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This study examined the feasibility of using pregnancy-associated glycoproteins (PAG) in milk within breeding for pregnancy maintenance and assessed the genetic variation in pregnancy loss traits. A total of 374,206 PAG samples from 41,889 Swedish Red (SR) and 82,187 Swedish Holstein (SH) cows were collected at monthly test-day milkings in 1,119 Swedish herds. Pregnancy status was defined based on PAG levels and confirmed by data on artificial insemination (AI), calving, and culling from d 1 postinsemination to calving. Pregnancy loss traits were defined as embryonic loss (diagnosed 28 d to 41 d after AI), fetal loss (42 d after AI until calving), and total pregnancy loss. Least squares means (± standard error, %) and genetic parameters were estimated using mixed linear models. Heritability was estimated to be 0.02, 0.02, and 0.03 for embryonic loss, fetal loss, and total pregnancy loss, respectively. Cows with pregnancy loss had lower PAG concentrations than cows which successfully maintained pregnancy and calved. PAG recording was limited to monthly test-day milking, resulting in low estimated embryonic loss (17.5 ± 0.4 and 18.7 ± 0.4 in SR and SH, respectively) and higher fetal loss (32.8 ± 0.5 and 35.1 ± 0.5 in SR and SH, respectively). Pregnancy loss might have occurred earlier but remained undetected until the next test-day milking, when it was recorded as fetal loss rather than embryonic loss. Estimated genetic correlation between embryonic and fetal pregnancy loss traits and classical fertility traits were in general high. Identification of novel genetic traits from PAG data can be highly specific, as PAG are only secreted by the placenta. Thus, PAG could be useful indicators in selection to genetically improve pregnancy maintenance and reduce reproductive losses in milk production. Further studies are needed to clarify how these results could be applied in breeding programs concurrent with selection for classical fertility traits., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

High-yielding dairy cows are often fed high proportions of cereal grain and pulses. For several reasons, it would be desirable to replace these feed sources with forage, which is not suitable for human consumption. Feeding large amounts of forage to dairy cows could also make dairy production more publicly acceptable in the future. In this study, we estimated genetic parameters for total dry matter intake (DMI), DMI from forage (DMI For ), energy-corrected milk (ECM), and ECM produced from forage (ECM For ). A total of 1,177 lactations from 575 cows of Swedish Red (SR) and Holstein (HOL) dairy breeds were included in the study. Mixed linear animal random regression models were used, with fixed effect of calving season and lactation week nested within parity 1 and 2+, fixed effect of calving year, and random regression coefficients for breeding value (up to linear) and permanent environmental effect (up to quadratic) of the cow. Heritability for DMI and DMI For was generally higher for HOL than for SR in all-parity data and in later parities; however, the opposite was true for first parity. Heritability for DMI and DMI For during the first 8 wk averaged 0.11 and 0.15, respectively, in all-parity data for the 2 breeds. Corresponding values for ECM For and ECM were 0.21 and 0.29, respectively. In first parity, values were 0.32, 0.36, 0.28, and 0.51, respectively. The genetic correlation between DMI and DMI For was high, above 0.83, and fairly constant across the lactation. The genetic correlation between ECM For and ECM was close to unity in the later part of lactation for both breeds, but was around 0.8 in the early lactation for both breeds; it decreased for HOL to 0.54 in wk 17. The genetic correlations between DMI and ECM For and between DMI For and ECM For were low and negative for HOL (absolute value ∼0.2-0.3), but changed for SR from weakly positive in early lactation to negative values and back to positive toward the end of lactation. For most traits, the correlation between wk 1 and wk 8 into the lactation was very high; the lowest value was for DMI in HOL at 0.81. The genetic correlation between parities was rather high in the first part of the lactation. During the first 8 wk, the correlation was lower for HOL than for SR, except for ECM. We found that DMI For and ECM For showed reasonably large heritability, and future work should explore the possibility of genomic evaluations., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

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., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Evaluating fertility traits based on endocrine progesterone profiles is becoming a promising option to improve dairy cow fertility. Several studies have been conducted on endocrine fertility traits, mainly in the Holstein breed. In this study, focusing also on the Swedish Red (SR) breed, genetic parameters were estimated for classical and endocrine fertility traits, the latter based on in-line milk progesterone records obtained for 14 Swedish herds using DeLaval Herd Navigator (DeLaval International, Tumba, Sweden). A total of 210,403 observations from 3,437 lactations of 1,107 SR and 1,538 Holstein cows were used. Mixed linear animal models were used for estimation of genetic parameters. Least squares means analysis showed that Holstein cows had a 2.5-d-shorter interval from calving to commencement of luteal activity (C-LA) and longer length of first inter-ovulatory interval (IOI) than SR cows. The highest mean interval for C-LA, IOI, and first luteal phase length (LPL) was observed in the fourth parity. The incidence of short (<18 d), normal, (18-24 d), and long (>24 d) IOI was 29.3, 40.7, and 30%, respectively. Genetic analysis indicated moderate heritability (h 2 ) for C-LA (h 2 = 0.24), luteal activity during the first 60 d in milk (LA60, h 2 = 0.15), proportion of samples with luteal activity (PLA, h 2 = 0.13), and calving to first heat (CFH, h 2 = 0.18), and low heritability estimates for LPL (h 2 = 0.08) and IOI (h 2 = 0.03) in the combined data set for both breeds. Similar heritability estimates were obtained for each breed separately except for IOI and LPL in SR cows, for which heritability was estimated to be zero. Swedish Red cows had 0.01 to 0.06 higher heritability estimates for C-LA, LA60, and PLA than did Holstein cows. Calving interval had moderate heritability among the classical traits for Holstein and the combined data set, but h 2 was zero for SR. Commencement of luteal activity had a strong genetic correlation with LA60 (mean ± SE; -0.88 ± 0.06), PLA (-0.72 ± 0.11), and CFH (0.90 ± 0.04). Similarly, CFH had a strong genetic correlation with IOI (0.98 ± 0.20). Number of inseminations per series showed a weak genetic correlation with all endocrine traits except IOI. Overall, endocrine traits had higher heritability estimates than classical traits in both breeds, and may have a better potential to explain the actual reproductive status of dairy cows than classical traits. This might favor inclusion of some endocrine fertility traits-especially those related to commencement of luteal activity-as selection criteria and breeding goal traits if recording becomes more common in herds. Further studies on genetic and genomic evaluations for endocrine fertility traits may help to provide firm conclusions. A prerequisite is that the data from automatic devices be made available to recording and breeding organizations in the future and included in a central database., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Female fertility has a major role in dairy production and affects the profitability of dairy cattle. The genetic progress obtained by traditional selection can be slow because of the low heritability of classical fertility traits. Endocrine fertility traits based on progesterone concentration in milk have higher heritability and more directly reflect the cow's own reproductive physiology. The aim of our study was to identify genomic regions for 7 endocrine fertility traits in dairy cows by performing a genome-wide association study with 54,000 SNP. The next step was to fine-map targeted genomic regions with significant SNP using imputed sequences to identify potential candidate genes associated with the normal and atypical progesterone profiles. The association between a SNP and a phenotype was assessed by a single SNP analysis, using a linear mixed model that included a random polygenic effect. Phenotypes and genotypes were available for 1,126 primiparous and multiparous Holstein-Friesian cows from research herds in Ireland, the Netherlands, Sweden, and the United Kingdom. In total, 44 significant SNP associated with 7 endocrine fertility traits were identified on Bos taurus autosome (BTA) 1-4, 6, 8-9, 11-12, 14-17, 19, 21-24, and 29. Three chromosomes, BTA8, BTA17, and BTA23, were imputed from 54,000 SNP genotypes to the whole-genome sequence level with Beagle version 4.1. The fine-mapping identified several significant associations with delayed cyclicity, cessation of cyclicity, commencement of luteal activity, and inter-ovulatory interval. These associations may contribute to an index of markers for genetic improvement of fertility. Several potential candidate genes reported to affect reproduction were also identified in the targeted genomic regions. However, due to high linkage disequilibrium, it was not possible to identify putative causal genes or polymorphisms for any of the regions., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

In this study, we aimed to estimate and compare the genetic parameters of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) as 3 feed efficiency-related traits across lactation in 3 dairy cattle breeds (Holstein, Nordic Red, and Jersey). The analyses were based on weekly records of DMI, ECM, and BW per cow across lactation for 842 primiparous Holstein cows, 746 primiparous Nordic Red cows, and 378 primiparous Jersey cows. A random regression model was applied to estimate variance components and genetic parameters for DMI, ECM, and BW in each lactation week within each breed. Phenotypic means of DMI, ECM, and BW observations across lactation showed to be in very similar patterns between breeds, whereas breed differences lay in the average level of DMI, ECM, and BW. Generally, for all studied breeds, the heritability for DMI ranged from 0.2 to 0.4 across lactation and was in a range similar to the heritability for ECM. The heritability for BW ranged from 0.4 to 0.6 across lactation, higher than the heritability for DMI or ECM. Among the studied breeds, the heritability estimates for DMI shared a very similar range between breeds, whereas the heritability estimates for ECM tended to be different between breeds. For BW, the heritability estimates also tended to follow a similar range between breeds. Among the studied traits, the genetic variance and heritability for DMI varied across lactation, and the genetic correlations between DMI at different lactation stages were less than unity, indicating a genetic heterogeneity of feed intake across lactation in dairy cattle. In contrast, BW was the most genetically consistent trait across lactation, where BW among all lactation weeks was highly correlated. Genetic correlations between DMI, ECM, and BW changed across lactation, especially in early lactation. Energy-corrected milk had a low genetic correlation with both DMI and BW at the beginning of lactation, whereas ECM was highly correlated with DMI in mid and late lactation. Based on our results, genetic heterogeneity of DMI, ECM, and BW across lactation generally was observed in all studied dairy breeds, especially for DMI, which should be carefully considered for the recording strategy of these traits. The genetic correlations between DMI, ECM, and BW changed across lactation and followed similar patterns between breeds., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: Pregnancy loss is a major source of infertility in dairy cows. Despite a fertilization rate after insemination (AI) of approximately 90%, calving rates are 30%-50%, indicating the occurrence of extensive embryonic and foetal losses. The aim of this study was to establish the extent and pattern of embryonic and foetal loss in Swedish Red (SR) and Swedish Holstein (SH) dairy cows, as well as, the relationship to oestrus intensity (OI) and progesterone (P4) concentration. In total, 2130 AIs and 16,176 milk P4 samples from 359 SR and 212 SH dairy cows were included in the study. Pregnancy losses were estimated using data from P4 values combined with AI information and calving data., Results: Total pregnancy loss from AI to the day of calving was 65%. Early embryonic loss, late embryonic loss and foetal loss were estimated to be 29, 14 and 13%, respectively. There is strong evidence in the literature that P4 concentrations at different time points are associated with pregnancy loss. In the present study, cows with pregnancy losses had significantly higher P4 levels at the day of AI and significantly lower P4 concentration at days 10, 21 and 30 after AI compared to pregnant cows. Swedish Red cows had significantly lower total pregnancy losses compared to SH cows (62% and 68% respectively, P = 0.017). Early embryonic loss was 6.7% points lower for cows inseminated at a stronger OI (OI = 3) compared to at a weaker OI (OI = 2, P = 0.006). Cows inseminated at ovulation number ≥ 5 had significantly lower early pregnancy losses compared to cows inseminated at first or second ovulation (11.5 and 8% points, respectively, P < 0.05). With an increase of one SD of milk (448 kg ECM) during the first 60 days in milk, early embryonic loss increased by 4.7% points (P = 0.006)., Conclusions: It is important to increase the number of cows calving per insemination by reducing embryo/foetal loss. This outcome can be achieved by management and breeding for optimal P4 levels at critical time points, and by considering oestrus expression in the breeding programmes to facilitate the correct timing of insemination.

The main objective of this study was to investigate the benefit of accuracy of genomic prediction when combining records for an intermediate physiological phenotype in a training population with records for a traditional phenotype. Fertility was used as a case study, where commencement of luteal activity (C-LA) was the physiological phenotype, whereas the interval from calving to first service and calving interval were the traditional phenotypes. The potential accuracy of across-country genomic prediction and optimal recording strategies of C-LA were also investigated in terms of the number of farms and number of repeated records for C-LA. Predicted accuracy was obtained by estimating population parameters for the traits in a data set of 3,136 Holstein Friesian cows with 8,080 lactations and using a deterministic prediction equation. The effect of genetic correlation, heritability, and reliability of C-LA on the accuracy of genomic prediction were investigated. When the existing training population was 10,000 bulls with reliable estimated breeding value for the traditional trait, predicted accuracy for the physiological trait increased from 0.22 to 0.57 when 15,000 cows with C-LA records were added to the bull training population; but, when the interest was in predicting the traditional trait, we found no benefit from the additional recording. When the genetic correlation was higher between the physiological and traditional traits (0.7 instead of 0.3), accuracy increased less when adding the 15.000 cows with C-LA (from 0.51 to 0.63). In across-country predictions, we observed little to no increase in accuracy of the intermediate physiological phenotype when the training population from Sweden was large, but when accuracy increased the training population was small (200 cows), from 0.19 to 0.31 when 15,000 cows were added from the Netherlands (genetic correlation of 0.5 between countries), and from 0.19 to 0.48 for genetic correlation of 0.9. The predicted accuracy initially increased substantially when recording on the same farm was extended and multiple C-LA records per cow were used in prediction compared with single records; that is, accuracy increased from 0.33 with single records to 0.38 with multiple records (on average 1.6 records per cow) from 2 yr of recording C-LA. But, when the number C-LA per cow increased beyond 2 yr of recording, we noted no substantial benefit in accuracy from multiple records. For example, for 5 yr of recording (on average 2.5 records per cow), accuracy was 0.47; on doubling the recording period to 10 yr (on average 3.1 records per cow), accuracy increased by 0.07 units, whereas when C-LA was recorded for 15 yr (on average 3.3 records per cow) accuracy increased only by 0.05 units. Therefore, for genomic prediction using expensive equipment to record traits for training populations, it is important to optimize the recording strategy. The focus should be on recording more cows rather than continuous recording on the same cows., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to define endocrine fertility traits from in-line milk progesterone (P4) records and to estimate genetic parameters for these traits. Correlations of classical fertility (calving interval and calving to first service) and milk production traits with endocrine fertility traits were also estimated. In-line milk P4 records (n=160,952) collected from June 2009 through November 2013 for 2,273 lactations of 1,561 Holstein-Friesian cows in 12 commercial herds in the Netherlands were analyzed for (the log of) the number of days from calving till commencement of luteal activity (lnC-LA), proportion of samples between 25 and 60 d in milk with luteal activity (PLA), presence or absence of luteal activity for a cow between 25 and 60 d in milk, interval from commencement of luteal activity to first service (CLAFS), first luteal phase length, length of first interluteal interval, and length of first interovulatory interval. Milk P4 records were sampled, on average, every 2 d. Genetic parameters were estimated using a mixed linear animal model. Heritability estimates (±SE) of endocrine fertility traits were 0.12±0.05 for lnC-LA, 0.12±0.05 for PLA, and 0.11±0.06 for CLAFS, and their repeatability estimates were 0.29±0.04, 0.21±0.04, and 0.15±0.06, respectively. The genetic correlation of lnC-LA with PLA was -0.91±0.06 and with CLAFS was -0.56±0.25. The genetic correlations of lnC-LA were 0.26±0.33 with calving interval and 0.37±0.21 with calving to first service. Genetic correlations of the milk production traits with lnC-LA ranged from 0.04 to 0.18 and 0.07 to 0.65 with classical fertility traits. The phenotypic correlations of all endocrine fertility traits with milk production traits were close to zero (0.01 to 0.07). This study shows that in-line P4 records can be used to define and explore several heritable endocrine fertility traits in dairy cows and might help in selection for improved fertility., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein-Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all <0.021. Posterior QTL probabilities of 0.060 and 0.045 were observed for CLA on SNPs each on chromosome 2 and chromosome 21, respectively, in the univariate analyses; these probabilities increased when CLA was included in the bivariate analyses with the traditional fertility traits. For example, in the bivariate analysis with CI, the posterior QTL probability of the two aforementioned SNPs were 0.662 and 0.123. Candidate genes in the vicinity of these SNPs are discussed. The results from this study suggest that the power of genome-wide association studies in cattle may be increased by sharing of data and also possibly by using physiological measures of the trait under investigation.

The association between somatic cell count (SCC) and daily milk yield in different stages of lactation was investigated in cows free of clinical mastitis (CM). Data were recorded between 1989 and 2004 in a research herd, and consisted of weekly test-day (TD) records from 1,155 lactations of Swedish Holstein and Swedish Red cows. The main data set (data set A) containing 36,117 records excluded TD affected by CM. In this data set, the geometric mean SCC was 55,000 and 95,000 cells/mL in primiparous and multiparous cows, respectively. A subset of data set A (data set B), containing 27,753 records excluding all TD sampled in lactations affected by CM, was created to investigate the effect of subclinical mastitis (SCM) in lactations free of CM. Daily milk yields were analyzed using a mixed linear model with lactation stage; linear, quadratic and cubic regressions of log(2)-transformed and centered SCC nested within lactation stage; weeks in lactation; TD season; parity; breed; pregnancy status; year-season of calving; calving, reproductive, metabolic and claw disorders; and housing system as fixed effects. A random regression was included to further improve the modeling of the lactation curve. Primiparous and multiparous cows were analyzed separately. The magnitude of daily milk loss associated with increased SCC depended on stage of lactation and parity, and was most extensive in late lactation irrespective of parity. In data set A, daily milk loss at an SCC of 500,000 cells/mL ranged from 0.7 to 2.0 kg (3 to 9%) in primiparous cows, depending on stage of lactation. In multiparous cows, corresponding loss was 1.1 to 3.7 kg (4 to 18%). Regression coefficients of primiparous cows estimated from data set B were consistent with those obtained from data set A, whereas data set B generated more negative regression coefficients of multiparous cows suggesting a higher milk loss associated with increased SCC in lactations in which the cow did not develop CM. The 305-d milk loss in the average lactation affected with SCM was 155 kg of milk (2%) in primiparous cows and 445 kg of milk (5%) in multiparous cows. It was concluded that multiparous cows in late lactation can be expected to be responsible for the majority of the herd-level production loss caused by SCM, and that preventive measures need to focus on reducing the incidence of SCM in such cows.

We examined whether infrequent milk sampling for progesterone analysis could be used as a management or diagnostic tool by the dairy farmer to predict delayed ovarian cyclicity in dairy cows. The data included 1040 lactations from 324 Swedish Red cows and 183 Swedish Holstein cows and were randomly divided into two datasets. A logistic regression model was fit to the first dataset and the model was then validated on the other dataset. The model was also validated using a British dataset comprising 1212 lactations from 1080 British Holstein-Friesian cows. The dependent variable was whether delayed ovarian cyclicity occurred or not, delayed ovarian cyclicity defined as progesterone levels below threshold value for the first 56 or 45 days postpartum in the Swedish or British dataset, respectively. The basic model included the effects of breed, parity, season and housing type. To the basic model various progesterone-based measurements were added. These were the interval from calving to commencement of luteal activity and the percentage of samples with luteal activity within 60 days after calving, using all samples in the databases or one sample per month. The accuracy of the conditional probability of delayed ovarian cyclicity calculated with the different models was obtained using receiver operating characteristic (ROC) curves and calculating the area under curve. Sensitivity and specificity were calculated for cut-off probabilities using the ROC analyses. The accuracy was highest (0.94-0.99) when including the progesterone measurements based on milk sampling for progesterone analysis 2-3 times per week. The accuracy was between 0.85 and 0.88 when the progesterone measurements with monthly milk sampling were added to the model and 0.76 or 0.67 with the basic model. This study clearly shows that infrequent milk sampling for progesterone analysis, such as once a month as in the regular milk recording system, could be used to predict delayed ovarian cyclicity in dairy cows. This increases the opportunity for earlier treatment of anovulatory dairy cows and may therefore decrease involuntarily extended calving intervals in the herd.

The effects of the first lactational incidence of clinical mastitis (CM) on milk, fat, and protein production were studied in the Swedish Red and Swedish Holstein breeds. The data consisted of 38,535 weekly production records from 1,192 lactations (506 cows), sampled from 1987 to 2004 in one of the university's research herds. Daily yields were analyzed using a repeated-measures mixed model with an interaction between mastitis index and lactational stage, breed, parity, reproductive status, year-season of calving, and various indices for other disorders as independent variables. The indices were used to distinguish between cows with and without the studied diagnoses, as well as to indicate time (test day) in relation to day of diagnosis. Inclusion of the interaction made it possible to study the effects of CM occurring in different weeks of lactation. Primiparous and multiparous cows were analyzed separately, and the yields of nonmastitic cows were used as a reference for the production level in healthy cows. Lactational (305-d) yield losses were extrapolated from the daily estimates. High milk yield was predisposing to CM. Daily milk yield started to decline 2 to 4 wk before diagnosis. On the day of clinical onset, the milk yield of mastitic cows was reduced by 1 to 8 kg. After a case of CM, milk yield was suppressed throughout lactation. The magnitude of the yield losses was determined by the week of lactation at clinical onset. The greatest losses occurred when primiparous cows developed CM in wk 6, whereas multiparous cows experienced the greatest losses when diseased in wk 3. The 305-d milk, fat, and protein production in mastitic primiparous cows were reduced by 0 to 9, 0 to 8, and 0 to 7%, respectively. The corresponding reductions in mastitic multiparous cows were 0 to 11, 0 to 12, and 0 to 11%, respectively.

The incidence of normal and atypical progesterone profiles in Swedish dairy cows was studied. Data were collected from an experimental herd over 15 yr, and included 1,049 postpartum periods from 183 Swedish Holstein and 326 Swedish Red and White dairy cows. Milk progesterone samples were taken twice weekly until initiation of cyclical ovarian activity and less frequently thereafter. Progesterone profiles were 1) normal profile: first rise in milk progesterone above the threshold value before d 56 postpartum, followed by regular cyclical ovarian activity (70.4%); 2) delayed onset of cyclical ovarian activity: low milk progesterone the first 56 d postpartum (15.6%); 3) cessation of cyclical ovarian activity: ovarian activity resumed within 56 d postpartum, but ceased for a period of 14 d or more (6.6%); and 4) prolonged luteal phase: ovarian activity resumed within 56 d postpartum, but milk progesterone remained elevated in the nonpregnant cow for a period of 20 d or more (7.3%). Swedish Holsteins had 1.5 times higher risk of atypical profile than Swedish Red and Whites. Risk of atypical profiles was 0.5 and 0.7 times lower for older cows compared with first-parity cows; 2.3 times higher for cows in tie-stalls compared with those in loose housing; 2.6 times higher for cows calving during winter compared with summer; 0.5 times lower for cows in earlier (1994-1999) calving-year groups compared with the most recent (2000-2002); 2.5 times higher for cows with planned extended calving interval compared with conventional calving interval; and 2.2 times higher for an atypical profile in previous lactation compared with a normal profile. Cows with atypical profiles had a 15-d increase in interval from calving to first artificial insemination and an 18-d increase in interval from calving to conception. Progesterone samples taken within the first 60 d postpartum were used to calculate the percentage of samples above the threshold value of luteal activity. This measure had a significantly different mean in profiles and can be used to separate delayed onset of cyclical ovarian activity profiles and prolonged luteal phase profiles from normal. Thereby, it may be a more effective tool than measurements based only on the onset of ovarian cyclical activity in genetic evaluation of early postpartum fertility in dairy cows.

The objective of this study was to estimate genetic parameters for measures of luteal activity during the first 60 d postpartum. Analyses were made with different sampling intervals to investigate the possibility of combining progesterone measurement with routinely performed milk recording. Progesterone level in milk as an indicator of female fertility when selecting sires in a progeny-testing scheme was also examined. Data were collected from 1996 to 1999, and comprised 1,212 lactations from 1,080 British Holstein-Friesian cows at 8 commercial dairy farms in the United Kingdom. Milk samples for progesterone analysis were collected thrice weekly. Mixed linear animal models were used to analyze the data. Heritability for the percentage of samples with luteal activity during the first 60 d postpartum (PLA) was 0.30 and decreased with more infrequent sampling to 0.25, 0.20, and 0.14 for weekly, twice- monthly, and monthly sampling, respectively. Measures of PLA had a high negative genetic correlation with prolonged anovulation (-0.53 for monthly sampling, < -0.87 otherwise) and a moderate positive genetic correlation with persistent corpus luteum in the first estrus cycle (>0.65 if at least twice-monthly sampling). Genetic correlations with interval from calving to commencement of luteal activity were close to -1 for all PLA measurements and the selection index calculations showed that monthly progesterone sampling could be used with high accuracy (0.80 with 50 daughters per bull) to predict breeding values for commencement of luteal activity. Progesterone analysis at the time of regular milk recording could thereby be used to select for an early interval from calving to commencement of luteal activity and, at the same time, a decreased frequency of prolonged anovulation during the postpartum period. [ABSTRACT FROM AUTHOR]