Ranking Of Bulls Research Articles

325 Understanding Variation in Residual Feed Intake of Brahman Steers Fed Two Different Diets

Abstract Improving feed efficiency in cattle is a long-standing aim of the beef industry. However, discrepancies in the ranking of bulls based on the diets used and calculation methods employed for defining feed efficiency have been observed. Hence, the objective of this research was to assess the extent to which different factors influence feed efficiency in Brahman steers. Individual DMI and ADG were measured, and residual feed intake (RFI) calculated using linear regression of DMI on ADG and midtest metabolic body weight. Eight-nine steers were initially fed a low-protein diet (LP, 8.8% CP) for 70 days, followed by a high-protein (HP, 13.5% CP) diet for another 70 days. The genetic variation among steers and the proportion of Bos indicus genes were determined using the GGP Indicus 50K assay (Neogen Corporation, Ipswich, Australia). Rumen bacteriome variation was determined with 16S rRNA gene sequencing of rumen fluid collected before and after feeding. Daily diet refusals were visually scored for the proportion of pellets versus hay and expressed as a diet selection index (1 to 3). Organic matter digestibility (OMD) was determined using total feces collection in metabolism crates. Plasma urea nitrogen (PUN) was measured in blood samples collected at four time points. The diet-animal fractionation of nitrogen isotopes (Δ15Ntail hair) was quantified in tail hair using isotope-ratio mass spectroscopy to indicate differences in nitrogen metabolism. Finally, carcass characteristics were determined at slaughter. Including carcass fat depth improved (P = 0.01) DMI prediction (R2 increased from 78.4 to 80.4%) in the LP diet but had a smaller effect (P = 0.39) in the HP diet (R2 increased from 75.0 to 75.2%). In the LP diet, the variation in carcass fat-correct RFI (RFIc) could be explained by DMI (14.5% of the variation), rumen bacteriome (5.89%), diet selection index (3.08%), PUN (1.88%), genetic distance (0.76%) and OMD (0.50%), accounting for a total of 26.6% of the RFIc variation. In the HP diet, the regression model explained 35.3% of the RFIc variation, with DMI explaining 25.9%, diet selection index 7.19%, rumen bacteriome 1.02%, genetic distance 0.73% and Δ15Ntail hair 0.50%. In conclusion, this study highlights the importance of measuring diet selection to accurately determine RFI in cattle. The results also suggest that variations in the rumen bacteriome and nitrogen metabolism parameters play a role in individual variations of feed efficiency. However, most of the variation in feed efficiency remained unexplained.

РАСПРЕДЕЛЕНИЕ БЫКОВ АЙРШИРСКОЙ ПОРОДЫ ПЛЕМЕННЫХ ПРЕДПРИЯТИЙ РОССИИ ПО КРОВНОСТИ РОДСТВЕННЫХ ПОРОД

He significance of blood count of related breeds of the Ayrshire group of dairy cattle investigated. Information processed on the pedigrees of 181 bulls whose sperm found in 10 breeding enterprises of the RF. The average blood pressure of bulls in the Finnish Ayrshire breed was 54.2%, Swedish red - 9.9%, Norwegian red - 8.8%, Canadian - 25.7% and others - 1.4%. Bulls distributed according to the presence in their pedigrees of blood residues of related breeds of the Ayrshire group in the age aspect depending on the place of their birth and breeding enterprises, and the level of breeding value by milk of their daughters and pedigree. Most of the bulls were born in Russia and Finland (45.9 and 46.4% respectively) in their pedigrees the Finnish Ayrshire breed predominates. The ranking of bulls by official breeding value (BV) and by pedigree (BV PED ) revealed that in the best bulls according to these indicators (+100 kg and more) bloodline prevails in pedigrees by Finnish Ayrshire breed compared to the worst group (less than -100 kg). This regularity noted in pedigrees in 57.2 and 77.6% of bulls, depending on the assessment options. The BV of bulls and predicted positively and reliably correlates with the presence of Finnish blood in the pedigree (+ 0.233 * and + 0.308 **) and significantly negatively with Canadian (-0.252 * and -0.467 **). The findings are consistent with previous 2013 studies on the best rated Viking Genetics bulls.

Genetic evaluation of milking buffaloes (Bubalus bubalis): bull ranking

ABSTRACT The objective of this study was to evaluate genetic aspects related to production and reproductive efficiency of Murrah and Mediterranean buffaloes and their crosses. A ranking of bulls from Embrapa Eastern Amazonia was also composed to guide assisted mating. Birth records of 2,322 Murrah, Mediterranean, and crossbred buffaloes from the Embrapa Eastern Amazon herd, from 1953 to 2013, as well as information on production and reproductive traits were used. Genetic analyzes were performed in the WOMBAT software using the animal model with two-trait analysis. While heritability (h2) for total milk production (TMP) and fat milk percentage (F) were generally high, for reproductive traits h2 tended to be low. Genetic correlations for TMP with the other traits were low and negative, except for TMP with calving interval (CI) and service period (SP) in the Mediterranean breed and with age at first calving (AFC) and SP in crossbred, which were positive and high. Bull 1001 had high predicted transmitting ability (PTA) for TMP, so it should transmit a greater volume of milk to his offspring, although it had a lower PTA for F. There was sufficient variability within the herd to work with genetic management for both production and reproductive efficiency.

Evaluation of genotype by environment interactions on milk production traits of Holstein cows in southern Brazil.

ObjectiveThis study assessed the possible existence of genotype by environment interactions for milk, fat and protein yields in Holstein cattle raised in one of the most important milk production basins in Brazil.MethodsChanges in the genetic parameters and breeding values were evaluated for 57,967 animals from three distinct regions of southern Brazil, divided according to differences in climate. The genotype by environment interaction was determined by genetic correlations between regions, estimated by the restricted maximum likelihood, considering the animal model. Bull rankings were investigated to verify the ratio of coincident selected animals between regions for each trait.ResultsThe estimates of heritability coefficients were similar between two regions, but were lower in the third evaluated area, for all traits. Genetic correlations between regions were high, ranging from 0.91 to 0.99 for milk, fat and protein yields, representing the absence of a genotype by environment interaction for productive traits. The percentage of selection error between regions for the top 10% of animals ranged from 0.88% to 2.07% for milk yield, 0.99% to 2.46% for fat yield and 0.59% to 3.15% for protein yield.ConclusionA slight change in genotype between areas was expected since no significant genotype by environment interactions were identified, facilitating the process of selecting Holstein cattle in southern Brazil.

Genetic parameters, predictions, and rankings for semen production traits in a Thailand multi-breed dairy population using genomic-polygenic and polygenic models

The objectives were to compare estimates of variance components, genetic parameters, prediction accuracies, and rankings of bulls for semen volume (VOL), number of sperm (NS), and motility (MOT) using genomic-polygenic (GPRM) and polygenic repeatability models (PRM). The dataset comprised 13,535 VOL, 12,773 NS, and 12,660 MOT from 131 bulls collected from 2001 to 2017 in the Semen Production and Dairy Genetic Evaluation Center of the Dairy Farming Promotion Organization of Thailand. Genotypic data encompassed 76,519 actual and imputed SNP from 72 animals. The three-trait GPRM and PRM included the fixed effects of contemporary group, ejaculate order, age of bull, ambient temperature, and heterosis. Random effects were animal additive genetic, permanent environmental, and residual. Variance components and genetic parameters were estimated using AIREMLF90. GPRM heritabilities were slightly greater than PRM for MOT (0.27 compared with 0.24), and slightly less for VOL (0.11 compared with 0.12), and NS (0.17 compared with 0.19). Repeatabilities were slightly less for GPRM than PRM (0.44 compared with 0.45 for MOT, 0.26 compared with 0.28 for NS, and 0.20 compared with 0.21 for VOL). Additive genetic correlations were high between NS and MOT (GPRM: 0.76, PRM: 0.78), moderate between VOL and NS (GPRM: 0.43, PRM: 0.55), and near zero between VOL and MOT (GPRM: -0.13, PRM: 0.04). Rank correlations between GPRM and PRM estimated breeding values (EBV) were high for all traits. The similarity between GPRM and PRM results suggested that SNP data from the small number of genotyped animals had a minimal impact on genetic predictions in this population.

Validation of consistency of Mendelian sampling variance

Experiences from international sire evaluation indicate that the multiple-trait across-country evaluation method is sensitive to changes in genetic variance over time. Top bulls from birth year classes with inflated genetic variance will benefit, hampering reliable ranking of bulls. However, none of the methods available today enable countries to validate their national evaluation models for heterogeneity of genetic variance. We describe a new validation method to fill this gap comprising the following steps: estimating within-year genetic variances using Mendelian sampling and its prediction error variance, fitting a weighted linear regression between the estimates and the years under study, identifying possible outliers, and defining a 95% empirical confidence interval for a possible trend in the estimates. We tested the specificity and sensitivity of the proposed validation method with simulated data using a real data structure. Moderate (M) and small (S) size populations were simulated under 3 scenarios: a control with homogeneous variance and 2 scenarios with yearly increases in phenotypic variance of 2 and 10%, respectively. Results showed that the new method was able to estimate genetic variance accurately enough to detect bias in genetic variance. Under the control scenario, the trend in genetic variance was practically zero in setting M. Testing cows with an average birth year class size of more than 43,000 in setting M showed that tolerance values are needed for both the trend and the outlier tests to detect only cases with a practical effect in larger data sets. Regardless of the magnitude (yearly increases in phenotypic variance of 2 or 10%) of the generated trend, it deviated statistically significantly from zero in all data replicates for both cows and bulls in setting M. In setting S with a mean of 27 bulls in a year class, the sampling error and thus the probability of a false-positive result clearly increased. Still, overall estimated genetic variance was close to the parametric value. Only rather strong trends in genetic variance deviated statistically significantly from zero in setting S. Results also showed that the new method was sensitive to the quality of the approximated reliabilities of breeding values used in calculating the prediction error variance. Thus, we recommend that only animals with a reliability of Mendelian sampling higher than 0.1 be included in the test and that low heritability traits be analyzed using bull data sets only.

Index proposal for Nelore bulls classification using traits groups of weight, carcass and reproductive

Genetic breeding programs generally evaluate animals considering the same objectives, but the indices used in the selection criteria can vary. This can give rise to distinct bull rankings in each program. Thus, we aimed to create alternatives for the referral of bulls for mating through multivariate analyses. We used information from the summaries of two genetic evaluation programs (1 and 2) with Nellore bulls. Characteristics were separated into groups: weight, carcass and reproduction. Groups were formed using the mean Euclidean distance and the Tocher optimization method. The means of each trait among the animals allocated to the same group were used to compose the following subindexes: weight gain (siWG), carcass (siCG) and reproduction (siRG). Based on the mean of the group subindex classification, we calculated the average index of group classification (iAGc). When classifying the best groups by characteristics, we observed an increase (superiority of some bulls) in the estimated breeding values (EBVs) by comparison with the total number of bulls evaluated by the programs, selected animals and best 10 final indexes of the programs. There was a change in bull classification when using the iAGc in relation to the classification using the final indexes of the programs. The coefficient of simple coincidence showed that there was a change in bull classification between programs, both between deciles of the final indexes and groups of characteristics. The subindexes siWG, siCG and siRG are important for correcting specific problems in herds. The iAGc should be used instead of the final index of the programs, providing more options for the selection of bulls for mating.

Detrimental effect of selection for milk yield on genetic tolerance to heat stress in purebred Zebu cattle: Genetic parameters and trends

In an attempt to determine the possible detrimental effects of continuous selection for milk yield on the genetic tolerance of Zebu cattle to heat stress, genetic parameters and trends of the response to heat stress for 86,950 test-day (TD) milk yield records from 14,670 first lactations of purebred dairy Gir cows were estimated. A random regression model with regression on days in milk (DIM) and temperature–humidity index (THI) values was applied to the data. The most detrimental effect of THI on milk yield was observed in the stage of lactation with higher milk production, DIM 61 to 120 (−0.099kg/d per THI). Although modest variations were observed for the THI scale, a reduction in additive genetic variance as well as in permanent environmental and residual variance was observed with increasing THI values. The heritability estimates showed a slight increase with increasing THI values for any DIM. The correlations between additive genetic effects across the THI scale showed that, for most of the THI values, genotype by environment interactions due to heat stress were less important for the ranking of bulls. However, for extreme THI values, this type of genotype by environment interaction may lead to an important error in selection. As a result of the selection for milk yield practiced in the dairy Gir population for 3 decades, the genetic trend of cumulative milk yield was significantly positive for production in both high (51.81kg/yr) and low THI values (78.48kg/yr). However, the difference between the breeding values of animals at high and low THI may be considered alarming (355kg in 2011). The genetic trends observed for the regression coefficients related to general production level (intercept of the reaction norm) and specific ability to respond to heat stress (slope of the reaction norm) indicate that the dairy Gir population is heading toward a higher production level at the expense of lower tolerance to heat stress. These trends reflect the genetic antagonism between production and tolerance to heat stress demonstrated by the negative genetic correlation between these components (−0.23). Monitoring trends of the genetic component of heat stress would be a reasonable measure to avoid deterioration in one of the main traits of Zebu cattle (i.e., high tolerance to heat stress). On the basis of current genetic trends, the need for future genetic evaluation of dairy Zebu animals for tolerance to heat stress cannot be ruled out.

Conventional and fluorescent based semen quality assessment in Karan Fries bulls.

Aim:The present study was carried out on semen ejaculates of 15 Karan Fries (KF) bulls maintained at Artificial Breeding Research Centre, National Dairy Research Institute, Karnal, India with an objective to evaluate the relationship between the conventional and fluorescent based semen quality analysis of the bulls.Materials and Methods:A total of 96 ejaculates were collected from 15 KF (Holstein Friesian [HF] crossbred) bulls. Semen were evaluated for color, volume, mass activity (MA) and percentage of individual motility (IM), sperm concentration, percent live spermatozoa, hypo-osmotic swelling test and acrosome integrity, chromatin integrity, sperm viability, and membrane integrity. Data were analyzed using SPSS software package for descriptive analysis. The correlation between rankings of sires based on conventional and fluorescent semen parameters were calculated by Spearman’s rank correlation coefficient.Results:The average ejaculates volume (ml), sperm concentration (106/ml), MA, IM (%), live (%), morphological abnormalities (%), host (%), acrosome integrity (%), chromomycin A3 (CMA3) (%), SYBR-PI (%), and fluorescent isothiocyanate-peanut agglutinin (FITC-PNA) (%) were 4.57±0.36, 1162.98±97.93, 2.95±0.09, 60.8±1.22, 71.41±2.10, 9.31±1.15, 65.5±1.81, 86.6±1.59, 3.53±0.43, 65.39±2.23 and 74.47±2.53, respectively. Rank correlations were found to be significant for SYBR-PI and FITC-PNA with most of the parameters evaluated by conventional methods. Overall, among conventional criteria, IM revealed ranking of bulls almost similar to that of fluorescent criteria.Conclusion:Overview of our results indicated that, among conventional criteria, MA and IM revealed ranking of bulls almost similar to that of fluorescent criteria.

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.

The sensitivity of predicted financial and genetic gains in Holsteins to changes in the economic value of traits

The objective of this study was to assess the impact of using different relative economic values (REVs) in selection indices on predicted financial and trait gains from selection of sires of cows and on the choice of leading Holstein bulls available in the UK dairy industry. Breeding objective traits were milk yield, fat yield, protein yield, lifespan, mastitis, non-return rate, calving interval and lameness. Relative importance of a trait, as estimated by a.h(2), was only moderately related to the rate of financial loss or total economic merit (ΔTEM) per percentage under- or overestimation of REV (r = 0.38 and 0.29, respectively) as a result of the variance-covariance structure of traits. The effects on TEM of under- or overestimating trait REVs were non-symmetrical. TEM was most sensitive to incorrect REVs for protein, fat, milk and lifespan and least sensitive to incorrect calving interval, lameness, non-return and mastitis REVs. A guide to deciding which dairy traits require the most rigorous analysis in the calculation of their REVs is given. Varying the REVs within a fairly wide range resulted in different bulls being selected by index and their differing predicted transmitting abilities would result in the herds moving in different directions in the long term (20 years). It is suggested that customized indices, where the farmer creates rankings of bulls tailored to their specific farm circumstances, can be worthwhile.

Principal component and factor analytic models in international sire evaluation.

BackgroundInterbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.MethodsPrincipal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.ResultsIn total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.ConclusionsGenetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.

Short communication: Quantifying bias in a single-trait international model ignoring covariances from multiple-trait national models

The current method in use for international genetic evaluations, called single-trait multiple across-country evaluation (ST-MACE), does not consider residual covariances among traits, making possible only the inclusion of one trait per country in an analysis. The aim of this study was to quantify the effect of bias resulting from treating traits from the same country as nationally independent in an international genetic evaluation. Data from the September 2007 Interbull test evaluation for Holstein female fertility traits were used. Data included were 1 trait from Belgium, Switzerland, Spain, and the United States of America, and 2 traits from Canada, Germany-Austria, and Denmark-Finland-Sweden. The biased results were obtained from a 10-variate ST-MACE analysis including all country traits. The unbiased results were obtained from 8 different 7-variate ST-MACE analyses, each including only 1 trait per country. Average absolute bias in the genetic correlations among 2-trait countries (0.11) was higher than for between 1-trait countries and 2-trait countries (0.07) and for among 1-trait countries (0.03). The results of the biased and the unbiased analyses were different, not only due to bias, but also because of different number of traits involved in the analyses. Differences were considerable (on average, 0.08 to 6.91) for reliabilities, which were higher for traits with lower heritability. Average differences were minor (−0.04 to 0.03 standard deviations) for predicted genetic merits. However, for the top 100 bulls in each country trait, these differences were important (on average, −0.26 to 0.11 standard deviation of predicted genetic merit), which caused considerable changes in bull rankings. The results of this study showed that the effect of bias, caused by ignoring covariances from multiple-trait national models in an ST-MACE analysis, is of such a magnitude that necessitates the use of another method such as multiple-trait multiple across-country evaluation.

Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region–age at calving–season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.

Calculation of lifetime net income per year (LTNI/year) of Australian Holstein cows to validate the Australian profit ranking of their sires. 2. Validation of the Australian profit ranking of sires and test for genotype by environment interaction based on LTNI/year

In Australia, the Australian Dairy Herd Improvement Scheme publishes the Australian profit ranking (APR), which is an economic index combining all economic traits. The APR is used to select sires that will breed the most profitable daughters. The objectives of this study were to test the predictive power of the APR by looking at the relationship between the APR of sires and lifetime net income per year (LTNI/year) of their future daughters and to test if this prediction was accurate in different types of herds. The importance of re-ranking was also tested by considering LTNI/year of cows in different herds as different traits and estimating the genetic correlation between LTNI/year in different herd types (herds differing in calving systems, region or production level). An additional objective was to test the value of individual trait Australian breeding values (ABV) of sires to predict LTNI/year of their future daughters. The results show that overall the relationship between the APR of bulls and the LTNI/year of their daughters was as expected. That is, the regression of daughter’s LTNI/year on sire’s APR equals 0.5 as expected because cows inherit half their genes from their sire. When LTNI/year of cows was regressed on sire’s APR separately for different herd types, the regression coefficients varied from 0.36 (low production herds) to 0.62 (high production herds). The genetic correlation for LTNI/year between herd types was always high indicating little re-ranking of sires. The lowest was 0.89 between LTNI/year of cows in seasonal and year-round calving systems as well as between Regions 3 (Gippsland and Tasmania) and Region 1 (New South Wales, Queensland, South Australia and Western Australia) herds. Thus, an APR calculated across all herd types will give a good ranking of bulls in any herd type. When LTNI/year of cows was regressed on individual sire ABV the regression coefficients were approximately half the economic weight for each trait as expected. Within seasonal calving and low production herds the regression on sire’s fertility ABV was higher than expected and the regression in year-round and high production herds was less than expected. In conclusion, over all herds the sire’s APR predicts LTNI/year of future daughters as expected. Owners of low production, seasonal calving herds will get slightly less benefit from 1 unit increase in APR than owners of high production, year-round calving herds and could perhaps benefit from placing additional emphasis on the fertility ABV when selecting sires.

Binational evaluation of type traits from Germany and France with a single-trait MACE animal model

Binational genetic evaluation between Germany and France were performed for each type trait using a single-trait MACE (multiple across-country evaluation) model. Daughter yield deviations (DYD) of bulls having 30 equivalent daughter contributions or more were the data for parameter estimation. Full pedigree information of bulls was used via sire and dam relationships. In general, across-country genetic correlation estimates were in agreement with what is observed by Interbull. The estimated correlations were over 0.93 for stature, rump angle, udder depth, front teat placement, teat length and rear teat placement. These traits have been classified in both countries for a long period of time. However, some other type traits were included later in the French type classification system (most of them since 2000): chest width, body depth, angularity, rump width, rear leg rear view, fore udder and rear udder height. The estimated correlations for these traits were relatively low. In order to check changes in genetic correlations over time, data from bulls born until the end of 1995 were discarded. Higher genetic correlation estimates between both countries were obtained by using more recent data especially for traits having lower genetic correlation, e.g. body depth correlation increased from 0.55 to 0.83. Once genetic correlations were estimated, binational genetic evaluation between Germany and France were performed for each type trait using DYD of bulls. The rankings of bulls obtained from this evaluation had some differences with Interbull rankings but a similar proportion of bulls from each country was found. Finally, more computationally demanding binational evaluations were performed using yield deviations of cows for binational cow comparison. The rankings obtained were influenced by the number of daughters per bull and heritabilities used in each country.

Linear–threshold animal model for birth weight, gestation length and calving ease in United Kingdom Limousin beef cattle data

Breeding value evaluation for UK Limousin beef cattle data was carried out by multiple-trait linear–threshold animal model with variance components assumed to be known. Polychotomous calving ease with five categories was analysed with two continuous traits: birth weight and gestation length. Field data consisted of 220,799 animals with observations with every possible combination of traits, and 270,035 animals in the pedigree. The threshold model was solved either with Newton Raphson or Expectation Maximisation algorithm, and solutions were compared to evaluation by a linear model with original and normalised scores. There were insignificant differences in solutions between the two algorithms for threshold model analyses. Furthermore, solutions of the continuous traits were similar by the threshold and linear models. For the categorical trait, correlations for random effects from the threshold and linear models were high. In case of normalised scores (original scores case in brackets) correlations with solutions from the threshold and linear model were 0.97 (0.94) and 0.97 (0.93) for direct and maternal genetic effects and 0.95 (0.89) for permanent maternal effects. Even so, at least one third of the top 1% ranking of bulls differed between the linear and the threshold models. Predictive abilities as correlations between estimated breeding values and pedigree indices were almost equal between the linear and threshold models for both continuous and categorical traits. In conclusion, despite the higher computational demand, the linear–threshold animal model can be seen worthwhile in the genetic evaluation of the national UK beef cattle data set.

Genetic evaluation of growth in a multibreed beef cattle population using random regression-linear spline models1

The objective of this study was to examine the feasibility of using random regression-spline (RR-spline) models for fitting growth traits in a multibreed beef cattle population. To meet the objective, the results from the RR-spline model were compared with the widely used multitrait (MT) model when both were fit to a data set (1.8 million records and 1.1 million animals) provided by the American Gelbvieh Association. The effect of prior information on the EBV of sires was also investigated. In both RR-spline and MT models, the following effects were considered: individual direct and maternal additive genetic effects, contemporary group, age of the animal at measurement, direct and maternal heterosis, and direct and maternal additive genetic mean effect of the breed. Additionally, the RR-spline model included an individual direct permanent environmental effect. When both MT and RR-spline models were applied to a data set containing records for weaning weight (WWT) and yearling weight (YWT) within specified age ranges, the rankings of bulls' direct EBV (as measured via Pearson correlations) provided by both models were comparable, with slightly greater differences in the reranking of bulls observed for YWT evaluations (>or=0.99 for BWT and WWT and >or=0.98 for YWT); also, some bulls dropped from the top 100 list when these lists were compared across methods. For maternal effects, the estimated correlations were slightly smaller, particularly for YWT; again, some drops from the top 100 animals were observed. As in regular MT multibreed genetic evaluations, the heterosis effects and the additive genetic effects of the breed could not be estimated from field data, because there were not enough contemporary groups with the proper composition of purebred and crossbred animals; thus, prior information based on literature values had to be included. The inclusion of prior information had a negligible effect in the overall ranking for bulls with greater than 20 birth weight progeny records; however, the effect of prior information for breeds or groups poorly represented in the data was important. The Pearson correlations for direct and maternal WWT and YWT ranged from 0.95 to 0.98 when comparing evaluations of data sets for which the out-of-range age records were removed or retained. Random regression allows for avoiding the discarding of records that are outside the usual age ranges of measurement; thus, greater accuracies are achieved, and greater genetic progress could be expected.

Selection of Sahiwal Cattle Bulls on Pedigree and Progeny

rhe objective of the study was to compare ranking of Sahiwal bulls selected on the basis of highest lactation milk yield of their dams with their estimated breeding values (EBVs) using an animal model. Data on 23,761 lactation milk yield records of 5,936 cows from five main Livestock Experiment Stations in Punjab province of Pakistan (1964-2004) were used for the study. At present the young A.I bulls are required to be from A-category bull-dams. Dams were categorized as A, B, C and D if they had highest lactation milk yield of≥2,700, 2,250-2,699, 1,800-2,249 and <1,800 litres, respectively. The EBVs for lactation milk yield were estimated for all the animals using an individual animal model having fixed effect of herd-year and season of calving and random effect of animal. Fixed effect of parity and random effect of permanent environment were incorporated when multiple lactation were used. There were 396 young bulls used for semen collection and A.I during 1973-2004. However, progeny with lactation yields recorded, were available only for 91 bulls and dams could be traced for only 63 bulls. Overall lactation milk yield averaged 1,440.8 kg. Milk yield was 10% heritable with repeatability of 39%. Ranking bulls on highest lactation milk yield of their dams, the in-vogue criteria of selecting bulls, had a rank correlation of 0.167 (p<0.190) with ranking based on EBVs from animal model analysis. Bulls' EBVs for all lactations had rank correlation of 0.716 (p<0.001) with EBVs based on first lactation milk yield and 0.766 (p<0.001) with average EBVs of dam and sire (pedigree index). Ranking of bulls on highest lactation yield of their dams has no association with their ranking based on animal model evaluation. Young Sahiwal bulls should be selected on the basis of pedigree index instead of highest lactation yield of dams. This can help improve the genetic potential of the breed accruing to conservation and development efforts.

Adjustment of heterogeneous variances and a calving year effect in test-day models for national genetic evaluation of dairy cattle in South Africa

South Africa implemented test-day models for genetic evaluations of production traits, using a Fixed Regression Test-Day Model (FRTDM), which assumes equal variances of the response variable at different days in milk, the explanatory variable. Data at the beginning and at the end of lactation period, have higher variances than tests in the middle of the lactation. Furthermore, first lactations have lower mean and variances compared to second and third lactations. This is a deviation from the basic assumptions required for the application of repeatability models. A modification was therefore implemented to reduce the effect of deviating from this assumption. Test-day milk, butterfat and protein yield records of Jersey cows, participating in the South African Milk Recording Scheme, were therefore pre-adjusted such that the variances are on the same scale. Variance components estimated using the adjusted records were higher than using unadjusted records. Convergence of breeding value estimation is reached significantly faster when using adjusted data (± 4000 iterations) compared to unadjusted records (± 15 000 iterations). Although cow and bull rankings were not influenced much, significant changes in breeding values for individual animals and genetic trends of especially young animals, were found. South African Journal of Animal Science Vol. 36(3) 2006: 165-174