability for MUN was stable across lactation (between 0.16 and 0.18), whereas MY, FY, and PY had low heritability at the beginning of lactation, which increased with time and stabilized after 100 d in milk, at 0.47, 0.36, and 0.44, respectively. Fertility traits had low heritabilities (0.02 to 0.05). Phenotypic correlations of MUN and milk production traits were between 0.13 (beginning of lactation) and 0.00 (end of lactation). Genetic correlations of MUN and MY, FY, and PY followed similar trends and were positive (0.22) at the beginning and negative (−0.15) at the end of lactation. Phenotypic correlations of MUN and fertility were close to zero. A surprising result was that genetic correlations of MUN and fertility traits suggest a positive relationship between the 2 traits for most of the lactation, indicating that animals with breeding values for increased MUN also had breeding values for improved fertility. This result was obtained with a random regression model as well as with a multi-trait model. The analyzed group of cows had a moderate level of MUN concentration. In such a population MUN concentration may increase slightly due to selection for improved fertility. Conversely, selection for increased MUN concentration may improve fertility slightly.     

Processing characteristics of dairy cow milk are moderately heritable

Milk processing attributes represent a group of milk quality traits that are important to the dairy industry to inform product portfolio. However, because of the resources required to routinely measure such quality traits, precise genetic parameter estimates from a large population of animals are lacking for these traits. Milk processing characteristics considered in the present study—rennet coagulation time, curd-firming time, curd firmness at 30 and 60 min after rennet addition, heat coagulation time, casein micelle size, and milk pH—were all estimated using mid-infrared spectroscopy prediction equations. Variance components for these traits were estimated using 136,807 test-day records from 5 to 305 d in milk (DIM) from 9,824 cows using random regressions to model the additive genetic and within-lactation permanent environmental variances. Heritability estimates ranged from 0.18 ± 0.01 (26 DIM) to 0.38 ± 0.02 (180 DIM) for rennet coagulation time; from 0.26 ± 0.02 (5 DIM) to 0.57 ± 0.02 (174 DIM) for curd-firming time; from 0.16 ± 0.01 (30 DIM) to 0.56 ± 0.02 (271 DIM) for curd firmness at 30 min; from 0.13 ± 0.01 (30 DIM) to 0.48 ± 0.02 (271 DIM) for curd firmness at 60 min; from 0.08 ± 0.01 (17 DIM) to 0.24 ± 0.01 (180 DIM) for heat coagulation time; from 0.23 ± 0.02 (30 DIM) to 0.43 ± 0.02 (261 DIM) for casein micelle size; and from 0.20 ± 0.01 (30 DIM) to 0.36 ± 0.02 (151 DIM) for milk pH. Within-trait genetic correlations across DIM weakened as the number of days between compared intervals increased but were mostly >0.4 except between the peripheries of the lactation. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix for all traits revealed that at least the 80% of the genetic variation among animals in lactation profiles was associated with the height of the lactation profile. Curd-firming time and curd firmness at 30 min were weakly to moderately genetically correlated with milk yield (from 0.33 ± 0.05 to 0.59 ± 0.05 for curd-firming time, and from ?0.62 ± 0.03 to ?0.21 ± 0.06 for curd firmness at 30 min). Milk protein concentration was strongly genetically correlated with curd firmness at 30 min (0.84 ± 0.02 to 0.94 ± 0.01) but only weakly genetically correlated with milk heat coagulation time (?0.27 ± 0.07 to 0.19 ± 0.06). Results from the present study indicate the existence of exploitable genetic variation for milk processing characteristics. Because of possible indirect deterioration in milk processing characteristics due to selection for greater milk yield, emphasis on milk processing characteristics is advised.     

The effect of intramammary infection with coagulase-negative staphylococci in early lactating heifers on milk yield throughout first lactation revisited

The objective of this study was to further scrutinize the previously found positive association between intramammary infection (IMI) caused by coagulase-negative staphylococci (CNS) in early lactating heifers and test-day daily milk yield (MY) throughout first lactation, with a specific focus on the effect of the heifers’ genetically determined milk production levels and the incidence of clinical mastitis. Two precise longitudinal data sets were analyzed using a series of statistical models including potential confounding and intermediate variables. The final database included the IMI status at calving, composite milk somatic cell count (SCC) and MY records at test day up to 285 d in milk (DIM), farmer-recorded clinical mastitis (CM) cases between 14 and 285 DIM, estimated new IMI incidence based on a SCC threshold of 100,000 cells/mL between 14 and 285 DIM, DIM, average 305-d MY at the herd level, and the heifers’ genetic merit for MY from 240 dairy heifers from 29 dairy herds. Seventy-one (29.6%) early lactating heifers were noninfected, 108 heifers (45.0%) were CNS infected, and 61 heifers (25.4%) were infected with any major pathogen. The positive effect of CNS IMI in early lactation on test-day MY was estimated at 1.32 kg/d using a first basic mixed regression model. Correcting for the confounder genetic merit for MY reduced this effect to 1.17 kg. Interestingly, taking into account the confounding effect of herd resulted in an increase of the estimate from 1.32 to 2.2 kg/d. The positive effect of CNS IMI in early lactation on MY after correcting the model for both confounders was estimated at 2.05 kg/d. Heifers infected with CNS in the first DIM tended to have fewer CM cases throughout lactation compared with the noninfected herd mates. Including the intermediate variable CM in the model explained 0.16 kg/d of the corrected effect of 2.05 kg/d. Inclusion of test-day SCC, another intermediate variable, however, increased the estimate by 0.11 kg/d. With an appropriate correction for several confounders and biologically understood intermediate variables such as CM, test-day SCC, and new IMI based on SCC threshold of 100,000 cells/mL, an unexplained test-day MY difference between CNS-infected and noninfected heifers of 2.0 kg/d remained.     

Bivariate analysis of conception rates and test-day milk yields in Holsteins using a threshold-linear model with random regressions

The objective of this study was to estimate genetic correlations between conception rates (CR) and test-day (TD) milk yields in Holsteins for different days in milk (DIM) in small and large herds. The data included 217,213 first-parity service records of 94,984 cows in New York State between 1999 and 2003. The CR was defined as the outcome of a single insemination. Conception rate and TD milk were analyzed using a series of threshold-linear models with fixed effects that included herd-test-date for TD milk and herd-year for CR, age, service month, cubic regressions on DIM using Legendre polynomials and with random effects that included herd × sire interaction, sire additive genetic and permanent environments with quadratic random regressions on DIM, service sire for CR, and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Herds were categorized into small (≤80 cows) and large operations. Large herds produced a higher TD milk (34.0 vs. 30.8 kg), had lower CR (29.5 vs. 34.4%), began breeding earlier (75 vs. 92 d to first service), and had fewer days open (138 vs. 145 d). The average CR was 20% at 50 DIM, increased to about 38% at DIM 100, and then leveled off. Estimated genetic correlations between CR and TD milk stayed around −0.15 for small herds but changed from positive (0.3) at 60 DIM to negative (−0.3) at 120 DIM for large herds. Genetic correlations for CR between small and large herds were highest at 80 DIM and lowest at 140 DIM. The chi-square test showed that the frequency of service records was significantly different during a given week for 71% of large herds and for 15% of small herds, suggesting more timed artificial insemination services in large herds. For the top 15% of cows for milk, fertility peaked around 100 DIM in large herds and at around 100 and 170 DIM in small herds. It seems that optimum breeding practices in large herds of breeding cows earlier are already followed.     

Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in The Netherlands

With random regression models, genetic parameters of test-day milk production records of dairy cattle can be estimated directly from the data. However, several researchers that used this method have reported unrealistically high variances at the borders of the lactation trajectory and low genetic correlations between beginning and end of lactation. Recently, it has been proposed to include herd-specific regression curves in the random regression model. The objective was to study the effect of including random herd curves on estimated genetic parameters. Genetic parameters were estimated with 2 models; both included random regressions for the additive genetic and permanent environmental effect, whereas the second model also included a random regression effect for herd x 2-yr period of calving. All random regressions were modeled with fourth-order Legendre polynomials. Bayesian techniques with Gibbs sampling were used to estimate all parameters. The data set comprised 857,255 test-day milk, fat, and protein records from lactations 1, 2, and 3 of 43,990 Holstein cows from 544 herds. Genetic variances estimated by the second model were lower in the first 100 d and at the end of the lactation, especially in lactations 2 and 3. Genetic correlations between d 50 and the end of lactation were around 0.25 higher in the second model and were consistent with studies where lactation stages are modeled as different traits. Subsequently, estimated heritabilities for persistency were up to 0.14 lower in the second model. It is suggested to include herd curves in a random regression model when estimating genetic parameters of test-day production traits in dairy cattle.