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.     

Comparison of non-linear models to describe the lactation curves for milk yield and composition in buffaloes (Bubalus bubalis)

In order to describe the lactation curves of milk yield (MY) and composition in buffaloes, seven non-linear mathematical equations (Wood, Dhanoa, Sikka, Nelder, Brody, Dijkstra and Rook) were used. Data were 116 117 test-day records for MY, fat (FP) and protein (PP) percentages of milk from the first three lactations of buffaloes which were collected from 893 herds in the period from 1992 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly production records of dairy buffaloes using the NLIN and MODEL procedures in SAS and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination root means square error (RMSE), Durbin–Watson statistic and Akaike’s information criterion (AIC). The Dijkstra model provided the best fit of MY and PP of milk for the first three parities of buffaloes due to the lower values of RMSE and AIC than other models. For the first-parity buffaloes, Sikka and Brody models provided the best fit of FP, but for the second- and third-parity buffaloes, Sikka model and Brody equation provided the best fit of lactation curve for FP, respectively. The results of this study showed that the Wood and Dhanoa equations were able to estimate the time to the peak MY more accurately than the other equations. In addition, Nelder and Dijkstra equations were able to estimate the peak time at second and third parities more accurately than other equations, respectively. Brody function provided more accurate predictions of peak MY over the first three parities of buffaloes. There was generally a positive relationship between 305-day MY and persistency measures and also between peak yield and 305-day MY, calculated by different models, within each lactation in the current study. Overall, evaluation of the different equations used in the current study indicated the potential of the non-linear models for fitting monthly productive records of buffaloes.     

Modelling of lactation curves of dairy cows based on monthly test day milk yield records under inconsistent milk recording scenarios

The objective of this study was to describe the lactation curve of dairy cattle in Kenya using a suitable lactation function in order to facilitate inclusion of partial lactations in national dairy cattle evaluation and to assess the effect of data characteristics on lactation curve parameters. Six functions were fitted to test day (TD) milk yield records from six parities of Ayrshire, Guernsey, Holstein Friesian, Jersey and Sahiwal cattle. Five datasets: DS-1 (12-TD dataset with randomly missing records), DS-2 (10-TD dataset without missing records), DS-3 (10-TD dataset with randomly missing records), DS-4 (7-TD dataset, with only TD 4 to 10 records) and DS-5 (7-TD dataset, with TD 1 to 4, 6, 8 and 10 records) depicting various recording circumstances were derived to assess the effects of data characteristics on lactation curves and to assess the feasibility of reducing the number of TD samples per lactation. The fit of the functions was evaluated using adjusted R(2) and their predictive abilities were compared using mean square prediction error, percentage of squared bias and the correlation between the predicted and actual milk yield. These criteria plus the changes in the parameters of curve functions and their associated standard errors were used in determining the effects of data characteristics on lactation curves. The mechanistic functions of Dijkstra (DIJ) and Pollott (APOL), and the incomplete gamma function of Wood (WD) had the highest adjusted R(2) > 0.75. The APOL function was eliminated due to convergence failures when analysis of individual lactations within breeds was carried out. Both DIJ and WD had good predictive ability, although DIJ performed slightly better. Convergence difficulties were noted in some DIJ analysis where data were limiting. Missing records, especially at the beginning of a lactation, greatly influenced parameters a and b of the functions. It also resulted in estimates with large standard errors. Missing records in later lactation hardly affected the parameter estimates. The WD and DIJ functions showed superior fit to the data. The WD function demonstrated higher adaptability to various data characteristics than DIJ and could be used in situations where animal recording is not consistently practised and where recording of animal performance is routinely practised. DIJ function had high data requirements, which restricts it to dairy systems with consistent recording, despite easy physiological interpretation of its parameters. The number of TD per lactation could be reduced by minimising sampling frequency in the later lactation while maintaining the monthly sampling frequency in early lactation.     

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.     

The effect of breed and parity on curves of body condition during lactation estimated using a non-linear function

The objectives of this study were to see if the body condition score curve during lactation could be described using a model amenable to biological interpretation, a non-linear function assuming exponential rates of change in body condition with time, and to quantify the effect of breed and parity on curves of body condition during lactation. Three breeds were represented: Danish Holstein (n = 112), Danish Red (n = 97) and Jerseys (n = 8). Cows entered the experiment at the start of first lactation and were studied during consecutive lactations (average number of lactations 2, minimum 1, maximum 3). They remained on the same dietary treatment throughout. Body condition was scored to the nearest half unit on the Danish scale (see Kristensen (1986); derived from the Lowman et al. (1976) system) from 1 to 5 on days: 2, 14, 28, 42, 56, 84, 112, 168, 224 after calving. Additionally, condition score was recorded on the day of drying off the cow, 35, 21, and 7 days before expected calving and finally on the day of calving. All condition scores were made by the trained personal on the research farm, where the same person made 92% of the scores. The temporal patterns in condition score were modelled as consisting of two underlying processes, one related to days from calving, referred to as lactation only, the other to days from (subsequent) conception, referred to as pregnancy. Both processes were assumed to be exponential functions of time. Each process was modelled separately using exponential functions, i.e. one model for lactation only and one for pregnancy, and then a combined model for both lactation only and pregnancy was fitted. The data set contained 467 lactation periods and 378 pregnancy periods. The temporal patterns in condition score of cows kept under stable and sufficient nutritional conditions were successfully described using a two component non-linear function. First lactation cows had shallower curves, they had greater condition scores at the nadir of the curve. Danish Holstein and Jersey were thinner at the end of the mobilisation period having lost more body condition than the Danish Red breed. Although the dairy breeds ended up being thinner there were no significant differences in the rate at which they lost body condition.     

Estimation of genetic parameters for test day records of dairy traits in the first three lactations

Application of test-day models for the genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (co)variance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (co)variance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate genetic parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (co)variance matrices for the test-day model and can provide consistent genetic parameters.     

Effect of different feeding strategies on lactation performance of Holstein and Normande dairy cows

The dairy farming systems of Western Europe are based on a simple feeding system composed of grazed and preserved grass, maize silage and concentrates in variable proportions. There is, nevertheless, a great diversity of feeding strategies between dairy farms. Over 5 years, we studied the direct and delayed effects of four feeding strategies on the lactation and reproduction performances of Holstein and Normande dairy cows. The four feeding strategies (denoted Hh, Hl, Lh and Ll) correspond to two total mixed rations applied in winter from calving to turnout (maize silage with 30% concentrate or grass silage with 15% concentrate), which were subsequently crossed with two levels of concentrate supplementation at grazing to 210 days. Each year, 72 dairy cows managed in grouped winter calving were assigned to the four strategies. Finally, the results of 325 lactations and 295 inseminated cows were analysed. The four strategies resulted in considerable variation in nutrient intake and, in particular, in differences in concentrates consumed, with values of 1407, 1026, 773 and 392 kg dry matter per cow for strategies Hh, Lh, Hl and Ll, respectively. Total milk production (7567, 7015, 6720 and 6238 kg per cow for treatments Hh, Lh, Hl and Ll, respectively), milk fat content (39.0, 37.1, 40.3 and 38.5 g/kg, respectively), milk protein content (33.0, 31.8, 33.1 and 31.6 g/kg, respectively), and the character of the lactation and body condition curves were all highly sensitive to the strategies applied. While no significant interaction was detected on total lactation yield, the Holstein cows reacted more dramatically to each dietary change at each period, compared with the Normande cows. Winter feeding did not affect the production of milk at pasture whereas, at pasture, the milk from the cows of the H groups in winter was higher in milk fat and protein content. Reproduction performance was unaffected by feeding strategy. The Holstein cows, well fed and producing the most milk (Hh and Hb), had the lowest rate of success at first artificial inseminations (21.5%). The dual-purpose Normande cows had a pregnancy rate 10 points higher than Holstein cows. This comparison of strongly contrasting feeding strategies confirms the immediate reactivity of dairy cows (in terms of milk performance and body condition) to variations of nutritive intake throughout lactation, with a weak carryover effect from feeding levels early in lactation. In contrast, reproduction performance was less sensitive to variation in nutrient supply.     

Profile and genetic parameters of dairy cattle locomotion score and lameness across lactation

This study investigated the profile of locomotion score and lameness before the first calving and throughout the first (n=237) and second (n=66) lactation of 303 Holstein cows raised on a commercial farm. Weekly heritability estimates of locomotion score and lameness, and their genetic and phenotypic correlations with milk yield, body condition score, BW and reproduction traits were derived. Daughter future locomotion score and lameness predictions from their sires’ breeding values for conformation traits were also calculated. First-lactation cows were monitored weekly from 6 weeks before calving to the end of lactation. Second-lactation cows were monitored weekly throughout lactation. Cows were locomotion scored on a scale from one (sound) to five (severely lame); a score greater than or equal to two defined presence of lameness. Cows’ weekly body condition score and BW was also recorded. These records were matched to corresponding milk yield records, where the latter were 7-day averages on the week of inspection. The total number of repeated records amounted to 12 221. Data were also matched to the farm’s reproduction database, from which five traits were derived. Statistical analyses were based on uni- and bivariate random regression models. The profile analysis showed that locomotion and lameness problems in first lactation were fewer before and immediately after calving, and increased as lactation progressed. The profile of the two traits remained relatively constant across the second lactation. Highest heritability estimates were observed in the weeks before first calving (0.66 for locomotion score and 0.54 for lameness). Statistically significant genetic correlations were found for first lactation weekly locomotion score and lameness with body condition score, ranging from −0.31 to −0.65 and from −0.44 to −0.76, respectively, suggesting that cows genetically pre-disposed for high body condition score have fewer locomotion and lameness issues. Negative (favourable) phenotypic correlations between first lactation weekly locomotion score/lameness and milk yield averaged −0.27 and −0.17, respectively, and were attributed to management factors. Also a phenotypic correlation between lameness and conception rate of −0.19 indicated that lame cows were associated with lower success at conceiving. First-lactation daughter locomotion score and/or lameness predictions from sires’ estimated breeding values for conformation traits revealed a significant linear effect of rear leg side view, rear leg rear view, overall conformation, body condition score and locomotion, and a quadratic effect of foot angle.     

Effects of feeding propylene glycol on dry matter intake, lactation performance, energy balance and blood metabolites in early lactation dairy cows

The objectives of this study were to evaluate effects of feeding propylene glycol (PG) on feed intake, milk yield and milk composition, blood metabolites and energy balance in Holstein dairy cows from 1 to 63 days in milk. Thirty-two multiparous cows, blocked by lactation number, previous 305-day milk production and expected calving date, were arranged into four groups in a randomized block design. Treatments were: control, low PG, medium PG and high PG with 0, 150, 300 and 450 ml PG per cow per day, respectively. The supplement of food grade PG (0.998 g/g PG) was hand-mixed into the top one-third of the daily ration. Cows were fed ad libitum a total mixed ration consisting of forage and concentrate (50 : 50, dry matter basis). Feed intake, milk yield and milk components were not affected (P > 0.05) by PG supplementation. Overall, body weight (BW) loss tended (P < 0.08) to be linearly reduced, and energy status was linearly improved with increasing PG supplementation. Concentrations of glucose in plasma were higher for cows fed PG relative to control (55.6 v. 58.9 mg/dl) and linearly increased (P < 0.01) with increasing PG supplementation. Plasma concentrations of non-esterified fatty acids and beta-hydroxybutyrate were linearly increased, but urine acetoacetate concentration was quadratically changed with the highest for control diet and the lowest for 450 ml/day of PG. These results indicated that supplementation of PG in the early lactating cow diets had minimal effects on feed intake and milk production, but may potentially reduce contents of milk fat and milk protein. Supplementation of early lactating dairy cow diets with PG is beneficial in terms of improving energy status and reducing BW loss.     

The effects of energy concentration in roughage and allowance of concentrates on performance,health and energy efficiency of pluriparous dairy cows during early lactation

The aim of this study was to investigate the effects of different energy supplies from roughage and concentrates on performance, health and energy efficiency during early lactation. For this purpose an experiment was conducted containing 64 pluriparous German Holstein cows from 3 weeks prepartum until 16 weeks postpartum. During dry period all cows received an equal dry cow ration. After calving, cows were assigned in a 2 × 2 factorial arrangement to one of four groups, receiving either a moderate (MR, 6.0 MJ NE_L) or a high (HR, 6.4 MJ NE_L) energy concentration in roughage and furthermore moderate (MC, 150 g/kg energy-corrected milk (ECM)) or high amounts of concentrates (HC, 250 g/kg ECM) on dry matter (DM) basis, which were allocated from an automatic feeding system. Higher allocation of concentrates resulted in an increase of DM intake at expense of roughage intake. HC cows had a higher milk yield than MC cows, whereas ECM was higher in HR cows due to a decrease of milk fat yield in MR groups. Energy balance and body condition score were elevated in HC cows, but no differences occurred in development of subclinical ketosis. Furthermore, energy efficiency variables were lower in HC groups because the greater energy intake was not associated with a considerable elevation of milk yield. Consistency of faeces did not indicate digestive disorders in any of the treatment groups although the faecal manure score was significantly lower in HR groups. Our results underline the importance of a high energy uptake from roughage, which can contribute to an adequate performance and beneficial efficiency, especially at lower amounts of concentrates in ration. Feeding concentrates on an average amount of 9.4 kg/d compared to 6.4 kg/d on DM basis improved the energy balance in our trial, but without consequences for metabolic blood variables and general health of the cows.     

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.     

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100 to 350 animals, with lactation records dating back to 1968. Different types of GEM were compared, namely: (1) multivariate v. repeatability model when using the first three lactations, (2) an animal v. a sire model, (3) different fixed effects models to account for effects such as herd, year and season; and (4) fitting a model with genetic parameters fixed v. estimating the genetic parameters as part of the model fitting process. Two methods were used for the comparison of models. The first method used simulated data based on the Pakistani progeny testing system and compared estimated breeding values with true breeding values. The second method used cross-validation to determine the best model in subsets of actual Australian herd-recorded data. Subsets were chosen to reflect the Pakistani data in terms of herd size and number of herds. Based on the simulation and the cross-validation method, the multivariate animal model using fixed genetic parameters was generally the superior GEM, but problems arise in determining suitable values for fixing the parameters. Using mean square error of prediction, the best fixed effects structure could not be conclusively determined. The simulation method indicated the simplest fixed effects structure to be superior whereas in contrast, the cross-validation method on actual data concluded that the most complex one was the best. In conclusion it is difficult to propose a universally best GEM that can be used in any data set of this size. However, some general recommendations are that it is more appropriate to estimate the genetic parameters when evaluating for selection purposes, the animal model was superior to the sire model and that in the Pakistani situation the repeatability model is more suitable than a multivariate.     

Random regression models for genetic evaluation of clinical mastitis in dairy cattle

A genetic analysis of longitudinal binary clinical mastitis (CM) data recorded on about 90 000 first-lactation Swedish Holstein cows was carried out using linear random regression models (RRM). This method for genetic evaluation of CM has theoretical advantages compared to the method of linear cross-sectional models (CSM), which is currently being used. The aim of this study was to investigate the feasibility and suitability of estimating genetic parameters and predicting breeding values for CM with a linear sire RRM. For validation purposes, the estimates and predictions from the RRM were compared to those from linear sire longitudinal multivariate models (LMVM) and CSM. For each cow, the period from 10 days before to 241 days after calving was divided into four 1-week intervals followed by eight 4-week intervals. Within each interval, presence or absence of CM was scored as '1' or '0'. The linear RRM used to explain the trajectory of CM over time included a set of explanatory variables plus a third-order Legendre polynomial function of time for the sire effect. The time-dependent heritabilities and genetic correlations from the chosen RRM corresponded fairly well with estimates obtained from the linear LMVM for the separate intervals. Some discrepancy between the two methods was observed, with the more unstable results being obtained from the linear LMVM. Both methods indicated clearly that CM was not genetically the same trait throughout lactation. The correlations between predicted sire breeding values from the RRM, summarized over different time periods, and from linear CSM were rather high. They were, however, less than unity (0.74 to 0.96), which indicated some re-ranking of sires. Sire curves based on the time-specific breeding values from the RRM illustrated differences in intercept and slope among the best and the worst sires. To conclude, a linear sire RRM seemed to work well for genetic evaluation purposes, but was sensitive for estimation of genetic parameters.     

Development and evaluation of the herd dynamic milk model with focus on the individual cow component

The herd dynamic milk (HDM) model is a dynamic model capable of simulating the performance of individual dairy animals (from birth to death), with a daily time step. Within this study, the HDM model is described and evaluated in relation to milk production, body condition score (BCS) and BCS change throughout lactation by comparing model simulations against data from published experimental studies. The model’s response to variation in genetic potential, herbage allowance and concentrate supplementation was tested in a sensitivity analysis. Data from experiments in Ireland and France over a 3-year period (2009–11) were used to complete the evaluation. The aim of the Irish experiment was to determine the impact of different stocking rates (SRs) (SR1: 3.28 cow/ha, SR2: 2.51 cow/ha) on key physical, biological and economic performance. The aim of the French experiment was to evaluate over a prolonged time period, the ability of two breeds of dairy cows (Holstein and Normande) to produce and to reproduce under two feeding strategies (high level and low level) in the context of compact calving. The model evaluation was conducted at the herd level with separate evaluations for the primiparous and multiparous cows. The evaluation included the two extreme SRs for the Irish experiment, and an evaluation at the overall herd and individual animal level for the different breeds and feeding levels for the French data. The comparison of simulation and experimental data for all scenarios resulted in a relative prediction error, which was consistently <15% across experiments for weekly milk production and BCS. In relation to BCS, the highest root mean square error was 0.27 points of BCS, which arose for Holstein cows in the low feeding group in late lactation. The model responded in a realistic fashion to variation in genetic potential for milk production, herbage allowance and concentrate supplementation.     

Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle

Phenotypic variation in milk production traits has been described over the course of a lactation as well as between different parities. The objective of this study was to investigate whether variation in production is affected by different loci across lactations. A genome-wide association study (GWAS) using a 50-k SNP chip was conducted in 152 divergent German Holstein Friesian cows to test for association with milk production traits over different lactations. The first four lactations were analysed regarding milk yield, fat, protein, lactose, milk urea nitrogen yield and content as well as somatic cell score. Two approaches were used: (i) Wilmink curve parameters were used to assess the genetic effects over the course of a lactation and (ii) test-day yield deviations (YD) were used as a normative approach for a GWAS. The significant effects were largest for markers affecting curve parameters for which there was a statistical power <0.8 of detection even in this small design. While significant markers for YDs were detected in this study, the power to detect effects of a similar magnitude was only 0.11, suggesting that many loci may have been missed with this approach in the present design. Furthermore, all significant effects were specific for a single lactation, leading to the conclusion that the variance explained by a certain locus changes from lactation to lactation. We confirm the common evidence that most production traits vary in the degree of persistency after the peak as a result of genetic influence.     

An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends (Open Access publication)

In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.     

Environmental and genetic variation factors of artificial insemination success in French dairy sheep

Artificial inseminations (n = 678 168) recorded during 5 years in five French artificial insemination (AI) centres (2 'Lacaune', 1 'Manech tête rousse', 1 'Manech tête noire' and 1 'Basco béarnaise') were analysed to determine environmental and genetic factors affecting the insemination results. Analyses within centre-breed were performed using a linear model, which jointly estimates male and female fertility. This model combined four categories of data: the environmental effects related to the female, those related to the male, the non-sex-specific effects and finally the pedigree data of these males and females. After selection, the environmental female effects considered were age, synchronisation (0/1) on the previous year, total number of synchronisations during the female reproductive life, time interval between previous lambing and insemination, already dry or still lactating (0/1) when inseminated, and milk quantity produced during the previous year expressed as quartiles intra herd * year. The environmental male effects were motility and concentration of the semen. The non-sex-specific effects were the inseminator, the interaction herd * year nested within the inseminator, considered as random effects and the interaction year * season considered as a fixed effect. The main variation factors of AI success were relative to non-sex-specific effects and to female effects. Heritability estimates varied from 0.001 to 0.005 for male fertility and from 0.040 to 0.078 for female fertility. Repeatability estimates varied from 0.007 to 0.015 for male fertility and from 0.104 to 0.136 for female fertility. These parameters indicate that genetic improvement of AI results through a classical polygenic selection would be difficult. Moreover, in spite of the large quantity of variation factors fitted by the joint model, a very large residual variance remained unexplained.     

High- and low-purity glycerine supplementation to dairy cows in early lactation: effects on silage intake,milk production and metabolism

This study evaluated the effects of supplemental low- and high-purity glycerine on silage intake, milk yield and composition, plasma metabolites and body condition score (BCS) in dairy cows. A total of 42 cows of the Swedish Red Breed, housed in individual tie stalls, were fed 0.25 kg of low- or high-purity glycerine on top of concentrate, twice daily, during the first 4 weeks of lactation. One-third of the cows acted as controls, receiving no glycerine. Silage was fed for ad libitum intake and concentrate was fed at restricted level of intake, about 6 kg/day for primiparous cows and 7 kg/day for multiparous cows. Feed refusals were weighed daily. Cows were milked twice daily, milk yield was recorded on four occasions per week and milk samples were collected simultaneously. Blood samples were drawn from the coccygeal vessel once a week. Low- and high-purity glycerine had no effect on silage or total dry matter intake (P = 0.38 and P = 0.75, respectively) or on BCS (P = 0.45). Cows fed high-purity glycerine tended to have higher milk yield than control cows (P = 0.06). Milk composition tended to differ among treatments. No main effects of treatment on concentration of glycerine (P = 0.44), glucose (P = 0.78), insulin (P = 0.33), non-esterified fatty acids (P = 0.33) and β-hydroxybutyrate (P = 0.15) in plasma. These data indicate that high-purity glycerine has the potential to increase milk yield, as well as enhance the milk protein concentration and milk fat + protein yield.     

Immunological effects of altering the concentrate inclusion level in a grass silage-based diet for early lactation Holstein Friesian cows

Concentrate inclusion levels in dairy cow diets are often adjusted so that the milk yield responses remain economic. While changes in concentrate level on performance is well known, their impact on other biological parameters, including immune function, is less well understood. The objective of this study was to evaluate the effect of concentrate inclusion level in a grass silage-based mixed ration on immune function. Following calving 63 (45 multiparous and 18 primiparous) Holstein Friesian dairy cows were allocated to one of three isonitrogenous diets for the first 70 days of lactation. Diets comprised of a mixture of concentrates and grass silage, with concentrates comprising either a low (30%, LC), medium (50%, MC) or high (70%, HC) proportion of the diet on a dry matter (DM) basis. Daily DM intakes, milk yields and BW were recorded, along with weekly body condition score, milk composition and vaginal mucus scores. Blood biochemistry was measured using a chemistry analyzer, neutrophil phagocytic and oxidative burst assessed using commercial kits and flow cytometry, and interferon-γ production evaluated by ELISA after whole blood stimulation. Over the study period cows on HC had a higher total DM intake, milk yield, fat yield, protein yield, fat+protein yield, protein content, mean BW and mean daily energy balance, and a lower BW loss than cows on MC, whose respective values were higher than cows on LC. Cows on HC and MC had a lower serum non-esterified fatty acid concentration than cows on LC (0.37, 0.37 and 0.50 mmol/l, respectively, P=0.005, SED=0.032), while cows on HC had a lower serum β-hydroxybutyrate concentration than cows on MC and LC (0.42, 0.55 and 0.55 mmol/l, respectively, P=0.002, SED=0.03). Concentrate inclusion level had no effect on vaginal mucus scores. At week 3 postpartum, cows on HC tended to have a higher percentage of oxidative burst positive neutrophils than cows on LC (43.2% and 35.3%, respectively, P=0.078, SED=3.11), although at all other times concentrate inclusion level in the total mixed ration had no effect on neutrophil phagocytic or oxidative burst characteristics, or on interferon-γ production by pokeweed mitogen stimulated whole blood culture. This study demonstrates that for high yielding Holstein Friesian cows managed on a grass silage-based diet, concentrate inclusion levels in early lactation affects performance but has no effect on neutrophil or lymphocyte immune parameters.