Developing flexible models for genetic evaluations in smallholder crossbred dairy farms.

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats.

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

Genetic relationships between weight loss in early lactation and daily milk production throughout lactation in Holstein cows.

After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first- (L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model. In this model, the random effects were fitted by second-order Legendre polynomials on a weekly basis from wk 1 to 44. Nadir of BW was found to be earlier than reported in the literature, at 29 d in milk, and BW loss from calving to nadir was also lower than generally assumed, close to 29 kg. To estimate genetic correlations between body weight loss and production, we defined BWL5 as the loss of weight between wk 1 and 5 after calving. Genetic correlations between BWL5 and MY24 ranged from -0.26 to 0.05 in L1 and from -0.11 to 0.10 in L2, according to days in milk. These moderate to low values suggest that it may be possible to select for milk production without increasing early body mobilization.

Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle.

A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model.

Influence of production, reproduction, morphology, and health traits on true and functional longevity in French Holstein cows.

In the long term, resilient animals are able to maintain their normal biological processes when confronted with environmental perturbations, reducing their risk of being culled. Therefore, longevity can be proposed as an indicator of long-term resilience. Decisions to remove a given dairy cow from the herd are mainly related to low milk production (i.e., voluntary culling) or to reasons other than production (i.e., involuntary culling). The aptitude of animals to delay any culling is defined as true longevity (TL), whereas functional longevity (FL) is the ability to avoid involuntary culling. The aim of the study was to investigate the influence of production, reproduction, morphology, and health traits on TL and FL, to identify risk factors for culling. Data included 278,217 lactations from 122,461 Holstein Friesian cows reared in 640 herds. The length of productive life, calculated as the time between first calving and culling, or censoring, was used as the measure of longevity. Survival analysis was performed using proportional hazards models assuming a piecewise Weibull distribution of the baseline hazard function, with or without adjustment for milk production to evaluate FL and TL. Insemination status, calving ease, mastitis, somatic cell count, displaced abomasum, and udder depth had significant relationships with TL and FL. Differences in estimates of relative risk between TL and FL showed that milk production often influenced culling decisions: farmers are more prone to cull animals with low production even when they had good other characteristics. The culling risk factors identified in the present study can be used to study resilience in dairy cattle and to improve genetic evaluations of functional or total longevity.

Exploring underlying drivers of genotype by environment interactions in feed efficiency traits for dairy cattle with a mechanistic model involving energy acquisition and allocation.

Feed efficiency (FE) is a complex phenotype made up of multiple traits for which there is potential for substantial genotype by environment interaction (G × E). The objective of this study is to evaluate the extent of G × E for FE traits with a simulation approach. We used a mechanistic model of the dairy cow that simulates trajectories of phenotypes throughout lifetime, depending on trajectories of resource acquisition and allocation, driven by 4 genetic scaling parameters, and depending on the nutritional environment (quantity and quality of feed resources). The cow model, calibrated for a grass-based farming system and seasonal calving, was combined with a genetic module. This simulated genetic variation in the 4 genetic scaling parameters related to resource acquisition and allocation, based on a simple balanced pedigree structure (200 paternal half-sib groups each of 100 daughters). The population of 20,000 cows generated was simulated in 4 nutritional environment scenarios, representing a gradient of feeding constraints. In each scenario, 6 traits derived from the model outputs were analyzed to obtain population genetic parameters. Genetic correlations between second-lactation production and FE were positive and high in all scenarios and increased as the nutritional environment became more constraining. A measure of lifetime FE was positively correlated with second-lactation production under a less constrained environment, but these correlations decreased as the environment became more constraining. The genetic correlation between body reserves at second calving, and lifetime FE was positive and low in the least constraining scenario and increased as the environment became more constraining. In addition to genetic parameters, we looked at the distributions of acquisition and allocation parameters among the best performing cows for lactation and life FE, in the 2 most contrasted scenarios. The 4 subpopulations of best cows had acquisition and allocation strategies different from the whole population. In conclusion, this simulation study identifies the potential underlying biological basis for important G × E in FE traits. This highlights the importance of having a balanced breeding goal when undertaking selection that should also be based on phenotypes relevant to the target performance environment.

A new method to estimate residual feed intake in dairy cattle using time series data.

In dairy, the usual way to measure feed efficiency is through the residual feed intake (RFI) method. However, this method is, in its classical form, a linear regression, which, by construction, does not take into account the evolution of the RFI components across time, inducing approximations in the results. We present here a new approach that incorporates the dynamic dimension of the data. Using a multitrait random regression model, the correlations between milk, live weight, DM intake (DMI) and body condition score (BCS) were investigated across the lactation. In addition, at each time point, by a matrix regression on the variance-covariance matrix and on the animal effects from the three predictor traits, a predicted animal effect for intake was estimated, which, by difference with the actual animal effect for intake, gave a RFI estimation. This model was tested on historical data from the Aarhus University experimental farm (1 469 lactations out of 740 cows). Correlations between animal effects were positive and high for milk and DMI and for weight and DMI, with a maximum mid-lactation, stable across time at around 0.4 for weight and BCS, and slowly decreasing along the lactation for milk and weight, DMI and BCS, and milk and BCS. At the Legendre polynomial coefficient scale, the correlations were estimated with a high accuracy (averaged SE of 0.04, min = 0.02, max = 0.05). The predicted animal effect for intake was always extremely highly correlated with the milk production and highly correlated with BW for the most part of the lactation, but only slightly correlated with BCS, with the correlation becoming negative in the second half of the lactation. The estimated RFI possessed all the characteristics of a classical RFI, with a mean at zero at each time point and a phenotypic independence from its predictors. The correlation between the averaged RFI over the lactation and RFI at each time point was always positive and above 0.5, and maximum mid-lactation (>0.9). The model performed reasonably well in the presence of missing data. This approach allows a dynamic estimation of the traits, free from all time-related issues inherent to the traditional RFI methodology, and can easily be adapted and used in a genetic or genomic selection context.

Improved dairy cattle mating plans at herd level using genomic information.

From 2012 to 2018, 223 180 Montbéliarde females were genotyped in France and the number of newly genotyped females increased at a rate of about 33% each year. With female genotyping information, farmers have access to the genomic estimated breeding values of the females in their herd and to their carrier status for genetic defects or major genes segregating in the breed. This information, combined with genomic coancestry, can be used when planning matings in order to maximize the expected on-farm profit of future female offspring. We compared different mating allocation approaches for their capacity to maximize the expected genetic gain while limiting expected progeny inbreeding and the probability to conceive an offspring homozygous for a lethal recessive allele. Three mate allocation strategies (random mating (RAND), sequential mating (gSEQ€) and linear programing mating (gLP€)) were compared on 160 actual Montbéliarde herds using male and female genomic information. Then, we assessed the benefit of using female genomic information by comparing matings planned using only female pedigree information with the equivalent strategy using genomic information. We measured the benefit of adding genomic expected inbreeding and risk of conception of an offspring homozygous for a lethal recessive allele to Net merit in mating plans. The influence of three constraints was tested: by relaxing the constraint on availability of a particular semen type (sexed or conventional) for bulls, by adding an upper limit of 8.5% coancestry between mate pairs or by using a more stringent maximum use of a bull in a herd (5% vs 10%). The use of genomic information instead of pedigree information improved the mate allocation method in terms of progeny expected genetic merit, genetic diversity and risk to conceive an offspring homozygous for a lethal recessive allele. Optimizing mate allocation using linear programming and constraining coancestry to a maximum of 8.5% per mate pair reduced the average coancestry with a small impact on expected Net Merit. In summary, for male and female selection pathways, using genomic information is more efficient than using pedigree information to maximize genetic gain while constraining the expected inbreeding of the progeny and the risk to conceive an offspring homozygous for a lethal recessive allele. This study also underlines the key role of semen type (sexed vs conventional) and the associated constraints on the mate allocation algorithm to maximize genetic gain while maintaining genetic diversity and limiting the risk to conceive an offspring homozygous for a lethal recessive allele.

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies.

Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.

Estimation of genetic parameters for dairy traits and somatic cell score in the first 3 parities using a random regression test-day model in French Alpine goats.

Lactation curve shape can affect an animal's health, feed requirements, and milk production throughout the year. We implemented a random regression model for the genetic evaluation of lactation curve shapes of dairy traits in French Alpine goats for their first 3 parities. Milk, fat, and protein yields, fat and protein contents, somatic cell score, and fat/protein ratio were considered. The data consisted of test-day records from 49,849 first lactation Alpine goats during their first 3 lactations. The reference model used a Legendre polynomial of order 2 for each parity to describe the genetic and permanent environmental effects, and was compared with a model that combined the second and third parities. A rank reduction of the variance-covariance matrix was also performed using an eigenvalue decomposition for each parity from the 2 models. Genetic parameters were consistent between the models tested. With a reduction to rank 2 and combining the second and third parities, the first 2 principal components correctly summarized the genetic variability of milk yield level and persistency, with a near-nil correlation between the 2, and with a much shorter computation time than the reference model. A favorable correlation of +0.43 between milk yield persistency and fat/protein ratio persistency at the beginning of the lactation was found from buck estimated breeding values.

Estimation of genetic parameters for functional longevity in the South African Holstein cattle using a piecewise Weibull proportional hazards model.

Non-genetic factors influencing functional longevity and the heritability of the trait were estimated in South African Holsteins using a piecewise Weibull proportional hazards model. Data consisted of records of 161,222 of daughters of 2,051 sires calving between 1995 and 2013. The reference model included fixed time-independent age at first calving and time-dependent interactions involving lactation number, region, season and age of calving, within-herd class of milk production, fat and protein content, class of annual variation in herd size and the random herd-year effect. Random sire and maternal grandsire effects were added to the model to estimate genetic parameters. The within-lactation Weibull baseline hazards were assumed to change at 0, 270, 380 days and at drying date. Within-herd milk production class had the largest contribution to the relative risk of culling. Relative culling risk increased with lower protein and fat per cent production classes and late age at first calving. Cows in large shrinking herds also had high relative risk of culling. The estimate of the sire genetic variance was 0.0472 ± 0.0017 giving a theoretical heritability estimate of 0.11 in the complete absence of censoring. Genetic trends indicated an overall decrease in functional longevity of 0.014 standard deviation from 1995 to 2007. There are opportunities for including the trait in the breeding objective for South African Holstein cattle.

Genomic and single-step evaluations of carcass traits of young bulls in dual-purpose cattle.

Genetic evaluations for carcass traits of young bulls in Normande and Montbeliarde breeds are currently being developed in France. In order to determine a suitable genomic evaluation for three carcass traits of young bulls, genomic breeding values were estimated for young candidates to selection using different approaches. Records of 111,789 Normande and 118,183 Montbeliarde were used. Average progeny pre-adjusted performances (DYD) were calculated for sires. Evaluation approaches were compared based on an assessment of their accuracy (correlation between DYD and estimated breeding values [EBVs]) and bias (regression coefficient of DYD on EBVs) on the 20% youngest AI sires. All genomic approaches were generally more accurate than BLUP (+.045 to +.116 correlation points), except for age at slaughter where single-step GBLUP (SSGBLUP) was the only genomic method leading to a greater accuracy (+.038 to +.126 points). The best setting of the SSGBLUP relationship matrix was characterized by a weight of 30% for pedigree information in the genomic relationship matrix. SSGBLUP was the most valuable evaluation approach for the evaluation of carcass traits of Normande and Montbeliarde young bulls.

Ability of dairy cows to ensure pregnancy according to breed and genetic merit for production traits under contrasted pasture-based systems.

The present study aimed to assess and measure the effects of breed, genetic merit for production traits, and feeding systems (FS) on the ability of dairy cows to ensure pregnancy through its components (fertilization, embryonic losses, recalving). An experiment was conducted over 9 yr on Normande and Holstein cows assigned to contrasted FS. Diets were based on maize silage in winter and grazing plus concentrate in spring in the high FS group, and on grass silage in winter and grazing with no concentrate during spring in the low FS group. Within breeds, cows were classified into 2 groups with similar estimated breeding values (EBV) for milk solids: cows with high EBV for milk yield were included in a milk group and those with high EBV for fat and protein contents were included in a content group. Holstein cows produced more milk throughout lactation than Normande cows (the differential was greater in the high FS group, +2,294 kg, compared with +1,280 kg in the low FS group) and lost more body condition to nadir (the differential was greater in the high FS group, -1.00 point, compared with -0.80 point in the low FS group). Within breeds, milk solids production was similar between genetic groups. Cows in the high FS group produced more milk (+2,495 kg for Holstein and +1,481 kg for Normande cows) and had a higher body condition score at nadir (+0.40 point for Holstein and +0.60 point for Normande) than cows in the low FS group. Holstein cows had a lower recalving rate than Normande cows (-19 percentage units). We found no effect of genetic group and FS on fertility of Normande cows. However, according to FS, Holstein cows in the content group exhibited different fertility failure patterns. In the low FS group, Holstein cows in the content group had more nonfertilizations or early embryo mortality (+26 percentage units at first and second services) than Holstein cows in the milk group. In the high FS group, Holstein cows in the content group had a higher proportion of late embryo mortality than in the milk group (+10 percentage units at first and second services). We observed no effect of FS on recalving rate; however, indicators of energy balance (protein content or body condition score) were positively associated with successful conception and pregnancy. This suggested a link between genetic merit for fat and protein content and lower ability of dairy cows to ensure pregnancy because of more nonfertilizations and early or late embryo mortality.

Genomic evaluation of regional dairy cattle breeds in single-breed and multibreed contexts.

An important prerequisite for high prediction accuracy in genomic prediction is the availability of a large training population, which allows accurate marker effect estimation. This requirement is not fulfilled in case of regional breeds with a limited number of breeding animals. We assessed the efficiency of the current French routine genomic evaluation procedure in four regional breeds (Abondance, Tarentaise, French Simmental and Vosgienne) as well as the potential benefits when the training populations consisting of males and females of these breeds are merged to form a multibreed training population. Genomic evaluation was 5-11% more accurate than a pedigree-based BLUP in three of the four breeds, while the numerically smallest breed showed a < 1% increase in accuracy. Multibreed genomic evaluation was beneficial for two breeds (Abondance and French Simmental) with maximum gains of 5 and 8% in correlation coefficients between yield deviations and genomic estimated breeding values, when compared to the single-breed genomic evaluation results. Inflation of genomic evaluation of young candidates was also reduced. Our results indicate that genomic selection can be effective in regional breeds as well. Here, we provide empirical evidence proving that genetic distance between breeds is only one of the factors affecting the efficiency of multibreed genomic evaluation.

Carcass traits of young bulls in dual-purpose cattle: genetic parameters and genetic correlations with veal calf, type and production traits.

The profitability of dual-purpose breeding farms can be increased through genetic improvement of carcass traits. To develop a genetic evaluation of carcass traits of young bulls, breed-specific genetic parameters were estimated in three French dual-purpose breeds. Genetic correlations between these traits and veal calf, type and milk production traits were also estimated. Slaughter performances of 156 226 Montbeliarde, 160 361 Normande and 8691 Simmental young bulls were analyzed with a multitrait animal model. In the three breeds, heritabilities were moderate for carcass weight (0.12 to 0.19±0.01 to 0.04) and carcass conformation (0.21 to 0.26±0.01 to 0.04) and slightly lower for age at slaughter (0.08 to 0.17±0.01 to 0.03). For all three breeds, genetic correlations between carcass weight and carcass conformation were moderate and favorable (0.30 to 0.52±0.03 to 0.13). They were strong and favorable (-0.49 to -0.71±0.05 to 0.15) between carcass weight and age at slaughter. Between age at slaughter and carcass conformation, they were low and unfavorable to moderate and favorable (-0.25 to 0.10±0.06 to 0.18). Heavier young bulls tend to be better conformed and slaughtered earlier. Genetic correlations between corresponding young bulls and veal production traits were moderate and favorable (0.32 to 0.70±0.03 to 0.09), implying that selecting sires for veal calf production leads to select sires producing better young bulls. Genetic correlations between young bull carcass weight and cow size were moderately favorable (0.22 to 0.45±0.04 to 0.10). Young bull carcass conformation had moderate and favorable genetic correlations (0.11 to 0.24±0.04 to 0.10) with cow width but moderate and unfavorable genetic correlations (-0.21 to -0.36±0.03 to 0.08) with cow height. Taller cows tended to produce heavier young bulls and thinner cows to produce less conformed ones. Genetic correlations between carcass traits of young bulls and cow muscularity traits were low to moderate and favorable. Finally, genetic correlations between carcass traits of young bulls and milk production traits were low and unfavorable to moderate and favorable. These results indicate the existence for all three breeds of genetic variability for the genetic improvement of carcass traits of young bulls as well as favorable genetic correlations for their simultaneous selection and no strong unfavorable correlation with milk production traits.

Ability of dairy cows to be inseminated according to breed and genetic merit for production traits under contrasting pasture-based feeding systems.

Strong genetic selection on production traits is considered to be responsible for the declined ability of dairy cows to ensure reproduction. The present study aimed to quantify the effect of genetic characteristics (breeds and genetic merit for production traits) and feeding systems (FS) on the ability of dairy cows to be inseminated. An experiment was conducted during 9 years on Normande and Holstein cows assigned to contrasted pasture-based FS. Diets were based on maize silage in winter and grazing plus concentrate in spring in the High FS; and on grass silage in winter and grazing with no concentrate during spring in the low FS. Within breed, cows were classified into two genetic groups with similar estimated breeding values (EBV) for milk solids: cows with high EBV for milk yield were included in a Milk-Group and those with high EBV for fat and protein contents were included in a Content-Group. Holstein produced more milk throughout lactation than Normande cows (+2294 kg in the High FS and +1280 kg in the Low FS, P<0.001) and lost more body condition to nadir (-1.00 point in the High FS and -0.80 kg in the Low FS, P<0.001). They also showed a poorer ability to be inseminated because of both a delayed commencement of luteal activity (CLA) and delayed first service (more days from start of the breeding season to first service, DAI1). Cows in the Milk-Group produced more milk than cows in the Content-Group, but milk solids production was similar. Cows in the Content-Group had earlier CLA than cows in the Milk-Group (P<0.01). Genetic group neither affected ovulation detection rate nor DAI1. Within breed and FS, cows with high genetic merit for milk yield had later CLA and DAI1. Cows in the High FS produced more milk and lost less condition to nadir than cows in the Low FS. FS did not affect dairy cows' ability to be inseminated. However, cows with higher milk protein content, and presumably better energy balance, had earlier CLA (P<0.01) and DAI1 (P<0.10). In addition, higher milk yield was associated with poorer ovulation detection rate and oestrus intensity (P<0.05). The study showed that at similar EBV level for milk solids, selection for increased milk fat and protein content resulted in improved cyclicity and similar oestrous expression and submission rates compared with selection for increased milk yield.

Genetic changes of survival traits over the past 25 yr in Dutch dairy cattle.

Genetic correlations and heritabilities for survival were investigated over a period of 25 yr to evaluate if survival in first lactation has become a different trait and if this is affected by adjusting for production level. Survival after first calving until 12mo after calving (surv_12mo) and survival of first lactation (surv_1st_lac) were analyzed in Dutch black-and-white cows. The data set contained 1,108,745 animals for surv_12mo and 1,062,276 animals for surv_1st_lac, with first calving between 1989 and 2013. The trait survival as recorded over 25 yr was split in five 5-yr intervals to enable a multitrait analysis. Bivariate models using subsets of the full data set and multitrait and autoregressive models using the full data set were used. Survival and functional survival were analyzed. Functional survival was defined as survival adjusted for within-herd production level for 305-d yield of combined kilograms of fat and protein. Mean survival increased over time, whereas genetic variances and heritability decreased. Bivariate models yielded large standard errors on genetic correlations due to poor connectedness between the extreme 5-yr intervals. The more parsimonious models using the full data set gave nonunity genetic correlations. Genetic correlations for survival were below 0.90 between intervals separated by 1 or more 5-yr intervals. Genetic correlations for functional survival did not indicate that definition of survival changed (≥0.90). The difference in genetic correlations between survival and functional survival is likely explained by lower emphasis of dairy farmers on culling in first lactation for low yield in more recent years. This suggests that genetic evaluation for longevity using historical data should analyze functional survival rather than survival.

Short communication: Improving accuracy of predicting breeding values in Brazilian Holstein population by adding data from Nordic and French Holstein populations.

The present study investigated the improvement of prediction reliabilities for 3 production traits in Brazilian Holsteins that had no genotype information by adding information from Nordic and French Holstein bulls that had genotypes. The estimated across-country genetic correlations (ranging from 0.604 to 0.726) indicated that an important genotype by environment interaction exists between Brazilian and Nordic (or Nordic and French) populations. Prediction reliabilities for Brazilian genotyped bulls were greatly increased by including data of Nordic and French bulls, and a 2-trait single-step genomic BLUP performed much better than the corresponding pedigree-based BLUP. However, only a minor improvement in prediction reliabilities was observed in nongenotyped Brazilian cows. The results indicate that although there is a large genotype by environment interaction, inclusion of a foreign reference population can improve accuracy of genetic evaluation for the Brazilian Holstein population. However, a Brazilian reference population is necessary to obtain a more accurate genomic evaluation.

Multiple trait genetic evaluation of clinical mastitis in three dairy cattle breeds.

In 2010, a routine genetic evaluation on occurrence of clinical mastitis in three main dairy cattle breeds-- Montbéliarde (MO), Normande (NO) and Holstein (HO)--was implemented in France. Records were clinical mastitis events reported by farmers to milk recording technicians and the analyzed trait was the binary variable describing the occurrence of a mastitis case within the first 150 days of the first three lactations. Genetic parameters of clinical mastitis were estimated for the three breeds. Low heritability estimates were found: between 2% and 4% depending on the breed. Despite its low heritability, the trait exhibits genetic variation so efficient genetic improvement is possible. Genetic correlations with other traits were estimated, showing large correlations (often>0.50, in absolute value) between clinical mastitis and somatic cell score (SCS), longevity and some udder traits. Correlation with milk yield was moderate and unfavorable (ρ=0.26 to 0.30). High milking speed was genetically associated with less mastitis in MO (ρ=-0.14) but with more mastitis in HO (ρ=0.18). A two-step approach was implemented for routine evaluation: first, a univariate evaluation based on a linear animal model with permanent environment effect led to pre-adjusted records (defined as records corrected for all non-genetic effects) and associated weights. These data were then combined with similar pre-adjusted records for others traits in a multiple trait BLUP animal model. The combined breeding values for clinical mastitis obtained are the official (published) ones. Mastitis estimated breeding values (EBV) were then combined with SCSs EBV into an udder health index, which receives a weight of 14.5% to 18.5% in the French total merit index (ISU) of the three breeds. Interbull genetic correlations for mastitis occurrence were very high (ρ=0.94) with Nordic countries, where much stricter recording systems exist reflecting a satisfactory quality of phenotypes as reported by the farmers. They were lower (around 0.80) with countries supplying SCS as a proxy for the international evaluation on clinical mastitis.

Toward improved postpartum cyclicity of primiparous dairy cows: Effects of genetic merit for production traits under contrasting feeding systems.

Milk genetic merit is known to affect commencement of luteal activity (C-LA) in dairy cows. This effect is considered to be due to energy exported in milk production. The present study aimed to identify and quantify the effects of genetic characteristics [breed and estimated breeding value (EBV) for milk yield and fat and protein contents] and feeding system on C-LA of primiparous cows. From 2006 to 2013, an experiment was conducted on 97 primiparous dairy (Holstein) and 97 primiparous dual-purpose (Normande) cows. Within breed, cows were classified into 2 groups: cows with high EBV for milk yield were included in a "milk group" and those with high EBV for fat and protein contents were included in a "content group." Within breed, exported energy in milk and body weight (BW) loss were similar for both genetic groups. Two grazing-based strategies were used, a high feeding system (maize silage in winter and grazing plus concentrate) and a low feeding system (grass silage in winter and grazing with no concentrate). Interval from calving to C-LA was studied performing survival analyses. Milk progesterone profile, milk yield, and body condition were analyzed using χ(2)-test and analysis of covariance. Holstein cows produced more milk (+1,810 kg in the high feeding system and +1,120 kg in the low feeding system) and lost more BW from wk 1 to 14 of lactation (-1.4 kg/wk) than Normande cows, whereas Normande cows had earlier C-LA than Holstein cows. Within breed, cows in the content group had earlier C-LA (associated hazard ratio=2.0) than cows in the milk group. Body weight at calving and loss from wk 1 to 14 of lactation tended to be associated with later C-LA. Cows in the high feeding system produced more milk (+2,040 kg for the Holstein cows and +1,350 kg for Normande cows) and lost less BW from wk 1 to 14 of lactation (+3.8 kg/wk) than cows in the low feeding system. No effect of feeding system or milk yield was observed on C-LA. Prolonged luteal phases were frequent (18% of cows) and were not associated with either breed or genetic group. Ovarian cycles were longer for Holstein than for Normande cows (+1.7d) because of a longer luteal phase and a longer interluteal interval. Results of the study could be useful to establish strategies to manage declining reproductive performances at genetic and environmental levels. This study showed that cows with a genetic predisposition to export milk energy through fat and protein contents had earlier C-LA than predisposed to export milk energy through yield.