The oral microbiome as a proxy for feed intake in dairy cattle

Genetic material from rumen microorganisms can be found within the oral cavity, and hence there is potential in using the oral microbiome as a proxy of the ruminal microbiome. Feed intake (FI) influences the composition of rumen microbiota and might directly influence the oral microbiome in dairy cattle. Ruminal content samples (RS) from 29 cows were collected at the beginning of the study and also 42 d later (RS0 and RS42, respectively). Additionally, 18 oral samples were collected through buccal swabbing at d 42 (OS42) from randomly selected cows. Samples were used to characterize and compare the taxonomy and functionality of the oral microbiome using Nanopore sequencing and to evaluate the feasibility of using the oral microbiome to estimate FI. Up to 186 taxonomical features were found differentially abundant (DA) between RS and OS42. Similar results were observed when comparing OS42 to RS collected at different days. Microorganisms associated with the liquid fraction of the rumen were less abundant in OS42 as these were probably swallowed after regurgitation. Up to 1,102 KEGGs were found to be DA between RS and OS42 and these results differed when comparing time of collection, but differentially abundant KEGGs were mainly associated to metabolism in both situations. Models based on the oral microbiome and rumen microbiome differed in their selection of microbial groups and biological pathways to predict FI. In the rumen, fiber-associated microorganisms are considered suitable indicators of feed intake. On the other hand, biofilm formers like Gammaproteobacteria or Bacteroidia classes are deemed appropriate proxies for predicting feed intake from oral samples. Models from RS exhibited some predictive ability to estimate FI, but OS significantly outperformed them. The best lineal model to estimate FI was obtained with the relative abundance of taxonomical feature at genera level, achieving an average R2 equal to 0.88 within the training data, and a root mean square error equal to 3.46 ± 0.83 (standard deviation; SD) kg of DM/ as well as a Pearson correlation coefficient between observed and estimated FI of 0.48 ± 0.30 in the test data. The results from this study suggest that oral microbiome has potential to predict FI in dairy cattle, and it encourages validating this potential in other populations.

Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins

This study aimed to find the parsimonious random regression model (RRM) to evaluate the genetic potential for milk yield (MY), fat content (FC), and protein content (PC) in Tunisian Holstein cows. For this purpose, 551,139; 331,654; and 302,396 test day records for MY, FC, and PC were analysed using various RRMs with different Legendre polynomials (LP) orders on additive genetic (AG) and permanent environmental (PE) effects, and different types of residual variances (RV). The statistical analysis was performed in a Bayesian framework with Gibbs sampling, and the model performances were assessed, mainly, on the predictive ability criteria. The study found that the optimal model for evaluating these traits was an RRM with a third LP order and nine classes of heterogeneous RV. In addition, the study found that heritability estimates for MY, FC, and PC ranged from 0.11 to 0.22, 0.11 to 0.17, and 0.12 to 0.18, respectively, indicating that genetic improvement should be accompanied by improvements in the production environment. The study also suggested that new selection rules could be used to modify lactation curves by exploiting the canonical transformation of the random coefficient covariance (RC) matrix or by using the combination of slopes of individual lactation curves and expected daily breeding values.

Invited review: Recursive models in animal breeding: Interpretation, limitations, and extensions

Structural equation models allow causal effects between 2 or more variables to be considered and can postulate unidirectional (recursive models; RM) or bidirectional (simultaneous models) causality between variables. This review evaluated the properties of RM in animal breeding and how to interpret the genetic parameters and the corresponding estimated breeding values. In many cases, RM and mixed multitrait models (MTM) are statistically equivalent, although subject to the assumption of variance-covariance matrices and restrictions imposed for achieving model identification. Inference under RM requires imposing some restrictions on the (co)variance matrix or on the location parameters. The estimates of the variance components and the breeding values can be transformed from RM to MTM, although the biological interpretation differs. In the MTM, the breeding values predict the full influence of the additive genetic effects on the traits and should be used for breeding purposes. In contrast, the RM breeding values express the additive genetic effect while holding the causal traits constant. The differences between the additive genetic effect in RM and MTM can be used to identify the genomic regions that affect the additive genetic variation of traits directly or causally mediated for another trait or traits. Furthermore, we presented some extensions of the RM that are useful for modeling quantitative traits with alternative assumptions. The equivalence of RM and MTM can be used to infer causal effects on sequentially expressed traits by manipulating the residual (co)variance matrix under the MTM. Further, RM can be implemented to analyze causality between traits that might differ among subgroups or within the parametric space of the independent traits. In addition, RM can be expanded to create models that introduce some degree of regularization in the recursive structure that aims to estimate a large number of recursive parameters. Finally, RM can be used in some cases for operational reasons, although there is no causality between traits.

Mitigation of greenhouse gases in dairy cattle via genetic selection: 1. Genetic parameters of direct methane using noninvasive methods and proxies of methane

Records of methane emissions from 1,501 cows on 14 commercial farms in 4 regions of Spain were collected from May 2018 to June 2019. Methane concentrations (MeC) were measured using a nondispersive infrared methane detector installed within the feed bin of the automatic milking system during 14- to 21-d periods. Rumination time (RT; min/d) was collected using collars with a tag that registered time (minutes) spent eating and ruminating. The means of MeC and methane production (MeP) were 1,254.28 ppm and 182.49 g/d, respectively; mean RT was 473.38 min/d. Variance components for MeC, MeP, and RT were estimated with REML using pedigree and genomic information in a single-step model. Heritabilities for MeC and MeP were 0.11 and 0.12, respectively. Rumination time showed a slightly larger heritability estimate (0.17). The genetic correlation between MeP and MeC was high (>0.95), suggesting that selection on either trait would lead to a positive correlated response on the other. Negative correlations were estimated between RT and MeC (-0.24 ± 0.38) and MeP (-0.43 ± 0.35). Methane concentration and MeP had slightly positive correlations with milk yield (0.17 ± 0.39 and 0.21 ± 0.36), protein percentage (0.08 ± 0.32 and 0.30 ± 0.45), protein yield (0.22 ± 0.41 and 0.31 ± 0.35), fat percentage (0.02 ± 0.40 and 0.27 ± 0.36), and fat yield (0.27 ± 0.28 and 0.29 ± 0.28) from bivariate analyses. Rumination time had positive correlations with milk yield (0.41 ± 0.75) and protein yield (0.26 ± 0.57) and negative correlations with fat yield (-0.45 ± 0.32), protein percentage (-0.15 ± 0.38), and fat percentage (-0.40 ± 0.47). A positive approximated genetic correlation was estimated between fertility and MeC (0.10 ± 0.05) and MeP (0.18 ± 0.05), resulting in slightly higher CH₄ production when selecting for better fertility [days open estimated breeding values (EBV) are expressed with mean 100 and SD 10, inversely related to days from calving to conception; that is, greater days open EBV implies better fertility]. Positive correlations were also estimated for stature with MeC and MeP (0.30 ± 0.04 and 0.43 ± 0.04, respectively). Other type traits (chest width, udder depth, angularity, and capacity) were positively correlated with methane traits, possibly because of higher milk yield and higher feed intake from these animals. Rumination time showed positive EBV correlations with production traits and type traits, and negative correlations with somatic cell count and body condition score. Based on the genetic correlations and heritabilities estimated in this study, methane is measurable and heritable, and estimates of genetic correlations suggest no strong opposition to current breeding objectives in Spanish Holsteins.

Changes in the rumen and colon microbiota and effects of live yeast dietary supplementation during the transition from the dry period to lactation of dairy cows

The first objective of this study was to evaluate the dynamics and their potential association with animal performance of the microbiota in both the rumen and colon of dairy cows as they move from a nonlactation to a lactation ration. The second objective was to assess the potential effects on the microbiota of live yeast supplementation. Twenty-one Holstein cows were split in 2 treatments consisting of 1 × 10¹⁰ cfu/d of live yeast (LY; n = 10) or no supplementation (control; n = 11) starting 21 d before until 21 d after calving. At 14 d before and 7 and 21 d after calving, samples of rumen and colon digesta were obtained from each cow using an endoscope. Total DNA was extracted and submitted to high-throughput sequencing. Shannon diversity index, in both the rumen and colon, was unaffected by LY; however, in the rumen it was lowest 7 d after calving and returned to precalving values at 21 d in milk, whereas in the colon it was greatest 14 d before calving but decreased after calving. In the rumen, LY supplementation increased the relative abundance (RA) of Bacteroidales (group UCG-001), Lachnospiracea (groups UCG-002 and UCG-006), and Flexilinea 14 d before calving, and increased RA of Streptococcus 21 d after calving compared with control cows. However, changes in the ruminal microbiota were more drastic across days relative to calving than as influenced by the dietary treatment, and the effect of LY in the colon was milder than in the rumen. The ruminal RA of several genera was associated with postcalving DMI, and that of Gastranaerophilales was the only order positively associated with milk yield. Several genera were positively correlated with feed efficiency, with Clostridiales (unclassified) being the only genus negatively associated with feed efficiency. In the colon, Prevotellaceae (group Ga6A1) was the only genus positively associated with feed efficiency. The ruminal RA of Prevotella 7 and Ruminobacter 14 d precalving was negatively correlated with dry matter intake and milk yield postcalving. The RA of Parabacteroides in the colon 14 d before calving was negatively correlated with milk yield, whereas the RA of Eggerthellaceae (unclassified) and Erysipelotrichaceae (groups c and unclassified) were positively correlated with feed efficiency. Interestingly, LY supplementation doubled the RA of Eggerthellaceae (unclassified) in the colon. It is concluded that microbial diversity in the rumen experiences a transient reduction after calving, whereas in the colon, the reduction is maintained at least until 21 d in milk. Most of the effects of LY on rumen microbiota were observed before calving, whereas in the colon, LY effects were more moderate but consistent and independent of the stage of production. The microbial community of the rumen after calving is more associated with feed intake, milk yield, and feed efficiency than that of the colon. However, the colon microbiota before calving is more associated with feed efficiency after calving than that of the rumen.

Including nonadditive genetic effects in mating programs to maximize dairy farm profitability

We compared the outcome of mating programs based on different evaluation models that included nonadditive genetic effects (dominance and heterozygosity) in addition to additive effects. The additive and dominance marker effects and the values of regression on average heterozygosity were estimated using 632,003 single nucleotide polymorphisms from 7,902 and 7,510 Holstein cows with calving interval and production (milk, fat, and protein yields) records, respectively. Expected progeny values were computed based on the estimated genetic effects and genotype probabilities of hypothetical progeny from matings between the available genotyped cows and the top 50 young genomic bulls. An index combining the traits based on their economic values was developed and used to evaluate the performance of different mating scenarios in terms of dollar profit. We observed that mating programs with nonadditive genetic effects performed better than a model with only additive effects. Mating programs with dominance and heterozygosity effects increased milk, fat, and protein yields by up to 38, 1.57, and 1.21 kg, respectively. The inclusion of dominance and heterozygosity effects decreased calving interval by up to 0.70 d compared with random mating. The average reduction in progeny inbreeding by the inclusion of nonadditive genetic effects in matings compared with random mating was between 0.25 to 1.57 and 0.64 to 1.57 percentage points for calving interval and production traits, respectively. The reduction in inbreeding was accompanied by an average of A$8.42 (Australian dollars) more profit per mating for a model with additive, dominance, and heterozygosity effects compared with random mating. Mate allocations that benefit from nonadditive genetic effects can improve progeny performance only in the generation where it is being implemented, and the gain from specific combining abilities cannot be accumulated over generations. Continuous updating of genomic predictions and mate allocation programs are required to benefit from nonadditive genetic effects in the long term.

Improving the reliability of female fertility breeding values using type and milk yield traits that predict energy status in Australian Holstein cattle

The objectives of this study were (1) to propose changing the selection criteria trait for evaluating fertility in Australia from calving interval to conception rate at d 42 after the beginning of the mating season and (2) to use type traits as early fertility predictors, to increase the reliability of estimated breeding values for fertility. The breeding goal in Australia is conception within 6 wk of the start of the mating season. Currently, the Australian model to predict fertility breeding values (expressed as a linear transformation of calving interval) is a multitrait model that includes calving interval (CVI), lactation length (LL), calving to first service (CFS), first nonreturn rate (FNRR), and conception rate. However, CVI has a lower genetic correlation with the breeding goal (conception within 6 wk of the start of the mating season) than conception rate. Milk yield, type, and fertility data from 164,318 cow sired by 4,766 bulls were used. Principal component analysis and genetic correlation estimates between type and fertility traits were used to select type traits that could subsequently be used in a multitrait analysis. Angularity, foot angle, and pin set were chosen as type traits to include in an index with the traits that are included in the multitrait fertility model: CVI, LL, CFS, FNRR, and conception rate at d 42 (CR42). An index with these 8 traits is expected to achieve an average bull first proof reliability of 0.60 on the breeding objective (conception within 6 wk of the start of the mating season) compared with reliabilities of 0.39 and 0.45 for CR42 only or the current 5-trait Australian model. Subsequently, we used the first eigenvector of a principal component analysis with udder texture, bone quality, angularity, and body condition score to calculate an energy status indicator trait. The inclusion of the energy status indicator trait composite in a multitrait index with CVI, LL, CFS, FNRR, and CR42 achieved a 12-point increase in fertility breeding value reliability (i.e., increased by 30%; up to 0.72 points of reliability), whereas a lower increase in reliability (4 points, i.e., increased by 10%) was obtained by including angularity, foot angle, and pin set in the index. In situations when a limited number of daughters have been phenotyped for CR42, including type data for sires increased reliabilities compared with when type data were omitted. However, sires with more than 80 daughters with CR42 records achieved reliability estimates close to 80% on average, and there did not appear to be a benefit from having daughters with type records. The cost of phenotyping to obtain such reliabilities (assuming a cost of AU$14 per cow with type data and AU$5 per cow with pregnancy diagnosed) is lower if more pregnancy data are collected in preference to type data. That is, efforts to increase the reliability of fertility EBV are most cost effective when directed at obtaining a larger number of pregnancy tests.

Comparison of methods for the implementation of genome-assisted evaluation of Spanish dairy cattle

The aim of this study was to evaluate methods for genomic evaluation of the Spanish Holstein population as an initial step toward the implementation of routine genomic evaluations. This study provides a description of the population structure of progeny tested bulls in Spain at the genomic level and compares different genomic evaluation methods with regard to accuracy and bias. Two bayesian linear regression models, Bayes-A and Bayesian-LASSO (B-LASSO), as well as a machine learning algorithm, Random-Boosting (R-Boost), and BLUP using a realized genomic relationship matrix (G-BLUP), were compared. Five traits that are currently under selection in the Spanish Holstein population were used: milk yield, fat yield, protein yield, fat percentage, and udder depth. In total, genotypes from 1859 progeny tested bulls were used. The training sets were composed of bulls born before 2005; including 1601 bulls for production and 1574 bulls for type, whereas the testing sets contained 258 and 235 bulls born in 2005 or later for production and type, respectively. Deregressed proofs (DRP) from January 2009 Interbull (Uppsala, Sweden) evaluation were used as the dependent variables for bulls in the training sets, whereas DRP from the December 2011 DRPs Interbull evaluation were used to compare genomic predictions with progeny test results for bulls in the testing set. Genomic predictions were more accurate than traditional pedigree indices for predicting future progeny test results of young bulls. The gain in accuracy, due to inclusion of genomic data varied by trait and ranged from 0.04 to 0.42 Pearson correlation units. Results averaged across traits showed that B-LASSO had the highest accuracy with an advantage of 0.01, 0.03 and 0.03 points in Pearson correlation compared with R-Boost, Bayes-A, and G-BLUP, respectively. The B-LASSO predictions also showed the least bias (0.02, 0.03 and 0.10 SD units less than Bayes-A, R-Boost and G-BLUP, respectively) as measured by mean difference between genomic predictions and progeny test results. The R-Boosting algorithm provided genomic predictions with regression coefficients closer to unity, which is an alternative measure of bias, for 4 out of 5 traits and also resulted in mean squared errors estimates that were 2%, 10%, and 12% smaller than B-LASSO, Bayes-A, and G-BLUP, respectively. The observed prediction accuracy obtained with these methods was within the range of values expected for a population of similar size, suggesting that the prediction method and reference population described herein are appropriate for implementation of routine genome-assisted evaluations in Spanish dairy cattle. R-Boost is a competitive marker regression methodology in terms of predictive ability that can accommodate large data sets.

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets

In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy and bias. This modification may be used to speed the calculus of genome-assisted evaluation in large data sets such us those obtained from consortiums.

Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R(2)) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R(2) values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.

Inbreeding depression on female fertility and calving ease in Spanish dairy cattle

Inbreeding depression on female fertility and calving ease in Spanish dairy cattle was studied by the traditional inbreeding coefficient (F) and an alternative measurement indicating the inbreeding rate (DeltaF) for each animal. Data included records from 49,497 and 62,134 cows for fertility and calving ease, respectively. Both inbreeding measurements were included separately in the routine genetic evaluation models for number of insemination to conception (sequential threshold animal model) and calving ease (sire-maternal grandsire threshold model). The F was included in the model as a categorical effect, whereas DeltaF was included as a linear covariate. Inbred cows showed impaired fertility and tended to have more difficult calvings than low or noninbred cows. Pregnancy rate decreased by 1.68% on average for cows with F from 6.25 to 12.5%. This amount of inbreeding, however, did not seem to increase dystocia incidence. Inbreeding depression was larger for F greater than 12.5%. Cows with F greater than 25% had lower pregnancy rate and higher dystocia rate (-6.37 and 1.67%, respectively) than low or noninbred cows. The DeltaF had a significant effect on female fertility. A DeltaF = 0.01, corresponding to an inbreeding coefficient of 5.62% for the average equivalent generations in the data used (5.68), lowered pregnancy rate by 1.5%. However, the posterior estimate for the effect of DeltaF on calving ease was not significantly different from zero. Although similar patterns were found with both F and DeltaF, the latter detected a lowered pregnancy rate at an equivalent F, probably because it may consider the known depth of the pedigree. The inbreeding rate might be an alternative choice to measure inbreeding depression.

Impact of Calving Ease on Functional Longevity and Herd Amortization Costs in Basque Holsteins Using Survival Analysis

The aim of this study was to analyze the impact of calving ease (CE) on functional longevity of Basque Holsteins, using a Weibull proportional hazards model. The data considered for the analysis were 53,353 calving records from 25,810 Holstein cows distributed across 781 herds and sired by 746 bulls. The effects included in the statistical model were age at first calving, stage of lactation, interaction between year and season of calving, 305-d adjusted milk yield, CE, herd, and sire. Calving ease was considered as a time-dependent covariate and, as was the case for the rest of covariates included in the model, had a significant effect on functional longevity. Calvings needing assistance or surgery increased culling risk by 18%, when compared with unassisted calvings. The effect of CE on length of productive life in primiparous and multiparous cows was also investigated. A second analysis was performed replacing the CE effect with the interaction between parity and CE to evaluate the effect of CE in primiparous and multiparous cows. An increase in calving difficulty had a greater impact on culling during first lactations than in subsequent ones. Therefore, difficult calvings, mainly at first parities, had a high impact on herd amortization costs, increasing them by 10% in relation to easy calvings. Therefore, calving difficulty should be avoided as much as possible, especially in primiparous cows, to avoid reduction of profitability.

Genetic Analysis of the Twenty-One-Day Pregnancy Rate in US Holsteins Using an Ordinal Censored Threshold Model with Unknown Voluntary Waiting Period

Genetic variation in the number of 21-d opportunity periods required to achieve pregnancy after the voluntary waiting period (VWP) had passed was examined using 44,901 lactation records of 29,422 lactating Holstein cows on 61 large commercial dairy farms in the United States. Cows were allowed a maximum of 8 opportunity periods, and the cumulative percentages of cows that became pregnant by the end of the first, second, third, fourth, and fifth opportunity periods were 19, 29, 37, 43, and 47%, respectively. In addition, 38% of records were censored because of culling or failure to achieve pregnancy after 8 opportunity periods. Mean days open was 128 d for complete records, whereas mean days to last service was 148 d for censored records. An ordinal censored threshold model was developed, in which duration of the VWP was estimated simultaneously with prediction of sire breeding values. The posterior mean of intraherd-year heritability for the number of 21-d opportunity periods required to achieve pregnancy was 0.06, with a posterior standard deviation of 0.01. Posterior means for duration of the VWP ranged from 28 to 74 d postpartum among the 116 herd-parity classes represented in the study, whereas farmer-reported survey values for duration of the VWP ranged from 30 to 78 d postpartum. Sires' predicted transmitting abilities were computed, assuming an unknown VWP (i.e., estimated from the data), a VWP fixed at 60 d postpartum, or a VWP fixed at farmer survey values. Correlations among sire predicted transmitting abilities from different models were > or = 0.98, although some reranking occurred among top sires. In summary, the proposed model for genetic evaluation of female fertility can accommodate heterogeneity in duration of the VWP between herds, as well as heterogeneity that may arise within herds owing to management practices such as intentional delay of first insemination in high-producing cows or cows with poor body condition, and it can also accommodate censored records for nonpregnant cows.

Selection for Female Fertility Using Censored Fertility Traits and Investigation of the Relationship with Milk Production

Bivariate models (censored linear-linear and censored threshold-linear) were used to estimate genetic parameters for production and fertility traits in the Spanish Holstein population. Records on 71,217 lactations from 41,515 cows were used: 30 and 36% of lactations were censored for days open (DO) and number of inseminations to conception (INS), respectively. Heritability estimates for production traits (milk, fat, protein) ranged between 0.18 and 0.25. Heritability of days to first service (DFS) and DO was 0.05; heritability of INS on the liability scale was 0.04. Genetic correlations between fertility traits were 0.41, 0.71, and 0.87 for DFS-INS, DO-INS, and DO-DFS, respectively. Days open had a larger genetic correlation (ranging from 0.63 to 0.76) with production traits than did DFS (0.47 to 0.59) or INS (0.16 to 0.23). Greater antagonism between production and DO may be due to voluntary management decisions for high-yielding cows, resulting in longer lactation lengths. Inseminations to conception appeared to be less correlated with milk production than were the other 2 female fertility traits. Including INS in a total merit index would be expected to increase genetic gain in terms of profit, but profit would decrease if either DO or DO and DFS were included in the index. Thus, INS is the trait to be preferred when selecting for female fertility. The genetic correlation between actual milk yield and 305-d standardized milk yield was 0.96 in the present study, suggesting that some reranking of sires could occur. Because the target of attaining a 12-mo calving interval, as implied by a 305-d standardized lactation length, is changing in the dairy industry, routine genetic evaluation of actual total lactation milk yield should be considered.

Genetic and Phenotypic Relationships Among Locomotion Type Traits, Profit, Production, Longevity, and Fertility in Spanish Dairy Cows

The phenotypic and genetic relationships of 3 locomotion traits with profit, production, longevity, and fertility traits were studied to determine the importance of locomotion traits for dairy producers. Two data sets including official milk records and type classification scores of 62,293 cows, and reproductive records of 24,561 cows from the Basque and Navarra Autonomous Regions were analyzed. Higher scores for feet and legs (FL), foot angle (FA), and rear legs set (RLS) were positively related to production and functional traits, whereas fertility was not significantly affected. The cows that scored the highest for FL were $213/yr more profitable, produced 575 kg more milk per year, and remained in the herd for 307 more functional days than the cows scoring the lowest. Feet and legs was the trait most genetically correlated to profit, although a low value (0.10) was obtained, whereas RLS was the trait most correlated to milk production (0.12). Genetic correlations among FL, FA, RLS, and longevity traits (from -0.10 to 0.05) were low. Quadratic curves were the best fit for both profit and functional herd life for EBV of each of the 3 locomotion traits. Further studies dealing with profitability and lameness, instead of using conformation traits, could be performed directly if a larger data pool of lameness was routinely recorded.

Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle

Genetic correlations among female fertility traits (linear and binary) were estimated using 225,085 artificial insemination records from 120,713 lactations on 63,160 Holstein cows. Fertility traits were: calving interval, days open, a linear transformation of days open, days to first insemination, interval between first and last insemination, number of inseminations per service period, pregnancy within 56 and 90 d after first insemination, and success in first insemination. A bivariate animal model was implemented using Bayesian methods in the case of binary traits. Low heritabilities (0.02 to 0.06) were estimated for these fertility traits. Strong genetic correlations (0.89 to 0.99) were found among traits, except for days to first service, where the genetic correlation with other fertility traits ranged from -0.52 to -0.18 for binary traits, and from 0.50 to 0.82 for days to first service, calving interval, and days open. Four fertility indices were proposed utilizing information from insemination records; these indices combined one indicator of the beginning of the service period and one indicator of conception rate. Two additional indices used information from the milk-recording scheme, including calving interval and a linear transformation of days open. The fertility index composed of days to first service and pregnancy within 56 d achieved the highest genetic gain for reducing fertility cost, reducing days to first service, and reducing the number of inseminations per lactation ($8.60, -1.31 d, and -0.03 AI, respectively). This index achieved at least 15% higher genetic gain than obtained from indices with information from the milk recording scheme only (calving interval and days open).

Number of Inseminations to Conception in Holstein Cows Using Censored Records and Time-Dependent Covariates

Three methodologies that accommodate censoring or time-dependent covariates were used to estimate variance components for number of inseminations to conception. Data included 80,071 lactation records and 143,927 artificial inseminations in 47,509 Spanish Holstein cows. Up to 4 inseminations to conception, along with their respective censoring information, were analyzed. An ordinal-censored threshold model (CTM), a sequential threshold model (STM), and a grouped survival analysis via a discrete proportional hazards model (DPH) were implemented. Sire variance estimates on the liability scale were 0.016 and 0.010 for CTM and STM, respectively, and 0.012 for DPH on the logarithmic scale. Heritability estimates on the liability scale were 0.050 and 0.038 with CTM and STM, respectively. All models led to similar rankings of sires, and the strong correlations (0.97 to 0.98) between methodologies suggested robustness in ranking of sires of cows. Service sire variance estimates were 0.021 for both CTM and STM; DPH led to an approximate service sire variance of 0.020. Rankings for service sires between methodologies ranged from 0.76 to 0.90. These lower values are most likely due to differences in the treatment of time-dependent covariates. The STM had greater predictive ability of daughter fertility at first insemination than the other methodologies. However, the CTM predicted daughter fertility more accurately in subsequent inseminations. The DPH and STM had a similar predictive ability of daughter fertility in second and subsequent inseminations.

Genetic analysis of an artificial insemination progeny test program

The success of the progeny test (PT) program from one Spanish artificial insemination (AI) organization was evaluated. The annual genetic trend for the organization was compared with PT programs from other countries. The relationships among parents' estimated breeding values (EBV) and PT results for sons were also studied. Estimated breeding values for type and production traits were obtained from international genetic evaluations from February 2004. The annual genetic gain of the Spanish PT program was similar to that of other international programs. The Spanish AI organization graduated 13% of its sampled bulls, and 52% of primiparous cows were daughters of Spanish bulls (32% from proven bulls and 20% from sampling bulls). Correlations between EBV for PT bulls and their pedigree indices (0.52 to 0.70) were slightly lower than correlations between EBV for PT bulls and their parent averages (0.63 to 0.73). Both young and mature cows contributed to genetic progress. Success of PT bulls (defined by number of second-crop daughters) depended mainly on their EBV for final score, protein yield, and the type-production index. Significant correlations of sire EBV were found for final score and type-production index with the number of second-crop daughters (0.22 and 0.17). Likewise, significant correlations of dam EBV for final score and type-production index with the number of second crop daughters were found (0.25 and 0.18). Final score and protein yield were the main factors in success of a PT bull. The type-production index for PT bulls was not important for success unless it was 2.5 standard deviations above average. The PT bulls with low EBV for type-production index were used as proven bulls when they had higher EBV either for protein or final score.

Economic value of female fertility and its relationship with profit in Spanish dairy cattle

A data file of 225,085 inseminations and 120,713 lactations from 63,160 Holstein cows was analyzed to obtain female fertility economic value according to number of inseminations per service period (INS). Fertility cost (FCOST) was included in a bioeconomic model, taking into account number of doses of semen, hormonal treatments, fertility culling cost, and delayed milk and calf sales. A profit equation was elaborated to estimate fertility cost and profit according to INS. Fertility in Spanish dairy cattle has worsened >10% over the last 14 yr. Days open have increased by about 15 d, and INS has increased from 1.7 to 2.0. A quadratic relationship was found between FCOST and INS. Similar profitability was estimated for cows who needed one or 2 INS, but when >3 INS were needed, profit decreased by >205 (US dollars)/yr per cow. Cows that needed more INS had higher milk yield per lactation, but also had a higher culling risk and lower productive life and lifetime production, therefore, lower profit. Calving interval (CI) and INS economic values were, respectively, -4.90 and -67.32 (US dollars)/yr per cow and per one unit of change. The economic values of productive traits were 4.04, 1.02, and 1.19 (US dollars)/yr per cow and per one unit of change for kg protein, kg fat, and days in milk, respectively. A mature body weight economic value of -0.67 (US dollars)/yr per cow and per kg was estimated. The relative importance of fertility traits with respect to protein was 64% for CI and 24% for INS, although the CI economic value is highly influenced by phenotypic standard deviation considered.