Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r_auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and r_auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r_auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.     

Observational study on associations between resilience indicators based on daily milk yield in first lactation and lifetime profitability

Resilience is the ability of cows to be minimally affected by disturbances, such as pathogens, heat waves, and changes in feed quality, or to quickly recover. Obvious advantages of resilience are good animal welfare and easy and pleasant management for farmers. Furthermore, economic effects are also expected, but these remain to be determined. The goal of this study was to investigate the association between resilience and lifetime gross margin, using indicators of resilience calculated from fluctuations in daily milk yield using an observational study. Resilience indicators and lifetime gross margin were calculated for 1,325 cows from 21 herds. These cows were not alive anymore and, therefore, had complete lifetime data available for many traits. The resilience indicators were the natural log-transformed variance (LnVar) and the lag-1 autocorrelation (r_auto) of daily milk yield deviations from cow-specific lactation curves in parity 1. Good resilience is indicated by low LnVar (small yield response to disturbances) and low r_auto (quick yield recovery to baseline). Lifetime gross margin was calculated as the sum of all revenues minus the sum of all costs throughout life. Included revenues were from milk, calf value, and slaughter of the cow. Included costs were from feed, rearing, insemination, management around calving, disease treatments, and destruction in case of death on farm. Feed intake was unknown and, therefore, lifetime feed costs had to be estimated based on milk yield records. The association of each resilience indicator with lifetime gross margin, and also with the underlying revenues and costs, was investigated using analysis of covariance (ANCOVA) models. Mean daily milk yield in first lactation, herd, and year of birth were included as covariates and factors. Natural log-transformed variance had a significantly negative association with lifetime gross margin, which means that cows with stable milk yield (low LnVar, good resilience) in parity 1 generated on average a higher lifetime gross margin than cows that had the same milk yield level but with more fluctuations. The association with lifetime gross margin could be mainly attributed to higher lifetime milk revenues for cows with low LnVar, due to a longer lifespan. Unlike LnVar, r_auto was not significantly associated with lifetime gross margin or any of the underlying lifetime costs and revenues. However, it was significantly associated with yearly treatment costs, which is important for ease of management. In conclusion, the importance of resilience for total profit generated by a cow at the end of life was confirmed by the significant association of LnVar with lifetime gross margin, although effects of differences in feed efficiency between resilient and less resilient cows remain to be studied. The economic advantage can be mainly ascribed to benefits of long lifespan.     

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.     

Relationships between fertility and production in Holstein dairy cattle in different lactations

Record of Performance and Dairy Herd Improvement Corporation production records of Ontario Holstein cows were merged with breeding receipts of three Ontario AI units from September 1981 through December 1985. Relationships between fertility and production in the first three lactations were investigated for 97,368 daughters of 3806 sires in 22,768 herd-hear-seasons of calving. Fertility traits were days from calving to first insemination, number of inseminations per conception, and days open. Production traits were age and month of calving adjusted 305-d milk and fat yields and fat percentage. Multiple-trait maximum likelihood was used to estimate variances and covariances. Heritabilities for the first three lactations were .18, .18, and .19 for milk yield; .20, .19, and .19 for fat yield; and .58, .52, and .48 for fat percentage. Heritabilities of fertility traits ranged from .03 to .06. Genetic and phenotypic correlations between fertility and production traits in all three lactations were essentially 0. Genetic correlations between different lactation production traits ranged from .2 to .65. Repeatabilities of fertility traits ranged from .05 to .16 in different lactations. Repeatabilities for production traits in different lactations ranged from .51 to .77. Genetic and phenotypic correlations between fertility and production in the subsequent lactation and between production and subsequent lactation fertility were also very low or zero.     

Genetic and Phenotypic Relationships Between Lactation Cell Counts and Milk Yield and Composition of Holstein Cows

Monthly somatic cell count data were collected between February 1977 and February 1982 for Holstein cows in 928 herds enrolled on the Quebec Dairy Herd Analysis Service. The geometric mean of the log monthly cell counts was calculated for each lactation. Official lactation records for 305-day milk, fat, and protein yields, and fat and protein percents were obtained for same cows. There were 18,189 cows in first lactation representing 257 sires, 13,225 in second lactation representing 206 sires, and 8,683 in third lactation representing 151 sires. Heritabilities of yield traits and protein percent increased across three lactations. Heritability of fat percent was similar in first and third lactations but decreased slightly in second lactations. Heritability of lactation cell count was small, being least in second lactations. Genetic correlations between lactation cell count and yield traits were positive in first lactations, small and negative in second lactations, and small and positive in third lactations. Genetic correlations between lactation cell count and fat and protein contents were small in the three lactations. Phenotypic correlations between lactation cell count and production traits were small in each of the three lactations. Genetic correlations between yield traits in early lactation and lactation cell count in a subsequent lactation were positive. The genetic correlation between protein percent in an early lactation and cell count in a later lactation was large between first and second lactations, decreased between second and third lactation, and small between first and third lactations. Genetic correlations were small and negative for fat percent.     

Genetic analysis of milk production traits of polish black and white cattle using large-scale random regression test-day models

Genetic parameters for milk, fat, and protein yield and persistency in the first 3 lactations of Polish Black and White cattle were estimated. A multiple-lactation model was applied with random herd-test-day effect, fixed regressions for herd-year and age-season of calving, and random regressions for the additive genetic and permanent environmental effects. Three data sets with slightly different edits on minimal number of days in milk and the size of herd-year class were used. Each subset included more than 0.5 million test-day records and more than 58,000 cows. Estimates of covariance components and genetic parameters for each trait were obtained by Bayesian methods using the Gibbs sampler. Due to the large size and a good structure of the data, no differences in estimates were found when additional criteria for record selection were applied. More than 95% of the genetic variance for all traits and lactations was explained by the first 2 principal components, which were associated with the mean yield and lactation persistency. Heritabilities of 305-d milk yield in the first 3 lactations (0.18, 0.16, 0.17) were lower than those for fat (0.12, 0.11, 0.12) and protein (0.13, 0.14, 0.15). Estimates of daily heritabilities increased in general with days in milk for all traits and lactations, with no apparent abnormalities at the beginning or end of lactation. Genetic correlations between yields in different lactations ranged from 0.74 (fat yield in lactations 1 and 3) to 0.89 (milk yield in lactations 2 and 3). Persistency of lactation was defined as the linear regression coefficient of the lactation curve. Heritability of persistency increased with lactation number for all traits and genetic correlations between persistency in different lactations were smaller than those for 305d yield. Persistency was not genetically correlated with the total yield in lactation.     

Heritabilities and correlations among body condition scores, production traits, and reproductive performance

The objectives of this study were to estimate the heritability of body condition scores (BCS) from producer and consultant-recorded data and to describe the genetic and phenotypic relationships among BCS, production traits, and reproductive performance. Body condition scores were available at calving, postpartum, first service, pregnancy check, before dry off, and at dry off from the Dairy Records Management Systems in Raleigh, NC, through the PCDART program. Heritabilities, genetic correlations, and phenotypic correlations were estimated assuming an animal model using average information REML. Herd-year-season effects and age at calving were included in all models. Prior calving interval was included in models for second and third lactations. Analyses that included reproductive traits were conducted with and without mature equivalent milk as a covariable. Heritability estimates for BCS ranged from 0.09 at dry-off to 0.15 at postpartum in first lactation. Heritability estimates ranged from 0.07 before dry-off to 0.20 at pregnancy check in second lactation and from 0.08 before dry-off to 0.19 at first service in third lactation. Genetic correlations between adjacent BCS within first lactation were greater than 0.96 with the exception of calving and postpartum (0.74). In second lactation, adjacent genetic correlations were 1.0 with the exception of calving and postpartum (0.84). Genetic correlations across lactations were greater than 0.77. Phenotypic correlations between scoring periods were highest for adjacent scoring periods and when BCS was lowest. Phenotypic correlations were lower than genetic correlations, i.e., less than 0.70. Higher BCS during the lactation were negatively related to production, both genetically and phenotypically, but the relationship was moderate. Higher BCS were favorably related genetically to reproductive performance during the lactation.     

Genetic (co-)variation of methane emissions,efficiency, and production traits in Danish Holstein cattle along and across lactations

Methane emissions in ruminant livestock has become a hot topic, given the pressure to reduce greenhouse gas emissions drastically in the European Union over the next 10 to 30 yr. During the 2021 United Nations Climate Change conference, countries also made collective commitments to curb methane emissions by 2050. Genetic selection for low-methane-emitting animals, particularly dairy cows, is one possible strategy for mitigation. However, it is essential to understand how methane emissions in lactating animals vary along lactation and across lactations. This understanding is useful when making decisions for future phenotyping strategies, such as the frequency and duration of phenotyping within and across lactations. Therefore, the objectives of this study were to estimate (1) genetic parameters for 2 methane traits: methane concentration (MeC) and methane production (MeP) at 2 parity levels in Danish Holstein cows across the entire lactation using random regression models; (2) genetic correlations within and between methane traits across the entire lactation; and (3) genetic correlations between the methane traits and economically important traits throughout first lactation. Methane concentration (n = 19,639) records of 575 Danish Holstein cows from a research farm measured between 2013 and 2020 were available. Subsequently, CH₄ production in grams/day (MeP; n = 13,866) was calculated; MeP and MeC for first and second lactation (L1 and L2) were analyzed as separate traits: MeC_L1, MeP_L1, MeC_L2, and MeP_L2. Heritabilities, variance components, and genetic correlations within and between the 4 CH₄ traits were estimated using random regression models with Legendre polynomials. The additive genetic and permanent environmental effects were modeled using second-order Legendre polynomial for lactation weeks. Estimated heritabilities for MeP_L1 ranged between 0.11 and 0.49, for MeC_L1 between 0.10 and 0.28, for MeP_L2 between 0.14 and 0.36, and for MeC_L2 between 0.13 and 0.29. In general, heritability estimates of MeC traits were lower and more stable throughout lactation and were similar between lactations compared with MeP. Genetic correlations (within trait) at different lactation weeks were generally highly positive (0.7) for most of the first lactation, except for the correlation of early lactation (<10 wk) with late lactation (>40 wk) where the correlation was the lowest (<0.5). Genetic correlations between methane traits were moderate to highly correlated during early and mid lactation. Finally, MeP_L1 has stronger genetic correlations with energy-corrected milk and dry matter intake compared with MeC_L1. In conclusion, both traits are different along (and across) lactation(s) and they correlated differently with production, maintenance, and intake traits, which is important to consider when including one of them in a future breeding objective.     

Phenotypic and genetic statistics of components of milk and two measures of somatic cell concentration

Heritabilities, genotypic and phenotypic correlations for milk, fat, and protein yields, and two traits related to somatic cell concentration (cumulative lactation score and lactational somatic cell concentration) were estimated. A total of 18,416 first lactations of Holstein cows were analyzed by a new procedure for estimating variance components. Heritabilities were .21, .23, .19, .17, and .61 for milk, fat, and protein yields, cumulative lactation score, and lactational somatic cell concentration. Addition of protein yields to the current selection for two traits with nil economic value for protein would improve genetic gains for fat and milk yields in the northeastern United States. If cumulative lactation score and lactational somatic cell concentration were incorporated in current selection for two traits, restricted selection indexes should be used to avoid reduction in genetic gains for milk and fat yields.     

Estimation of genetic parameters for concentrations of milk urea nitrogen

The objective of this study was to use field data collected by dairy herd improvement programs to estimate genetic parameters for concentrations of milk urea nitrogen (MUN). Edited data were 36,074 test-day records of MUN and yields of milk, fat, and protein obtained from 6102 cows in Holstein herds in Ontario, Canada. Data were divided into three sets, for the first three lactations. Two analyses were performed on data from each lactation. The first procedure used ANOVA to estimate the significance of the effects of several environmental factors on MUN. Herd-test-day effects had the most significant impact on MUN. Effects of stage of lactation were also important, and MUN levels tended to increase from the time of peak yield until the end of lactation. The second analysis used a random regression model to estimate heritabilities and genetic correlations of MUN and the yield traits. Heritability estimates for MUN in lactations one, two, and three were 0.44, 0.59, and 0.48, respectively. Heritabilities for the yield traits were of a similar magnitude. Little relationship was observed between MUN and yield. Raw phenotypic correlations were all <0.10 (absolute value). Genetic correlations with production traits were close to zero in lactations one and three and only slightly positive in lactation two. The results indicate that selection on MUN is possible, but relationships between MUN and other economically important traits such as metabolic disease and fertility are needed.     

Milk urea nitrogen concentration: heritability and genetic correlations with reproductive performance and disease

The objectives of this study were to estimate the heritability of milk urea nitrogen (MUN) concentration and describe the genetic relationship between MUN and reproductive performance and between MUN and diseases in Holsteins. Dairy Records Management Systems (Raleigh, NC) provided lactation data. The Danish Agricultural Advisory Center provided breeding value estimates for diseases. Infrared (IR) and wet chemistry (WC) data were analyzed separately. Heritabilities and genetic correlations for 2 different measures of MUN and reproductive performance were estimated with an animal model using ASREML. Heritabilities for MUN were estimated using all lactations combined (lactations 1 through 5) and separately for first lactation and second lactation. Genetic correlations with reproduction and health were estimated separately for parities 1 and 2. Herd-test-day or herd-year-season along with age at calving and days in milk were included as fixed effects in all models. Heritability estimates for all lactations combined were 0.15 for WC MUN and 0.22 for IR MUN. Genetic correlations between WC MUN and 2 measures of reproductive performance, days to first service, and first service conception were not different from zero. In contrast, the genetic correlation between WC MUN and days open of 0.21 in first lactation and 0.41 in second lactation indicated that higher WC MUN values were associated with increased days open. Correlations among estimated breeding values for MUN and estimated breeding values for Danish diseases identified no significant relationships. Although the results of this study indicate that heritable variation for MUN exists, the inability to identify significant genetic relationships with several measures of disease or reproductive performance appears to limit the value of MUN in selection for disease resistance and improved reproduction.     

Genetic parameters for claw and leg health, foot and leg conformation, and locomotion in Danish Holsteins

The purpose of this study was to estimate the genetic correlations among claw and leg health and potential indicator traits. Claw health was defined as absence of heel horn erosion, interdigital dermatitis, interdigital phlegmon, interdigital hyperplasia, laminitis, and sole ulcer. Leg health was defined as absence of hock infection, swollen hock, and bruising. The potential indicators were locomotion and foot and leg conformation, represented by rear leg side view, rear leg rear view, foot angle, and apparent hock quality and bone structure. The study was conducted using records from 429,877 Danish Holstein cows in first lactation. Binary health traits were divided into 3 subcategories: claw health, leg health, and absence of all claw and leg disorders. Genetic (r_g) and phenotypic correlations were estimated using a bivariate linear sire model and REML. Estimated heritabilities were 0.01 for all 3 combined claw and leg health traits (on the observed binary scale), 0.09 for locomotion, 0.14 for rear leg rear view, 0.19 for rear leg side view, 0.13 for foot angle, 0.22 for apparent hock quality, and 0.27 for apparent bone structure. Heritabilities were 0.06 and 0.01 for claw health and leg health, respectively, when transformed to the underlying continuous scale. Claw and leg disorders are an increasing problem for Danish Holsteins, but genetic improvement of claw and leg health is challenging because the traits have low heritabilities. Claw and leg health were separate but correlated traits (r_g = 0.35). Locomotion and rear leg rear view were useful indicator traits for claw health (r_g = 0.46 and r_g = 0.21, respectively), whereas hock quality and bone structure were useful indicators for leg health (r_g = 0.42 and 0.26, respectively). Claw and leg health should be considered as separate traits in genetic evaluations that also include the useful indicator traits to compensate for low heritability of the health traits.     

Genetics of alternative definitions of feed efficiency in grazing lactating dairy cows

The objective of the present study was to estimate genetic parameters across lactation for measures of energy balance (EB) and a range of feed efficiency variables as well as to quantify the genetic inter-relationships between them. Net energy intake (NEI) from pasture and concentrate intake was estimated up to 8 times per lactation for 2,481 lactations from 1,274 Holstein-Friesian cows. A total of 8,134 individual feed intake measurements were used. Efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NEI minus predicted energy requirements [e.g., net energy of lactation (NE_L), maintenance, and body tissue anabolism] or supplied from body tissue mobilization; residual energy production was defined as the difference between actual NE_L and predicted NE_L based on NEI, maintenance, and body tissue anabolism/catabolism. Energy conversion efficiency was defined as NE_L divided by NEI. Random regression animal models were used to estimate residual, additive genetic, and permanent environmental (co)variances across lactation. Heritability across lactation stages varied from 0.03 to 0.36 for all efficiency traits. Within-trait genetic correlations tended to weaken as the interval between lactation stages compared lengthened for EB, REI, residual energy production, and NEI. Analysis of eigenvalues and associated eigenfunctions for EB and the efficiency traits indicate the ability to genetically alter the profile of these lactation curves to potentially improve dairy cow efficiency differently at different stages of lactation. Residual energy intake and EB were moderately to strongly genetically correlated with each other across lactation (genetic correlations ranged from 0.45 to 0.90), indicating that selection for lower REI alone (i.e., deemed efficient cows) would favor cows with a compromised energy status; nevertheless, selection for REI within a holistic breeding goal could be used to overcome such antagonisms. The smallest (8.90% of genetic variance) and middle (11.22% of genetic variance) eigenfunctions for REI changed sign during lactation, indicating the potential to alter the shape of the REI lactation profile. Results from the present study suggest exploitable genetic variation exists for a range of efficiency traits, and the magnitude of this variation is sufficiently large to justify consideration of the feed efficiency complex in future dairy breeding goals. Moreover, it is possible to alter the trajectories of the efficiency traits to suit a particular breeding objective, although this relies on very precise across-parity genetic parameter estimates, including genetic correlations with health and fertility traits (as well as other traits).     

Productive life including all lactations and longer lactations with diminishing credits

Alternative measures of productive life (PL) were compared, and life expectancy factors were updated to replace estimates from 1993. Alternatives were proposed with extra credits for lactations longer than 10 mo and beyond 84 mo of age and for each calving so that an extremely long lactation would not receive more credits than multiple shorter lactations with dry periods between. Maximum credits per lactation of 10 mo (original PL), 12 mo, and unlimited were compared. The unlimited credits option either included or excluded a calf value equal to 2 mo of production and had credits given for all days either uniformly or based on lactation curves (diminishing credits). Standard lactation curves (first, second, and greater lactations) were estimated based on the test-day yields of Holstein cows remaining in lactation from a set of 903,579 lactation records. For the diminishing credits alternative, credit for a given day of a parity was derived using the predicted yield of the day proportional to the average daily yield of the first 305 d of second parity. Daily yields were deviations from a baseline of 13.62 kg. Heritabilities and genetic correlations were estimated by multitrait REML for alternative measures of PL, for longevity censored at various ages, and for yield traits and SCS in first parity. Data for REML analysis included records from 1,098,329 Holsteins born from 1994 through 1997 from 5,109 sires, and a relationship matrix among sires was included in the model. Lactations beyond 84 mo added little information. Heritability of PL was 0.073 with 10 mo, 0.069 with 12 mo, 0.068 and 0.067 with unlimited (uniform) lactation credits (with and without calf credits, respectively), and 0.070 with unlimited diminishing credits. Corresponding correlations among predicted transmitting abilities for PL and protein yield were 0.07, 0.06, 0.12, 0.23, and 0.09, all much lower than the 0.46 estimated in 1993. Heritability of PL with diminishing credits improved from 0.017 to 0.070 when censoring age increased from 36 to 96 mo. There was no further increase in heritability beyond 96 mo. Genetic correlation with the final PL was 0.87 when PL was censored at 36 mo, but the estimate increased steadily with the censoring age. The PL with diminishing credits, which was favorable in both economic and genetic aspects, was desirable in crediting cows for complete lactations.     

Genetic analysis of clinical mastitis, milk fever, ketosis, and retained placenta in three lactations of Norwegian red cows

The objectives were to infer heritability and genetic correlations between clinical mastitis (CM), milk fever (MF), ketosis (KET), and retained placenta (RP) within and between the first 3 lactations and to estimate genetic change over time for these traits. Records of 372,227 daughters of 2411 Norwegian Red (NRF) sires were analyzed with a 12-variate (4 diseases × 3 lactations) threshold model. Within each lactation, absence or presence of each of the 4 diseases was scored based on the cow's health recordings. Each disease was assumed to be a different trait in each of the 3 lactations. The model for liability had trait-specific effects of year-season of calving and age of calving (first lactation) or month-year of calving and calving interval (second and third lactations), herd-5-yr, sire of the cow, and a residual. Posterior means of heritability of liability in first, second, and third lactations were 0.08, 0.07, and 0.07, respectively, for CM; 0.09, 0.11, and 0.13 for MF; 0.14, 0.16, and 0.15 for KET, and 0.08 in all 3 lactations for RP. Posterior means of genetic correlations between liability to CM, MF, KET, and RP, within disease between lactations, ranged from 0.19 to 0.86, and were highest between KET in different lactations. Correlations involving first lactation MF were low and had higher standard deviations. Genetic correlations between diseases were low or moderate (from −0.10 to 0.40), within as well as between lactations; the largest estimates were for MF and KET, and the lowest involved MF or KET and RP. Positive genetic correlations between diseases suggest that some general disease resistance factor with a genetic component exists. Trends of average sire posterior means by birth-year of daughters were used to assess genetic change, and the results indicated genetic improvement of resistance to CM and KET and no genetic change for MF and RP in the NRF population.     

Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection

The objective of the present study was to investigate the phenotypic inter- and intra-relationships within and among alternative feed efficiency metrics across different stages of lactation and parities; the expected effect of genetic selection for feed efficiency on the resulting phenotypic lactation profiles was also quantified. A total of 8,199 net energy intake (NE_I) test-day records from 2,505 lactations on 1,290 cows were used. Derived efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NE_I minus predicted energy requirements based on lactation performance; residual energy production (REP) was defined as net energy for lactation minus predicted energy requirements based on lactation performance. Energy conversion efficiency was defined as net energy for lactation divided by NE_I. Pearson phenotypic correlations among traits were computed across lactation stages and parities, and the significance of the differences was determined using the Fisher r-to-z transformation. Sources of variation in the feed efficiency metrics were investigated using linear mixed models, which included the fixed effects of contemporary group, breed, parity, stage of lactation, and the 2-way interaction of parity by stage of lactation. With the exception of REI, parity was associated with all efficiency and production traits. Stage of lactation, as well as the 2-way interaction of parity by stage of lactation, were associated with all efficiency and production traits. Phenotypic correlations among the efficiency and production traits differed not only by stage of lactation but also by parity. For example, the strong phenotypic correlation between REI and energy balance (EB; 0.89) for cows in parity 3 or greater and early lactation was weaker for parity 1 cows at the same lactation stage (0.81), suggesting primiparous cows use the ingested energy for both milk production and growth. Nonetheless, these strong phenotypic correlations between REI and EB suggested negative REI animals (i.e., more efficient) are also in more negative EB. These correlations were further supported when assessing the effect on phenotypic performance of animals genetically divergent for feed intake and efficiency based on parental average. Animals genetically selected to have lower REI resulted in cows who consumed less NE_I but were also in negative EB throughout the entire lactation. Nonetheless, such repercussions of negative EB do not imply that selection for negative REI (as defined here) should not be practiced, but instead should be undertaken within the framework of a balanced breeding objective, which includes traits such as reproduction and health.     

Genetic parameters for clinical mastitis, somatic cell score, and production in the first three lactations of Swedish holstein cows

Using a mixed linear animal model, genetic parameters were estimated for clinical mastitis (MAST), lactation average somatic cell score (LSCS), and milk production traits in the first 3 lactations of more than 200,000 Swedish Holstein cows with first calving from 1995 to 2000. Heritability estimates for MAST (0.01 to 0.03) were distinctly lower than those for LSCS (0.10 to 0.14) and production traits (0.23 to 0.36). The genetic correlation between MAST and LSCS was high for all lactations (mean 0.70), implying that selection for low LSCS will reduce the incidence of mastitis. Undesirable genetic relationships with production were found for both MAST and LSCS with genetic correlations ranging from 0.01 to 0.45. This emphasizes the need for including udder health traits in the breeding goal. Genetic correlations across lactations for the same trait were positive and high for both MAST (>0.7), LSCS (>0.8), and production traits (>0.9), with the strongest correlations between second and third parity for all traits (>0.9 for udder health traits and close to unity for production traits).     

Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level

Resilient cows are minimally affected in their functioning by disturbances, and if affected, they quickly recover. Previously, the variance and autocorrelation of daily deviations from a lactation curve were proposed as resilience indicators. These traits were heritable and genetically associated with good health and longevity. However, it was unknown if selection for these indicators would lead to desired changes in the phenotype. The first aim of this study was to investigate if forward prediction of the resilience indicators in another environment was possible. Therefore, the resilience indicator records were split into 2 subsets, each containing half of the daughters of each sire, split within sire into cows that calved in early year-seasons and cows that calved in more recent year-seasons. Genetic correlations between the subsets were then estimated for each resilience indicator. The second aim was to estimate genetic correlations between the resilience indicators and traits describing production responses to actual disturbances. The disturbances were a heat wave in July 2015 and yield disturbances at herd level. The latter were selected by decreases in mean yield of all primiparous cows in a herd, indicating that a disturbance occurred. The data set used for calculation of the resilience indicators and the traits describing yield responses contained 62,932,794 daily milk yield records on 199,104 primiparous cows. Genetic correlations (r_g) between recent and earlier daughter groups were 1 for both resilience indicators, which suggests that selection will result in changes in the phenotype in the next generation. Furthermore, low variance was genetically correlated with weak response in milk yield to both the heat wave and herd disturbances (r_g 0.47 to 0.97). Low autocorrelation was genetically correlated with reduced perturbation length and quick recovery after the heat wave and herd disturbances (0.28 to 0.97). These results suggest that variance and autocorrelation cover different aspects of resilience, and should be combined in a resilience index. In conclusion, genetic selection for the resilience indicators will likely result in favorable changes in the traits themselves, and in response and recovery to actual disturbances, which confirms that they are useful resilience indicators.     

Heritability of lactation cell count measures and their relationships with milk yield and composition in Ayrshire cows

Lactation measures of somatic cell count were calculated from monthly test-day observations (transformed to a log scale) taken between February 1977 and February 1981 in Ayrshire cows in 115 herds enrolled in the Quebec Dairy Herd Analysis Service. Analyses were separate within three groups: 1137 first lactations, representing 37 sires; 1728 second and later lactations, representing 57 sires; and 2510 all lactations, representing 74 sires. Heritabilities of lactation measures were estimated from sire and error variances obtained by iterative minimum norm quadratic unbiased estimation. Heritabilities ranged from .09 to .16 in first lactations and averaged .09 for the group of second and later lactations and .07 for all lactations. Genetic correlations of lactation measures of cell count with milk, fat, protein yield, fat percent, and protein percent averaged .36, .68, .74, .38, and .45, in first lactations; -.97, -.27, -.56, .52, and .03 in second and later lactations; and -.50, -.54, -.73, .43, and .19 in all lactations. Respective average phenotypic correlations were low and negative for milk, fat, protein yield, and fat percent and low and positive for protein percent.     

Genetics of parity-dependant production increase and its relationship with health, fertility, longevity, and conformation in swiss holsteins

Genetic analysis of production increase (ProdI), defined as an increase in production from early to later lactations, was conducted using data from the Holstein Association of Switzerland. This production increase describes the maturity rate of the cow. The data set contained 42,807 cows with a ProdI value. All cows had completed the first 3 lactations. Different formulas were derived for the computation of ProdI using 1) milk yields or energy-corrected milk yields and 2) yields from all 3 lactations or only 2 of them (first and second, first and third, second and third). Heritabilities of ProdI and genetic and phenotypic correlations of ProdI with somatic cell score, days to first service, nonreturn rate, longevity, and 27 conformation traits were estimated by univariate and bivariate sire models that included relationship among sires. Heritabilities for ProdI were low (0.06 to 0.08), but genetic variation among sires existed. For nonreturn rate and longevity, regressions on the sire estimated breeding values were estimated. Additive genetic correlations of ProdI were moderately favorable with somatic cell score (-0.22 to -0.33) and chest width (0.21 to 0.30), i.e., with traits often associated with long-lasting cows. Unfavorable correlations were found with angularity (-0.18 to -0.26). Regression coefficients from regressing ProdI on sire estimated breeding values for longevity tend to show favorable relationships between these 2 traits (0.10 to 0.20). Results show that animals can be selected for ProdI, as there is good genetic variation between bulls. ProdI is a potential trait to be included in selection indices, as it has favorable genetic relationships with economically important functional traits such as health, conformation, and longevity.