Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

A total of 19 376 test day (TD) milk yield records from the first three lactations of 1618 cows daughters of 162 sires were used to estimate genetic and phenotypic parameters and determine the relationship between daily milk yield and lactation milk yield in the Sahiwal cattle in Kenya. Variance components were estimated using animal models based on a derivative free restricted maximum likelihood procedure. Variance components were estimated using various univariate and multi-trait fixed regression test day models (TDM) that defined contemporary groups either based on the year-season of calving (YSCV) or on the year-season of TD milk sampling (YSTD). Variance components were influenced by CG which resulted in differences in heritability and repeatability estimates between TDM. Models considering YSTD resulted in higher additive genetic variances and lower residual variances compared with models in which YSCV was considered. Heritability estimates for daily yield ranged from 0.28 to 0.46, 0.38 to 0.52 and 0.33 to 0.52 in the first, second and third lactation, respectively. In the first and second lactation, the heritability estimates were highest between TD 2 and TD 4. Genetic correlations among daily milk yields ranged from 0.41 to 0.93, 0.50 to 0.83 and 0.43 to 86 in the first, second and third lactation, respectively. The phenotypic correlations were correspondingly lower. Genetic correlations were different from unit when fitting multi-trait TDM. Therefore, a multiple trait model would be more ideal in determining the genetic merit of dairy sires and bulls based on daily yield records. Genetic and phenotypic correlations between daily yield and lactation yields were high and positive. Genetic correlations ranged from 0.84 to 0.99, 0.94 to 1.00 and 0.94 to 0.97 in the first, second and third lactations, respectively. The corresponding phenotypic correlation estimates ranged from 0.50 to 0.85, 0.50 to 0.83 and 0.53 to 0.87. The high genetic correlation between daily yield and lactation yield imply that both traits are influenced by similar genes. Therefore daily yields records could be used in genetic evaluation in the Sahiwal cattle breeding programme.     

Genetic (co)variances between milk mineral concentration and chemical composition in lactating Holstein-Friesian dairy cows

Milk mineral concentration is important from both the perspective of processing milk into dairy products and its nutritive value for human consumption. Precise estimates of genetic parameters for milk mineral concentration are lacking because of the considerable resources required to collect vast phenotypes quantities. The milk concentration of calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and phosphorus (P) in the present study was quantified from mid-IR spectroscopy on 12 223 test-day records from 1717 Holstein-Friesian cows. (Co)variance components were estimated using random regressions to model both the additive genetic and within-lactation permanent environmental variances of each trait. The coefficient of genetic variation averaged across days-in-milk (DIM) was 6.93%, 3.46%, 6.55%, 5.20% and 6.68% for Ca, K, Mg, Na and P concentration, respectively; heritability estimates varied across lactation from 0.31±0.05 (5 DIM) to 0.67±0.04 (181 DIM) for Ca, from 0.18±0.03 (60 DIM) to 0.24±0.05 (305 DIM) for K, from 0.08±0.03 (15 DIM) to 0.37±0.03 (223 DIM) for Mg, from 0.16±0.03 (30 DIM) to 0.37±0.04 (305 DIM) for Na and from 0.21±0.04 (12 DIM) to 0.57±0.04 (211 DIM) for P. Genetic correlations within the same trait across different DIM were almost unity between adjacent DIM but weakened as the time interval between pairwise compared DIM lengthened; genetic correlations were weaker than 0.80 only when comparing both peripheries of the lactation. The analysis of the geometry of the additive genetic covariance matrix revealed that almost 90% of the additive genetic variation was accounted by the intercept term of the covariance functions for each trait. Milk protein concentration and mineral concentration were, in general, positively genetically correlated with each other across DIM, whereas milk fat concentration was positively genetically correlated throughout the entire lactation with Ca, K and Mg; the genetic correlation with fat concentration changed from negative to positive with Na and P at 243 DIM and 50 DIM, respectively. Genetic correlations between somatic cell score and Na ranged from 0.38±0.21 (5 DIM) to 0.79±0.18 (305 DIM). Exploitable genetic variation existed for all milk minerals, although many national breeding objectives are probably contributing to an indirect positive response to selection in milk mineral concentration.     

Genetic modelling of test day records in dairy sheep using orthogonal Legendre polynomials

Test day milk yields of three lactations in Sfakia sheep were analyzed fitting a random regression (RR) model, regressing on orthogonal polynomials of the stage of the lactation period, i.e. days in milk. Univariate (UV) and multivariate (MV) analyses were also performed for four stages of the lactation period, represented by average days in milk, i.e. 15, 45, 70 and 105 days, to compare estimates obtained from RR models with estimates from UV and MV analyses. The total number of test day records were 790, 1314 and 1041 obtained from 214, 342 and 303 ewes in the first, second and third lactation, respectively. Error variances and covariances between regression coefficients were estimated by restricted maximum likelihood. Models were compared using likelihood ratio tests (LRTs). Log likelihoods were not significantly reduced when the rank of the orthogonal Legendre polynomials (LPs) of lactation stage was reduced from 4 to 2 and homogenous variances for lactation stages within lactations were considered. Mean weighted heritability estimates with RR models were 0.19, 0.09 and 0.08 for first, second and third lactation, respectively. The respective estimates obtained from UV analyses were 0.14, 0.12 and 0.08, respectively. Mean permanent environmental variance, as a proportion of the total, was high at all stages and lactations ranging from 0.54 to 0.71. Within lactations, genetic and permanent environmental correlations between lactation stages were in the range from 0.36 to 0.99 and 0.76 to 0.99, respectively. Genetic parameters for additive genetic and permanent environmental effects obtained from RR models were different from those obtained from UV and MV analyses.     

Genetic parameters for milkability from the first three lactations in Fleckvieh cows

Test-day records for average flow rate (AFR) from the routine dairy recording from Bavarian Fleckvieh cows were analysed. Two data sets with observations on approximately 20 000 cows each were sampled from the total data set. For the estimation of variance parameters, a two-step approach was applied. In a first step multiple-trait restricted maximum likelihood (REML) analyses were carried out. For each of the first three lactations, six time periods with up to 33 days were defined. An algorithm for iterative summing of expanded part matrices was applied in order to combine the estimates. In a second step covariance functions (CF) for additive-genetic variances and non-genetic animal variances were derived using second-order Legendre polynomials plus an exponential term. Estimates of test-day heritability for AFR ranged from 0.21 to 0.40, and were largest in lactation 1. For lactations 1 and 3, heritabilities decreased considerably towards the end of lactation. Genetic correlation estimates within lactation decreased as the distance between days in milk (DIM) increased. Genetic correlations between corresponding DIM in the three lactations were generally large, ranging from 0.80 to 0.99. The largest estimates were found between DIM from lactations 2 and 3. Results from this study suggest that including AFR data from second and third lactations in genetic evaluation systems could the improve accuracy of genetic selection.     

Relationships between conception rate in Holstein heifers and cows and milk yield at various stages of lactation

We investigated the relationships between conception rates (CRs) at first service in Japanese Holstein heifers (i.e. animals that had not yet had their first calf) and cows and their test-day (TD) milk yields. Data included records of artificial insemination (AI) for heifers and cows that had calved for the first time between 2000 and 2008 and their TD milk yields at 6 through 305 days in milk (DIM) from first through third lactations. CR was defined as a binary trait for which first AI was a failure or success. A threshold-linear animal model was applied to estimate genetic correlations between CRs of heifers or cows and TD milk yield at various lactation stages. Two-trait genetic analyses were performed for every combination of CR and TD milk yield by using the Bayesian method with Gibbs sampling. The posterior means of the heritabilities of CR were 0.031 for heifers, 0.034 for first-lactation cows and 0.028 for second-lactation cows. Heritabilities for TD milk yield increased from 0.324 to 0.433 with increasing DIM but decreased slightly after 210 DIM during first lactation. These heritabilities from the second and third lactations were higher during late stages of lactation than during early stages. Posterior means of the genetic correlations between heifer CR and all TD yields were positive (range, 0.082 to 0.287), but those between CR of cows and milk yields during first or second lactation were negative (range, −0.121 to −0.250). Therefore, during every stage of lactation, selection in the direction of increasing milk yield may reduce CR in cows. The genetic relationships between CR and lactation curve shape were quite weak, because the genetic correlations between CR and TD milk yield were constant during the lactation period.     

Genetic correlations between production and disease traits during first lactation in Holstein cows

The aim of this study was to estimate genetic correlations between milk yield, somatic cell score (SCS), mastitis, and claw and leg disorders (CLDs) during first lactation in Holstein cows by using a threshold–linear random regression test-day model. We used daily records of milk, fat and protein yields; somatic cell count (SCC); and mastitis and CLD incidences from 46 771 first-lactation Holstein cows in Hokkaido, Japan, that calved between 2000 and 2009. A threshold animal model for binary records (mastitis and CLDs) and linear animal model for yield traits were applied in our multiple trait analysis. For both liabilities and yield traits, additive genetic effects were used as random regression on cubic Legendre polynomials of days on milk. The highest positive genetic correlations between yields and disease incidences (0.36 for milk and mastitis, 0.56 for fat and mastitis, 0.24 for protein and mastitis, 0.32 for milk and CLD, 0.44 for fat and CLD and 0.31 for protein and CLD) were estimated at about the time of peak milk yield (36 to 65 days in milk). Selection focused on early lactation yield may therefore increase the risk of mastitis and CLDs. The positive genetic correlations of SCS with mastitis or CLD incidence imply that selection to reduce SCS in the early stages of lactation would decrease the incidence of both mastitis and CLD.     

Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model

Genetic parameters were estimated with restricted maximum likelihood for individual test-day milk, fat, and protein yields and somatic cell scores with a random regression cubic spline model. Test-day records of Holstein cows that calved from 1994 through early 1999 were obtained from Dairy Records Management Systems in Raleigh, North Carolina, for the analysis. Estimates of heritability for individual test-days and estimates of genetic and phenotypic correlations between test-days were obtained from estimates of variances and covariances from the cubic spline analysis. Estimates were calculated of genetic parameters for the averages of the test days within each of the ten 30-day test intervals. The model included herd test-day, age at first calving, and bovine somatropin treatment as fixed factors. Cubic splines were fitted for the overall lactation curve and for random additive genetic and permanent environmental effects, with five predetermined knots or four intervals between days 0, 50, 135, 220, and 305. Estimates of heritability for lactation one ranged from 0.10 to 0.15, 0.06 to 0.10, 0.09 to 0.15, and 0.02 to 0.06 for test-day one to test-day 10 for milk, fat, and protein yields and somatic cell scores, respectively. Estimates of heritability were greater in lactations two and three. Estimates of heritability increased over the course of the lactation. Estimates of genetic and phenotypic correlations were smaller for test-days further apart.     

Genetic parameters of a biological lactation model: early lactation and secretion rate traits of dairy heifers

Early lactation parameters are difficult to estimate from commercial dairy records due to the small number of records available before the peak of production. A biological model of lactation was used with weekly milk records from a single Holstein herd to estimate these early lactation parameters and the secretion rate of milk from the average cell throughout lactation. A genetic analysis of the lactation curve parameters, calculated curve characteristics and secretion rate traits was undertaken. Early lactation traits were found to have little genetic variation and effectively zero heritability. Secretion rate traits for milk, protein, lactose and water were all moderately heritable and highly genetically correlated (>0.87) but fat secretion rate had lower genetic correlations with the other secretion rates. A similar pattern of correlations was seen between total lactation yield traits for fat, protein, lactose and water. The genetic correlations between the lactation curve traits and the secretion rate traits were calculated. Total milk yield, peak yield and maximum secretion potential were all highly correlated with milk, lactose and water secretion rates but less so with fat and protein secretion rates. In particular, fat secretion rate had a moderate to low genetic correlation with these lactation curve traits. Persistency of lactation was highly correlated with fat and protein secretion rates, more persistent lactations being associated with lower rates of secretion of these milk components. Similar levels of heritability were found, where trait genetic parameters were directly equivalent to those derived from the same dataset by random regression methods. However, by using a biological model of lactation to analyse lactation traits new insights into the biology of lactation are possible and ways to select cows on a range of lactation traits may be achieved.     

A note on the effect of variation of lactation length on the efficiency of tropical cattle selection for milk yield

Summary The effects of procedures generally used to reduce variation of lactation length on the efficiency of selection for milk yield are examined applying existing theory to a set of average literature estimates of heritabilities and correlations between lactation yield and length. Adjustment of milk yield for lactation length should be expected to remove more genetic than phenotypic variation, thus reducing selection efficiency in relation to unadjusted yield. Selecting individuals on an optimum index of lactation yield and length would be more efficient for improving yield than selecting on yield alone, while both criteria would have practically the same efficiency for selection on progeny test. This result could be applied to reduce milk recording frequency without losing selection accuracy. Culling on lactation length before selecting on yield would have little effect on individual selection efficiency. However, excluding short lactation records should be expected to reduce both selection accuracy of the progeny test and genetic variation in yield.     

Analysis of feed intake and energy balance of high-yielding first lactating Holstein cows with fixed and random regression models

At the dairy research farm Karkendamm, the individual roughage intake was measured since 1 September 2005 using a computerised scale system to estimate daily energy balances as the difference between energy intake and calculated energy requirements for lactation and maintenance. Data of 289 heifers with observations between the 11th and 180th day of lactation over a period of 487 days were analysed. Average energy-corrected milk yield, feed intake, live weight and energy balance were 31.8kg, 20.6kg, 584 kg and 13.6 MJ NEL (net energy lactation), respectively, per day. Fixed and random regression models were used to estimate repeatabilities, correlations between cow effects and genetic parameters. The resulting genetic correlations in different lactation stages demonstrate that feed intake and energy balance at the beginning and the middle of lactation are genetically different traits. Heritability of feed intake is low with h2=0.06 during the first days after parturition and increases in the middle of lactation, whereas the energy balance shows the highest heritability with h2=0.34 in the first 30 days of lactation. Genetic correlations between energy balance and feed intake and milk yield, respectively, illustrate that energy balance depends more on feed intake than on milk yield. Genetic correlation between body condition score and energy balance decreases rapidly within the first 100 days of lactation. Hence, to avoid negative effects on health and reproduction as consequences of strong energy deficits at the beginning of lactation, the energy balance itself should be measured and used as a selection criterion in this lactation stage. Since the number of animals is rather small for a genetic analysis, the genetic parameters have to be evaluated on a more comprehensive dataset.     

Effects of stage of pregnancy on variance components,daily milk yields and 305-day milk yield in Holstein cows,as estimated by using a test-day model

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.     

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Test-day milk yield and somatic cell count data over extended lactation (lactation to 540-600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.     

Bayesian segregation analysis of milk flow in Swiss dairy cattle using Gibbs sampling

Segregation analyses with Gibbs sampling were applied to investigate the mode of inheritance and to estimate the genetic parameters of milk flow of Swiss dairy cattle. The data consisted of 204 397, 655 989 and 40 242 lactation records of milk flow in Brown Swiss, Simmental and Holstein cattle, respectively (4 to 22 years). Separate genetic analyses of first and multiple lactations were carried out for each breed. The results show that genetic parameters especially polygenic variance and heritability of milk flow in the first lactation were very similar under both mixed inheritance (polygenes + major gene) and polygenic models. Segregation analyses yielded very low major gene variances which favour the polygenic determinism of milk flow. Heritabilities and repeatabilities of milk flow in both Brown Swiss and Simmental were high (0.44 to 0.48 and 0.54 to 0.59, respectively). The heritability of milk flow based on scores of milking ability in Holstein was intermediate (0.25). Variance components and heritabilities in the first lactation were slightly larger than those estimates for multiple lactations. The results suggest that milk flow (the quantity of milk per minute of milking) is a relevant measurement to characterise the cows milking ability which is a good candidate trait to be evaluated for a possible inclusion in the selection objectives in dairy cattle.     

Genotype by environment interaction due to heat stress during gestation and postpartum for milk production of Holstein cattle

Remarkable increases in the production of dairy animals have negatively impacted their tolerance to heat stress (HS). The evaluation of the effect of HS on milk yield is based on the direct impact of HS on performance. However, in practical terms, HS also exerts its influence during gestation (indirect effect). The main purpose of this study was to identify and characterize the genotype by environment interaction (G × E) due to HS during the last 60 days of gestation (THI_g) and also the HS postpartum (THI_m) over first lactation milk production of Brazilian Holstein cattle. A total of 389 127 test day milk yield (TD) records from 1572 first lactation Holstein cows born in Brazil (daughters of 1248 dams and 70 sires) and the corresponding temperature–humidity index (THI) obtained between December 2007 and January 2013 were analyzed using different random regression models. Cows in the cold environment (THI_g = 64 to 73) during the last 60 days of gestation produced more milk than those cows in a hot environment (THI_g = 74 to 84), particularly during the first 150 days of lactation (DIM). The heritabilities (h²) of TD were similar throughout DIM for cows in THI_g hot (0.11 to 0.20) or (0.10 to 0.22), while the genetic correlations (r_g) for TD between these two environments ranged from 0.11 to 0.52 along the first 250 DIM. The h² estimates for TD across THI_m were similar for cows in THI_g hot (0.07 to 0.25) and THI_g cold (0.08 to 0.19). The r_g estimates ranged from 0.17 to 0.42 along THI_m between TD of cows in cold and hot THI_g. The results were consistent in demonstrating the existence of an additional source of G × E for TD due to THI_g and THI_m. The present study is probably the first to provide evidence of this source of G × E; further research is needed because of its importance when the breeding objective is to select animals that are more tolerant to HS.     

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

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

Genetic parameters for test day milk yields of first lactation Holstein cows by random regression models

Covariance components for test day milk yield using 263 390 first lactation records of 32 448 Holstein cows were estimated using random regression animal models by restricted maximum likelihood. Three functions were used to adjust the lactation curve: the five-parameter logarithmic Ali and Schaeffer function (AS), the three-parameter exponential Wilmink function in its standard form (W) and in a modified form (W*), by reducing the range of covariate, and the combination of Legendre polynomial and W (LEG+W). Heterogeneous residual variance (RV) for different classes (4 and 29) of days in milk was considered in adjusting the functions. Estimates of RV were quite similar, rating from 4.15 to 5.29 kg². Heritability estimates for AS (0.29 to 0.42), LEG+W (0.28 to 0.42) and W* (0.33 to 0.40) were similar, but heritability estimates used W (0.25 to 0.65) were highest than those estimated by the other functions, particularly at the end of lactation. Genetic correlations between milk yield on consecutive test days were close to unity, but decreased as the interval between test days increased. The AS function with homogeneous RV model had the best fit among those evaluated.     

Genetics of heat tolerance for milk yield and quality in Holsteins

Tropical and sub-tropical climates are characterized by high temperature and humidity, during at least part of the year. Consequently, heat stress is common in Holstein cattle and productive and reproductive losses are frequent. Our objectives were as follows: (1) to quantify losses in production and quality of milk due to heat stress; (2) to estimate genetic correlations within and between milk yield (MY) and milk quality traits; and (3) to evaluate the trends of genetic components of tolerance to heat stress in multiple lactations of Brazilian Holstein cows. Thus, nine analyses using two-trait random regression animal models were carried out to estimate variance components and genetic parameters over temperature–humidity index (THI) values for MY and milk quality traits (three lactations: MY×fat percentage (F%), MY×protein percentage (P%) and MY×somatic cell score (SCS)) of Brazilian Holstein cattle. It was demonstrated that the effects of heat stress can be harmful for traits related to milk production and milk quality of Holstein cattle even though most herds were maintained in a modified environment, for example, with fans and sprinklers. For MY, the effect of heat stress was more detrimental in advanced lactations (−0.22 to −0.52 kg/day per increase of 1 THI unit). In general, the mean heritability estimates were higher for lower THI values and longer days in milk for all traits. In contrast, the heritability estimates for SCS increased with increasing THI values in the second and third lactation. For each trait studied, lower genetic correlations (different from unity) were observed between opposite extremes of THI (THI 47 v. THI 80) and in advanced lactations. The genetic correlations between MY and milk quality trait varied across the THI scale and lactations. The genotype×environment interaction due to heat stress was more important for MY and SCS, particularly in advanced lactations, and can affect the genetic relationship between MY and milk quality traits. Selection for higher MY, F% or P% may result in a poor response of the animals to heat stress, as a genetic antagonism was observed between the general production level and specific ability to respond to heat stress for these traits. Genetic trends confirm the adverse responses in the genetic components of heat stress over the years for milk production and quality. Consequently, the selection of Holstein cattle raised in modified environments in both tropical and sub-tropical regions should take into consideration the genetic variation in heat stress.     

Heritability and repeatability of milk coagulation properties predicted by mid-infrared spectroscopy during routine data recording,and their relationships with milk yield and quality traits

The aim of this study was to estimate (co)variance components for milk coagulation properties (MCP) predicted by mid-infrared spectroscopy (MIRS) during routine milk recording, and to assess their relationships with yield and quality traits. A total of 63 470 milk samples from Holstein-Friesian cows were analyzed for MCP, pH and quality characteristics using MIRS. Casein to protein and protein to fat ratios were calculated from information obtained by MIRS. Records were collected across 1 year on 16 089 cows in 345 herds. The model used for genetic analysis included fixed effects of parity and stage of lactation, and random effects of herd-test-day, cow permanent environmental, animal additive genetic and residual. (Co)variance components were assessed in a Bayesian framework using the Gibbs Sampler. Estimates of heritabilities were consistent with those reported in the literature, being moderate for MCP (0.210 and 0.238 for rennet coagulation time (RCT) and curd firmness (a₃₀), respectively), milk contents (0.213 to 0.333) and pH (0.262), and low for somatic cell score (0.093) and yield traits (0.098 to 0.130). Repeatabilities were 0.391 and 0.434 for RCT and a₃₀, respectively, and genetic correlations were generally low, with estimates greater than 0.30 (in absolute value) only for a₃₀ with fat, protein and casein contents. Overall, results suggest that genetic evaluation for MCP predicted by MIRS is feasible at population level, and several repeated measures per cow during a lactation are required to estimate reliable breeding values for coagulation traits.     

Analysis of lactation shapes in extended lactations

In order to describe the temporal evolution of milk yield (MY) and composition in extended lactations, 21 658 lactations of Italian Holstein cows were analyzed. Six empirical mathematical models currently used to fit 305 standard lactations (Wood, Wilmink, Legendre, Ali and Schaeffer, quadratic and cubic splines) and one function developed specifically for extended lactations (a modification of the Dijkstra model) were tested to identify a suitable function for describing patterns until 1000 days in milk (DIM). Comparison was performed on individual patterns and on average curves grouped according to parity (primiparous and multiparous) and lactation length (standard ⩽305 days, and extended from 600 to 1000 days). For average patterns, polynomial models showed better fitting performances when compared with the three or four parameters models. However, LEG and spline regression, showed poor prediction ability at the extremes of the lactation trajectory. The Ali and Schaeffer polynomial and Dijkstra function were effective in modelling average curves for MY and protein percentage, whereas a reduced fitting ability was observed for fat percentage and somatic cell score. When individual patterns were fitted, polynomial models outperformed nonlinear functions. No detectable differences were observed between standard and extended patterns in the initial phase of lactation, with similar values of peak production and time at peak. A considerable difference in persistency was observed between 200 and 305 DIM. Such a difference resulted in an estimated difference between standard and extended cycle of about 7 and 9 kg/day for daily yield at 305 DIM and of 463 and 677 kg of cumulated milk production at 305 DIM for the first- and second-parity groups, respectively. For first and later lactation animals, peak yield estimates were nearly 31 and 38 kg, respectively, and occurred at around 65 and 40 days. The asymptotic level of production was around 9 kg for multiparous cows, whereas the estimate was negative for first parity.     

Data transformation for rank reduction in multi-trait MACE model for international bull comparison

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations.