Imputation of missing milk Fourier transform mid-infrared spectra using existing milk spectral databases: A strategy to improve the reliability of breeding values and predictive models.

The use of milk Fourier transform mid-infrared (FT-MIR) spectrometry to develop management and breeding tools for dairy farmers and industry is growing and supported by the availability of numerous new predicted phenotypes to assess the nutritional quality of milk and its technological properties, but also the animal health and welfare status and its environmental fingerprint. For genetic evaluations, having a long-term and representative spectral dairy herd improvement (DHI) database improves the reliabilities of estimated breeding values (EBV) from these phenotypes. Unfortunately, most of the time, the raw spectral data used to generate these estimations are not stored. Moreover, many reference measurements of those phenotypes, needed during the FT-MIR calibration step, are available from past research activities but lack spectra records. So, it is impossible to use them to improve the FT-MIR models. Consequently, there is a strong interest in imputing those missing spectra. The innovative objective of this study was to use the existing large spectral DHI database to estimate missing spectra by selecting probable spectra using, as the match criteria, common dairy traits recorded for a long time by DHI organizations. We tested 4 match criteria combinations. Combination 1 required to have equal fat and protein contents between the sample for which a spectrum was to be estimated and the reference samples in the DHI database. Combination 2 also required an equal urea content. Combination 3 requested equal fat, protein, and lactose contents. Finally, combination 4 included all criteria. When more than one spectrum was found during the search, their average was the estimated spectrum for the query sample. Concretely, this study estimated missing spectra for 1,700 samples using 2,000,000 spectral DHI records. For assessing the effect of this spectral estimation on the prediction quality, FT-MIR equations were used to predict 11 phenotypes, selected as their quantification used different FT-MIR regions. They were related to the milk fat and mineral composition, lactoferrin content, quantity of eructed methane, body weight (BW), and dry matter intake. The accuracy between predictions obtained from actual and estimated spectra was evaluated by calculating the mean absolute error (MAE). The criteria in the fourth and second combinations were too strict to estimate a spectrum for most samples. Indeed, for many samples, no spectra with the same values for those matching criteria was found. The third match criteria combination had a poorer prediction performance for all studied traits and spectral absorptions than the first combination due to fewer matched samples available to compute the missing spectrum. By allowing a range for matching lactose content (±0.1 g/dL milk), we showed that this new combination increased the number of selected samples to compute missing spectra and predict better the infrared absorption at different wavenumbers, especially those related to the lactose quantification. The prediction performance was further improved by performing queries on the entire Walloon DHI spectral database (6,625,570 spectra), and it varied among the studied phenotypes. Without considering the traits used for the matching, the best predictions were obtained for the content of saturated fatty acids (MAE = 0.15 g/dL milk) and BW (MAE = 12.80 kg). Yet, the predictions for the unsaturated fatty acids were less accurate (MAE = 0.13 and 0.018 g/dL milk for monounsaturated and polyunsaturated fatty acids), likely because of the poorer predictions of spectral regions related to long-chain fatty acids. Similarly, poorer predictions were observed for the amount of methane eructed by dairy cows (MAE = 47.02 g/d), likely because it is not directly related to fat content or composition. Prediction accuracies for the remaining traits were also low. In conclusion, we observed that increasing the number of relevant matching criteria helps improve the quality of FT-MIR predicted phenotypes and the number of spectra used during the search. So, it would be of great interest to test in the future the suitability of the developed methodology with large-scale international spectral databases to improve the reliability of EBV from these FT-MIR-based phenotypes and the robustness of FT-MIR predictive models.

Researching 100 t cows: An innovative approach to identify intrinsic cows factors associated with a high lifetime milk production.

Longevity is an important trait both from an economic and social perspective. Modern dairy cows are criticized for their short productive lifespan: only a minority of animals survives to a fourth lactation, implying that most cows are culled before reaching their maximal potential. In contrast, the population of 100 t cows (HT), reaching the threshold of 100,000 kg lifetime milk yield, is growing rapidly. As these cows combine a long lifespan with high functionality, a better understanding of their intrinsic characteristics might help us to improve the overall lifespan and lifetime production in dairy cows. The aim of the present research was to compare HT with their less-producing herd mates in order to identify intrinsic cow factors associated with longevity and high lifetime production. Therefore, we matched 26,248 HT with 691,597 herd mates, born in the same year in the same herd. Data were provided by Coöperatie rundveeverbetering (CRV) and contained birth dates, calving dates, milk yield and dam information. In addition, scores for conformation traits based on classifications in the first lactation and breeding values (for milk yield, fertility, udder health and claw health) were provided. Multivariable conditional logistic regression models were built to identify factors associated with reaching a lifetime milk yield of 100,000 kg. Results revealed cows born in September and born out of heifers to have the highest odds to become a HT. When cows received a score ≥ 83 (population average 80) for udder and feet & legs conformation, they had higher odds of reaching the 100,000 kg threshold. While a greater body condition and larger rump angle increased the odds of becoming a HT, this was decreased in cows with a large body depth. Finally, breeding values for milk yield, fertility, udder health and claw health were positively associated with the likelihood of reaching a lifetime milk yield of 100,000 kg. In conclusion, to increase lifetime milk yield in dairy herds, farmers should select heifers with high scores for conformation traits like udder and feet & legs and high breeding values for milk yield, fertility and udder health. Furthermore, our data suggest that being born in September out of a heifer potentially contributes to reaching a high lifetime milk yield.

Genetic analysis of resilience indicators based on milk yield records in different lactations and at different lactation stages.

Resilience is the ability of cows to cope with disturbances, such as pathogens or heat waves. To breed for improved resilience, it is important to know whether resilience genetically changes throughout life. Therefore, the aim was to perform a genetic analysis on 2 resilience indicators based on data from 3 periods of the first lactation (d 11-110, 111-210, and 211-340) and the first 3 full lactations, and to estimate genetic correlations with health traits. The resilience indicators were the natural log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily deviations in milk yield from an expected lactation curve. Low LnVar and rauto indicate low variability in daily milk yield and quick recovery, and were expected to indicate good resilience. Data of 200,084 first, 155,784 second, and 89,990 third lactations were used. Heritabilities were similar based on different lactation periods (0.12-0.15 for LnVar, 0.05-0.06 for rauto). However, the heritabilities of the resilience indicators based on full first lactation were higher than those based on lactation periods (0.20 for LnVar, 0.08 for rauto), due to lower residual variances. Heritabilities decreased from 0.20 in full lactation 1 to 0.19 in full lactation 3 for LnVar and from 0.08 to 0.06 for rauto. For LnVar, as well as for rauto, the strongest genetic correlation between lactation periods was between period 2 and 3 (0.97 for LnVar, 0.96 for rauto) and the weakest between period 1 and 3 (0.81 for LnVar, 0.65 for rauto). Similarly, for both traits the genetic correlation between full lactations was strongest between lactations 2 and 3 (0.99 for LnVar, 0.95 for rauto) and weakest between lactations 1 and 3 (0.91 for LnVar, 0.71 for rauto). For LnVar, genetic correlations with resilience-related traits, such as udder health, ketosis, and longevity, adjusted for correlations with milk yield, were almost always favorable (-0.59 to 0.02). In most cases these genetic correlations were stronger based on full lactations than on lactation periods. Genetic correlations were similar across full lactations, but the correlation with udder health increased substantially from -0.31 in lactation 1 to -0.51 in lactation 3. For rauto, genetic correlations with resilience-related traits were always favorable in lactation period 1 and in most full lactations, but not in the other lactation periods. However, correlations were weak (-0.27 to 0.15). Therefore, as a resilience indicator for breeding, LnVar is preferred over rauto. A multitrait index based on estimated breeding values for LnVar in lactations 1, 2, and 3 is recommended to improve resilience throughout the lifetime of a cow.

A large-scale study on the effect of age at first calving, dam parity, and birth and calving month on first-lactation milk yield in Holstein Friesian dairy cattle.

Milk yield during first lactation is an important economical trait. Age at first calving (AFC) is considered an important predictor of subsequent milk yield. In addition, both season of birth, as well as season of calving, have been shown to influence milk production, with conflicting results. Finally, higher parity of the dam has been associated with a lower performance of the offspring. The aim of the present study was to assess the effect of the above-mentioned factors based on a large-scale study and to rank the most important determinants for first-lactation milk yield. Data on 3,810,678 Holstein Friesian heifers, born in Belgium and the Netherlands between 2000 and 2015, were provided by Cooperative CRV and CRV BV (Arnhem, the Netherlands) and consisted of birth dates, calving dates, and first-lactation productions. In addition, herd, sire, and dam information was provided. Linear regression models were built with herd-calving year and sire as random effects and 305-d energy-corrected milk (ECM) yield during first lactation as outcome variable. Birth month, calving month, parity of the dam, and AFC were included as fixed effects in the model and a dominance analysis was performed to rank the associated factors according to importance. Results revealed AFC to be the most important factor (R2 = 0.047), with an increase in ECM up to an age of 33 mo. Calving month was a more important predictor than birth month (R2 = 0.010 vs. R2 = 0.002, respectively), with the highest first-lactation production in heifers calving in October to December, and the lowest in heifers calving in June and July. Birth month had a limited effect on first-lactation milk yield (R2 = 0.002), potentially masked by rearing strategies during early life. Finally, parity of the dam ≥3 was associated with a reduced ECM of the offspring (R2 = 0.002). In conclusion, our results show AFC to be an important determinant of milk yield during first lactation. In addition, seasonal patterns in milk production are seen, which should be further explored to identify the underlying mechanism.

Exploration of variance, autocorrelation, and skewness of deviations from lactation curves as resilience indicators for breeding.

The ability of a cow to cope with environmental disturbances, such as pathogens or heat waves, is called resilience. To improve resilience through breeding, we need resilience indicators, which could be based on the fluctuation patterns in milk yield resulting from disturbances. The aim of this study was to explore 3 traits that describe fluctuations in milk yield as indicators for breeding resilient cows: the variance, autocorrelation, and skewness of the deviations from individual lactation curves. We used daily milk yield records of 198,754 first-parity cows, recorded by automatic milking systems. First, we estimated a lactation curve for each cow using 4 different methods: moving average, moving median, quantile regression, and Wilmink curve. We then calculated the log-transformed variance (LnVar), lag-1 autocorrelation (rauto), and skewness (Skew) of the daily deviations from these curves as resilience indicators. A genetic analysis of the resilience indicators was performed, and genetic correlations between resilience indicators and health, longevity, fertility, metabolic, and production traits were estimated. The heritabilities differed between LnVar (0.20 to 0.24), rauto (0.08 to 0.10), and Skew (0.01 to 0.02), and the genetic correlations among the indicators were weak to moderate. For rauto and Skew, genetic correlations with health, longevity, fertility, and metabolic traits were weak or the opposite of what we expected. Therefore, rauto and Skew have limited value as resilience indicators. However, lower LnVar was genetically associated with better udder health (genetic correlations from -0.22 to -0.32), better longevity (-0.28 to -0.34), less ketosis (-0.27 to -0.33), better fertility (-0.06 to -0.17), higher BCS (-0.29 to -0.40), and greater dry matter intake (-0.53 to -0.66) at the same level of milk yield. These correlations support LnVar as an indicator of resilience. Of all 4 curve-fitting methods, LnVar based on quantile regression systematically had the strongest genetic correlations with health, longevity, and fertility traits. Thus, quantile regression is considered the best curve-fitting method. In conclusion, LnVar based on deviations from a quantile regression curve is a promising resilience indicator that can be used to breed cows that are better at coping with disturbances.

Predicting survival in dairy cattle by combining genomic breeding values and phenotypic information.

Advances in technology and improved data collection have increased the availability of genomic estimated breeding values (gEBV) and phenotypic information on dairy farms. This information could be used for the prediction of complex traits such as survival, which can in turn be used in replacement heifer management. In this study, we investigated which gEBV and phenotypic variables are of use in the prediction of survival. Survival was defined as survival to second lactation, plus 2 wk, a binary trait. A data set was obtained of 6,847 heifers that were all genotyped at birth. Each heifer had 50 gEBV and up to 62 phenotypic variables that became gradually available over time. Stepwise variable selection on 70% of the data was used to create multiple regression models to predict survival with data available at 5 decision moments: distinct points in the life of a heifer at which new phenotypic information becomes available. The remaining 30% of the data were kept apart to investigate predictive performance of the models on independent data. A combination of gEBV and phenotypic variables always resulted in the model with the highest Akaike information criterion value. The gEBV selected were longevity, feet and leg score, exterior score, udder score, and udder health score. Phenotypic variables on fertility, age at first calving, and milk quantity were important once available. It was impossible to predict individual survival accurately, but the mean predicted probability of survival of the surviving heifers was always higher than the mean predicted probability of the nonsurviving group (difference ranged from 0.014 to 0.028). The model obtained 2.0 to 3.0% more surviving heifers when the highest scoring 50% of heifers were selected compared with randomly selected heifers. Combining phenotypic information and gEBV always resulted in the highest scoring models for the prediction of survival, and especially improved early predictive performance. By selecting the heifers with the highest predicted probability of survival, increased survival could be realized at the population level in practice.

Excision of both pretreatment marked positive nodes and sentinel nodes improves axillary staging after neoadjuvant systemic therapy in breast cancer.

BACKGROUND: Marking the axilla with radioactive iodine seed and sentinel lymph node (SLN) biopsy have been proposed for axillary staging after neoadjuvant systemic therapy in clinically node-positive breast cancer. This study evaluated the identification rate and detection of residual disease with combined excision of pretreatment-positive marked lymph nodes (MLNs) together with SLNs. METHODS: This was a multicentre retrospective analysis of patients with clinically node-positive breast cancer undergoing neoadjuvant systemic therapy and the combination procedure (with or without axillary lymph node dissection). The identification rate and detection of axillary residual disease were calculated for the combination procedure, and for MLNs and SLNs separately. RESULTS: At least one MLN and/or SLN(s) were identified by the combination procedure in 138 of 139 patients (identification rate 99·3 per cent). The identification rate was 92·8 per cent for MLNs alone and 87·8 per cent for SLNs alone. In 88 of 139 patients (63·3 per cent) residual axillary disease was detected by the combination procedure. Residual disease was shown only in the MLN in 20 of 88 patients (23 per cent) and only in the SLN in ten of 88 (11 per cent), whereas both the MLN and SLN contained residual disease in the remainder (58 of 88, 66 per cent). CONCLUSION: Excision of the pretreatment-positive MLN together with SLNs after neoadjuvant systemic therapy in patients with clinically node-positive disease resulted in a higher identification rate and improved detection of residual axillary disease.

ANTECEDENTES: En el cáncer de mama con ganglios positivos clínicamente tras el tratamiento neoadyuvante sistémico, se ha propuesto la utilización de iodo radioactivo (Marking Axilla with Radioactive Iodine, MARI) y de la biopsia de ganglio linfático centinela para la estadificación axilar. En este estudio se evaluó la tasa de identificación y detección de enfermedad residual cuando se combinó la exéresis de los ganglios linfáticos marcados antes del tratamiento (marked lymph nodes, MLN) junto con los ganglios centinela (sentinel lymph nodes, SLN). MÉTODOS: Se realizó un análisis retrospectivo multicéntrico de pacientes con cáncer de mama con ganglios positivos clínicamente que se sometieron a tratamiento neoadyuvante sistémico y en las que se combinaron ambas técnicas (con o sin disección axilar). Se calcularon las tasas de identificación y detección de enfermedad residual axilar para MLN y SLN por separado y en conjunto. RESULTADOS: En 138/139 pacientes se identificaron ≥ 1 MLN y/o SLN combinando ambas técnicas (tasa de identificación del 99,3%). La tasa de identificación fue de 92,8% para MLN y del 87,8% para SLN. Combinando ambas técnicas se detectó enfermedad axilar residual en 88/139 (63,3%) pacientes. Se detectó enfermedad residual en 20/88 (22,7%) pacientes utilizando únicamente MLN, en 10/88 (11,4%) pacientes utilizando únicamente SLN y en 58/88 (65,9%) combinando ambas técnicas. CONCLUSIÓN: La exéresis conjunta de los ganglios marcados con iodo radioactivo antes del tratamiento neoadyuvante sistémico y de los ganglios centinela después del tratamiento en pacientes con cN+ logró una tasa de identificación más alta y una mejor detección de la enfermedad axilar residual.

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle.

In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set used for prediction contained 6,847 heifers born between January 2012 and June 2013, and had known survival outcomes. Each animal had 50 genomic estimated breeding values available at birth and up to 65 phenotypic variables that accumulated over time. Survival was predicted at 5 moments in life: at birth, at 18 mo, at first calving, at 6 wk after first calving, and at 200 d after first calving. The data sets were randomly split into 70% training and 30% testing sets to evaluate model performance for 20-fold validation. The methods were compared for accuracy, sensitivity, specificity, area under the curve (AUC) value, contrasts between groups for the prediction outcomes, and increase in surviving animals in a practical scenario. At birth and 18 mo, all methods had overlapping performance; no method significantly outperformed the other. At first calving, 6 wk after first calving, and 200 d after first calving, random forest and naive Bayes had overlapping performance, and both machine-learning methods outperformed multiple logistic regression. Overall, naive Bayes has the highest average AUC at all decision points up to 200 d after first calving. Random forest had the highest AUC at 200 d after first calving. All methods obtained similar increases in survival in the practical scenario. Despite this, the methods appeared to predict the survival of individual heifers differently. All methods improved over time, but the changes in mean model outcomes for surviving and non-surviving animals differed by method. Furthermore, the correlations of individual predictions between methods ranged from r = 0.417 to r = 0.700; the lowest correlations were at first calving for all methods. In short, all 3 methods were able to predict survival at a population level, because all methods improved survival in a practical scenario. However, depending on the method used, predictions for individual animals were quite different between methods.

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.

Changes in the genetic level and the effects of age at first calving and milk production on survival during the first lactation over the last 25 years.

Survival during the first year after first calving was investigated over the last 25 years, 1989-2013, as well as how the association of survival with season of calving, age at first calving (AFC) and within-herd production level has changed over that period. The data set contained 1 108 745 Dutch black-and-white cows in 2185 herds. Linear models were used to estimate (1) effect of year and season and their interaction and (2) effect of AFC, within-herd production level, and 5-year intervals and their two-way interactions, and the genetic trend. All models contained AFC and percentage of Holstein Friesian as a fixed effect, and herd-year-season, sire and maternal grandsire as random effects. Survival and functional survival were analysed. Functional survival was defined as survival adjusted for within-herd production level. Survival rate increased by 8% up to 92% in the last 25 years. When accounting for pedigree, survival showed no improvement up to 1999, but improved since then. Genetically, survival increased 3% to 4% but functional survival did not increase over the 25 years. We found an interesting difference between the genetic trends for survival and functional survival for bulls born between 1985 and 1999, where the trend for survival was still increasing, but was negative for functional survival. Since 1999, genetic trend picked up again for both survival and functional survival. AFC, season of calving and within-herd production level affected survival. Survival rate decreased 0.6%/month for survival and 1.5% for functional survival between AFC of 24 and 32 months. Calving in summer resulted in 2.0% higher survival than calving in winter. Within herd, low-producing cows had a lower survival rate than high-producing cows. However, these effects became less important during the recent years. Based on survival optimum AFC is around 24 months, but based on functional survival it is better to have an AFC<24 months. Overall, survival rate of heifers has improved considerably in the past 25 years, initially due to the focus on a high milk production. More recently, the importance of a high milk production has been reduced towards attention for functional survival.

Genotype by environment interaction for production, somatic cell score, workability, and conformation traits in Dutch Holstein-Friesian cows between farms with or without grazing.

In this study, genotype by environment interaction was investigated for production traits, somatic cell score (SCS), workability traits, and conformation traits for Holstein-Friesian cows producing on farms with or without grazing in the Netherlands. Additionally, heritabilities and repeatabilities were estimated in both farm systems. Data were available for 1,019 Dutch farms, and farm type was known for those farms, 142 farms without grazing and 877 farms with grazing. The data set consisted of 428,600 test-day records for production from 49,412 cows, and from this data set a subset for SCS was created, consisting of 374,734 test-day records from 45,955 cows. For workability and conformation traits, the data set consisted of 30,180 cows. Bivariate mixed models with multiple fixed effects and random sire and random permanent environment effects were applied. The majority of sires had daughters in both farm types. The heritabilities for milk yield (0.27), fat yield (0.19), and protein yield (0.20) were higher in farms with grazing than in farms without grazing with heritabilities of 0.24 for milk yield, 0.18 for fat yield, and 0.18 for protein yield. Repeatability was lower in the grazing farms for milk yield, fat yield, and protein yield, probably because of alternating quality of dry matter intake during grazing. Genetic correlations between grazing and no grazing were 0.99, 0.98, 0.97, and 1.00 for milk yield, fat yield, protein yield, and SCS, respectively. Genetic correlations for workability traits and conformation traits between grazing and no grazing varied between 0.93 and 1.00. For all traits, genetic correlations were close to unity, indicating no genotype by environment interaction between farms with or without grazing for production traits, SCS, workability traits, and conformation traits in Dutch Holstein-Friesians. Therefore, the same sires can be used for farms both with and without grazing.

Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations--the Netherlands and United States.

To include feed-intake-related traits in the breeding goal, accurate estimates of genetic parameters of feed intake, and its correlations with other related traits (i.e., production, conformation) are required to compare different options. However, the correlations between feed intake and conformation traits can vary depending on the population. Therefore, the objective was to estimate genetic correlations between 6 feed-intake-related traits and 7 conformation traits within dairy cattle from 2 countries, the Netherlands (NL) and the United States (US). The feed-intake-related traits were dry matter intake (DMI), residual feed intake (RFI), milk energy output (MilkE), milk yield (MY), body weight (BW), and metabolic body weight (MBW). The conformation traits were stature (ST), chest width (CW), body depth (BD), angularity (ANG), rump angle (RA), rump width (RW), and body condition score (BCS). Feed intake data were available for 1,665 cows in NL and for 1,920 cows in US, from 83 nutritional experiments (48 in NL and 35 in US) conducted between 1991 and 2011 in NL and between 2007 and 2013 in US. Additional conformation records from relatives of the animals with DMI records were added to the database, giving a total of 37,241 cows in NL and 28,809 in US with conformation trait information. Genetic parameters were estimated using bivariate animal model analyses. The model included the following fixed effects for feed-intake-related traits: location by experiment-ration, age of cow at calving modeled with a second order polynomial by parity class, location by year-season, and days in milk, and these fixed effects for the conformation traits: herd by classification date, age of cow at classification, and lactation stage at classification. Both models included additive genetic and residual random effects. The highest estimated genetic correlations involving DMI were with CW in both countries (NL=0.45 and US=0.61), followed by ST (NL=0.33 and US=0.57), BD (NL=0.26 and US=0.49), and BCS (NL=0.24 and US=0.46). The MilkE and MY were moderately correlated with ANG in both countries (0.33 and 0.47 in NL, and 0.36 and 0.48 in US). Finally, BW was highly correlated with CW (0.77 in NL and 0.84 in US) and with BCS (0.83 in NL and 0.85 in US). Feed-intake-related traits were moderately to highly genetically correlated with conformation traits (ST, CW, BD, and BCS) in both countries, making them potentially useful as predictors of DMI.

Genotype by environment interaction for livability of dairy calves from first parity cows.

Death of calves around parturition is a matter of concern for dairy farmers. Relatively high stillbirth rates and unfavourable trends have been reported for Holstein heifers in the Netherlands and several other countries. In our study, we investigated herd differences, genetic parameters and genotype by environment interaction for heifer calf livability. A large dataset with data from calvings between 1993 and 2012 of Dutch dairy farms was used. There were considerable differences between herds in livability of calves from heifers, with averages ranging from 74% to 95%. Both herds with relatively high and low averages showed the same negative trend between 1993 and 2012, with largest declines in herds with the lowest averages. We found that heritability and genetic variation of first parity livability were substantially larger in herd environments where the likelihood of stillbirth was high v. environments where stillbirth was at a low level. The genetic correlations between herd environment levels were all very close to unity, indicating that ranking of sires was similar for all environments. However, for herds with a relatively high stillbirth incidence selecting sires with favourable breeding values is expected to be twice as profitable as in herds with a relatively low stillbirth incidence.

Genetic analysis of longevity in Dutch dairy cattle using random regression.

Longevity, productive life, or lifespan of dairy cattle is an important trait for dairy farmers, and it is defined as the time from first calving to the last test date for milk production. Methods for genetic evaluations need to account for censored data; that is, records from cows that are still alive. The aim of this study was to investigate whether these methods also need to take account of survival being genetically a different trait across the entire lifespan of a cow. The data set comprised 112,000 cows with a total of 3,964,449 observations for survival per month from first calving until 72 mo in productive life. A random regression model with second-order Legendre polynomials was fitted for the additive genetic effect. Alternative parameterizations were (1) different trait definitions for the length of time interval for survival after first calving (1, 3, 6, and 12 mo); (2) linear or threshold model; and (3) differing the order of the Legendre polynomial. The partial derivatives of a profit function were used to transform variance components on the survival scale to those for lifespan. Survival rates were higher in early life than later in life (99 vs. 95%). When survival was defined over 12-mo intervals survival curves were smooth compared with curves when 1-, 3-, or 6-mo intervals were used. Heritabilities in each interval were very low and ranged from 0.002 to 0.031, but the heritability for lifespan over the entire period of 72 mo after first calving ranged from 0.115 to 0.149. Genetic correlations between time intervals ranged from 0.25 to 1.00. Genetic parameters and breeding values for the genetic effect were more sensitive to the trait definition than to whether a linear or threshold model was used or to the order of Legendre polynomial used. Cumulative survival up to the first 6 mo predicted lifespan with an accuracy of only 0.79 to 0.85; that is, reliability of breeding value with many daughters in the first 6 mo can be, at most, 0.62 to 0.72, and changes of breeding values are still expected when daughters are getting older. Therefore, an improved model for genetic evaluation should treat survival as different traits during the lifespan by splitting lifespan in time intervals of 6 mo or less to avoid overestimated reliabilities and changes in breeding values when daughters are getting older.