Genetic correlations between the cumulative pseudo-survival rate,milk yield,and somatic cell score during lactation in Holstein cattle in Japan using a random regression model

Trends in genetic correlations between longevity, milk yield, and somatic cell score (SCS) during lactation in cows are difficult to trace. In this study, changes in the genetic correlations between milk yield, SCS, and cumulative pseudo-survival rate (PSR) during lactation were examined, and the effect of milk yield and SCS information on the reliability of estimated breeding value (EBV) of PSR were determined. Test day milk yield, SCS, and PSR records were obtained for Holstein cows in Japan from 2004 to 2013. A random subset of the data was used for the analysis (825 herds, 205,383 cows). This data set was randomly divided into 5 subsets (162–168 herds, 83,389–95,854 cows), and genetic parameters were estimated in each subset independently. Data were analyzed using multiple-trait random regression animal models including either the residual effect for the whole lactation period (H0), the residual effects for 5 lactation stages (H5), or both of these residual effects (HD). Milk yield heritability increased until 310 to 351 d in milk (DIM) and SCS heritability increased until 330 to 344 DIM. Heritability estimates for PSR increased with DIM from 0.00 to 0.05. The genetic correlation between milk yield and SCS increased negatively to under ?0.60 at 455 DIM. The genetic correlation between milk yield and PSR increased until 342 to 355 DIM (0.53–0.57). The genetic correlation between the SCS and PSR was ?0.82 to ?0.83 at around 180 DIM, and decreased to ?0.65 to ?0.71 at 455 DIM. The reliability of EBV of PSR for sires with 30 or more recorded daughters was 0.17 to 0.45 when the effects of correlated traits were ignored. The maximum reliability of EBV was observed at 257 (H0) or 322 (HD) DIM. When the correlations of PSR with milk yield and SCS were considered, the reliabilities of PSR estimates increased to 0.31–0.76. The genetic parameter estimates of H5 were the same as those for HD. The rank correlation coefficients of the EBV of PSR between H0 and H5 or HD were greater than 0.9. Additionally, the reliabilities of EBV of PSR of H0 were similar to those for H5 and HD. Therefore, the genetic parameter estimates in H0 were not substantially different from those in H5 and HD. When milk yield and SCS, which were genetically correlated with PSR, were used, the reliability of PSR increased. Estimates of the genetic correlations between PSR and milk yield and between PSR and SCS are useful for management and breeding decisions to extend the herd life of cows.     

Genetic parameter estimation for milk yield over multiple parities and various lengths of lactation in Danish Jerseys by random regression models

The objectives of this study were to test for heterogeneity of genetic and environmental variance among completed and extended records from different lactations or different days in milk (DIM) and to build a model that accounts for this heterogeneity. A total of 147,457 305-d milk yield records from Danish Jersey cows calving between 1984 and early 1999 from two regions of Denmark were used in this study. Results showed that DIM and parity influenced parameters estimated from an animal model with repeated records. Therefore, the data were analyzed using random-regression models that allow the covariance between measurements to change gradually with DIM and parity. Random regressions were fitted for additive genetic effects and permanent environmental effects using second- or third-order normalized Legendre polynomials for DIM and parity. Variances of random-regression coefficients associated with all orders of the polynomials were significant. Based on these parameter estimates, a covariance function (CF) was defined. The CF showed that the heritability decreases over parities, but within each parity heritability increases with DIM, whereas variance of permanent environmental effects increases over parities and decreases with DIM. Generally, genetic correlations were higher between records with similar DIM and parity. The results indicate that there are problems with the extension procedure used to predict 305-d milk yields. Using the covariance functions estimated in this study, breeding values could be predicted that take into account the covariance structure between records from different parities and different DIM.     

Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits

Keeping dairy cows in grassland systems relies on detailed analyses of genetic resistance against endoparasite infections, including between- and within-breed genetic evaluations. The objectives of this study were (1) to compare different Black and White dairy cattle selection lines for endoparasite infections and (2) the estimation of genetic (co)variance components for endoparasite and test-day milk production traits within the Black and White cattle population. A total of 2,006 fecal samples were taken during 2 farm visits in summer and autumn 2015 from 1,166 cows kept in 17 small- and medium-scale organic and conventional German grassland farms. Fecal egg counts were determined for gastrointestinal nematodes (FEC-GIN) and flukes (FEC-FLU), and fecal larvae counts for the bovine lungworm Dictyocaulus viviparus (FLC-DV). The lowest values for gastrointestinal nematode infections were identified for genetic lines adopted to pasture-based production systems, especially selection lines from New Zealand. Heritabilities were low for FEC-GIN (0.05–0.06 ± 0.04) and FLC-DV (0.05 ± 0.04), but moderate for FEC-FLU (0.33 ± 0.06). Almost identical heritabilities were estimated for different endoparasite trait transformations (log-transformation, square root). The genetic correlation between FEC-GIN and FLC-DV was 1.00 ± 0.60, slightly negative between FEC-GIN and FEC-FLU (?0.10 ± 0.27), and close to zero between FLC-DV and FEC-FLU (0.03 ± 0.30). Random regression test-day models on a continuous time scale [days in milk (DIM)] were applied to estimate genetic relationships between endoparasite and longitudinal test-day production traits. Genetic correlations were negative between FEC-GIN and milk yield (MY) until DIM 85, and between FEC-FLU and MY until DIM 215. Genetic correlations between FLC-DV and MY were negative throughout lactation, indicating improved disease resistance for high-productivity cows. Genetic relationships between FEC-GIN and FEC-FLU with milk protein content were negative for all DIM. Apart from the very early and very late lactation stage, genetic correlations between FEC-GIN and milk fat content were negative, whereas they were positive for FEC-FLU. Genetic correlations between FEC-GIN and somatic cell score were positive, indicating similar genetic mechanisms for susceptibility to udder and endoparasite infections. The moderate heritabilities for FEC-FLU suggest inclusion of FEC-FLU into overall organic dairy cattle breeding goals to achieve long-term selection response for disease resistance.     

Defining a nitrogen efficiency index in Holstein cows and assessing its potential effect on the breeding program of bulls

The purposes of this study were (1) to explore the relationship between 3 milk mid-infrared predicted features including nitrogen intake (NINT), milk true protein N (MTPN), and milk urea-N yield (MUNY); (2) to integrate these 3 features into an N efficiency index (NEI) and analyses approximate genetic correlations between the NEI and 37 traits (indices) of interest; and (3) to assess the potential effect of including the NEI into breeding programs of bulls. The edited data were 1,043,171 test-day records on 342,847 cows in 1,931 herds and 143,595 test-day records on 53,660 cows in 766 herds used for estimating breeding values (EBV) and variance components, respectively. The used records were within 5 to 50 d in milk. The records were grouped into primiparous and multiparous. The genetic parameters for the included mid-infrared features and EBV of the animals included in the pedigree were estimated using a multiple-trait repeatability animal model. Then, the EBV of the NINT, MTPN, MUNY were integrated into the NEI using a selection index assuming weights based on the N partitioning. The approximate genetic correlations between the NEI and 37 traits of interest were estimated using the EBV of the selected bulls. The bulls born from 2011 to 2014 with NEI were selected and the NEI distribution of these bulls having EBV for the 8 selected traits (indices) was checked. The heritability and repeatability estimates for NINT, MTPN, and MUNY ranged from 0.09 to 0.13, and 0.37 to 0.65, respectively. The genetic and phenotypic correlations between NINT, MTPN, and MUNY ranged from ?0.31 to 0.87, and ?0.02 to 0.42, respectively. The NEI ranged from ?13.13 to 12.55 kg/d. In total, 736 bulls with reliability ≥0.50 for all included traits (NEI and 37 traits) and at least 10 daughters distributed in at least 10 herds were selected to investigate genetic aspects of the NEI. The NEI had positive genetic correlations with production yield traits (0.08–0.46), and negative genetic correlations with the investigated functional traits and indices (?0.71 to ?0.07), except for the production economic index and functional type economic index. The daughters of bulls with higher NEI had lower NINT and MUNY, and higher MTPN. Furthermore, 26% of the bulls (n = 50) with NEI born between 2011 to 2014 had higher NEI and global economic index than the average in the selected bulls. Finally, the developed NEI has the advantage of large-scale prediction and therefore has the potential for routine application in dairy cattle breeding in the future.     

Exploiting genetic variability in the trajectory of lactation yield and somatic cell score with each progressing parity

The inclusion of reproductive performance in dairy cow breeding schemes has resulted in a cumulative improvement in genetic merit for reproductive performance; this improvement should manifest in longer productive lives through a reduced requirement for involuntary culling. Nonetheless, the average length of dairy cow productive life has not changed in most populations, suggesting that risk factors for culling, especially in older cows, are possibly more associated with lower yield or high somatic cell score (SCS) than compromised reproductive performance. The objective of the present study was to understand the dynamics of lactation yields and SCS in dairy cows across parities and, in doing so, quantify the potential to alter this trajectory through breeding. After edits, 3,470,520 305-d milk, fat, and protein yields, as well as milk fat and protein percentage and somatic cell count records from 1,162,473 dairy cows were available for analysis. Random regression animal models were used to identify the parity in which individual cows reached their maximum lactation yields, and highest average milk composition and SCS; also estimated from these models were the (co)variance components for yield, composition, and SCS per parity across parities. Estimated breeding values for all traits per parity were calculated for cows reaching ≥fifth parity. Of the cows included in the analyses, 91.0%, 92.2%, and 83.4% reached maximum milk, fat, and protein yield in fifth parity, respectively. Conversely, 95.9% of cows reached their highest average fat percentage in first parity and 62.9% of cows reached their highest average protein percentage in third parity. In contrast to both milk yield and composition traits, 98.4% of cows reached their highest average SCS in eighth parity. Individual parity estimates of heritability for milk yield traits, milk composition, and SCS ranged from 0.28 to 0.44, 0.47 to 0.69, and 0.13 to 0.23, respectively. The strength of the genetic correlations per trait among parities was inversely related to the interval between the parities compared; the weakest genetic correlation was 0.67 (standard error = 0.02) between milk yield in parities 1 and 8. Eigenvalues and eigenfunctions of the additive genetic covariance matrices for all investigated traits revealed potential to alter the trajectory of parity profiles for milk yield, milk composition, and SCS. This was further demonstrated when evaluating the trajectories of animal estimated breeding values per parity.     

Genetic and genomic dissection of dry matter intake at different lactation stages in primiparous Holstein cows

Dry matter intake (DMI) and feed efficiency are economically relevant traits. Simultaneous selection for low DMI and high milk yield might improve feed efficiency, but bears the risk of aggravating the negative energy balance and related health problems in early lactation. Lactation stage-specific selection might provide a possibility to optimize the trajectory of DMI across days in milk (DIM), but requires in-depth knowledge about genetic parameters within and across lactation stages. Within the current study, daily heritabilities and genetic correlations between DMI records from different lactation stages were estimated using random regression models based on 910 primiparous Holstein cows. The heritability estimates from DIM 11 to 180 follow a slightly parabolic curve varying from 0.26 (DIM 121) to 0.37 (DIM 11 and 180). Genetic correlations estimated between DIM 11, 30, 80, 130, and 180 were all positive, ranging from 0.29 (DIM 11 and 180) to 0.97 (DIM 11 and 30; i.e., the correlations are inversely related to the length of the interval between compared DIM). Deregressed estimated breeding values for the same lactation days were used as phenotypes in sequential genome-wide association studies using 681 cows drawn from the study population and genotyped for the Illumina SNP50 BeadChip (Illumina Inc., San Diego, CA). A total of 21 SNP on 10 chromosomes exceeded the chromosome-wise significance threshold for at least 1 analyzed DIM, pointing to some interesting candidate genes directly involved in the regulation of feed intake. Association signals were restricted to certain lactation stages, thus supporting the genetic correlations. Partitioning the explained variance onto chromosomes revealed a large contribution of Bos taurus autosome 7 not harboring any associated marker in the current study. The results contribute to the knowledge about the genetic architecture of the complex phenotype DMI and might provide valuable information for future selection efforts.     

Genetic parameters for cheese-making properties and milk composition predicted from mid-infrared spectra in a large data set of Montbéliarde cows

Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH_0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH_{0 PPC} had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH_{0 PPC} and CY or coagulation traits were weak (?0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.     

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.     

Genetic and phenotypic parameters for type and production in Guernsey dairy cows.

Heritabilities; genetic and phenotypic correlations for milk, fat, and protein production; and linear type traits were estimated from a sire model including sire relationships using multiple-trait REML. For the milk production traits, 68,109 first parity records were analyzed. Heritabilities ranged from .31 to .37, genetic correlations between the milk production traits ranged from .80 to .92, and phenotypic correlations ranged from .86 to .94. Linear type traits from 12,996 cows on 15 traits were used to estimate heritabilities and genetic and phenotypic correlations between linear type traits. The heritabilities ranged from .53 for stature to .09 for foot angle. Rear udder height and rear udder width had the highest positive genetic correlation (.85), whereas dairy form and udder depth had the highest negative genetic correlation (-.41). When the first parity production records were merged with type records for cows, 9867 records on 18 traits were obtained. Dairy form, rear udder height, and rear udder width had strong to moderate positive genetic correlations with the three production traits. Fore udder attachment and udder depth had moderate negative genetic correlations with the three milk production traits. These results suggest that selection for improvement of milk production will lead to correlated increases in dairy form, rear udder height, rear udder width, and udder depth and to correlated decreases in the strength of fore udder attachment.     

Genetic parameters across lactation for feed intake,fat- and protein-corrected milk,and liveweight in first-parity Holstein cattle

Breeding values for dry matter intake (DMI) are important to optimize dairy cattle breeding goals for feed efficiency. However, generally, only small data sets are available for feed intake, due to the cost and difficulty of measuring DMI, which makes understanding the genetic associations between traits across lactation difficult, let alone the possibility for selection of breeding animals. However, estimating national breeding values through cheaper and more easily measured correlated traits, such as milk yield and liveweight (LW), could be a first step to predict DMI. Combining DMI data across historical nutritional experiments might help to expand the data sets. Therefore, the objective was to estimate genetic parameters for DMI, fat- and protein-corrected milk (FPCM) yield, and LW across the entire first lactation using a relatively large data set combining experimental data across the Netherlands. A total of 30,483 weekly records for DMI, 49,977 for FPCM yield, and 31,956 for LW were available from 2,283 Dutch Holstein-Friesian first-parity cows between 1990 and 2011. Heritabilities, covariance components, and genetic correlations were estimated using a multivariate random regression model. The model included an effect for year-season of calving, and polynomials for age of cow at calving and days in milk (DIM). The random effects were experimental treatment, year-month of measurement, and the additive genetic, permanent environmental, and residual term. Additive genetic and permanent environmental effects were modeled using a third-order orthogonal polynomial. Estimated heritabilities ranged from 0.21 to 0.40 for DMI, from 0.20 to 0.43 for FPCM yield, and from 0.25 to 0.48 for LW across DIM. Genetic correlations between DMI at different DIM were relatively low during early and late lactation, compared with mid lactation. The genetic correlations between DMI and FPCM yield varied across DIM. This correlation was negative (up to −0.5) between FPCM yield in early lactation and DMI across the entire lactation, but highly positive (above 0.8) when both traits were in mid lactation. The correlation between DMI and LW was 0.6 during early lactation, but decreased to 0.4 during mid lactation. The highest correlations between FPCM yield and LW (0.3–0.5) were estimated during mid lactation. However, the genetic correlations between DMI and either FPCM yield or LW were not symmetric across DIM, and differed depending on which trait was measured first. The results of our study are useful to understand the genetic relationship of DMI, FPCM yield, and LW on specific days across lactation.     

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.     

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 parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems

Lactose is a major component of milk (typically around 5% of composition) that is not usually directly considered in national genetic improvement programs of dairy cattle. Daily test-day lactose yields and percentage data from pasture-based seasonal calving herds in Australia were analyzed to assess if lactose content can be used for predicting fitness traits and if an additional benefit is achieved by including lactose yield in selecting for milk yield traits. Data on lactose percentage collected from 2007 to 2014, from about 600 herds, were used to estimated genetic parameters for lactose percentage and lactose yield and correlations with other milk yield traits, somatic cell count (SCC), calving interval (CIV), and survival. Daily test-day data were analyzed using bivariate random regression models. In addition, multi-trait models were also performed mainly to assess the value of lactose to predict fitness traits. The heritability of lactose percentage (0.25 to 0.37) was higher than lactose yield (0.11 to 0.20) in the first parity. Genetically, the correlation of lactose percentage with protein percentage varied from 0.3 at the beginning of lactation to ?0.24 at the end of the lactation in the first parity. Similar patterns in genetic correlations were also observed in the second and third parity. At all levels (i.e., genetic, permanent environmental, and residual), the correlation between milk yield and lactose yield was close to 1. The genetic and permanent environmental correlations between lactose percentage and SCC were stronger in the second and third parity and toward the end of the lactation (?0.35 to ?0.50) when SCC levels are at their maximum. The genetic correlation between lactose percentage in the first 120 d and CIV (?0.23) was similar to correlation of CIV with protein percentage (?0.28), another component trait with the potential to predict fertility. Furthermore, the correlations of estimated breeding values of lactose percentage and estimated breeding values of traits such as survival, fertility, SCC, and angularity suggest that the value of lactose percentage as a predictor of fitness traits is weak. The results also suggest that including lactose yield as a trait into the breeding objective is of limited value due to the high positive genetic correlation between lactose yield and protein yield, the trait highly emphasized in Australia. However, recording lactose percentage as part of the routine milk recording system will enable the Australian dairy industry to respond quickly to any future changes and market signals.     

Relationships between energy balance during early lactation and cow performance,blood metabolites,and fertility: A meta-analysis of individual cow data

This study was designed to contribute to the understanding of the relationships between energy balance (EB) in early lactation [4 to 21 d in milk (DIM)] and fertility traits [interval to start of luteal activity (SLA), interval to first observed heat (FOH), and conception to first artificial insemination (AI)], and their associated relationships with cow performance and blood metabolites between 4 to 150 DIM. Individual cow data (488 primiparous and 1,020 multiparous lactations) from 27 experiments was analyzed. Data on cow performance, EB (on a metabolizable energy basis), and fertility traits were available for all cows, whereas milk progesterone data (to determine SLA) and periodic blood metabolite data were available for 1,042 and 1,055 lactations, respectively. Data from primiparous and multiparous cows were analyzed separately, with the data sets for the 2 parity groups divided into quartiles (Q1–Q4) according to the average EB during 4 to 21 DIM (EB range for Q1 to Q4: primiparous, ?120 to ?49, ?49 to ?24, ?24 to ?3, and ?3 to 92 MJ/d, respectively: multiparous, ?191 to ?79, ?79 to ?48, ?48 to ?22, and ?22 to 93 MJ/d, respectively). Differences between EB quartiles for production and fertility traits were compared. In early lactation (4 to 21 DIM), moving from Q1 to Q4 mean DMI and metabolizable energy intake increased whereas mean ECM decreased. During the same period, moving from Q1 to Q4 milk fat content, milk fat-to-protein ratio, and plasma nonesterified fatty acid and β-hydroxybutyrate concentrations decreased, whereas milk protein content and plasma glucose concentrations increased in both primiparous and multiparous cows. When examined over the entire experimental period (4 to 150 DIM), many of the trends in intakes and milk production remained, although the magnitude of the difference between quartiles was much reduced, whereas milk fat content did not differ between quartiles in primiparous cows. The percentage of cows with FOH before 42 DIM increased from Q1 to Q4 (from 46 to 72% in primiparous cows, and from 41 to 58% in multiparous cows). Interval from calving to SLA and to FOH decreased with increasing EB during 4 to 21 DIM, with these occurring 9.8 and 10.2 d earlier, respectively, in Q4 compared with Q1 (primiparous cows), and 7.4 and 5.9 d earlier, respectively, in Q4 compared with Q1 (multiparous cows). For each 10 MJ/d decrease in mean EB during 4 to 21 DIM, FOH was delayed by 1.2 and 0.8 d in primiparous and multiparous cows, respectively. However, neither days to first AI nor the percentage of cows that conceived to first AI were affected by daily EB during 4 to 21 DIM in either primiparous or multiparous cows, and this is likely to reflect a return to a less metabolically stressed status at the time of AI. These results demonstrate that interval from calving to SLA and to FOH were reduced with increasing EB in early lactation, whereas early lactation EB had no effect on conception to the first service.     

Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows

Automatic milking systems record an enormous amount of data on milk yield and the cow itself. These type of big data are expected to contain indicators for health and resilience of cows. In this study, the aim was to define and estimate heritabilities for traits related with fluctuations in daily milk yield and to estimate genetic correlations with existing functional traits, such as udder health, fertility, claw health, ketosis, and longevity. We used daily milk yield records from automatic milking systems of 67,025 lactations in the first parity from 498 herds in the Netherlands. We defined 3 traits related to the number of drops in milk yield using Student t-tests based on either a rolling average (drop rolling average) or a regression (drop regression) and the natural logarithm of the within-cow variance of milk yield (LnVar). Average milk yield was added to investigate the relationships between milk yield and these new traits. ASReml was used to estimate heritabilities, breeding values (EBV), and genetic correlations among these new traits and average milk yield. Approximate genetic correlations were calculated using correlations between EBV of the new traits and existing EBV for health and functional traits correcting for nonunity reliabilities using the Calo method. Partial genetic correlations controlling for persistency and average milk yield and relative contributions to reliability were calculated to investigate whether the new traits add new information to predict fertility, health, and longevity. Heritabilities were 0.08 for drop rolling average, 0.06 for drop regression, and 0.10 for LnVar. Approximate genetic correlations between the new traits and the existing health traits differed quite a bit, with the strongest correlations (?0.29 to ?0.52) between LnVar and udder health, ketosis, persistency, and longevity. This study shows that fluctuations in daily milk yield are heritable and that the variance of milk production is best among the 3 fluctuations traits tested to predict udder health, ketosis, and longevity. Using the residual variance of milk production instead of the raw variance is expected to further improve the trait to breed healthy, resilient, and long-lasting dairy cows.     

Estimating milk, fat, and protein lactation curves with a test day model.

Test day models were used to estimate lactation curves for milk, fat, protein, fat percentage, and protein percentage and to study the influence of age, season, and herd productivity on Holstein lactation curves. Random effects of lactation within herd and fixed effects of herd test date were absorbed. Fixed effects of cow's age on test day and either DIM (57 divisions) by parity (1, 2, greater than or equal to 3) class or season of calving (winter or summer) by DIM by parity class were estimated. Lactation curves for yield traits derived from DIM solutions were flatter for first versus later lactation, even without addition of age effects. Differences between lactation curves for the two seasons were slight, suggesting that most observed seasonal differences are caused by seasonal productivity accounted for by herd test date effects. At peak, winter calving cows yielded slightly more milk of similar fat percentage but of lower protein percentage than those calving in summer. Data were also partitioned into nine subsets based on rolling herd milk and fat percentage. Lactation curves for yield traits, but not percentage traits, varied with rolling herd milk. Lactation curves for fat yield and percentage varied with rolling herd fat percentage.     

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.     

Integration of Interbull's multiple across-country evaluation approach breeding values into the multiple-trait single-step random regression test-day genetic evaluation for yield traits of Australian Red breeds

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model.     

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ～2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from ?0.61 to ?0.41 and from ?0.55 to ?0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.     

Genetic and phenotypic correlations between milk coagulation properties, milk production traits, somatic cell count, casein content, and pH of milk

Genetic and phenotypic correlations between milk coagulation properties (MCP: coagulation time and curd firmness), milk yield, fat content, protein content, ln(somatic cell count) (SCS), casein content, and pH of milk and heritability of these traits were estimated from data consisting of milk samples of 4664 Finnish Ayrshire cows sired by 91 bulls. In addition, differences in average estimated breeding values (EBV) for the above traits between the cows with noncoagulating (NC) milk and those with milk that coagulated (CO samples) were examined. The estimations were carried out to study the possibilities of indirect genetic improvement of MCP by use of the above characteristics. The genetic and phenotypic correlations between MCP and the milk production traits were low or negligible. The genetic associations between desirable MCP and low SCS were rather strong (-0.45 to 0.29). Desirable MCP correlated both genetically and phenotypically with low pH of milk (-0.51 to 0.50). The rather high heritability estimates for curd firmness in different forms (0.22 to 0.39), and the wide variation in the proportion of daughters producing NC milk between the sires (0 to 47%) suggested that noncoagulation of milk is partly caused by additive genetic factors. Based on the genetic correlations between curd firmness and SCS and the high EBV for SCS obtained for the cows with NC-milk, it is possible that the loci causing noncoagulation of milk and increasing somatic cell count of milk are closely linked or partly the same. One means to genetically improve MCP and to reduce the occurrence of NC milk could thus be selection for low somatic cell count of milk.