Heritabilities and genetic and phenotypic correlations for milk production and fertility traits of spring-calved once-daily or twice-daily milking cows in New Zealand

The objectives of this study were to estimate the genetic and phenotypic correlations and heritabilities for milk production and fertility traits in spring-calved once-daily (OAD) milking cows for the whole season in New Zealand and compare those estimates with twice-daily (TAD) milking cows. Data used in the study consisted of 69,252 first parity cows from the calving seasons 2015-2016 to 2017-2018 in 113 OAD and 531 TAD milking herds. Heritability estimates for production and fertility traits were obtained through single-trait animal models, and estimates of genetic and phenotypic correlations were obtained through bivariate animal models. Heritability estimates of production traits varied from 0.26 to 0.61 in OAD and from 0.13 to 0.63 in TAD. Heritability estimates for fertility traits were low in both OAD and TAD milking cow populations, and estimates were consistent (OAD: 0.01 to 0.10 and TAD: 0.01 to 0.08) across milking regimens. Estimates of phenotypic and genetic correlations among production traits were consistent across populations. In both populations, phenotypic correlations between milk production and fertility traits were close to zero, and most of the genetic correlations were antagonistic. In OAD milking cows, genetic correlations of milk and lactose yields with the start of mating to conception, 6-wk in-calf, not-in-calf, and 6-wk calving rate were close to zero. Interval from first service to conception was negatively genetically correlated with milk and lactose yields in OAD milking cows. Protein percentage was positively genetically correlated with 3-wk and 6-wk submission, 3-wk in-calf, 6-wk in-calf, first service to conception, 3-wk calving, and 6-wk calving rate in the TAD milking cow population, but these correlations were low in the OAD milking cow population. Further studies are needed to understand the relationship of protein percentage and fertility traits in the OAD milking system. The phenotypic correlations between fertility traits were similar in OAD and TAD milking populations. Genetic correlations between fertility traits were strong (≥0.70) in cows milked TAD, but genetic correlations varied from weak to strong in cows milked OAD. Further research is required to evaluate the interaction between genotype by milking regimen for fertility traits in terms of sire selection in the OAD milking cow population.

Relationships between milk protein concentrations at different phases of first to fourth lactation and subsequent calving intervals in Holstein cows

So far, no study has been conducted on the effect of milk protein concentrations (MP%) in different time periods of lactation on subsequent calving intervals (CI). The purpose of this study was to evaluate the association between MP% at different stages of the first to fourth lactations and subsequent calving intervals in primiparous and multiparous Holstein dairy cows. Test day records (3X milking frequency) of milk yield measured on primiparous (n = 503793) and multiparous (n = 389237, 257347, 156035 and 87750 for parity 2, 3 and 4, and 5 respectively) Holstein dairy cows collected during 1984-2019 were obtained from Animal Breeding Center of Iran. To investigate the relationship between MP% at different time intervals of lactation and subsequent calving intervals (up to fifth parity), each lactation was divided into four time periods (≤50d, 50 < d ≤ 100, 100 < d ≤ 200 and 200 < d ≤ 305). At each period, protein concentrations were also divided into three classes: 1-3%, 3-5%, 5-7%. There was a negative relationship between MP% in the first 50 days of first lactation in primiparous and multiparous dairy cows and subsequent calving intervals in a way that calving intervals decreased as MP% increased and the shortest calving intervals were observed for the third class of milk protein concentrations. After the first 50 days of first lactation, the shortest subsequent calving intervals were detected for the second class of MP% and the CI increased non-significantly when MP% increased to 5-7. In general, due to the significant relationship between MP% and subsequent CI, this relationship can be further considered in breeding programs in dairy cattle.

Development of a linear programming model for the optimal allocation of nutritional resources in a dairy herd

A linear programming model that selects the optimal cropping plan and feeds allocation for diets to minimize the whole dairy farm feed costs was developed. The model was virtually applied on 29 high-yielding Holstein-Friesian herds, confined, total mixed ration dairy farms. The average herd size was 313.2 ± 144.1 lactating cows and the average land size was 152.2 ± 92.5 ha. Farm characteristics such as herd structure, nutritional grouping strategies, feed consumption, cropping plan, intrinsic farm limitations (e.g., silage and hay storage availability, water for irrigation, manure storage) and on farm produced forage costs of production were collected from each farm for the year 2017. Actual feeding strategies, land availability, herd structure, crop production costs and yields, and milk and feed market prices for the year 2017 were used as model inputs. Through optimization, the feeding system was kept equal to the actual farm practice. The linear program formulated diets for each animal group to respect actual herd dry matter intake and fulfill actual consumption of crude protein, rumen-degradable and rumen-undegradable fractions of crude protein, net energy for lactation, neutral detergent fiber, acid detergent fiber, forage neutral detergent fiber, and nonfiber carbohydrate. Production levels and herd composition were considered to remain constant as the nutritional requirement would remain unchanged. The objective function was set to minimize the whole-farm feed costs including cash crop sales as income, and crop production costs and purchased feed costs as expenses. Optimization improved income over feed costs by reducing herd feed costs by 7.8 ± 6.4%, from baseline to optimized scenario, the improved was explained by lower feed costs per kilogram of milk produced due to a higher feed self-sufficiency and higher income from cash crop. In particular, the model suggested to maximize, starting from baseline to optimized scenario, the net energy for lactation (+8.5 ± 6.3%) and crude protein (+3.6 ± 3.1%) produced on farm, whereas total feed cost (€/100 kg of milk) was greater in the baseline (20.4 ± 2.3) than the optimized scenario (19.0 ± 1.9), resulting in a 6.7% feed cost reduction with a range between 0.49% and 21.6%. This meant €109 ± 96.9 greater net return per cow per year. The implementation of the proposed linear programming model for the optimal allocation of the nutritional resources and crops in a dairy herd has the potential to reduce feed cost of diets and improve the farm feed self-sufficiency.

Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle

Substantial evidence demonstrates that the genetic background of milk production traits changes during lactation. However, most GWAS for milk production traits assume that genetic effects are constant during lactation and therefore might miss those quantitative trait loci (QTL) whose effects change during lactation. The GWAS for genotype by lactation stage interaction are aimed at explicitly detecting the QTL whose effects change during lactation. The purpose of this study was to perform GWAS for genotype by lactation stage interaction for milk yield, lactose yield, lactose content, fat yield, fat content, protein yield, and somatic cell score to detect QTL with changing effects during lactation. For this study, 19,286 test-day records of 1,800 first-parity Dutch Holstein cows were available and cows were genotyped using a 50K SNP panel. A total of 7 genomic regions with effects that change during lactation were detected in the GWAS for genotype by lactation stage interaction. Two regions on Bos taurus autosome (BTA)14 and BTA19 were also significant based on a GWAS that assumed constant genetic effects during lactation. Five regions on BTA4, BTA10, BTA11, BTA16, and BTA23 were only significant in the GWAS for genotype by lactation stage interaction. The biological mechanisms that cause these changes in genetic effects are still unknown, but negative energy balance and effects of pregnancy may play a role. These findings increase our understanding of the genetic background of lactation and may contribute to the development of better management indicators based on milk composition.

Relative effect of milk constituents on fertility performance of milk-recorded, spring-calving dairy cows in Ireland

Several studies have reported associations between milk composition data and fertility performance. However, no work to date has estimated the effect of milk constituents on fertility performance in cows with low milk constituent concentrations. The objective of this study was to assess the association between milk constituents, animal characteristics, and time from mating start date (MSD) to conception using survival analysis. Furthermore, we aimed to investigate the relative effect of each variable by predicting median times to conception for animals with different combinations of characteristics and milk compositions. The final data set consisted of 87,227 cow lactation records from 64,519 cows in 2,049 herds with calving dates from 2010 to 2013. Milk recording data from each lactation were used, including test day recordings at 0 to 30, 31 to 60, and 61 to 90 d in milk (DIM). The analysis was limited to spring-calving cows (i.e., animals calving from January to May inclusive). Mating start date was determined for each unique herd in each year. A cow-specific MSD (MSD_cow) was defined taking into consideration the MSD for each herd and the calving date and a minimum calving to insemination interval of each herd year. The conception date for each cow was estimated using the subsequent calving date. Cows with no subsequent calving date were assumed not to have conceived. Time from MSD_cow to approximate conception date was analyzed using survival analysis. Cox proportional hazard models were constructed for each of the 3 recording windows: 0 to 30, 31 to 60, and 61 to 90 DIM. A fourth model was used to assess the dynamics of milk composition over the 3 windows. To investigate the effect of these variables, model outputs were used to create parametric accelerated failure time models to predict median survival times for animals at the 10th and 90th percentiles of the variable of interest but otherwise identical across the rest of the variables. Results demonstrated that fertility breeding subindex had the largest effect on time from MSD_cow to conception, with an additional 62 d open for those in the 10th percentile versus those in the 90th percentile. Of the milk constituents, milk lactose concentration had the greatest effect on MSD to conception, particularly when measured from 0 to 30 DIM. An additional 10 d open resulted from comparing those in the 10th and 90th percentiles. Milk protein concentration, although statistically significant, had a lower effect on fertility outcome when comparing cows in the 10th and 90th percentiles for this exposure variable. The greatest effect was found in the 61 to 90 DIM recording window, where cows in the 10th percentile had an additional 9 d open at the subsequent breeding season compared with those in the 90th percentile. Overall, our study shows that although the associations between milk constituents and fertility are statistically significant, their overall influence in determining MSD to conception in this study population is relatively modest, particularly compared with fertility breeding subindex, when comparing cows at the 10th and 90th percentiles. Of the milk constituents measured, milk lactose concentration measured at 0 to 30 DIM had the greatest effect in determining fertility outcome when comparing cows at the 10th and 90th percentiles. The predictive value of early-lactation test day milk composition data on hazard of pregnancy during the following breeding period, within a spring-calving context, appears to be relatively modest at the individual-cow level. Further work is required to test the usefulness of these associations at the herd level.

Fertility of fresh and frozen sex-sorted semen in dairy cows and heifers in seasonal-calving pasture-based herds

Our objective in this study was to evaluate the reproductive performance of dairy heifers and cows inseminated with fresh or frozen sex-sorted semen (SS) in seasonal-calving pasture-based dairy herds. Ejaculates of 10 Holstein-Friesian bulls were split and processed to provide (1) fresh conventional semen at 3 × 10⁶ sperm per straw (CONV); (2) fresh SS at 1 × 10⁶ sperm per straw (SS-1M); (3) fresh SS semen at 2 × 10⁶ sperm per straw (SS-2M); and (4) frozen SS at 2 × 10⁶ sperm per straw (SS-FRZ). Generalized linear mixed models were used to evaluate the effect of semen treatment and other explanatory variables on pregnancy per artificial insemination (P/AI) in heifers (n = 3,214) and lactating cows (n = 5,457). In heifers, P/AI was greater for inseminations with CONV (60.9%) than with SS-FRZ (52.8%) but did not differ from SS-1M (54.2%) or SS-2M (53.5%). Cows inseminated with CONV had greater P/AI (48.0%) than cows inseminated with SS, irrespective of treatment (SS-1M, SS-2M, and S-FROZEN; 37.6, 38.9, and 40.6%, respectively). None of the SS treatments differed from each other with regard to P/AI in either heifers or cows. The relative performance of SS compared with CONV was also examined [i.e., relative P/AI = (SS P/AI)/(CONV P/AI) × 100]. Frozen SS achieved relative P/AI >84%. Bull affected P/AI in both heifers and cows, but no bull by semen treatment interaction was observed. In heifers, P/AI increased with increasing predicted transmitting ability for milk protein percentage. In cows, P/AI increased with increasing Economic Breeding Index (EBI) and with days in milk (DIM) at AI but decreased with increasing EBI milk subindex, parity and with DIM². Cows in parity ≥5 had the lowest P/AI and differed from cows in parities 1, 2, or 3. Dispatch-to-AI interval of fresh semen did not affect P/AI in lactating cows, but a dispatch-to-AI interval by bull interaction was detected whereby P/AI was constant for most bulls but increased with greater dispatch-to-AI intervals for 2 bulls. In conclusion, frozen SS achieved greater P/AI relative to conventional semen than was previously reported in lactating cows. Fresh SS did not achieve greater P/AI than frozen SS, regardless of whether the sperm dose per straw was 1 × 10⁶ or 2 × 10⁶. A bull effect for all semen treatments, as well as a dispatch-to-AI interval by bull interaction for fresh semen, highlights the importance of using a large team of bulls for breeding management.

Genome-wide association studies for genetic effects that change during lactation in dairy cattle

Genetic effects on milk production traits in dairy cattle might change during lactation. However, most genome-wide association studies (GWAS) for milk production traits assume that genetic effects are constant during lactation. This assumption might lead to missing these quantitative trait loci (QTL) whose effects change during lactation. This study aimed to screen the whole genome specifically for QTL whose effects change during lactation. For this purpose, 4 different GWAS approaches were performed using test-day milk protein content records: (1) separate GWAS for specific lactation stages, (2) GWAS for estimated Wilmink lactation curve parameters, (3) a GWAS using a repeatability model where SNP effects are assumed constant during lactation, and (4) a GWAS for genotype by lactation stage interaction using a repeatability model and accounting for changing genetic effects during lactation. Separate GWAS for specific lactation stages suggested that the detection power greatly differs between lactation stages and that genetic effects of some QTL change during lactation. The GWAS for estimated Wilmink lactation curve parameters detected many chromosomal regions for Wilmink parameter a (protein content level), whereas 2 regions for Wilmink parameter b (decrease in protein content toward nadir) and no regions for Wilmink parameter c (increase in protein content after nadir) were detected. Twenty chromosomal regions were detected with effects on milk protein content; however, there was no evidence that their effects changed during lactation. For 5 chromosomal regions located on chromosomes 3, 9, 10, 14, and 27, significant evidence was observed for a genotype by lactation stage interaction and thus their effects on milk protein content changed during lactation. Three of these 5 regions were only identified using a GWAS for genotype by lactation stage interaction. Our study demonstrated that GWAS for genotype by lactation stage interaction offers new possibilities to identify QTL involved in milk protein content. The performed approaches can be applied to other milk production traits. Identification of QTL whose genetic effects change during lactation will help elucidate the genetic and biological background of milk production.

Components of the covariances between reproductive performance traits and milk protein concentration and milk yield in dairy cows

Reproductive performance in dairy cows can be improved through genetic selection and herd management. Milk protein concentration is strongly associated with various measures of reproductive performance, but the relative importance of genetic and environmental components of these associations have not been defined. The primary objective of this study was to estimate the magnitudes of correlations and covariances between 9 reproductive performance traits in dairy cows and each of milk protein concentration and milk yield at 4 levels: genetic, permanent environmental effects of cow, herd-year-season, and residual levels. A retrospective single cohort study was conducted using data collected from seasonally and split calving dairy herds. We used animal models to partition covariances for the relationships between 9 fertility traits and each of milk protein concentration and milk yield at lactation level, with up to 80,203 lactations from 27,244 cows that were 780 herd-year-seasons in 65 herds. For the fertility traits, of the explained covariance with milk protein concentration, between 33 and 79% (median 53%) was genetic and 21 to 67% (median 47%) was nongenetic. We concluded that research should be conducted to identify management strategies that capture the nongenetic components of relationships between milk protein concentration and reproductive performance. Genetic correlations with milk protein concentration were generally similar to genetic correlations with milk yield, but the correlation with milk protein concentration was closer (i.e., the absolute value of the correlation coefficient was nearer to 1) for pregnant by wk 6, a key trait for seasonally and split calving dairy herds (correlation coefficient ± standard error = 0.28 ± 0.05 and -0.17 ± 0.07 for milk protein concentration and milk yield, respectively). As the associations also have substantial genetic components, it is possible that reliabilities of estimated breeding values for fertility may be improved by including milk protein concentration in multitrait genetic evaluation models for fertility traits. From our preliminary analyses, reliabilities were only slightly higher when pregnancy by wk 6 of the breeding period was analyzed with milk protein concentration rather than alone or with milk yield, but further research should be considered to assess this question. Importantly, the benefits of these strong relationships can only be fully harnessed through joint use of both management strategies and genetic strategies.

Incidence of double ovulation during the early postpartum period in lactating dairy cows

In lactating cattle, the incidence of twin calving has many negative impacts on production and reproduction in dairy farming. In almost all cases, natural twinning in dairy cattle is the result of double ovulation. It has been suggested that the milk production level of cows influences the number of ovulatory follicles. The objective of the present study was to investigate the incidence of double ovulations during the early postpartum period in relation to the productive and reproductive performance of dairy cows. The ovaries of 43 Holstein cows (26 primiparous and 17 multiparous) were ultrasonographically scanned throughout the three postpartum ovulation sequences. The incidence of double ovulation in the unilateral ovaries was 66.7%, with a higher incidence in the right ovary than in the left, whereas that in bilateral ovaries was 33.3%. When double ovulations were counted dividing into each side ovary in which ovulations occurred, the total frequency of ovulations deviated from a 1:1 ratio (60.3% in the right side and 39.7% in the left side, P < 0.05). In multiparous cows, double ovulation occurred more frequently than in primiparous cows (58.8% vs. 11.5% per cow and 30.0% vs. 3.8% per ovulation, respectively P < 0.01). The double ovulators experienced more anovulatory repeated waves of follicles before their first ovulations than the single ovulators, which resulted in an extension of the period from parturition to third ovulation (81.5 days vs. 64.2 days, P < 0.05). In the multiparous cows, the double ovulators exhibited higher peak milk yield (P < 0.01) with lower milk lactose concentration (P < 0.05), indicating the prevalence of a more severe negative energy balance during the postpartum 3-month compared to the multiparous single ovulators. Our results showed that, regardless of their parity, double ovulation had no impact on the reproductive performance of the cows. Two multiparous cows that experienced double ovulation during the early postpartum period subsequently conceived twin fetuses. It can be speculated that the incidence of double ovulations during the early postpartum period partly contributes to the increased incidence of undesirable twin births in multiparous dairy cows.