Short communication: genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows

Postpartum energy status is critically important to health and fertility, and it remains a major task to find suitable indicator traits for energy balance. Therefore, genetic parameters for daily energy balance (EB) and dry matter intake (DMI), weekly milk fat to protein ratio (FPR), and monthly body condition score (BCS) were estimated using random regression on data collected from 682 Holstein-Friesian primiparous cows recorded between lactation d 11 to 180. Average energy-corrected milk (ECM), EB, DMI, BCS, and FPR were 32.0 kg, 9.6 MJ of NE_L, 20.3 kg, 2.95, and 1.12, respectively. Heritability estimates for EB, DMI, BCS, and FPR ranged from 0.03 to 0.13, 0.04 to 0.19, 0.34 to 0.59, and 0.20 to 0.54. Fat to protein ratio was a more valid measure for EB in early lactation than DMI, BCS, or single milk components. Correlations between FPR and EB were highest at the beginning of lactation [genetic correlation (r_g) = −0.62 at days in milk (DIM) 15] and decreased toward zero. Dry matter intake was the trait most closely correlated with EB in mid lactation (r_g = 0.73 at DIM 120 and 150). Energy balance in early lactation was negatively correlated to EB in mid lactation. The same applied to DMI. Genetic correlations between FPR across lactation stages were all positive; the lowest genetic correlation (0.55) was estimated between the beginning of lactation and early mid lactation. Hence, to improve EB at the beginning of lactation, EB and indicator traits need to be recorded in early lactation. We concluded that FPR is an adequate indicator for EB during the energy deficit phase. Genetic correlations of FPR with ECM, fat percentage, and protein percentage showed that a reduction of FPR in early lactation would have a slightly negative effect on ECM, whereas milk composition would change in a desirable manner.     

Genetic heterogeneity of feed intake,energy-corrected milk,and body weight across lactation in primiparous Holstein,Nordic Red,and Jersey cows

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.     

Genetics of grass dry matter intake, energy balance, and digestibility in grazing irish dairy cows

The objective of this study was to estimate genetic parameters for grass dry matter intake (DMI), energy balance (EB), and cow internal digestibility (IDG) in grazing Holstein-Friesian dairy cows. Grass DMI was estimated up to 4 times per lactation on 1,588 lactations from 755 cows on 2 research farms in southern Ireland. Simultaneously measured milk production and BW records were used to calculate EB. Cow IDG, measured as the ratio of feed and fecal concentrations of the natural odd carbon-chain n-alkane pentatriacontane, was available on 583 lactations from 238 cows. Random regression and multitrait animal models were used to estimate residual, additive genetic and permanent environmental (co)variances across lactations. Results were similar for both models. Heritability for DMI, EB, and IDG across lactation varied from 0.10 [8 days in milk (DIM)] to 0.30 (169 DIM), from 0.06 (29 DIM) to 0.29 (305 DIM), and from 0.08 (50 DIM) to 0.45 (305 DIM), respectively, when estimated using the random regression model. Genetic correlations within each trait tended to decrease as the interval between periods compared increased for DMI and EB, whereas the correlations with IDG in early lactation were weakest when measured midlactation. The lowest correlation between any 2 periods was 0.10, −0.36, and −0.04 for DMI, EB, and IDG, respectively, suggesting the effect of different genes at different stages of lactations. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix revealed considerable genetic variation among animals in the shape of the lactation profiles for DMI, EB, and IDG. Genetic parameters presented are the first estimates from dairy cows fed predominantly grazed grass and imply that genetic improvement in DMI, EB, and IDG in Holstein-Friesian cows fed predominantly grazed grass is possible.     

Analysis of the genetic architecture of energy balance and its major determinants dry matter intake and energy-corrected milk yield in primiparous Holstein cows

The focus of modern dairy cow breeding programs has shifted from being mainly yield based toward balanced goals that increasingly consider functional traits such as fertility, metabolic stability, and longevity. To improve these traits, a less pronounced energy deficit postpartum is considered a key challenge. On the other hand, feed efficiency and methane emissions are gaining importance, possibly leading to conflicts in the design of breeding goals. Dry matter intake (DMI) is one of the major determinants of energy balance (EB), and recently some efforts were undertaken to include DMI in genomic breeding programs. However, there is not yet a consensus on how this should be achieved as there are different goals in the course of lactation (i.e., reducing energy deficit postpartum vs. subsequently improving feed efficiency). Thus, the aim of this study was to gain more insight into the genetic architecture of energy metabolism across lactation by genetically dissecting EB and its major determinants DMI and energy-corrected milk (ECM) yield at different lactation stages applying random regression methodology and univariate and multivariate genomic analyses to data from 1,174 primiparous Holstein cows. Daily heritability estimates ranged from 0.29 to 0.49, 0.26 to 0.37, and 0.58 to 0.68 for EB, DMI, and ECM, respectively, across the first 180 d in milk (DIM). Genetic correlations between ECM and DMI were positive, ranging from 0.09 (DIM 11) to 0.36 (DIM 180). However, ECM and EB were negatively correlated (r_g = ?0.26 to ?0.59). The strongest relationship was found at the onset of lactation, indicating that selection for increased milk yield at this stage will result in a more severe energy deficit postpartum. The results also indicate that EB is more affected by DMI (r_g = 0.71 to 0.81) than by its other major determinant, ECM. Thus, breeding for a higher DMI in early lactation seems to be a promising strategy to improve the energy status of dairy cows. We found evidence that genetic regulation of energy homeostasis is complex, with trait- and lactation stage-specific quantitative trait loci suggesting that the trajectories of the analyzed traits can be optimized as mentioned above. Especially from the multivariate genomic analyses, we were able to draw some conclusions on the mechanisms involved and identified the genes encoding fumarate hydratase and adiponectin as highly promising candidates for EB, which will be further analyzed.     

Heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsylvania tie stalls

The objectives of this study were to calculate the heritability of feed efficiency and residual feed intake, and examine the relationships between feed efficiency and other traits of productive and economic importance. Intake and body measurement data were collected monthly on 970 cows in 11 tie-stall herds for 6 consecutive mo. Measures of efficiency for this study were: dry matter intake efficiency (DMIE), defined as 305-d fat-corrected milk (FCM)/305-d DMI, net energy for lactation efficiency (NE_LE), defined as 305-d FCM/05-d NE_L intake, and crude protein efficiency (CPE), defined as 305-d true protein yield/305-d CP intake. Residual feed intake (RFI) was calculated by regressing daily DMI on daily milk, fat, and protein yields, body weight (BW), daily body condition score (BCS) gain or loss, the interaction between BW and BCS gain or loss, and days in milk (DIM). Data were analyzed with 3- and 4-trait animal models and included 305-d FCM or protein yield, DM, NE_L, or CP intake, BW, BCS, BCS change between DIM 1 and 60, milk urea nitrogen, somatic cell score, RFI, or an alternative efficiency measure. Data were analyzed with and without significant covariates for BCS and BCS change between DIM 1 and 60. The average DMIE, NE_LE, and CPE were 1.61, 0.98, and 0.32, respectively. Heritability of gross feed efficiency was 0.14 for DMIE, 0.18 for NE_LE, and 0.21 for CPE, and heritability of RFI was 0.01. Body weight and BCS had high and negative correlations with the efficiency traits (−0.64 to −0.70), indicating that larger and fatter cows were less feed efficient than smaller and thinner cows. When BCS covariates were included in the model, cows identified as being highly efficient produced 2.3 kg/d less FCM in early lactation due to less early lactation loss of BCS. Results from this study suggest that selection for higher yield and lower BW will increase feed efficiency, and that body tissue mobilization should be considered.     

Comparison of 3-breed rotational crossbreds of Montbéliarde,Viking Red,and Holstein with Holstein cows fed 2 alternative diets for dry matter intake,production, and residual feed intake

The objective of this study was to compare 3-breed rotational crossbred (CB) cows of the Montbéliarde, Viking Red, and Holstein (HO) breeds with HO cows fed 2 alternative diets for dry matter intake (DMI), fat plus protein production (CFP), body weight (BW), body condition score (BCS), feed efficiency, and residual feed intake (RFI) from 46 to 150 days in milk (DIM) during first lactation. The CB cows (n = 17) and HO cows (n = 19) calved from September 2019 to March 2020. Cows were fed either a traditional total mixed ration diet (TRAD) or a higher fiber, lower starch total mixed ration diet (HFLS). The HFLS had 21% more corn silage, 47% more alfalfa hay, 44% less corn grain, and 43% less corn gluten feed than the TRAD. The 2 diets were analyzed for dry matter content, crude protein, forage digestibility, starch, and net energy for lactation. The BW and BCS were recorded once weekly. Daily milk, fat, and protein production were estimated from twice monthly milk recording with random regression. Measures of efficiency were CFP per kilogram of DMI and DMI per kilogram of BW. The RFI from 46 to 150 DIM was the residual error from regression of DMI on milk energy, metabolic BW, and the energy required for change in BW. Statistical analysis of all variables included the fixed effects of diet, breed group, and the interaction of diet and breed group. The CB cows fed HFLS had less DMI (?12%) and lower DMI/BW (?14%) compared with the HO cows fed TRAD. For CFP, CB and HO cows were not different when fed TRAD or HFLS. Furthermore, the CB cows fed HFLS had higher BW (+50 kg) compared with HO cows fed HFLS. The CB cows fed TRAD had higher BCS than HO cows fed TRAD and HO cows fed HFLS (+0.46 and +0.62, respectively). The HO cows fed TRAD had more DMI (+14%) and lower CFP per kilogram of DMI (?12%) compared with the HO cows fed HFLS. In addition, mean RFI from 46 to 150 DIM was lower and more desirable for CB cows fed HFLS (?120.0 kg) compared with HO cows fed TRAD (85.3 kg). Dairy producers may feed either TRAD or HFLS to CB cows without loss of CFP.     

Genetic parameters across lactation for feed intake,fat- and protein-corrected milk,and liveweight in first-parity Holstein cattle

Breeding values for dry matter intake (DMI) are important to optimize dairy cattle breeding goals for feed efficiency. However, generally, only small data sets are available for feed intake, due to the cost and difficulty of measuring DMI, which makes understanding the genetic associations between traits across lactation difficult, let alone the possibility for selection of breeding animals. However, estimating national breeding values through cheaper and more easily measured correlated traits, such as milk yield and liveweight (LW), could be a first step to predict DMI. Combining DMI data across historical nutritional experiments might help to expand the data sets. Therefore, the objective was to estimate genetic parameters for DMI, fat- and protein-corrected milk (FPCM) yield, and LW across the entire first lactation using a relatively large data set combining experimental data across the Netherlands. A total of 30,483 weekly records for DMI, 49,977 for FPCM yield, and 31,956 for LW were available from 2,283 Dutch Holstein-Friesian first-parity cows between 1990 and 2011. Heritabilities, covariance components, and genetic correlations were estimated using a multivariate random regression model. The model included an effect for year-season of calving, and polynomials for age of cow at calving and days in milk (DIM). The random effects were experimental treatment, year-month of measurement, and the additive genetic, permanent environmental, and residual term. Additive genetic and permanent environmental effects were modeled using a third-order orthogonal polynomial. Estimated heritabilities ranged from 0.21 to 0.40 for DMI, from 0.20 to 0.43 for FPCM yield, and from 0.25 to 0.48 for LW across DIM. Genetic correlations between DMI at different DIM were relatively low during early and late lactation, compared with mid lactation. The genetic correlations between DMI and FPCM yield varied across DIM. This correlation was negative (up to −0.5) between FPCM yield in early lactation and DMI across the entire lactation, but highly positive (above 0.8) when both traits were in mid lactation. The correlation between DMI and LW was 0.6 during early lactation, but decreased to 0.4 during mid lactation. The highest correlations between FPCM yield and LW (0.3–0.5) were estimated during mid lactation. However, the genetic correlations between DMI and either FPCM yield or LW were not symmetric across DIM, and differed depending on which trait was measured first. The results of our study are useful to understand the genetic relationship of DMI, FPCM yield, and LW on specific days across lactation.     

Genetic analysis of efficiency traits in Austrian dairy cattle and their relationships with body condition score and lameness

This study is part of a larger project whose overall objective was to evaluate the possibilities for genetic improvement of efficiency in Austrian dairy cattle. In 2014, a 1-yr data collection was carried out. Data from 6,519 cows kept on 161 farms were recorded. In addition to routinely recorded data (e.g., milk yield, fertility, disease data), data of novel traits [e.g., body weight (BW), body condition score (BCS), lameness score, body measurements] and individual feeding information and feed quality were recorded on each test-day. The specific objective of this study was to estimate genetic parameters for efficiency (related) traits and to investigate their relationships with BCS and lameness in Austrian Fleckvieh, Brown Swiss, and Holstein cows. The following efficiency (related) traits were considered: energy-corrected milk (ECM), BW, dry matter intake (DMI), energy intake (INEL), ratio of milk output to metabolic BW (ECM/BW^0.75), ratio of milk output to DMI (ECM/DMI), and ratio of milk energy output to total energy intake (LE/INEL, LE = energy in milk). For Fleckvieh, the heritability estimates of the efficiency (related) traits ranged from 0.11 for LE/INEL to 0.44 for BW. Heritabilities for BCS and lameness were 0.19 and 0.07, respectively. Repeatabilities were high and ranged from 0.30 for LE/INEL to 0.83 for BW. Heritability estimates were generally lower for Brown Swiss and Holstein, but repeatabilities were in the same range as for Fleckvieh. In all 3 breeds, more-efficient cows were found to have a higher milk yield, lower BW, slightly higher DMI, and lower BCS. Higher efficiency was associated with slightly fewer lameness problems, most likely due to the lower BW (especially in Fleckvieh) and higher DMI of the more-efficient cows. Body weight and BCS were positively correlated. Therefore, when selecting for a lower BW, BCS is required as additional information because, otherwise, no distinction between large animals with low BCS and smaller animals with normal BCS would be possible.     

Liability to diseases and their relation to dry matter intake and energy balance in German Holstein and Fleckvieh dairy cows

Dairy cow efficiency is increasingly important for future breeding decisions. The efficiency is determined mostly by dry matter intake (DMI). Reducing DMI seems to increase efficiency if milk yield remains the same, but resulting negative energy balance (EB) may cause health problems, especially in early lactation. Objectives of this study were to examine relationships between DMI and liability to diseases. Therefore, cow effects for DMI and EB were correlated with cow effects for 4 disease categories throughout lactation. Disease categories were mastitis, claw and leg diseases, metabolic diseases, and all diseases. In addition, this study presents relative percentages of diseased cows per days in milk (DIM), repeatability, and cow effect correlations for disease categories across DIM. A total of 1,370 German Holstein (GH) and 287 Fleckvieh (FV) primiparous and multiparous dairy cows from 12 dairy research farms in Germany were observed over a period of 2 yr. Farm staff and veterinarians recorded health data. We modeled health and production data with threshold random regression models and linear random regression models. From DIM 2 to 305 average daily DMI was 22.1 kg/d in GH and 20.2 kg/d in FV. Average weekly EB was 2.8 MJ of NE_L/d in GH and 0.6 MJ of NE_L/d in FV. Most diseases occurred in the first 20 DIM. Multiparous cows were more susceptible to diseases than primiparous cows. Relative percentages of diseased cows were highest for claw and leg diseases, followed by metabolic diseases and mastitis. Repeatability of disease categories and production traits was moderate to high. Cow effect correlations for disease categories were higher for adjacent lactation stages than for more distant lactation stages. Pearson correlation coefficients between cow effects for DMI, as well as EB, and disease categories were estimated from DIM 2 to 305. Almost all correlations were negative in GH, especially in early lactation. In FV, the course of correlations was similar to GH, but correlations were mostly more negative in early lactation. For the first 20 DIM, correlations ranged from ?0.31 to 0.00 in GH and from ?0.42 to ?0.01 in FV. The results illustrate that future breeding for dairy cow efficiency should focus on DMI and EB in early lactation to avoid health problems.     

Three-breed rotational crossbreds of Montbéliarde,Viking Red,and Holstein compared with Holstein cows for feed efficiency,income over feed cost,and residual feed intake

Rotational 3-breed crossbred cows of Montbéliarde, Viking Red, and Holstein (CB) were compared with Holstein (HO) cows for alternative measures of feed efficiency as well as income over feed cost (IOFC) and residual feed intake (RFI) during the first 150 d of first, second, and third lactations. Primiparous and multiparous CB (n = 63 and n = 43, respectively) and HO (n = 60 and n = 37, respectively) cows were fed the same total mixed ration twice daily with refusals weighed once daily. Feed was analyzed for dry matter content, net energy for lactation, and crude protein content. Body weight (BW) was recorded twice weekly. Daily production of milk, fat, and protein were estimated from monthly test days with best prediction. Measures of efficiency from 4 to 150 d in milk (DIM) were feed conversion efficiency (FCE), defined as fat plus protein production (kg) per kilogram of dry matter intake (DMI); ECM/DMI, defined as kilograms of energy-corrected milk (ECM) per kilogram of DMI; net energy for lactation efficiency (NE_LE), defined as ECM (kg) per megacalorie of net energy for lactation intake; crude protein efficiency (CPE), defined as true protein production (kg) per kilogram of crude protein intake; and DMI/BW, defined as DMI (kg) per kilogram of BW. The IOFC was defined as revenue from fat plus protein production minus feed cost. The RFI from 4 to 150 DIM for each lactation was the residual error remaining from regression of DMI on milk energy output (Mcal), metabolic BW, and energy required for change in BW (Mcal). Statistical analysis of measures of feed efficiency and RFI for primiparous cows included the fixed effects of year of calving and breed group. For multiparous cows, statistical analysis included breed as a fixed effect and cow as a repeated effect nested within breed group. Primiparous CB cows had higher means for FCE (+5.5%), ECM/DMI (+4.0%), NE_LE (+4.0%), and CPE (+5.2%) and a lower mean DMI/BW (–5.3%) than primiparous HO cows. Primiparous CB cows ($875) also had higher mean IOFC than primiparous HO cows ($825). In addition, mean RFI from 4 to 150 DIM was significantly lower (more desirable) for primiparous CB cows than HO cows. Likewise, multiparous CB cows had higher means for FCE (+8.2%), ECM/DMI (+5.9%), NE_LE (+5.8%), and CPE (+8.1%) and a lower mean for DMI/BW (–4.8%) than multiparous HO cows. Multiparous CB cows ($1,296) also had a higher mean for IOFC than multiparous HO cows ($1,208) and a lower mean for RFI from 4 to 150 DIM than HO cows.     

Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns

The objectives of this study were to determine the feasibility of measuring feed intake in commercial tie-stall dairies and infer genetic parameters of feed intake, yield, somatic cell score, milk urea nitrogen, body weight (BW), body condition score (BCS), and linear type traits of Holstein cows. Feed intake, BW, and BCS were measured on 970 cows in 11 Pennsylvania tie-stall herds. Historical test-day data from these cows and 739 herdmates who were contemporaries during earlier lactations were also included. Feed intake was measured by researchers once per month over a 24-h period within 7 d of 6 consecutive Dairy Herd Information test days. Feed samples from each farm were collected monthly on the same day that feed intake was measured and were used to calculate intakes of dry matter, crude protein, and net energy of lactation. Test-day records were analyzed with multiple-trait animal models, and 305-d fat-corrected milk yield, dry matter intake, crude protein intake, net energy of lactation intake, average BW, and average BCS were derived from the test-day models. The 305-d traits were also analyzed with multiple-trait animal models that included a prediction of 40-wk dry matter intake derived from National Research Council equations. Heritability estimates for 305-d intake of dry matter, crude protein, and net energy of lactation ranged from 0.15 to 0.18. Genetic correlations of predicted dry matter intake with 305-d dry matter, crude protein, and net energy of lactation intake were 0.84, 0.90, and 0.94, respectively. Genetic correlations among the 3 intake traits and fat-corrected milk yield, BW, and stature were moderate to high (0.52 to 0.63). Results indicate that feed intake measured in commercial tie-stalls once per month has sufficient accuracy to enable genetic research. High-producing and larger cows were genetically inclined to have higher feed intake. The genetic correlation between observed and predicted intakes was less than unity, indicating potential variation in feed efficiency.     

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.     

Genetic and environmental relationships between body condition score and milk production traits in Canadian Holsteins

The objective of this research was to estimate genetic parameters of first-lactation body condition score (BCS), milk yield, fat percentage (Fat%), protein percentage (Prot%), somatic cell score (SCS), milk urea nitrogen (MUN), lactose percentage (Lact%), and fat to protein ratio (F:P) using multiple-trait random regression animal models. Changes in covariances between BCS and milk production traits on a daily basis have not been investigated before and could be useful for determining which BCS estimated breeding values (EBV) might be practical for selection in the future. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Québec herds several times per cow throughout the lactation. Average daily heritabilities and genetic correlations among the various traits were similar to literature values. On an average daily basis, BCS was genetically unfavorably correlated with milk yield (i.e., increased milk yield was associated with lower body condition). The unfavorable genetic correlation between BCS and milk yield became stronger as lactation progressed, but was equivalent to zero for the first month of lactation. Favorable genetic correlations were found between BCS with Prot%, SCS, and Lact% (i.e., greater BCS was associated with greater Prot%, lower SCS, and greater Lact%). These correlations were strongest in early lactation. On an average daily basis, BCS was not genetically correlated with Fat% or MUN, but was negatively correlated with F:P. Furthermore, BCS at 5 and 50 d in milk (DIM) had the most favorable genetic correlations with milk production traits over the lactation (at 5, 50, 150, and 250 DIM). Thus, early lactation BCS EBV shows potential for selection. Regardless, this study showed that the level of association BCS has with milk production traits is not constant over the lactation. Simultaneous selection for both BCS and milk production traits should be considered, mainly due to the unfavorable genetic correlation between BCS with milk yield.     

Relationships among milk production, energy balance, plasma analytes, and reproduction in Holstein-Friesian cows

Associations were examined between components and indicators of early lactation energy balance (EB) and measures of fertility in Holstein cows. Milk production, dry matter intake (DMI), body condition score (BCS), and endocrine and metabolite data from 96 cows were analyzed using multivariate logistic regression and survival analysis. Fertility variables investigated were interval to commencement of luteal activity (C-LA), calving to conception interval (CCI), and conception rate to first service (CON1). Mean daily EB, milk protein content, and DMI during the first 28 d in milk were associated positively with CON1. Cows having poorer BCS (≤2.25) at first service had a lower CON1. Positive associations were identified among EB, milk protein content, DMI, and the likelihood of a shorter interval to C-LA. Cows having greater DMI and a more positive EB had an increased likelihood of a shorter CCI, whereas a lower nadir BCS was associated with an increased likelihood of a longer CCI. Milk yield was not associated with any of the fertility variables investigated. A greater plasma concentration of insulin-like growth factor I (IGF-I) during the first 2 wk of lactation was associated with a greater CON1 and an increased likelihood of a shorter interval to C-LA. In conclusion, we identified DMI as the principal component of EB influencing subsequent fertility. Furthermore, results indicate that milk protein content and plasma IGF-I concentration in early lactation may be useful indicators of reproductive efficiency.     

Reducing milking frequency from thrice to twice daily in early lactation improves the metabolic status of high-yielding dairy cows with only minor effects on yields

Reducing milk production during early lactation might be of interest to improve the energy balance (EB) of high-yielding dairy cows. Therefore, the objective of this study was to determine how reducing the milking frequency (MF) of high-yielding dairy cows from thrice to twice a day during the first 30 d in milk (DIM) affects yields, intake, efficiency, metabolic status, and carryover effects. To this end, 42 multiparous cows were divided into 2 groups according to their previous lactation performance, parity, and body weight. The control cows were milked 3 times a day (3ML) and the treated cows were milked twice a day (2ML) until 30 DIM and then both groups were milked 3 times a day. Milk samples were taken twice a week from 2 or 3 consecutive milkings until 45 DIM for analysis of milk solids, and both groups were followed until 100 DIM to determine the carryover effects of MF until 30 DIM. Individual dry matter intake (DMI), milk yield, and body weight were recorded daily. Blood samples were taken 3 times weekly from 14 d prepartum until 45 DIM. Milk yield during the first 30 DIM was 8.6% higher (49.3 and 45.4 kg/d, respectively), milk fat percentage was lower (3.96 and 4.27%, respectively), and the yields of all milk solids were higher in the 3ML cows than in the 2ML cows. Dry matter intake and 4% fat-corrected milk were similar between groups. The EB during the first 30 DIM was lower in the 3ML cows than in the 2ML cows, and milk yield, but not 4% fat-corrected milk yield, per unit of DMI was higher in the 3ML cows. No differences were observed between groups from 31 to 100 DIM in milk yield (～56.3 kg/d for both groups), milk solids yield, DMI, or milk/DMI; however, fat percentage was lower and EB was higher in the 3ML cows. Blood glucose concentrations between 0 and 30 DIM were lower and β-hydroxybutyrate concentrations were higher in the 3ML cows than in the 2ML cows, but nonesterified fatty acids concentrations were lower, which may be attributed to the lower clearance frequency of nonesterified fatty acids from the blood stream in the 2ML cows. A lower proportion of the 3ML cows (10%) ovulated ≤15 DIM compared with the 2ML cows (40%), with no beneficial effects on preovulatory follicle characteristics. Reducing the MF from thrice to twice a day during the first 30 DIM improved EB and metabolic status, with only minor effects on production.     

Relationships between energy balance during early lactation and cow performance,blood metabolites,and fertility: A meta-analysis of individual cow data

This study was designed to contribute to the understanding of the relationships between energy balance (EB) in early lactation [4 to 21 d in milk (DIM)] and fertility traits [interval to start of luteal activity (SLA), interval to first observed heat (FOH), and conception to first artificial insemination (AI)], and their associated relationships with cow performance and blood metabolites between 4 to 150 DIM. Individual cow data (488 primiparous and 1,020 multiparous lactations) from 27 experiments was analyzed. Data on cow performance, EB (on a metabolizable energy basis), and fertility traits were available for all cows, whereas milk progesterone data (to determine SLA) and periodic blood metabolite data were available for 1,042 and 1,055 lactations, respectively. Data from primiparous and multiparous cows were analyzed separately, with the data sets for the 2 parity groups divided into quartiles (Q1–Q4) according to the average EB during 4 to 21 DIM (EB range for Q1 to Q4: primiparous, ?120 to ?49, ?49 to ?24, ?24 to ?3, and ?3 to 92 MJ/d, respectively: multiparous, ?191 to ?79, ?79 to ?48, ?48 to ?22, and ?22 to 93 MJ/d, respectively). Differences between EB quartiles for production and fertility traits were compared. In early lactation (4 to 21 DIM), moving from Q1 to Q4 mean DMI and metabolizable energy intake increased whereas mean ECM decreased. During the same period, moving from Q1 to Q4 milk fat content, milk fat-to-protein ratio, and plasma nonesterified fatty acid and β-hydroxybutyrate concentrations decreased, whereas milk protein content and plasma glucose concentrations increased in both primiparous and multiparous cows. When examined over the entire experimental period (4 to 150 DIM), many of the trends in intakes and milk production remained, although the magnitude of the difference between quartiles was much reduced, whereas milk fat content did not differ between quartiles in primiparous cows. The percentage of cows with FOH before 42 DIM increased from Q1 to Q4 (from 46 to 72% in primiparous cows, and from 41 to 58% in multiparous cows). Interval from calving to SLA and to FOH decreased with increasing EB during 4 to 21 DIM, with these occurring 9.8 and 10.2 d earlier, respectively, in Q4 compared with Q1 (primiparous cows), and 7.4 and 5.9 d earlier, respectively, in Q4 compared with Q1 (multiparous cows). For each 10 MJ/d decrease in mean EB during 4 to 21 DIM, FOH was delayed by 1.2 and 0.8 d in primiparous and multiparous cows, respectively. However, neither days to first AI nor the percentage of cows that conceived to first AI were affected by daily EB during 4 to 21 DIM in either primiparous or multiparous cows, and this is likely to reflect a return to a less metabolically stressed status at the time of AI. These results demonstrate that interval from calving to SLA and to FOH were reduced with increasing EB in early lactation, whereas early lactation EB had no effect on conception to the first service.     

Heritabilities and correlations among body condition score,dairy form and selected linear type traits

The objectives of this study were to estimate the heritability of body condition score (BCS) with data that could be used to generate genetic evaluations for BCS in the US, and to estimate the relationship among BCS, dairy form and selected type traits. Body condition score and linear type trait records were obtained from Holstein Association USA Inc. Because BCS was a new trait for classifiers, scoring distribution and accuracy was not normal. Records from 11 of 29 classifiers were eliminated to generate a data set that should represent BCS data recorded in the future. Edited data included 128,478 records for analysis of first lactation cows and 207,149 records for analysis of all cows. Heritabilities and correlations were estimated with ASREML using sire models. Models included age at calving nested within lactation, 5th order polynomials of DIM, fixed herd-classification visit effects and random sire and error. Genetic correlation estimates were generated between first lactation data that had records from 11 classifiers removed and data with no classifiers removed. Genetic correlation estimates were 0.995 and above between data with and without classifiers removed for scoring distributions, but heritability estimates were higher with the classifiers edited from the data. Heritability estimates for type traits and final score were similar to previously reported estimates. The heritability estimate for BCS was 0.19 for first lactation cows and 0.22 for all cows. The genetic correlation estimate for first lactation cows between BCS and dairy form was -0.73, whereas the genetic correlation estimate between BCS and strength was 0.72. Genetic correlation estimates were nearly identical when cows from all lactations were included in the analyses. Body condition score had a genetic correlation with final score closer to zero (0.08) than correlations of final score with dairy form, stature or strength.     

Comparison of methane production,intensity, and yield throughout lactation in Holstein cows

Genetic selection to reduce methane (CH₄) emissions from dairy cows is an attractive means of reducing the impact of agricultural production on climate change. In this study, we investigated the feasibility of such an approach by characterizing the interactions between CH₄ and several traits of interest in dairy cows. We measured CH₄, dry matter intake (DMI), fat- and protein-corrected milk (FPCM), body weight (BW), and body condition score (BCS) from 107 first- and second-parity Holstein cows from December 2019 to November 2021. Methane emissions were measured using a GreenFeed device and expressed in terms of production (MeP, in g/d), yield (MeY, in g/kg DMI), and intensity (MeI, in g/kg FPCM). Because of the limited number of cows, only animal parameters were estimated. Both MeP and MeI were moderately repeatable (>0.45), whereas MeY presented low repeatability, especially in early lactation. Mid lactation was the most stable and representative period of CH₄ emissions throughout lactation, with animal correlations above 0.9. The average animal correlations of MeP with DMI, FPCM, and BW were 0.62, 0.48, and 0.36, respectively. The MeI was negatively correlated with FCPM (<−0.5) and DMI (>−0.25), and positively correlated with BW and BCS. The MeY presented stable and weakly positive correlations with the 4 other traits throughout lactation, with the exception of slightly negative animal correlations with FPCM and DMI after the 35th week. The MeP, MeI, and MeY were positively correlated at all lactation stages and, assuming animal and genetic correlations do not strongly differ, selection on one trait should lead to improvements in all. Overall, selection for MeI is probably not optimal as its change would result more from CH₄ dilution in increased milk yield than from real decrease in methane emission. Instead, MeY is related to rumen function and is only weakly associated with DMI, FPCM, BW, and BCS; it thus appears to be the most promising CH₄ trait for selection, provided that this would not deteriorate feed efficiency and that a system of large-scale phenotyping is developed. The MeP is easier to measure and thus may represent an acceptable alternative, although care would need to be taken to avoid undesirable changes in FPCM and BW.     

Genetic relationships among body condition score,body weight,milk yield,and fertility in dairy cows

Genetic (co)variances between body condition score (BCS), body weight (BW), milk production, and fertility-related traits were estimated. The data analyzed included 8591 multiparous Holstein-Friesian cows with records for BCS, BW, milk production, and/or fertility from 78 seasonal calving grass-based farms throughout southern Ireland. Of the cows included in the analysis, 4402 had repeated records across the 2 yr of the study. Genetic correlations between level of BCS at different stages of lactation and total lactation milk production were negative (-0.51 to -0.14). Genetic correlations between BW at different stages of lactation and total lactation milk production were all close to zero but became positive (0.01 to 0.39) after adjusting BW for differences in BCS. Body condition score at different stages of lactation correlated favorably with improved fertility; genetic correlations between BCS and pregnant 63 d after the start of breeding season ranged from 0.29 to 0.42. Both BW at different stages of lactation and milk production tended to exhibit negative genetic correlations with pregnant to first service and pregnant 63 d after the start of the breeding season and positive genetic correlations with number of services and the interval from first service to conception. Selection indexes investigated illustrate the possibility of continued selection for increased milk production without any deleterious effects on fertility or average BCS, albeit, genetic merit for milk production would increase at a slower rate.     

Updating predictions of dry matter intake of lactating dairy cows

Our objective was to model dry matter intake (DMI) by Holstein dairy cows based on milk energy (MilkE), body weight (BW), change in BW (ΔBW), body condition score (BCS), height, days in milk (DIM), and parity (primiparous and multiparous). Our database included 31,631 weekly observations on 2,791 cows enrolled in 52 studies from 8 states of the United States, mostly in the Upper Midwest. The means ± standard deviations of these variables were 24 ± 5 kg of DMI, 30 ± 6 Mcal of MilkE/d, 624 ± 83 kg of BW, 0.24 ± 1.50 kg of ΔBW/d, 3.0 ± 0.5 BCS, 149 ± 6 cm height, and 102 ± 45 DIM. Data analysis was performed using a mixed-effects model containing location, study within location, diet within study, and location and cow within study as random effects, whereas the fixed effects included the linear effects of the covariates described previously and all possible 2-way interactions between parity and the other covariates. A nonlinear (NLIN) mixed model analysis was developed using a 2-step approach for computational tractability. In the first step, we used a linear (LIN) model component of the NLIN model to predict DMI using only data from mid-lactation dairy cows (76–175 DIM) without including information on DIM. In the second step, a nonlinear adjustment for DIM using all data from 0 to 368 DIM was estimated. Additionally, this NLIN model was compared with an LIN model containing a fourth-order polynomial for DIM using data throughout the entire lactation (0–368 DIM) to assess the utility of an NLIN model for the prediction of DMI. In summary, a total of 8 candidate models were evaluated as follows: 4 ways to express energy required for maintenance (BW, BW^0.75, BW adjusted for a BCS of 3, and BW^0.75 adjusted for a BCS of 3) × 2 modeling strategies (LIN vs. NLIN). The candidate models were compared using a 5-fold across-studies cross-validation approach repeated 20 times with the best-fitting model chosen as the proposed model. The metrics used for evaluation were the mean bias, slope bias, concordance correlation coefficient (CCC), and root mean squared error of prediction (RMSEP). The proposed prediction equation was DMI (kg/d) = [(3.7 + parity × 5.7) + 0.305 × MilkE (Mcal/d) + 0.022 × BW (kg) + (?0.689 + parity × ?1.87) × BCS] × [1 – (0.212 + parity × 0.136) × exp^{(?0.053 × DIM)}] (mean bias = 0.021 kg, slope bias = 0.059, CCC = 0.72, and RMSEP = 2.89 kg), where parity is equal to 1 if the animal is multiparous and 0 otherwise. Finally, the proposed model was compared against the Nutrient Requirements of Dairy Cattle (2001) prediction equation for DMI using an independent data set of 9,050 weekly observations on 1,804 Holstein cows. The proposed model had smaller mean bias and RMSEP and higher CCC than the Nutrient Requirements of Dairy Cattle equation to predict DMI and has potential to improve diet formulation for lactating dairy cows.