Estimation of genetic parameters for concentrations of milk urea nitrogen

Milk coagulation properties are moderately heritable in dairy cows: a meta-analysis using the random-effects model

This study aimed to conduct a meta-analysis using the random-effects model to merge published genetic parameter estimates for milk coagulation properties (MCP: comprising rennet coagulation time (RCT), curd-firming time (k20), curd firmness 30 min after rennet addition (a30), titrable acidity (TA) and milk acidity or pH) in dairy cows. Overall, 80 heritability estimates and 157 genetic correlations from 23 papers published between 1999 and 2020 were used. The heritability estimates for RCT, a30, k20, TA, and pH were 0.273, 0.303, 0.278, 0.189 and 0.276, respectively. The genetic correlation estimates between RCT-a30, RCT-pH, and RCT-TA were 0.842, 0.549 and -0.565, respectively. Genetic correlation estimates between RCT and production traits were generally low and ranged from -0.142 (between RCT and casein content) to 0.094 (between RCT and somatic cell score). Moderate and significant genetic correlations were observed between a30-pH (-0.396) and a30-TA (0.662). Also, the genetic correlation estimates between a30 and production traits were low to moderate and varied from -0.165 (between a30 and milk yield) to 0.481 (between a30 and casein content). Genetic correlation estimates between pH and production traits were low and varied from -0.190 (between pH and milk protein percentage) to 0.254 (between pH and somatic cell score). The results of this meta-analysis indicated the existence of additive genetic variation for MCP that could be used in genetic selection programs for dairy cows. Because of the moderate heritability of MCP and small genetic correlations with production traits, it could be possible to improve MCP with negligible correlated effects on production traits.

Estimation of heritability for milk urea and genetic correlations with milk production traits in 3 Danish dairy breeds

The aim of this study was to estimate genetic parameters for milk urea (MU) content in 3 main Danish dairy breeds. As a part of the Danish milk recording system, milk samples from cows on commercial farms were analyzed for MU concentration (mmol/L) and the percentages of fat and protein. There were 323,800 Danish Holstein, 70,634 Danish Jersey, and 27,870 Danish Red cows sampled with a total of 1,436,580, 368,251, and 133,922 test-day records per breed, respectively, included in the data set. Heritabilities for MU were low to moderate (0.22, 0.18, and 0.24 for the Holstein, Jersey, and Red breeds, respectively). The genetic correlation was close to zero between MU and milk yield in Jersey and Red, and -0.14 for Holstein. The genetic correlations between MU and fat and protein percentages, respectively, were positive for all 3 dairy breeds. Herd-test-day explained 51%, 54%, and 49% of the variation in MU in Holstein, Jersey, and Red, respectively. This indicates that MU levels in milk can be reduced by farm management. The current study shows that there are possibilities to influence MU by genetic selection as well as by farm management.

Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBV_HMU vs. GBV_LMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBV_LMU cows, whereas GBV_HMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

Relationship between milk urea content and important milk traits in Holstein cattle

Breeding cattle with low nitrogen emissions has been proposed as a countermeasure against eutrophication due to dairy production. Milk urea content (MU) could potentially serve as a new readily measured indicator trait for nitrogen emissions by cows. Therefore, we estimated genetic parameters related to MU and its relationship with other milk traits. We analysed 4 178 735 milk samples collected between January 2008 and June 2019 from 261 866 German Holstein dairy cows during their first, second, and third lactations. Restricted maximum likelihood estimation was conducted using univariate and bivariate random regression sire models in WOMBAT. We obtained moderate average daily heritability estimates for the daily MU of 0.24 in first lactation cows, 0.23 in second lactation cows, and 0.21 in third lactation cows with average daily genetic SDs of 25.16 mg/kg, 24.93 mg/kg, and 23.75 mg/kg, respectively. Averaged over days in milk, the repeatability estimates were low at 0.41 in first, second, and third lactation cows. A strong positive genetic correlation was found between MU and milk urea yield (MUY; 0.72 on average). In addition, 305-day heritabilities were estimated as 0.50, 0.52, and 0.50 in first, second, and third lactation cows, respectively, with genetic correlations of 0.94 or higher for MU in different lactations. By contrast, the averaged estimates of the genetic correlations between MU and other milk traits were low (-0.07 to 0.15). Moderate heritability estimates clearly allow the possible selection for MU, and the near-zero estimates of genetic correlations indicate no risk of undesired correlated selection responses in other milk traits. However, a relationship still needs to be established between MU as an indicator trait and the target trait, defined as total individual nitrogen emissions.

A meta-analysis of the genetic contribution estimates to major indicators for ketosis in dairy cows

The present study aimed to perform a meta-analysis using the random-effects model to merge published genetic parameter estimates for major indicators of ketosis [milk concentrations of acetone (ACETm) and β-hydroxybutyrate (BHBAm), and blood concentration of β-hydroxybutyrate (BHBAb)] in dairy cows. Overall, 51 heritability estimates and 130 genetic correlations from 19 papers published between 2012 and 2022 were used in this study. The average heritability estimates for ACETm, BHBAm, and BHBAb were 0.164, 0.123, and 0.141, respectively. The genetic correlation estimates between BHBAm and milk yield (MY), milk protein percentage (PP), and body condition score (BCS) were negative and moderate (-0.252, -0.200, and - 0.314, respectively). Genetic correlation estimates between BHBAm and milk fat percentage (FP), milk fat to protein ratio (FPR), and ketosis (KET) were moderate to high (0.411, 0.512, and 0.614, respectively). The genetic correlation estimates between BHBAb and MY and FP were low and equal to 0.128 and 0.035, respectively. The genetic correlation estimates between ACETm-MY and ACETm-PP were negative and moderate (-0.374 and - 0.398, respectively). Estimates of genetic correlation between ACETm and FP, FPR, and KET were moderate to high (0.455, 0.626, and 0.876, respectively). The results of this meta-analysis indicated the existence of additive genetic variation for ketosis indicator metabolites which could be exploited in genetic selection programs to reduce ketosis in dairy cows. Moreover, the results propose that selection for lower concentrations of indicator traits could be an effective plan for indirect improvement of production and reproduction performance, and health in dairy cows.

Hepatic urea, creatinine and uric acid metabolism in dairy cows with divergent milk urea concentrations

Milk urea concentration is an indicator for dietary nitrogen (N)-supply and urinary N-excretion. Dairy cows with high (HMU) compared to low milk urea (LMU) concentration have greater plasma urea, creatinine and uric acid concentrations, but if the liver metabolism accounts for these differences is unknown. Eighteen HMU and 18 LMU cows were fed a diet with a low (LP) or normal (NP) crude protein concentration. A N balance study was performed and a ¹³C-urea bolus was administered to measure urea pool size. Liver samples were analyzed by 2D-gel-based proteomics and RT-qPCR. Although HMU cows had a greater urea pool, plasma urea, uric acid, and hippuric acid concentrations, these differences were not associated with altered expressions of genes related to urea cycling or N-metabolism. Instead, HMU cows had higher oxidative stress levels. Conclusively, other factors than hepatic urea metabolism account for milk urea concentrations. Despite higher plasma urea concentrations and argininosuccinate synthase 1 protein expression on the LP diet, urea cycle mRNA expressions were not affected, indicating that its activity is not controlled at transcriptional level. Feeding the LP diet resulted in increased expressions of enzymes catabolizing fatty acids, but the reason remains to be investigated in future studies.

Ruminal background of predisposed milk urea (MU) concentration in Holsteins

Efforts to reduce nitrogen (N) emissions are currently based on the optimization of dietary- N supply at average herd N requirements. The implementation of the considerable individual differences and predispositions in N- use efficiency and N- excretion in breeding programs is hampered by the difficulty of data collection. Cow individual milk urea (MU) concentration has been proposed as an easy-to-measure surrogate trait, but recent studies questioned its predictive power. Therefore, a deeper understanding of the biological mechanisms underlying predisposed higher (HMUg) or lower (LMUg) MU concentration in dairy cows is needed. Considering the complex N- metabolism in ruminants, the distinction between HMUg and LMUg could be based on differences in (i) the rumen microbial community, (ii) the host-specific transcription processes in the rumen villi, and (iii) the host-microbe interaction in the rumen. Therefore, rumen fluid and rumen epithelial samples from 10 HMUg and 10 LMUg cows were analyzed by 16S sequencing and HiSeq sequencing. In addition, the effect of dietary-N reduction on ruminal shifts was investigated in a second step. In total, 10 differentially abundant genera (DAG) were identified between HMUg and LMUg cows, elucidating greater abundances of ureolytic Succinivibrionaceae_UCG-002 and Ruminococcaceae_unclassified in LMUg animals and enhanced occurrences of Butyvibrio in HMUg cows. Differential expression analysis revealed genes of the bovine Major Histocompatibility Complex (BOLA genes) as well as MX1, ISG15, and PRSS2 displaying candidates of MU predisposition that further attributed to enhanced immune system activities in LMUg cows. A number of significant correlations between microbial genera and host transcript abundances were uncovered, including strikingly positive correlations of BOLA-DRA transcripts with Roseburia and Lachnospiraceae family abundances that might constitute particularly prominent microbial-host interplays of MU predisposition. The reduction of feed-N was followed by 18 DAG in HMUg and 19 DAG in LMUg, depicting pronounced interest on Shuttleworthia, which displayed controversial adaption in HMUg and LMUg cows. Lowering feed-N further elicited massive downregulation of immune response and energy metabolism pathways in LMUg. Considering breeding selection strategies, this study attributed information content to MU about predisposed ruminal N-utilization in Holstein-Friesians.

Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows

The aim of this study was to estimate genetic parameters of milk urea concentration (MU) and its genetic correlations with milk production traits, longevity, and functional traits in the first 3 parities in dairy cows. The edited data set consisted in 9,107,349 MU test-day records from the first 3 parities of 560,739 cows in 2,356 herds collected during the years 1994 to 2020. To estimate the genetic parameters of MU, data of 109 randomly selected herds, with a total of 770,016 MU test-day records, were used. Genetic parameters and estimated breeding values were estimated using a multiple-trait (parity) random regression model. Herd-test-day, age-year-season of calving, and days in milk classes (every 5 d as a class) were used as fixed effects, whereas effects of herd-year of calving, permanent environment, and animal were modeled using random regressions and Legendre polynomials of order 2. The average daily heritability and repeatability of MU during days in milk 5 to 365 in the first 3 parities were 0.19, 0.22, 0.20, and 0.48, 0.48, 0.47, respectively. The mean genetic correlation estimated among MU in the first 3 parities ranged from 0.96 to 0.97. The average daily estimated breeding values for MU of the selected bulls (n = 1,900) ranged from -9.09 to 7.37 mg/dL. In the last 10 yr, the genetic trend of MU has gradually increased. The genetic correlation between MU and 11 traits of interest ranged from -0.28 (milk yield) to 0.28 (somatic cell score). The findings of this study can be used as the first step for development of a routine genetic evaluation for MU and its inclusion into the genetic selection program in the Walloon Region of Belgium.

Nitrogen Use Efficiency and Excretion in Grazing Cows with High and Low Milk Urea Nitrogen Breeding Values

Milk urea nitrogen content is moderately heritable and is phenotypically related to urine nitrogen (UN). Based on this relationship, it has been suggested that genetic selection for lower milk urea nitrogen in grazing dairy cows could decrease UN concentration thereby reducing nitrogen excretions into the ground. The objective of this study was to compare the nitrogen use efficiency (NUE) and excretion in grazing cows with high and low milk urea nitrogen breeding values (MUNBV) in two farms of contrasting farming intensity. On the high-intensity farm (HIF) 68 and 70 cows with low and high MUNBV, respectively, were fed higher levels of supplementation and milked twice-daily, while on the low-intensity farm (LIF) 82 and 86 cows with low and high MUNBV, respectively, were fed lower levels of supplementation milked once-daily. Nitrogen use efficiency (g/g) was calculated as the ratio of daily milk N to daily N intake. Daily N intake (g/day) was derived from feed intake estimates based on energy requirements. The UN (g/day) was estimated by back-calculation from dietary N and subtracting milk N, faecal N, and N retained in body tissues. Irrespective of farm, cows with low MUNBV had significantly lower MY and milk urea nitrogen (p < 0.001) but this was not linked to significantly less UN. In the LIF, cows with low MUNBV had lower NUE (p < 0.001) than cows with high MUNBV, and this was explained by the reduced protein yield (p < 0.001). Selecting cows for low MUNBV was not an effective tool to reduce N losses and to increase the NUE in two dairy farms of contrasting farming intensity.

Nonlinear modeling to describe the pattern of 15 milk protein and nonprotein compounds over lactation in dairy cows

The protein profile of milk includes several caseins, whey proteins, and nonprotein nitrogen compounds, which influence milk's value for human nutrition and its cheesemaking properties for the dairy industry. To fill in the gap in current knowledge of the patterns of these individual nitrogenous compounds throughout lactation, we tested the ability of a parametric nonlinear lactation model to describe the pattern of each N compound expressed qualitatively (as % of total milk N), quantitatively (in g/L milk), and as daily yield (in g/d). The lactation model was tested on a data set of detailed milk nitrogenous compound profiles (15 fractions-12 protein traits and 3 nonproteins-for each expression mode: 45 traits) obtained from 1,342 cows reared in 41 multibreed herds. Our model was a modified version of Wilmink's model, often used for describing milk yield during lactation because of its reliability and ease of parameter interpretation from a biological point of view. We allowed the sign of the persistency coefficient (parameter c) that explained the variation in the long-term milk component (parameter a) to be positive or negative. We also allowed the short-term milk component (parameter b) to be positive or negative, and we estimated a specific speed of adaptation parameter (parameter k) for each trait rather than assumed a value a priori, as in the original model (k = 0.05). These 4 parameters were included in a nonlinear mixed model with cow breed and parity order as fixed effects, and herd-date as random. Combinations of the positive and negative signs of the b and c parameters allowed us to identify 4 differently shaped lactation curves, all found among the patterns exhibited by the nitrogenous fractions as follows: the "zenith" curve (with a maximum peak; for milk yield and 10 other N traits), the "nadir" curve (with a minimum point; for 20 traits, including almost all those expressed in g/L of milk), the "downward" curve (continuously decreasing; for 14 traits, including almost all those in g/d), and the "upward" curve (continuously increasing; only for κ-casein, in % N). Direct estimation of the k parameters specific to each trait showed the large variability in the adaptation speed of fresh cows and greatly increased the model's flexibility. The results indicated that nonlinear parametric mathematical models can effectively describe the different and complex patterns exhibited by individual nitrogenous fractions during lactation; therefore, they could be useful tools for interpreting milk composition variations during lactation.

Identification of Genomic Regions Influencing N-Metabolism and N-Excretion in Lactating Holstein- Friesians

Excreted nitrogen (N) of dairy cows contribute to environmental eutrophication. The main N-excretory metabolite of dairy cows is urea, which is synthesized as a result of N-metabolization in the liver and is excreted via milk and urine. Genetic variation in milk urea (MU) has been postulated but the complex physiology behind the trait as well as the tremendous diversity of processes regulating the N-metabolism impede the consistent determination of causal regions in the bovine genome. In order to map the genetic determinants affecting N-excretion, MU and eight other N-excretory metabolites in milk and urine were assessed in a genome-wide association study. Therefore phenotypes of 371 Holstein- Friesians were obtained in a trial on a dairy farm under near commercial conditions. Genotype data comprised SNP information of the Bovine 50K MD Genome chip (45,613 SNPs). Significantly associated genomic regions for MU concentration revealed GJA1 (BTA 9), RXFP1, and FRY1 (both BTA 12) as putative candidates. For milk urea yield (MUY) a promising QTL on BTA 17 including SH3D19 emerged, whereas RCAN2, CLIC5, ENPP4, and ENPP5 (BTA 23) are suggested to influence urinary urea concentration. Minor N-fractions in milk (MN) may be regulated by ELF2 and SLC7A11 (BTA 17), whilst ITPR2 and MYBPC1 (BTA 5), STIM2 (BTA 6), SGCD (BTA 7), SLC6A2 (BTA 18), TMCC2 and MFSD4A (BTA 16) are suggested to have an impact on various non-urea-N (NUN) fractions excreted via urine. Our results highlight genomic regions and candidate genes for N-excretory metabolites and provide a deeper insight into the predisposed component to regulate the N-metabolism in dairy cows.

The Effect of Feeding Management and Culling of Cows on the Lactation Curves and Milk Production of Primiparous Dairy Cows

Simple Summary

Curves for milk yield and milk constituents were plotted according to Wood’s model. The curves were compared for four herds with a high milk yield (over 10,000 kg). The farms used different feeding systems. Differences were noted in the number of days in milk at which the herds reached the peak of lactation. The herd that reached peak production latest had the highest peak milk yield as well as the highest average milk yield for the entire lactation. The effect of early culling of primiparous cows after 30, 60 and 90 days in milk was analysed in herd T1. Such early culling of primiparous cows prevents their negative impact on the average yield of the entire herd. This increases the chance of improving yield in the herd by quickly introducing new, genetically more valuable cows.

Abstract

The study attempted to estimate the lactation curves of primiparous dairy cows in relation to their feeding management. Therefore, the first aim of the study was to determine and compare the lactation curves of primiparous dairy cows using Wood’s model and to estimate the association between the lactation curves and feeding management. The second objective was to investigate the effect of the culling rate on improvement in the milk yield of primiparous dairy herds. The study was conducted on four commercial dairy farms of Polish Holstein–Friesian cows using different feeding systems (TMR—total mixed ration and PMR—partial mixed ration) and management (T1—one TMR throughout lactation; P1—one PMR throughout lactation; T2 and T3—three feed periods such as FRESH, TMR I and TMR II according to days in milk). The data used for the study were obtained from monthly milk performance evaluations of 1662 primiparous cows conducted by the Polish Federation of Cattle Breeding and Dairy Farmers throughout the year 2015. Wood’s lactation model was used to plot curves for milk yield, fat and protein content, lactose content, and milk urea contents. The highest milk yield for the whole lactation and in the peak lactation phase was recorded for cows in herd T1. This herd reached peak lactation on day 105 of milking, with an average milk yield of 42.1 kg, which was about 5 kg more milk than in the other herds. The study showed that the culling of primiparous cows in herd T1 after 30, 60 and 90 days of lactation prevented a significant reduction in milk yield in a 305-day lactation. It also increased average milk production by 1586.9 kg per primiparous dairy cow.

Genetic parameters for milk urea and its relationship with milk yield and compositions in Holstein dairy cows

The aim was to estimate genetic parameters for milk urea (MU) concentration and its relationship with milk yield and compositions in Holstein dairy Cows. Edited data were 90,594 test-day records of milk yield and composition collected during 2015 to 2018 on 13,737 lactations obtained from 7,850 Holstein cows in 50 herds. Random regression test-day model was used to estimate genetic parameters. (Co)variance components were estimated with the Bayesian Gibbs sampling method using a single chain of 400,000 iterates. The first 50,000 iterates of each chain were regarded as a burn-in period. Mean (SD) of MU was 23.03 (5.99) and 22.41 (5.74) mg/dl in primiparous and multiparous cows, respectively. Average heritability estimates for daily MU was 0.33 (SD = 0.02) ranged 0.29 to 0.36 and 0.32 (SD = 0.03) ranged 0.27 to 0.34, respectively, for primiparous and multiparous cows. The mean (SD) genetic correlation between MU and milk yield, fat yield, protein yield, lactose yield, fat percentage, protein percentage, lactose percentage, and somatic cell score was, respectively, -0.02 (0.03), -0.02 (0.01), 0.01 (0.04), 0.01 (0.03), 0.00 (0.07), -0.03 (0.04), 0.00 (0.01), -0.11 (0.06) in primiparous cows. The corresponding values in multiparous cows were -0.01 (0.02), -0.01 (0.03), -0.04 (0.04), -0.04 (0.04), 0.04 (0.04), 0.04 (0.07), -0.03 (0.09), 0.06 (0.11), respectively. The results indicate that selection on MU is possible with no effect on milk yield or compositions, however, relationships between MU and other important traits such as longevity, metabolic diseases, and fertility are needed.

Milk urea nitrogen variation explained by differences in urea transport into the gastrointestinal tract in lactating dairy cows

Milk urea nitrogen (MUN) and blood urea nitrogen are correlated with nitrogen balance and nitrogen excretion; however, there is also a genetic component to MUN concentrations that could be associated with differences in urea transport. It was hypothesized that a portion of the variation in MUN concentrations among cows is caused by variation in gastrointestinal and kidney urea clearance rates. Eight lactating cows with varying MUN concentrations while fed a common diet were infused with [¹⁵N¹⁵N]urea to determine urea N entry rate (UER), gastrointestinal entry rate, returned to ornithine cycle, urea N used for anabolism, urea N excretion in feces and urine. Urea clearance rates by the kidneys and gastrointestinal tract were calculated from isotopic enrichment of urea excretion in urine and gut entry rate, respectively, and plasma urea N concentrations (PUN). Over the course of the experiment, animals weighed an average of 506 ± 62 kg and produced 26.3 ± 4.39 kg of milk/d, with MUN concentrations ranging from 11.6 to 17.3 mg/dL (average of 14.9 ± 2.1 mg/dL). Plasma urea N was positively correlated with UER, urea N excretion in urine, and urea N used for anabolism. Plasma urea N and MUN were negatively correlated with gut clearance rates and ratio of gastrointestinal entry rate to UER. This relationship supports the hypothesis that differences in gut urea transport activity among animals causes variation in PUN and MUN concentrations, and that cows with high PUN and MUN are less efficient at recycling PUN to the gastrointestinal tract and thus may be more susceptible to ruminal N deficiencies when fed low RDP diets. Such biological variation in urea metabolism necessitates an adequate safety margin when setting regulations for maximal MUN levels as an indicator of herd N efficiency.

Non-genetic effects on daily milk yield and components of Holstein cows in Morocco

The objectives of this study were to determine milk yield and constituents' levels of Holstein cows under Moroccan conditions and to assess the effects of environmental factors influencing them. Data including 93,815 test day records of 6343 Holstein cows in 162 herds were analyzed for milk yield, contents of fat, protein, lactose and solids-not-fat (SNF), milk urea nitrogen (MUN), and somatic cell score (SCS). Averages were 25.1 ± 7.33 kg/day, 3.54 ± 0.76%, 3.02 ± 0.34%, 4.89 ± 0.24%, 8.72 ± 0.36%, 17.6 ± 8.17 mg/dl, and 4.12 ± 2.06 units, respectively. The effects of herd and calving year were found to be significant on all the studied traits. Parity was determined to have significant effects on all analyzed traits, with the exception of MUN concentration. Milk yield, fat content, protein content, and SCS increased with parity, whereas lactose percentage decreased in later parities. Except fat and lactose contents, all the other studied variables were significantly influenced by calving season. Milk yield and MUN were highest for cows calving during humid season, whereas protein content, SNF content, and SCS tended to be higher in dry season. All the studied traits changed significantly according to stage of lactation. The peaks of milk yield and lactose content occurred between 35 and 65 days, whereas that of MUN between 95 and 125 days after parturition. Protein and SNF contents and SCS attained their lowest values between days 35 and 65, while fat content between days 65 and 95. It was concluded that these environmental factors should be taken into consideration if the milk yield and quality in Morocco is to be optimized.

Genetic Analysis of Persistency for Milk Fat Yield in Iranian Buffaloes (Bubalus bubalis)

This study aimed to estimate heritabilities and genetic trends for different persistency measures for milk fat yield and their genetic correlations with 270-day milk yield in Iranian buffaloes. The records of test-day milk fat yield belonging to the first three lactations of buffaloes within 523 herds consisting of 43,818 records were got from the Animal Breeding Center and Promotion of Animal Products of Iran from 1996 to 2012. To fit the lactation curves based on a random regression test-day model, different orders of Legendre polynomial (LP) functions were selected. Three persistency measures were altered according to the specific condition of the lactation curve in buffaloes: (1) The average of estimated breeding values (EBVs) for test day fat yield from day 226 to day 270 as a deviation from the average of EBVs from day 44 to day 62 (PM₁), (2) A summation of contribution for each day from day 53 to day 247 as a deviation from day 248 (PM₂), and (3) The difference between EBVs for day 257 and day 80 (PM₃). The estimates of heritability for PM₁, PM₂, and PM₃ ranged from 0.20 to 0.48, from 0.36 to 0.47, and from 0.19 to 0.35 over the first three lactations, respectively. The estimate of genetic trends for different persistency measures of milk fat yield was not significant over the lactations (P > 0.05). Genetic correlation estimates between various measures of persistency were generally high over the first three lactations. Also, genetic correlations estimates between persistency measures and 270-day milk yield were mostly low and varied from 0.00 to 0.24 (between PM₁ and 270-day milk yield), from −0.19 to 0.13 (between PM₂ and 270-day milk yield), and from −0.02 to 0.00 (between PM₁ and 270-day milk yield) over the first three lactations, respectively. Persistency measures that showed low genetic correlations with milk fat yield were considered the most suitable measures in selection schemes. Besides, medium to high heritability estimates for different persistency measures for milk fat yield indicated that relevant genetic variations detected for these characters could be regarded in outlining later genetic improvement programs of Iranian buffaloes.

Factors associated with estrous expression and subsequent fertility in lactating dairy cows using automated activity monitoring

The objective of this observational study was to identify factors associated with estrous duration (DU) and intensity measured as the peak of activity (PA) change and subsequent fertility in lactating Holstein cows using a neck-mounted automated activity monitor (Heatime Pro, SCR Engineers Ltd., Netanya, Israel). Ambient temperature and relative humidity were recorded hourly to calculate the temperature-humidity index (THI). A total of 5,933 estrus events from 3,132 cows located on 8 commercial dairy farms in Germany were used for this study. Farms participated in monthly DHIA testing. Pregnancy diagnosis was performed either by transrectal palpation [farm 1: 42 ± 3 d; farm 3: 40 ± 3 d; farms 4 and 8: 38 ± 3 d; farm 5: 43 ± 3 d after artificial insemination (AI)] or transrectal ultrasonography (farms 2, 6, and 7: 30 ± 3 d after AI). Estrous intensity was categorized based on peak activity of estrus into low (35-89 index value), and high (90-100 index value) PA. Overall, 73.5% of estrus events were of high PA. The mean (± standard error of the mean) DU was 14.94 ± 0.06 h. There was a strong correlation between DU and PA (r = 0.67). In the final statistical model, only PA was associated with pregnancy per artificial insemination (P/AI), with 1.35 greater odds of pregnancy for cows with high PA compared with cows with low PA. Increased THI 1 wk before AI was associated with shorter DU, lower PA, and decreased P/AI. A small percentage of cows (4.7%) showed short interestrus intervals (i.e., more than 1 activity peak within 7 d close to the event of estrus), resulting in reduced DU, PA, and P/AI. The change of weighted rumination was associated with DU and PA, as a lower nadir was associated with a greater risk for high PA and long DU. There was no association, however, between the nadir of change of weighted rumination and P/AI. Whereas milk yield and somatic cell count from the DHIA test date before AI were negatively associated with estrous expression, neither milk yield nor somatic cell count was associated with P/AI. Surprisingly, multiparous cows expressed estrus with longer DU (13.15 ± 0.31 h) compared with primiparous cows (12.52 ± 0.32 h), whereas PA did not differ among parities. Pregnancy per AI was greater for primiparous (29.4%) than for multiparous (22.1%) cows. An estrus event with long DU or high PA was more likely later in lactation. Milk fat, milk protein, milk urea nitrogen, and lactose from the DHIA test date closest to AI had no association with estrous expression or P/AI. In conclusion, DU and PA were highly correlated, and cows with high PA were particularly associated with greater odds for pregnancy. A negative association between estrous expression and P/AI was identified for increased THI 1 wk before AI and cows with short interestrus intervals using automated activity monitor.

Proxy Measures and Novel Strategies for Estimating Nitrogen Utilisation Efficiency in Dairy Cattle

Simple Summary

Dairy cow diets contain nitrogen, mostly in the form of protein. However, dietary nitrogen is used with a low efficiency for milk production, and much of the unused nitrogen is converted to urea and excreted in urine and faeces (manure). Nitrogen within manure can then be lost to the environment, and this is a particular issue when dairy cows are offered diets containing excess dietary protein. As a result, there is increasing pressure on the dairy sector to improve the efficiency with which dairy cows utilise dietary nitrogen. While nitrogen utilisation efficiency can be measured accurately on research farms, this is more difficult on commercial farms. For that reason, there is much interest in developing low-cost and easy-to-use proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of proximate analyses that are already used as indicators of nitrogen use efficiency in dairy cows (e.g., blood urea and milk urea), and a number of more novel measures that may have potential for use in the future (including analysis of milk, blood, urine, breath, and predictions of intake). These ‘proxy’ measurements can be used to improve feeding management and might be used to monitor adherence to legislation.

Abstract

The efficiency with which dairy cows convert dietary nitrogen (N) to milk N is generally low (typically 25%). As a result, much of the N consumed is excreted in manure, from which N can be lost to the environment. Therefore there is increasing pressure to reduce N excretion and improve N use efficiency (NUE) on dairy farms. However, assessing N excretion and NUE on farms is difficult, thus the need to develop proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of these proximate measures. While a strong relationship exists between blood urea N and urinary N excretion, blood sampling is an invasive technique unsuitable for regular herd monitoring. Milk urea N (MUN) can be measured non-invasively, and while strong relationships exist between dietary crude protein and MUN, and MUN and urinary N excretion, the technique has limitations. Direct prediction of NUE using mid-infrared analysis of milk has real potential, while techniques such as near-infrared spectroscopy analysis of faeces and manure have received little attention. Similarly, techniques such as nitrogen isotope analysis, nuclear magnetic resonance spectroscopy of urine, and breath ammonia analysis may all offer potential in the future, but much research is still required.

Short communication: Genetic aspects of milk urea nitrogen and new indicators of nitrogen efficiency in dairy cows

Milk urea nitrogen (MUN), a trait routinely measured in the national milk recording system, is a useful indicator of nitrogen utilization efficiency of dairy cows, and selection for MUN and MUN-derived traits could be a valid strategy to produce better animals with regard to efficiency of nitrogen utilization. Therefore, the aim of the present study was to explore the genetic aspects of MUN and new potential indicators of nitrogen efficiency, namely ratios of protein to MUN, casein to MUN, and whey protein to MUN, in the Italian Brown Swiss population. A total of 153,175 test-day records of 10,827 cows in 500 herds were used for genetic analysis. Variance components and heritability of the investigated traits were estimated using single-trait repeatability animal models, whereas genetic and phenotypic correlations between the traits were estimated through bivariate repeatability animal models, including fixed effects of herd-test-date, stage of lactation, parity, calving year, and calving season, and the random effects of additive genetic animal, cow permanent environment, and the residual. Heritability estimates for MUN (0.20 ± 0.01) and the 3 new indicators of nitrogen utilization efficiency (0.15 ± 0.01 for protein-to-MUN and casein-to-MUN ratios, and 0.12 ± 0.01 for ratio of whey protein to MUN) suggested that additive genetic variation exists for these traits, and thus there is potential to select for greater organic nitrogen and lower inorganic nitrogen in milk. Genetic association between MUN and the 3 ratios was high (-0.87 ± 0.01) but not unity, suggesting that ratios could provide some further information beyond that provided by MUN with regard to efficiency of nitrogen utilization. Genetic trend of the investigated traits by year of birth of Brown Swiss sires showed how the selection applied in the last 30 yr has led to an increase of both quantity and quality of milk, and a decrease of somatic cell score and MUN. The inclusion of MUN in breeding programs could speed up the process of increasing organic nitrogen such as protein, which is useful for cheese-making, and reducing inorganic nitrogen (MUN) in milk.

The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle

Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

Genetic variation in milk urea nitrogen concentration of dairy cattle and its implications for reducing urinary nitrogen excretion

Nitrogen (N) leached into groundwater from urine patches of cattle grazing in situ is an environmental problem in pasture-based dairy industries. One potential mitigation is to breed cattle for lower urinary nitrogen (UN) excretion. Urinary nitrogen is difficult to measure, while milk urea nitrogen concentration (MUN) is relatively easy to measure. For animals fed diets of differing N content in confinement, MUN is moderately heritable and is positively related to UN. However, there is little information on the heritability of MUN, and its relationship with other traits such as milk yield and composition, for animals grazing fresh pasture. Milk urea nitrogen concentration data together with milk yield, fat, protein and lactose composition and somatic cell count was collected from 133 624 Holstein-Friesian (HF), Jersey (J) and HF×J (XBd) cows fed predominantly pasture over three full lactations and one part lactation. Mean MUN was 14.0; and 14.4, 13.2 and 13.9 mg/dl for HF, J and XBd cows, respectively. Estimates of heritability of MUN were 0.22 using a repeatability model that fitted year-of-lactation by month-of-lactation by cow-age with days-in-milk within month-of-lactation and cow-age, and 0.28 using a test-day model analysis with Gibbs sampling methods. Sire breeding values (BVs) ranged from -2.8 to +3.2 indicating that MUN could be changed by selection. The genetic correlation between MUN and percent true protein in milk was -0.22; -0.29 for J cows and -0.16 for HF cows. Should the relationship between MUN and UN observed in dietary manipulation studies hold similarly when MUN is manipulated by genetic selection, UN excretion could be reduced by 6.6 kg/cow per year in one generation of selection using sires with low MUN BVs. Although J cows had lower MUN than HF, total herd UN excretion may be similar for the same fixed feed supply because more J cows are required to utilise the available feed. The close relationship between blood plasma urea N concentration and MUN may enable early selection of bulls to breed progeny that excrete less UN.

Factors affecting the milk urea nitrogen concentration in Chinese Holstein cows

In order to investigate the factors affecting milk urea nitrogen in Chinese Holstein cows, a large commercial dairy farm participated in a 30-month study. In this study, the mean milk urea nitrogen concentration was 11.75 mg/dl. The milk urea nitrogen reached its maximum value on day 90 of lactation for the first parity and the third or higher parities, but it peaked at the end of lactation for the second parity. The milk urea nitrogen of the first parity was lower than that of other parities. The milk urea nitrogen showed its minimum level in January, and reached its maximum in July. The milk urea nitrogen at the first month of lactation in cows calving in summer was higher than other seasons, while at the fourth month of lactation, the milk urea nitrogen of cows calving in autumn was significantly lower than in cows calving in other seasons. Positive correlations were observed between daily milk yield, net energy for lactation, crude protein and milk urea nitrogen for the first and third parities, but negative correlations were observed in the second parity. The milk urea nitrogen showed significantly positive correlations with fat content, total solid content and daily matter intake for all parities. A negative correlation was observed between milk urea nitrogen and protein content, with the exception of the second parity. For all data, as milk urea nitrogen concentration increased, milk protein content decreased. It has been recommended that milk urea nitrogen concentration should be evaluated in combination with parity, days in milk, season (or month), daily matter intake and dietary nutritional components, in order to improve the management and economic benefits of dairy farm.

Bayesian estimates of genetic relationship between calving difficulty and productive and reproductive performance in Holstein cows

The objective of present study was to estimate genetic correlations between calving difficulty and productive and reproductive traits in Iranian Holsteins. Calving records from the Animal Breeding Center of Iran, collected from 1991 to 2011 and comprising 183 203 first-calving events of Holstein cows from 1470 herds were included in the dataset. Threshold animal models included direct genetic effect (Model 1) or direct and maternal genetic effects with covariance between them (Model 2) were fitted for the genetic analysis of calving difficulty. Also, linear animal models including direct genetic effect were fitted for the genetic analysis of productive and reproductive performance traits. A set of linear-threshold bivariate models was used for obtaining genetic correlation between calving difficulty and other traits. All analyses were implemented by Bayesian approach via Gibbs sampling methodology. A single Gibbs sampling chain with 300 000 rounds was generated by the TM program. Posterior mean estimates of direct heritabilities for calving difficulty were 0.056 and 0.066, obtained from different models. Also, posterior mean estimate of maternal heritability for this trait was 0.018. Estimate of correlation between direct and maternal genetic effects for calving difficulty was negative (–0.44). Posterior mean estimates of direct heritabilities for milk yield, fat yield, protein yield, days from calving to first service, days open and first calving interval were 0.257, 0.188, 0.235, 0.034, 0.042 and 0.050 respectively. The posterior means of direct genetic correlation between calving difficulty and milk yield, fat yield, protein yield, days from calving to first service, days open and first calving interval were low and equal to –0.135, 0.030, –0.067, –0.010, –0.075 and –0.074 respectively. The results of the current study indicated that exploitable genetic variation in calving difficulty, productive and reproductive traits could be applied in designing future genetic selection plans for Iranian Holsteins.

The eff ect of selected factors on urea concentration in the milk of Polish Holstein-Friesian cows

The objective of the study was to determine the relationships between milk urea concentration and factors such as lactation number, stage of lactation, month and season of the test day, age at calving, milk yield and protein percentage. Data for the calculations consisted of 7,731 test-day records from 1,078 Polish Holstein-Friesian cows. Test-day milking was performed for first, second and third lactations during the period from December 2010 to December 2011. Calculations were performed using the MIXED procedure in SAS/STAT. A mixed linear model using was applied in which parameters were estimated by the restricted maximum likelihood (REML) method. Least squares means for fixed eff ects in the model were compared by the Tukey-Kramer test. The first lactation diff ered significantly (p<0.05) from the second and third in terms of mean urea concentration, but there were no significant diff erences between the second and third lactations. For primiparous cows the milk urea concentration increased throughout lactation, but for older cows it increased only up to 7–8 months of lactation. Urea concentrations did not diff er significantly in the same stages of consecutive lactations, i.e. the first and second or second and third. Statistically significant diff erences were noted between the first and third lactations only in months 9 and 10 of lactation. Seasonal changes in milk urea content varied depending on the lactation number. In the first lactation the milk urea concentration was lowest in spring and highest in autumn. This tendency was not observed in the second and third lactation. Milk urea concentration was positively associated with both milk yield and protein percentage

Potential use of milk urea nitrogen to abate atmospheric nitrogen emissions from wisconsin dairy farms

Urinary urea N (UUN) is the principal nitrogen (N) source controlling emissions of ammonia (NH) and nitrous oxide (NO) from dairy manure. The objectives of this study were (i) to study the integrative nature of dietary crude protein (CP) management, secretion of milk urea N (MUN), excretion of UUN, and N emissions from dairy production systems; (ii) to evaluate how associative changes in dietary CP, MUN, and UUN affect atmospheric N emissions from dairy farms; and (iii) to discuss some of the challenges and opportunities to an expanded use of MUN to enhance dietary CP use and decrease UUN excretion and N emissions from dairy farms. Milk urea N records of 37,889 cows in 197 herds in Wisconsin revealed that approximately one half of tested cows were likely consuming dietary CP in excess of requirement. Farm simulations were used to quantify the effect of dietary CP on whole-farm N emissions. At a statewide average MUN of 12.5 mg dL, 48 to 87% of UUN was emitted as NH, with the lowest loss from pasture-based farms and the greatest loss from tie-stall farms. Each 1 mg dL decrease of MUN (range, 16-10 mg dL) provided an associated daily decrease in UUN of 16.6 g per cow, which decreased NH and NO emissions from manure by 7 to 12%. Although more site-specific information is required on herd MUN-UUN relationships and more a reliable interpretation of MUN assay results is needed, monitoring of MUN may be used to enhance dietary CP use and to reduce UUN excretion and N emissions from Wisconsin dairy farms.

Candidate gene association analysis for milk yield, composition, urea nitrogen and somatic cell scores in Brown Swiss cows

The aim of this study was to investigate 96 single-nucleotide polymorphisms (SNPs) from 54 candidate genes, and test the associations of the polymorphic SNPs with milk yield, composition, milk urea nitrogen (MUN) content and somatic cell score (SCS) in individual milk samples from Italian Brown Swiss cows. Milk and blood samples were collected from 1271 cows sampled once from 85 herds. Milk production, quality traits (i.e. protein, casein, fat and lactose percentages), MUN and SCS were measured for each milk sample. Genotyping was performed using a custom Illumina VeraCode GoldenGate approach. A Bayesian linear animal model that considered the effects of herd, days in milk, parity, SNP genotype and additive polygenic effect was used for the association analysis. Our results showed that 14 of the 51 polymorphic SNPs had relevant additive effects on at least one of the aforementioned traits. Polymorphisms in the glucocorticoid receptor DNA-binding factor 1 (GRLF1), prolactin receptor (PRLR) and chemokine ligand 2 (CCL2) were associated with milk yield; an SNP in the stearoyl-CoA desaturase (SCD-1) was related to fat content; SNPs in the caspase recruitment domain 15 protein (CARD15) and lipin 1 (LPIN1) affected the protein and casein contents; SNPs in growth hormone 1 (GH1), lactotransferrin (LTF) and SCD-1 were relevant for casein number; variants in beta casein (CSN2), GH1, GRLF1 and LTF affected lactose content; SNPs in beta-2 adrenergic receptor (ADRB2), serpin peptidase inhibitor (PI) and SCD-1 were associated with MUN; and SNPs in acetyl-CoA carboxylase alpha (ACACA) and signal transducer and activator of transcription 5A (STAT5A) were relevant in explaining the variation of SCS. Although further research is needed to validate these SNPs in other populations and breeds, the association between these markers and milk yield, composition, MUN and SCS could be exploited in gene-assisted selection programs for genetic improvement purposes.

Genetic parameters for milk urea concentration and milk traits in Polish Holstein-Friesian cows

Milk urea concentration (MU) used by dairy producers for management purposes can be affected by selection for milk traits. To assess this problem, genetic parameters for MU in Polish Holstein-Friesian cattle were estimated for the first three lactations. The genetic correlation of MU with milk production traits, lactose percentage, fat to protein ratio (FPR) and somatic cell score (SCS) were computed with two 5-trait random regression test-day models, separately for each lactation. Data used for estimation (159,044 daily observations) came from 50 randomly sampled herds. (Co)variance components were estimated with the Bayesian Gibbs sampling method. The coefficient of variation for MU in all three parities was high (40-41 %). Average daily heritabilities of MU were 0.22 for the first parity and 0.21 for the second and third lactations. Average genetic correlations for different days in milk in the first three lactations between MU and other traits varied. They were small and negative for protein percentage (from -0.24 to -0.11) and for SCS (from -0.14 to -0.09). The weakest genetic correlation between MU and fat percentage, and between MU and lactose percentage were observed (from -0.10 to 0.10). Negative average genetic correlation with the fat to protein ratio was observed only in the first lactation (-0.14). Genetic correlations with yield traits were positive and ranged from low to moderate for protein (from 0.09 to 0.33), fat (from 0.16 to 0.35) and milk yield (from 0.20 to 0.42). These results suggest that the selection on yield traits and SCS tends to increase MU slightly.

Cow and herd variation in milk urea nitrogen concentrations in lactating dairy cattle

Milk urea nitrogen (MUN) is correlated with N balance, N intake, and dietary N content, and thus is a good indicator of proper feeding management with respect to protein. It is commonly used to monitor feeding programs to achieve environmental goals; however, genetic diversity also exists among cows. It was hypothesized that phenotypic diversity among cows could bias feed management decisions when monitoring tools do not consider genetic diversity associated with MUN. The objective of the work was to evaluate the effect of cow and herd variation on MUN. Data from 2 previously published research trials and a field trial were subjected to multivariate regression analyses using a mixed model. Analyses of the research trial data showed that MUN concentrations could be predicted equally well from diet composition, milk yield, and milk components regardless of whether dry matter intake was included in the regression model. This indicated that cow and herd variation could be accurately estimated from field trial data when feed intake was not known. Milk urea N was correlated with dietary protein and neutral detergent fiber content, milk yield, milk protein content, and days in milk for both data sets. Cow was a highly significant determinant of MUN regardless of the data set used, and herd trended to significance for the field trial data. When all other variables were held constant, a percentage unit change in dietary protein concentration resulted in a 1.1mg/dL change in MUN. Least squares means estimates of MUN concentrations across herds ranged from a low of 13.6 mg/dL to a high of 17.3 mg/dL. If the observed MUN for the high herd were caused solely by high crude protein feeding, then the herd would have to reduce dietary protein to a concentration of 12.8% of dry matter to achieve a MUN concentration of 12 mg/dL, likely resulting in lost milk production. If the observed phenotypic variation is due to genetic differences among cows, genetic choices could result in herds that exceed target values for MUN when adhering to best management practices, which is consistent with the trend for differences in MUN among herds.

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

The objective of this study was to describe the genetic and phenotypic relationship between milk urea nitrogen (MUN) and reproductive traits in Iranian Holstein dairy cows. Test-day MUN data obtained from 57 301 dairy cows on 20 large dairy herds in Iran between January 2005 and June 2009. Genetic parameters for MUN and reproductive traits were estimated with a five-trait model using ASREML program. Random regression test-day models were used to estimate heritabilities separately for MUN from first, second and third lactations. Regression curves were modeled using Legendre polynomials of order 3. Herd-year-season along with age at calving was included as fixed effects in all models for reproductive traits. Heritabilities for MUN and reproductive traits were estimated separately for first lactation, second lactation and third lactation. The estimated heritabilities for MUN varied from 0.18 to 0.22. The heritability estimate was low for reproductive traits, which ranged from 0.02 to 0.06 for different traits and across parities. Except for days open, phenotypic and genetic correlations of MUN with reproductive performance traits were close to zero. Genetic correlations between MUN and days open were 0.23, 0.35 and 0.45 in first, second and third lactation, respectively. However, the phenotypic correlation between MUN at different parities was moderate (0.28 to 0.35), but the genetic correlation between MUN at different parities was high and ranged from 0.84 to 0.97. This study shows a limited application of MUN for use in selection programs to improve reproductive performance.

Genetic analysis of milk urea nitrogen and relationships with yield and fertility across lactation

The aim of this project was to investigate the relationship of milk urea nitrogen (MUN) with 3 milk production traits [milk yield (MY), fat yield (FY), protein yield (PY)] and 6 fertility measures (number of inseminations, calving interval, interval from calving to first insemination, interval from calving to last insemination, interval from first to last insemination, and pregnancy at first insemination). Data consisted of 635,289 test-day records of MY, FY, PY, and MUN on 76,959 first-lactation Swedish Holstein cows calving from 2001 to 2003, and corresponding lactation records for the fertility traits. Yields and MUN were analyzed with a random regression model followed by a multi-trait model in which the lactation was broken into 10 monthly periods. Heritability for MUN was stable across lactation (between 0.16 and 0.18), whereas MY, FY, and PY had low heritability at the beginning of lactation, which increased with time and stabilized after 100 d in milk, at 0.47, 0.36, and 0.44, respectively. Fertility traits had low heritabilities (0.02 to 0.05). Phenotypic correlations of MUN and milk production traits were between 0.13 (beginning of lactation) and 0.00 (end of lactation). Genetic correlations of MUN and MY, FY, and PY followed similar trends and were positive (0.22) at the beginning and negative (-0.15) at the end of lactation. Phenotypic correlations of MUN and fertility were close to zero. A surprising result was that genetic correlations of MUN and fertility traits suggest a positive relationship between the 2 traits for most of the lactation, indicating that animals with breeding values for increased MUN also had breeding values for improved fertility. This result was obtained with a random regression model as well as with a multi-trait model. The analyzed group of cows had a moderate level of MUN concentration. In such a population MUN concentration may increase slightly due to selection for improved fertility. Conversely, selection for increased MUN concentration may improve fertility slightly.

Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle

Phenotypic variation in milk production traits has been described over the course of a lactation as well as between different parities. The objective of this study was to investigate whether variation in production is affected by different loci across lactations. A genome-wide association study (GWAS) using a 50-k SNP chip was conducted in 152 divergent German Holstein Friesian cows to test for association with milk production traits over different lactations. The first four lactations were analysed regarding milk yield, fat, protein, lactose, milk urea nitrogen yield and content as well as somatic cell score. Two approaches were used: (i) Wilmink curve parameters were used to assess the genetic effects over the course of a lactation and (ii) test-day yield deviations (YD) were used as a normative approach for a GWAS. The significant effects were largest for markers affecting curve parameters for which there was a statistical power <0.8 of detection even in this small design. While significant markers for YDs were detected in this study, the power to detect effects of a similar magnitude was only 0.11, suggesting that many loci may have been missed with this approach in the present design. Furthermore, all significant effects were specific for a single lactation, leading to the conclusion that the variance explained by a certain locus changes from lactation to lactation. We confirm the common evidence that most production traits vary in the degree of persistency after the peak as a result of genetic influence.

Genome-wide scan to detect quantitative trait loci for milk urea nitrogen in Dutch Holstein-Friesian cows

Studies have reported genetic variation in milk urea nitrogen (MUN) between cows, suggesting genetic differences in nitrogen efficiency between cows. In this paper, the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in MUN and MUN yield are presented. Two to 3 morning milk samples were taken from 1,926 cows, resulting in 5,502 test-day records. Test-day records were corrected for systematic environmental effects using a repeatability animal model. Averages of corrected phenotypes of 849 cows, belonging to 7 sire families, were used in an across-family multimarker regression approach to detect QTL. Animals were successfully genotyped for 1,341 single nucleotide polymorphisms. The QTL analysis resulted in 4 chromosomal regions with suggestive QTL: Bos taurus autosomes (BTA) 1, 6, 21, and 23. On BTA 1, 2 suggestive QTL affecting MUN were detected at 60 and 140 cM. On BTA 6, 1 suggestive QTL affecting both MUN and MUN yield was detected at 103 cM. On BTA 21, 1 suggestive QTL affecting MUN yield was detected at 83 cM. On BTA 23, 1 suggestive QTL affecting MUN was detected at 54 cM. Quantitative trait loci for MUN and MUN yield were suggestive and each explained between 2 and 3% of the phenotypic variance.

Short communication: Genetic parameters of production traits in Chinese Holsteins using a random regression test-day model

The objective of this study was to estimate genetic parameters of production traits in the first 3 parities in Chinese Holsteins. Data were a random sample of complete herds (109,005 test-day records of 9,706 cows from 54 herds) extracted from the original data set, which included 362,304 test-day records of 30,942 Holstein cows from 105 herds. A test-day animal model with multiple-trait random regression and the Gibbs sampling method were used for parameter estimation. Regression curves were modeled using Legendre polynomials of order 4. The multiple-trait analysis included milk, fat, and protein yield, and somatic cell score (SCS). Average daily heritabilities ranged between 0.222 and 0.346 for the yield traits and between 0.092 and 0.187 for SCS. Heritabilities were higher in the third lactation for all traits. Within-parity genetic correlations were very high among the yield traits (>0.806) and were close to zero between SCS and yield traits, especially for first-parity cows. Results were similar to previous literature estimates from studies that used the same model as applied to this study. The estimates found in this study will be used to perform breeding value estimation for national genetic evaluations in Chinese Holsteins.

Short communication: effect of preadjusting test-day yields for stage of pregnancy on variance component estimation in Canadian Ayrshires

Preadjustment of phenotypic records is an alternative to accounting for the effect of pregnancy within the genetic evaluation model. Variance components used in the Canadian Test-Day Model may need to be re-estimated after preadjusting for pregnancy. The objective of this study was to assess the effect of preadjusting test-day yields on variance components and estimated breeding values using a random regression test-day model in a random sample of Ayrshire cows. A random sample of 981 Canadian Ayrshire cows from 18 complete herds (average of 54.5 cows/herd) was analyzed. Two data sets were created using the same animals, one with unadjusted milk, fat, and protein yields, and one data set with test-day records adjusted for pregnancy effects. Pregnancy effect estimates from a previous study were used for additive preadjustment of records. Variance components were estimated using both data sets. Results were very similar between the 2 data sets for all estimated genetic parameters (heritabilities, genetic, and permanent environmental correlations). The relative squared differences were very small: 0.05% for heritabilities, 0.20% for genetic correlations, and 0.18% for permanent environmental correlations. Furthermore, paired Student's t-tests showed that the differences between the genetic parameters of data sets adjusted and unadjusted for pregnancy effect were not significantly different from 0. Results from this study show that preadjusting data for pregnancy did not yield changes in covariance component estimates, thus suggesting that preadjusting test-day records could be a feasible solution to account for pregnancy in the Canadian Test-Day Model without changing the current model. Estimated breeding values (EBV) were calculated for both data sets to observe the impact of preadjusting for pregnancy. Overall, the largest changes in EBV seen when preadjusting for pregnancy (compared with unadjusted records) occurred for nonpregnant elite cows, whose EBV declined. Preadjusting for pregnancy before genetic evaluations improves the estimation of breeding values by adding the negative impact of pregnancy back onto pregnant cow test-day records, causing an increase in their production EBV.

Genetic and phenotypic relationships among milk urea nitrogen, fertility, and milk yield in holstein cows

The aims of the study were to evaluate the relationships among milk urea nitrogen and nonreturn rates at the phenotypic scale, and to estimate genetic parameters among milk urea nitrogen, milk yield, and fertility traits in the early period of lactation. Milk yield, protein percentage, the interval from calving to first service, and 56- and 90-d nonreturn rates were available from 73,344 Holstein cows from 2,178 different herds located in a region in northwestern Germany. Generalized linear models with a logit link function were applied to assess the phenotypic relationships. Bivariate threshold-threshold, linear-threshold, and linear-linear models, fitted in a Bayesian framework, were used to estimate genetic correlations among traits. Milk yield, protein percentage, and milk urea nitrogen were means from test-day 1 (on average 20.8 d in milk) and test-day 2 (on average 53.1 d in milk) after calving. An increase in milk urea nitrogen was associated with decreasing 56-d nonreturn rates on the phenotypic scale. At fixed levels of milk urea nitrogen, greater values of protein percentage, indicating a surplus of energy in the feed, were positively associated with nonreturn rates. Heritabilities were 0.03 for 56- and 90-d nonreturn rates, 0.07 for interval from calving to first service, 0.13 for milk urea nitrogen, and 0.19 for milk yield. Service sire explained a negligible part (below 0.15%) of the total variance for nonreturn rates. Genetic correlations between the interval from calving to first service and nonreturn rates were close to zero. The genetic correlation between nonreturn rates was 0.94, suggesting that a change from nonreturn after 90 d to nonreturn after 56 d in the national genetic evaluation would not result in any loss of information. The genetic correlation between milk yield and nonreturn after 56 d was -0.31, and between milk yield and calving to first service was 0.14, both indicating an antagonistic relationship between production and reproduction. The genetic correlation between milk yield and milk urea nitrogen was 0.44, reflecting an energy deficiency in early lactation. The genetic correlations between milk urea nitrogen and nonreturn rates were too weak (-0.19 for 56-d nonreturn rate, and -0.23 for 90-d nonreturn rate) to justify the use of milk urea nitrogen as an additional trait in genetic selection for fertility, as demonstrated by selection index calculations.