Synchronization of breeding and its impact on genetic parameters and evaluation of female fertility traits

Achieving an acceptable level of fertility in herds is difficult for many dairy producers because identifying cows in estrus has become challenging owing to poor estrus expression, increased herd size, and lack of time and skilled labor for estrus detection. As a result, synchronization of estrus is often used to manage reproduction. The aims of this study were (1) to identify artificial inseminations (AI) that were performed following synchronization and (2) to assess the effect of synchronization on genetic parameters and evaluation of fertility traits. This study used breeding data collected between 1995 and 2021 from over 4,600 Australian dairy herds that had at least 30 matings per year. Because breeding methods were not reported, the recording pattern of breeding dates showing a large proportion of the total AI being recorded on a single date of the year served as an indicator of synchronization. First, the proportion of AI recorded on each day of the year was calculated for each herd-year. Subsequently, synchronization was defined when a herd with, for instance, only 30 matings in a year, had at least 0.20 or more AI on the same day. As the number of breedings in a herd-year increased, the threshold for classifying AI was continuously reduced from 0.20 to as low as 0.03 under the assumption that mating of many cows on a single date becomes increasingly difficult without synchronization. From the current data, we deduced that 0.11 of all AI were possibly performed following synchronization (i.e., timed AI, TAI). The proportion of AI classified as TAI increased over time and with herd size. Although the deviation from equal numbers of mating on 7 d of the week was not used for classifying AI, 0.44 of AI being categorized as TAI were performed on just 2 d of the week. When data classified as TAI were used for estimating genetic parameters and breeding values, the interval between calving and first service (CFS) was found to be the most affected trait. The phenotypic and additive genetic variance and heritability, as well as variability and reliability of estimated breeding values of bulls and cows for CFS were lower for TAI than for AI performed following detected estrus (i.e., estrus-detected AI, EAI). For calving interval, first service nonreturn rate (FNRR), and successful calving rate to first service, genetic correlations between the same trait measured in TAI and EAI were close to 1, in contrast to 0.55 for CFS. The lower genetic variances and heritabilities for FNRR and calving interval in TAI than in EAI suggests that synchronization reduces the genetic variability of fertility. In conclusion, TAI makes CFS an ineffective measure of fertility. One approach to minimize this effect on genetic evaluations is to identify TAI (using the method described for example) and then set the CFS of these cows as missing records when running multitrait genetic evaluations of fertility traits that include CFS. In the long term, the most practical and accurate way to reduce the effect of synchronization on genetic evaluations is to record TAI along with mating data.     

Alternative subclinical mastitis traits for genetic evaluation in dairy cattle

Chronic subclinical mastitis (SCM), characterized by changes in milk composition and high somatic cell count (SCC) in milk for a prolonged period of time, is often caused by a bacterial infection. Different levels of SCC have been suggested and used as threshold to identify subclinical infection. The aim of this study was to examine different definitions of SCM based on test-day SCC and estimate genetic parameters for these traits and their genetic correlation to milk production. Test-day SCC records from 1,209,128 Norwegian Red cows in lactation 1 to 3 were analyzed. Twelve SCM traits were defined as binary with 2 test-day SCC in a row above SCC thresholds from 50,000 to 400,000 cells/mL (SCM50, SCM100, SCM150, SCM200, SCM250, SCM300, SCM350, and SCM400), with 3 test-day SCC in a row above 200,000 and 400,000 cells/mL (SCM200_3 and SCM400_3), and the number of days before the first case with SCM50 (D50) or SCM400 (D400). The heritability and genetic correlations were estimated for SCM traits and the mean lactation-average somatic cell score (LSCS) using linear animal repeatability models. The total mean frequency of SCM ranged from 1.2% to 51.8%, for different trait definitions, high for low SCC threshold (SCM50) and low for the highest SCC threshold (SCM400_3). For the 2 traits based on number of days, the mean values were 104 (D50) and 117 (D400) days. The mean LSCS was 4.4 (equivalent to around 82,000 SCC). Heritabilities for the 12 alternative SCM traits were low and varied from 0.01 (SCM400_3) to 0.1 (SCM100), whereas for LSCS the estimated heritability was 0.3 and standard error varied from 0.001 to 0.003. Genetic correlations among the SCM traits ranged from 0.7 (D50 and SCM400) to 1 (SCM350 and SCM400), whereas between SCM traits and milk production the correlation ranged from 0.07 (LSCS) to 0.3 (D400). The standard error for genetic correlations varied from 0.001 to 0.06. The heritability was low and the genetic correlations were strong among SCM traits. Genetic correlations lower than 1 suggest that the alternative SCM traits are genetically different from LSCS, the trait currently used in genetic evaluations for Norwegian Red. Hence, the alternative traits will add information and improve breeding for better udder health.     

Estimating genetic and phenotypic parameters of cellular immune-associated traits in dairy cows

Data collected from an experimental Holstein-Friesian research herd were used to determine genetic and phenotypic parameters of innate and adaptive cellular immune-associated traits. Relationships between immune-associated traits and production, health, and fertility traits were also investigated. Repeated blood leukocyte records were analyzed in 546 cows for 9 cellular immune-associated traits, including percent T cell subsets, B cells, NK cells, and granulocytes. Variance components were estimated by univariate analysis. Heritability estimates were obtained for all 9 traits, the highest of which were observed in the T cell subsets percent CD4⁺, percent CD8⁺, CD4⁺:CD8⁺ ratio, and percent NKp46⁺ cells (0.46, 0.41, 0.43 and 0.42, respectively), with between-individual variation accounting for 59 to 81% of total phenotypic variance. Associations between immune-associated traits and production, health, and fertility traits were investigated with bivariate analyses. Strong genetic correlations were observed between percent NKp46⁺ and stillbirth rate (0.61), and lameness episodes and percent CD8⁺ (?0.51). Regarding production traits, the strongest relationships were between CD4⁺:CD8⁺ ratio and weight phenotypes (?0.52 for live weight; ?0.51 for empty body weight). Associations between feed conversion traits and immune-associated traits were also observed. Our results provide evidence that cellular immune-associated traits are heritable and repeatable, and the noticeable variation between animals would permit selection for altered trait values, particularly in the case of the T cell subsets. The associations we observed between immune-associated, health, fertility, and production traits suggest that genetic selection for cellular immune-associated traits could provide a useful tool in improving animal health, fitness, and fertility.     

Effect of synchronized breeding on genetic evaluations of fertility traits in dairy cattle

Estrus detection has become more difficult over the years due to decreases in the estrus expression of high-producing dairy cows, and increased herd sizes and animal density. Through the use of hormonal synchronization protocols, also known as timed artificial insemination (TAI) protocols, it is possible to alleviate some of the challenges associated with estrus detection. However, TAI masks cows' fertility performance, resulting in an unfair comparison of treated animals and innately fertile animals. Consequently, genetically inferior and superior cows show similar phenotypes, making it difficult to distinguish between them. As genetic programs rely on the collection of accurate phenotypic data, phenotypes collected on treated animals likely add bias to genetic evaluations. In this study, to assess the effect of TAI, the rank correlation of bulls for a given trait using only TAI records were compared with the same trait using only heat detection records. A total of 270,434 records from 192,539 animals split across heifers, first and second parity cows were analyzed for the traits: calving to first service, first service to conception, and days open. Results showed large reranking across all traits and parities between bulls compared based on either having only TAI records or only heat detection records, suggesting that a bias does indeed exist. Large reranking was also observed for both the heat detection and TAI groups among the top 100 bulls in the control group, which included all records. Furthermore, breeding method was added to the model to assess its effect on bull ranking. However, there were only minor changes in the rank correlations between scenario groups. Therefore, more complex methods to account for the apparent bias created by TAI should be investigated; for this, the method by which these data are collected needs to be improved through creating a standardized way of recording breeding codes. Though the results of this study suggest the presence of bias within current fertility evaluations, additional research is required to confirm the findings of this study, including looking at high-reliability bulls specifically, to determine if the levels of reranking remain. Future studies should also aim to understand the potential genetic differences between the fertility traits split via management technology, possibly in a multiple-trait analysis.     

Genetic parameters for reproductive losses estimated from in-line milk progesterone profiles in Swedish dairy cattle

This study assessed the extent of reproductive losses and associated genetic parameters in dairy cattle, using in-line milk progesterone records for 14 Swedish herds collected by DeLaval's Herd Navigator. A total of 330,071 progesterone samples were linked to 10,219 inseminations (AI) from 5,238 lactations in 1,457 Swedish Red and 1,847 Swedish Holstein cows. Pregnancy loss traits were defined as early embryonic loss (1–24 d after AI), late embryonic loss (25–41 d after AI), fetal loss (42 d after AI until calving), and total pregnancy loss (from d 1 after AI until calving). The following classical fertility traits were also analyzed: interval from calving to first service, interval from calving to last service, interval between first and last service, calving interval, and number of inseminations per service period. Least squares means with standard error (LSM ± SE), heritabilities, and genetic correlations were estimated in a mixed linear model. Fixed effects included breed, parity (1, 2, ≥3), estrus cycle number when the AI took place, and a linear regression on 305-d milk yield. Herd by year and season of AI, cow, and permanent environmental effect were considered random effects. Extensive (approximately 45%) early embryonic loss was found, but with no difference between the breeds. Swedish Red was superior to Swedish Holstein in the remaining pregnancy loss traits with, respectively: late embryonic loss of 6.1 ± 1.2% compared with 13.3 ± 1.1%, fetal loss of 7.0 ± 1.2% compared with 12.3 ± 1.2%, and total pregnancy loss of 54.4 ± 1.4% compared with 60.6 ± 1.4%. Swedish Red also had shorter calving to first service and calving to last service than Swedish Holstein. Estimated heritability was 0.03, 0.06, and 0.02 for early embryonic, late embryonic, and total pregnancy loss, respectively. Milk yield was moderately genetically correlated with both early and late embryonic loss (0.52 and 0.39, respectively). The pregnancy loss traits were also correlated with several classical fertility traits (?0.46 to 0.92). In conclusion, Swedish Red cows had lower reproductive loss during late embryonic stage, fetal stage, and in total, and better fertility than Swedish Holstein cows. The heritability estimates for pregnancy loss traits were of the same order of magnitude as previously reported for classical fertility traits. These findings could be valuable in work to determine genetic variation in reproductive loss and its potential usefulness as an alternative fertility trait to be considered in genetic or genomic evaluations.     

Estimation of genetic parameters for novel functional traits in Brown Swiss cattle

The aim of this study was to estimate genetic parameters and accuracies of breeding values for a set of functional, behavior, and conformation traits in Brown Swiss cattle. These traits were milking speed, udder depth, position of labia, rank order in herd, general temperament, aggressiveness, milking temperament, and days to first heat. Data of 1,799 phenotyped Brown Swiss cows from 40 Swiss dairy herds were analyzed taking the complete pedigree into account. Estimated heritabilities were within the ranges reported in literature, with results at the high end of the reported values for some traits (e.g., milking speed: 0.42 ± 0.06, udder depth: 0.42 ± 0.06), whereas other traits were of low heritability and heritability estimates were of low accuracy (e.g., milking temperament: 0.04 ± 0.04, days to first heat: 0.02 ± 0.04). For most behavior traits, we found relatively high heritabilities (general temperament: 0.38 ± 0.07, aggressiveness: 0.12 ± 0.08, and rank order in herd: 0.16 ± 0.06). Position of labia, arguably an indicator trait for pathological urovagina, was genetically analyzed in this study for the first time, and a moderate heritability (0.28 ± 0.06) was estimated.     

Voluntary waiting period and adoption of synchronized breeding in dairy herd improvement herds

Voluntary waiting period and adoption of synchronized breeding (ovulation synchronization followed by timed artificial insemination) were characterized from 33 million services of Holsteins and Jerseys in Dairy Herd Improvement herds. Calving month, calving year, and parity had large effects on days to first service for both breeds. Holstein cows that calved during March and April were bred later than those that calved during other months (February and March for Jerseys), whereas cows that calved during September and October were bred earlier. First-parity cows had longer days to first service than did second-parity cows. Herd-year voluntary waiting period was measured as the days postpartum by which 10% of cows had received a first insemination. Median days to reach 10% of cows bred were 55.5 d. Over 65% of herds had 10% of cows inseminated by 60 d postpartum, the voluntary waiting period assumed for national evaluations for daughter pregnancy rate. Herd-years with synchronized breeding at first insemination were identified through χ² analysis based on deviation of observed frequency of first inseminations by day of the week from an expected equal frequency and by the maximum percentage of cows inseminated on a particular day of the week. Herds that were identified as having synchronized breeding had fewer days to first service (17.0), more services (0.16/cow), and fewer days open (9.1) than did herds that were classified as having traditional estrus detection. Synchronized herds also had a standard deviation for days to first service that was only 38% as large as that for herds that bred on observed estrus. Adoption of synchronized breeding for first services steadily increased from 1.9% of herd-years (2% of cows) for 1996 to 19.9% of herd-years (34.9% of cows) for 2005. Procedures for genetic evaluation of daughter pregnancy rate should be examined to determine if herd regimen for reproductive management affects results.     

Estimation of genetic parameters for productive life,reproduction, and milk-production traits in US dairy goats

Heritabilities and correlations for milk yield (MY), fat yield (FY), protein yield (PY), combined fat and protein yield (FPY), fat percentage (F%), protein percentage (P%), age at first kidding (AFK), interval between the first and second kidding (KI), and real and functional productive life at 72 mo (FPL72) of 33,725 US dairy goats, were estimated using animal models. Productive life was defined as the total days in production until 72 mo of age (PL72) for goats having the opportunity to express the trait. Functional productive life was obtained by correcting PL72 for MY, FY, PY, and final type score (FS). Six selection indexes were used, including or excluding PL72, with 6 groups of different economic weights, to estimate the responses to selection considering MY, FY, PY, and PL72 as selection criteria. The main criteria that determined the culling of a goat from the herd were low FS, MY, and FY per lactation. Heritability estimates were 0.22, 0.17, 0.37, 0.37, 0.38, 0.39, 0.54, 0.64, 0.09, and 0.16 for PL72, FPL72, MY, FY, PY, FPY, F%, P%, KI, and AFK, respectively. Most genetic correlations between the evaluated traits and PL72 or FPL72 were positive, except for F% (−0.04 and −0.06, respectively), P% (−0.002 and −0.03, respectively), and AFK (−0.03 and −0.01, respectively). The highest genetic correlations were between FPL72 and MY (0.39) and between PL72 and MY (0.33). Most phenotypic correlations between the traits evaluated and FPL72 and PL72 were positive (>0.23 and >0.26, respectively), except for F% (−0.004 and −0.02, respectively), P% (−0.05 and −0.02), KI (−0.01 and −0.07), and AFK (−0.08 and −0.08). The direct selection for PL72 increased it by 102.28 d per generation. The use of MY, FY, PY, KI, or AFK as selection criteria increased PL72 by 39.21, 27.33, 35.90, −8.28, or 2.77 d per generation, respectively. The inclusion of PL72 as selection criterion increased the expected response per generation from 0.15 to 17.35% in all selection indices studied.     

Genetic parameters for anovulation and pregnancy loss in dairy cattle

The objectives were to estimate heritabilities and genetic variances for anovulation at ∼50 d in milk and pregnancy loss occurring between first and second pregnancy diagnoses after artificial insemination. Data were originally collected for trials on reproductive management. Anovulation data consisted of 5,818 records from 13 studies in 8 herds with an overall prevalence of 23.3%. A Bayesian approach using Markov Chain Monte Carlo methods was used in mixed threshold models for both traits. The statistical model for anovulation included fixed effects [parity, herd-study-treatment, and body condition score (BCS)], covariates (inbreeding and milk yield), and random effects (sire and residual). A second statistical model included all terms in the first model except BCS. In addition, 2 bivariate, mixed sire models were used to analyze anovulation with BCS and anovulation with milk yield. The posterior mean heritability estimate for anovulation was 0.171 [posterior standard deviation (PSD) = 0.052]. Correlations of anovulation with milk yield were as follows: genetic = 0.168, PSD = 0.187; residual = −0.046, PSD = 0.022; and phenotypic = −0.036. Bivariate analysis of BCS with anovulation showed a genetic correlation (−0.301, PSD = 0.177) and phenotypic correlation (−0.192, PSD = 0.019). Pregnancy-loss data consisted of 3,775 records from 14 studies in 8 herds with an overall prevalence of 14.4%. Analysis of pregnancy loss used a sire-maternal grandsire threshold model with embryo survival as the subject of analysis. Independent variables consisted of fixed effects (parity and herd-study), covariates (embryo and maternal inbreeding), and random effects (sire of embryo, maternal grandsire of embryo, and residual). In addition, separate sire models were analyzed using embryo as the subject and cow as the subject of analysis. The sire-maternal grandsire model yielded a heritability for direct effect of 0.489 (PSD = 0.221) and for maternal effects of 0.166 (PSD = 0.113). In this study, the breeding value variance for embryo effects was 3 times the breeding value variance for maternal effects, indicating that, at the level of breeding values, the embryo's ability to survive has a greater effect on pregnancy loss than does the cow's ability to maintain the pregnancy. These results suggest that genetic improvement of reproductive performance could be enhanced by selection for fundamental measures such as abnormally long periods of postpartum anovulation and pregnancy loss. Larger studies of these traits are needed to obtain parameter estimates with greater precision.     

Genetic relationships and additive genetic variation of productive and reproductive traits in Guernsey dairy cattle

Data were first lactation production and reproduction records initiated from 1958 to 1981 in two experiment station Guernsey herds. Heritability estimates using paternal half sib groups were .24 +/- .12 for milk yield, .27 +/- .12 for fat yield, and .77 +/- .15 for fat percentage. Heritability estimates for reproductive traits ranged from .01 to .04 for number of services, service period, conception rate, and days open, but were higher for days in milk at first breeding (.12) and age at first calving (.13). Except for age at first calving, coefficients of additive genetic variation were larger for reproductive traits than for productive traits. Genetic correlations between measures of production and reproduction were moderate to large and antagonistic, except that the relationship between production and age at first calving was favorable. Breeding value estimates for milk yield and reproduction were negatively correlated for sires with above average breeding values for milk yield. Huge phenotypic variances for reproductive traits masked substantial additive genetic variation for these traits. When all things are considered it seems unwise to ignore reproductive performance in selection programs for dairy cattle.     

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from −0.48 to −0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (−0.58 ± 0.02) and MFAIL and FRM (−0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.     

Estimation of genetic parameters for individual udder quarter milk content traits in Brown Swiss cattle

The aim of this study was to estimate genetic parameters and accuracies of breeding values for milk content traits of individual udder quarters in Brown Swiss cattle. Data of 1,799 phenotyped cows from 40 Swiss dairy herds were analyzed, taking the complete pedigree into account. Fat, protein, lactose, and urea contents, somatic cell score (SCS), and information about hyperkeratosis were available for each udder quarter. The milk of rear udder quarters was found to have significantly higher lactose content and significantly lower fat content than milk of the front udder quarters. The same trend found for fat content was observed for protein content, whereas no differences between the udder quarters were observed for urea content, SCS, or hyperkeratosis. Heritabilities for each udder quarter were in the following ranges: fat content 0.09 ± 0.06 to 0.14 ± 0.06, protein content 0.20 ± 0.09 to 0.33 ± 0.07, lactose content 0.04 ± 0.03 to 0.16 ± 0.07, urea content 0.13 ± 0.07 to 0.22 ± 0.08, SCS 0.18 ± 0.06 to 0.32 ± 0.07, and hyperkeratosis 0.12 ± 0.04 to 0.26 ± 0.05. In our study, hyperkeratosis, protein content, and SCS showed higher heritabilities in the front udder quarters, fat content had higher heritabilities in the rear udder quarters, and no systematic pattern in heritability was observed for lactose content or urea content. Additive genetic correlations between all udder quarters were >0.90 for protein and urea contents, whereas they were remarkably low (<0.60) for SCS. For fat and lactose contents, the genetic correlations between the 2 front or between the 2 rear quarters, respectively, were notably higher than correlations between 1 front and 1 rear quarter, suggesting that the front and the rear udders could be considered as partly genetically different organs. The variability within the udder as such was found to be of low heritability (<0.10) in general, but repeatability was moderate to high for some traits (lactose content: 0.33 ± 0.05, protein content: 0.53 ± 0.05). Some of these findings can be explained by differences in the physiological background of the traits.     

Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins

Genetic improvement is a crucial tool to deal with the increasing demand for high quality, sustainably produced dairy. Breeding programs are based on genetic parameters, such as heritability and genetic correlations, for economically important traits in a population. In this study, we estimated population genetic parameters and genetic trends for 67 traits evaluated on heifers and first-lactation Canadian Holstein cows. The data consisted of approximately 500,000 records with pedigree information collected from 1980 to 2019. Genetic parameters were estimated using bivariate linear animal models under a Bayesian approach. Analyses for the 67 traits resulted in 2,211 bivariate combinations, from which the estimated genetic parameters are reported here. The most highly heritable traits were fat percent (0.66) and protein percent (0.69), followed by stature (0.47). Lowest heritabilities (0.01) were observed for disease-related traits, such as lameness and toe ulcer, and calf survival. The genetic correlations between gestation length, calf size, and calving ease measured on both heifer and cows were close to unity. On the other hand, traits such as body condition score and pin width, cystic ovaries and sole ulcer, rear teat placement, and toe ulcer were genetically unrelated. This study reports genetic parameters that have not been previously published for Canadian Holstein cows, and provides updates of those previously estimated. These estimates are useful for building new indexes, updating existing selection indexes, and for predicting correlated responses due to inclusion of novel traits in the breeding programs.     

A spreadsheet-based model demonstrating the nonuniform economic effects of varying reproductive performance in Ohio dairy herds

A spreadsheet-based model was developed to estimate the economic effect of varying reproductive performance in dairy herds. Scenarios were created to model an average cow with respect to production, herd lifetime, and reproductive events. Average milk yield per day of life as well as lifetime calf and replacement heifer production were examined. Additional inputs representing milk, feed, semen, calf, and salvage prices were used to calculate net cash flow for each day of herd life for the average cow in a scenario. Economic comparison of different scenarios was accomplished using an equivalent annual cash flow (annuity) methodology.Herd performance measures and prices representative of Ohio dairy herds were used to establish a baseline average cow that had a 160-d calving-to-conception interval [days open (DO)]. Alternative scenarios that differed from baseline in DO, annual culling rate, and feed and milk prices were created to characterize the effects of changes. Under scenario inputs representative of typical Ohio dairy herds, the model indicated that a lower annual culling rate (25%) was preferable to higher annual culling rates (34 or 45%). The model estimated maximum average milk yield per day of life to occur at 110 DO. At 34% annual culling rate, calves and replacement heifers produced per lifetime declined as DO increased; beyond 150 DO, the modeled cow produced less than 1 replacement heifer per lifetime. The model also estimated a loss of $1.37 per cow per year for a 1-d increase in DO beyond 160 d. At 20% higher feed and milk prices, the model estimated a loss of $1.52 per cow per year; at 20% lower feed and milk prices, the model estimated a loss of $1.23 per cow per year. Furthermore, the model suggested that the loss associated with a 1-d increase in DO changed as DO changed. Using baseline inputs, the model calculated losses for a 1-d increase of $0.44 per cow per year at 130 DO and $1.71 per cow per year at 190 DO. The nonuniform nature of the cost of additional DO is important to veterinarians and producers. The implication is that inefficient reproduction becomes marginally more costly to producers as performance declines and warrants increased attention. Conversely, marginal benefits of improved reproduction decrease as performance improves. Herds with strong reproductive performance have less opportunity to capture economic benefits of improvement.     

Strategies for estimating genetic parameters in marker-assisted best linear unbiased predictor models in dairy cattle

An appropriate strategy to estimate variance components and breeding values in genetic models with quantitative trait loci (QTL) was developed for a dairy cattle breeding scheme by utilizing simulated data. Reliable estimates for variance components in QTL models are a prerequisite in fine-mapping experiments and for marker-assisted genetic evaluations. In cattle populations, only a small fraction of the population is genotyped at genetic markers, and only these animals are included in marker-assisted genetic evaluation models. Phenotypic information in these models are precorrected phenotypes [daughter yield deviations (DYD) for bulls, yield deviations (YD) for cows] estimated by standard animal models from the entire population. Because DYD and YD may represent different amounts of information, the problem of weighting these 2 types of information appropriately arises. To detect the best combination of phenotypes and weighting factors, a stochastic simulation for a trait representing milk yield was used. The results show that DYD models are generally optimal for estimating QTL variance components, but properties of estimates depend strongly on weighting factors. An example for the benefit in selection of using YD is shown for the selection among paternal half-sibs inheriting alternative QTL alleles. Even if QTL effects are small, marker-assisted best unbiased linear prediction can improve the selection among half-sibs, because the Mendelian sampling variance within family can be exploited, especially in DYD-YD models. Marker-assisted genetic evaluation models should also include YD for cows to ensure that marker-assisted selection improves selection even for moderate QTL effects (≥10%). A useful strategy for practical implementation is to estimate variance components in DYD models and breeding values in DYD-YD models.     

Estimation of genetic parameters for cheese-making traits in Spanish Churra sheep

The global production of sheep milk is growing, and the main industrial use of sheep milk is cheese making. The Spanish Churra sheep breed is one of the most important native dairy breeds in Spain. The present study aimed to estimate genetic parameters for a wide range of traits influencing the cheese-making ability of Churra sheep milk. Using a total of 1,049 Churra ewes, we studied the following cheese-making traits: 4 traits related to milk coagulation properties (rennet coagulation time, curd-firming time, and curd firmness at 30 and 60 min after addition of rennet), 2 traits related to cheese yield (individual laboratory cheese yield and individual laboratory dried curd yield), and 3 traits measuring curd firmness over time (maximum curd firmness, time to attain maximum curd firmness, and syneresis). In addition, a list of milk traits, including the native pH of the milk and several milk production and composition traits (milk yield; the fat, protein, and dried extract percentages; and the somatic cell count), were also analyzed for the studied animals. After discarding the noncoagulating samples (only 3.7%), data of 1,010 ewes were analyzed with multiple-trait animal models by using the restricted maximum likelihood method to estimate (co)variance components, heritabilities, and genetic correlations. In general, the heritability estimates were low to moderate, ranging from 0.08 (for the individual laboratory dried curd yield trait) to 0.42 (for the fat percentage trait). High genetic correlations were found within pairs of related traits (i.e., 0.93 between fat and dried extract percentages, ?0.93 between the log of the curd-firming time and curd firmness at 30 min, 0.70 between individual laboratory cheese yield and individual laboratory dried curd yield, and ?0.94 between time to attain maximum curd firmness and syneresis). Considering all the information provided here, we suggest that in addition to the current consideration of the protein percentage trait for improving cheese yield traits, the inclusion of the pH of milk as a measured trait in the Churra dairy breeding program would represent an efficient strategy for improving the cheese-making ability of milk from this breed.     

Prepartum nutritional strategy affects reproductive performance in dairy cows

Negative energy balance during early postpartum is associated with reduced reproductive performance in dairy cows. A pooled statistical analysis of 7 studies completed in our group from 1993 to 2010 was conducted to investigate the association between prepartum energy feeding regimen and reproductive performance. The interval from calving to pregnancy (days to pregnancy, DTP) was the dependent variable to assess reproductive performance. Individual data for 408 cows (354 multiparous and 54 primiparous) were included in the analysis. The net energy for lactation (NE_L) intake was determined from each cow’s average dry matter intake and calculated dietary NE_L density. Treatments applied prepartum were classified as either controlled-energy (CE; limited NE_L intake to ≤100% of requirement) or high-energy (HE; cows were allowed to consume >100%) diets fed during the far-off (FO) or close-up (CU) dry periods. Cow was the experimental unit. The Cox proportional hazard model revealed that days to pregnancy was shorter for CE (median = 157 d) than HE (median = 167 d) diets during the CU period [hazard ratio (HR) = 0.70]. Cows fed HE diets during the last 4 wk prepartum lost more body condition score in the first 6 wk postpartum than those fed CE diets (−0.43 and −0.30, respectively). Cows fed CE diets during the FO period had lower nonesterified fatty acids concentrations in wk 1, 2, and 3 of lactation than cows fed HE diets. Higher nonesterified fatty acids concentration in wk 1 postpartum was associated with a greater probability of disease (n = 251; odds ratio = 1.18). Cows on the CE regimen during the FO period had greater plasma glucose concentrations during wk 1 and 3 after calving than cows fed the HE regimen. Higher plasma glucose (HG) concentration compared with lower glucose (LG) in wk 3 (HG: n = 154; LG: n = 206) and wk 4 (HG: n = 71; LG: n = 254) after calving was associated with shorter days to pregnancy (wk 3: median = 151 and 171 d for HG and LG, respectively, and HR = 1.3; wk 4: median = 148 and 167 d, respectively, and HR = 1.4). In the first 2 wk after calving, cows that received HE diets in the FO period had higher concentrations of total lipids and triglyceride and greater ratio of triglyceride to glycogen in liver than cows fed CE diets. In conclusion, cows fed CE diets during the CU period had a shorter interval between parturition and conception, which may be explained by increased NE_L intake during the first 4 wk postpartum and lower incidence of peripartal diseases. Lower body condition score loss during the first 6 wk postpartum and slightly higher glucose concentration at wk 3 likely contributed to improved reproductive performance.     

Stochastic economic analysis of dairy cattle artificial insemination reproductive management programs

Dairy herd reproductive performance and efficiency are closely tied to whole-farm profitability on commercial US dairy operations. Decision support assists farm managers seeking to determine economically optimal programs. Stochastic dominance is a risk efficiency criterion that was used to determine the preferred set of reproductive management programs. Stochastic dominance analysis of reproductive management programs, including visual heat detection without aids, Ovsynch, and Cosynch, was undertaken to facilitate management decisions regarding reproductive management programs. First-degree stochastic dominance identified the efficient set of reproductive management programs for those decision makers who simply prefer “more to less” or in this case prefer the higher value program. Second-degree stochastic dominance was used to identify the efficient set for decision makers who prefer “more to less” at a diminishing rate, reflecting risk aversion. For each program, artificial insemination submission rate and conception rate were the outcome variables that involved risk. Ovsynch and Cosynch were found to be first- and second-degree dominant over visual heat detection in the example base case. Comparing Ovsynch and Cosynch revealed first- and second-degree stochastic dominance of Ovsynch over Cosynch. Managers with all risk preferences would prefer Ovsynch until an additional 9 min of labor were required for Ovsynch. Risk-averse managers would prefer Ovsynch to Cosynch until an additional 25 min of labor were required to breed with Ovsynch. This highlights that, in the example base case, risk-averse managers are willing to incur additional labor costs to breed with Ovsynch and avoid potential downside conception rate risks associated with Cosynch. Risk preferences of the manager affect which programs remain in the efficient set. Risk preferences of farm managers must be recognized when making reproductive program recommendations. Because dairy farmers are likely to be risk averse, second-degree stochastic dominance is particularly important for their decisions.     

Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection

The objective of the present study was to investigate the phenotypic inter- and intra-relationships within and among alternative feed efficiency metrics across different stages of lactation and parities; the expected effect of genetic selection for feed efficiency on the resulting phenotypic lactation profiles was also quantified. A total of 8,199 net energy intake (NE_I) test-day records from 2,505 lactations on 1,290 cows were used. Derived efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NE_I minus predicted energy requirements based on lactation performance; residual energy production (REP) was defined as net energy for lactation minus predicted energy requirements based on lactation performance. Energy conversion efficiency was defined as net energy for lactation divided by NE_I. Pearson phenotypic correlations among traits were computed across lactation stages and parities, and the significance of the differences was determined using the Fisher r-to-z transformation. Sources of variation in the feed efficiency metrics were investigated using linear mixed models, which included the fixed effects of contemporary group, breed, parity, stage of lactation, and the 2-way interaction of parity by stage of lactation. With the exception of REI, parity was associated with all efficiency and production traits. Stage of lactation, as well as the 2-way interaction of parity by stage of lactation, were associated with all efficiency and production traits. Phenotypic correlations among the efficiency and production traits differed not only by stage of lactation but also by parity. For example, the strong phenotypic correlation between REI and energy balance (EB; 0.89) for cows in parity 3 or greater and early lactation was weaker for parity 1 cows at the same lactation stage (0.81), suggesting primiparous cows use the ingested energy for both milk production and growth. Nonetheless, these strong phenotypic correlations between REI and EB suggested negative REI animals (i.e., more efficient) are also in more negative EB. These correlations were further supported when assessing the effect on phenotypic performance of animals genetically divergent for feed intake and efficiency based on parental average. Animals genetically selected to have lower REI resulted in cows who consumed less NE_I but were also in negative EB throughout the entire lactation. Nonetheless, such repercussions of negative EB do not imply that selection for negative REI (as defined here) should not be practiced, but instead should be undertaken within the framework of a balanced breeding objective, which includes traits such as reproduction and health.     

Bivariate analysis of number of services to conception and days open in Norwegian red using a censored threshold-linear model

A bivariate censored threshold-linear model was used to study genetic parameters of number of services to conception (STC) and days open (DO) in first-lactation Norwegian Red (NRF) cows. Records of 1,454,916 NRF cows, with a first insemination from 1980 to 2004, were analyzed. It was assumed that every cow had at least a first insemination. The number of inseminations was recorded until a cow conceived or was culled, whichever occurred first. If a cow was culled before conception, it was considered censored at the number of services until culling was recorded. Twenty-one percent of cows were censored for both STC and DO. Using an univariate probit link function for STC, unobserved liabilities to STC and DO were modeled jointly as a linear function of age at first calving, month-year at first calving, herd-5-yr period, sire of cow and residual effects. Heritability of liability to STC and DO was 4% for each trait. The genetic and residual correlations between STC and DO were 0.77 and 0.68, respectively. There has been little or no genetic change for DO, whereas STC had favorable genetic and phenotypic trends.