Herd Test Day Research Articles

Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits.

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.

Lactoferrin concentration in bovine milk: validation of radial immunodiffusion technique, sources of variation, and association to udder health status

The present study aimed to evaluate analytical performances of radial immunodiffusion (RID) technique for the quantification of milk lactoferrin (LF), and to investigate the main sources of variation of LF concentration in individual milk samples of Holstein Friesian (HF, n = 1516) and Simmental (SI, n = 230) cows. Repeatability (RSDr) and reproducibility (RSDR) of RID method were assessed as relative standard deviation of 15 measurements within the same day and 45 measurements across 3 d, respectively. Sources of variation of milk LF were investigated through a mixed linear model which included the fixed effects of classes of stage of lactation and parity (and their interaction), cow breed, udder health status (UHS, based on somatic cell count and differential somatic cell count), and the random effects of the herd-test day nested within breed and the residual. Method performances were satisfactory in terms of repeatability (RSDr < 9%) and reproducibility (RSDR < 8%), suggesting that the RID can be considered as a consistent cow-side test for the quantification of milk LF. Milk LF was positively associated with somatic cell score (r = 0.40) and negatively with lactose content (r = −0.33). Lower concentration of LF was observed in milk of ‘healthy’ and ‘susceptible’ cows (13.03 and 12.87 mg/dL, respectively) compared to ‘mastitic’ and ‘chronic’ cows (17.91 and 17.33 mg/dL, respectively). In this light, milk LF has the potential to be a useful biomarker to detect early mastitis. Further research is advisable to deepen the association between milk LF content and observed clinical mastitis. Highlights Radial immunodiffusion method for milk lactoferrin quantification is repeatable and reproducible. Somatic cell count is positively associated with milk lactoferrin content. Milk lactoferrin is less concentrated in milk of healthy and susceptible than potentially mastitic and chronic cows.

Eigenvector selection indices for improving milk yield and persistency in Egyptian buffalo

The genetic gains were estimated for milk production and persistency, derived from random regression models, using eigenvector indices, and they were compared with the traditional selection index. The data set contained 4971 test day milk yield recorded for 691 buffalo cows, daughters of 120 sires and 532 dams. The model included the random effects of direct additive genetic, permanent environment and error, whereas the fixed effects were herd test day, year and season of calving and parity, and as a covariate, it was milk days. The first and the 2nd eigenvalues explained 73.1 and 22.9% of the variation of the random regression coefficients, respectively, suggesting that the use of the first two eigenvectors is sufficient. Genetic responses in total milk yield (TMY) based on the first eigenvector index (Ie1) and that based on the conventional selection (IMY) have close gain of about 171 kg in each index. The second eigenvector index (Ie2) showed an increase in TMY (9.91 kg), and thus an increase in the persistency (0.86 kg). The TMY and persistency are the two economically important traits in dairy production, additional genetic gains in persistency and high genetic gain for TMY could be obtained using the 2nd eigenvector index (I*2).

Comparison of high resolution observational and grid-interpolated weather data and application to thermal stress on herd average milk production traits in a temperate environment

To study the effects of heat stress in dairy cattle, animal performance records are merged with weather data from nearby meteorological stations. However, the stations location and distribution may render them less reliable. Alternatively, weather information from several meteorological stations can be interpolated using empirical algorithms to produce gridded estimates of weather parameters at desired spatio-temporal resolutions. The objective of this study was to compare and apply observational weather station data (WSD) and gridded numerical weather prediction (NWP) model data with high spatio-temporal resolution to identify thermal stress thresholds for milk production traits. Weather data were from the German meteorological service and reanalysis was based on the COnsortium for Small-Scale MOdelling (COSMO)-REA6 model. Milk performance data were from over 16 million monthly test-day records for the period 2010 to 2019 in southern Bavaria, Germany. Individual cow records were transformed to herd averages resulting to 797,455 herd test-days from 9,726 herds. These were merged with temperature and temperature humidity index (THI) data from 53 weather stations and corresponding gridded data. There was good agreement between WSD and NWP model data with correlation coefficients of 0.97 for both daily average temperature and THI and 0.84 for relative humidity. However, positive and negative biases were observed in the pre-Alpine regions. The average herd reaction norms were in good agreement and followed similar trends when estimated from WSD and NWP. The heat stress threshold at which milk yield and protein yield started to decline was 16.0 °C for temperature and 60 units for THI. The responses of fat yield, protein and fat contents were generally linear decline with no definite thresholds. Milk urea increased in a non-linear accelerating pattern and there was no undesired effects on SCS. The thresholds obtained in this study may be applied to implement necessary management strategies to mitigate losses.

Selective and deferred treatment of clinical mastitis in seven New Zealand dairy herds

Mastitis is the most frequent reason for antibiotic use in New Zealand dairy cattle and technologies reducing and targeting this use contribute to responsible product stewardship. Rapid identification of pathogen and antibiotic susceptibility facilitate targeted treatment but currently involve a minimum 24 h delay. Studies from confinement systems where Gram-negative organisms are responsible for a significant proportion of mastitis, indicate selective treatment can reduce antibiotic use without reducing clinical or bacteriological cure. However, in New Zealand’s seasonal, pastoral dairy system, mastitis is dominated by Gram-positive organisms and if treatment is deferred, it is vital both short- and long-term clinical health outcomes are not compromised.Mastatest® is a diagnostic system for bovine mastitis indicating the pathogen and its antibiotic sensitivity within 24 h of sampling. This study focused on evaluating this system’s ability to control antibiotic usage whilst achieving equivalent bacteriological and clinical cure rates alongside long term individual somatic cell count (ISCC) outcomes as conventional treatment choices.Mild to moderate mastitis cases in the 100 days after calving in 6467 cows from 7 farms were milk sampled and randomly allocated to a positive control group non-selective treatment or a culture-based treatment. All milk samples were processed using Mastatest®. For the positive control, the quarter was treated immediately with 3 treatments of procaine penicillin every 12 h. For the selective treatment group, treatment was delayed for 24 h and then informed by pathogen and antibiotic sensitivity from the Mastatest® result. Gram-negative and no-growth quarters were untreated. Gram-positive quarters were treated with the antibiotic for which the lowest in vitro antimicrobial sensitivity was reported.Re-sampling was carried out from affected quarter(s) approximately 21 days after initial diagnosis and cultured for bacterial identification. Clinical recurrence within 60 days and ISCC data was recorded at herd tests over the duration of the lactation. Antimicrobial usage and days of milk withhold pending clearance of antibiotic residues were also noted.There was no difference in bacteriological or clinical cure rate between the two treatment groups. Final herd test ISCC and days of milk withhold from supply did not differ between groups. Antibiotic usage was 24 % less (95 % predictive interval = 12–47 %) in the selective group.This study suggests that on farm decisions about deferred treatment of mastitis using Mastatest® to identify the intramammary pathogen can reduce the antimicrobial usage with no loss in bacterial or clinical cure and with no effect on ISCC over the lactation.

Effects of somatic cell score on milk yield and mid-infrared predicted composition and technological traits of Brown Swiss, Holstein Friesian, and Simmental cattle breeds

High milk somatic cell count (SCC) influences milk production and quality; however, very little is known about the effect of low SCC on milk quality, especially in terms of mineral content and coagulation properties. Thus, the present study aimed to investigate the effects of somatic cell score (SCS), calculated as log2(SCC/100) + 3, on milk yield, composition (fat, crude protein, casein, lactose, milk urea nitrogen, protein fractions, and mineral contents), and coagulation properties of Brown Swiss, Holstein Friesian, and Simmental cows from multibreed herds. Milk composition and coagulation traits were predicted using mid-infrared spectroscopy. The data set comprised 95,591 observations of 6,940 cows in 313 multibreed herds, collected from January 2011 to December 2017. Observations were divided into 8 classes based on SCS. Statistical analysis was performed using a linear mixed model, which included breed, parity, stage of lactation, SCS class, and their interactions as fixed effects, and herd test day, cow, and residual as random effects. The probability that cows experienced SCS > 4.00 at 30 ± 5, 60 ± 5, or 90 ± 5 d after the observation test day was calculated for each SCS class, and odds ratios to the reference class (-1.00 < SCS ≤ 0.00) were reported. Results showed that the relationship between SCS and milk traits followed a third-order polynomial regression. The average loss of milk, fat, and crude protein yields were 0.43, 0.01, and 0.01 kg/d, respectively, for each SCS unit higher than 1.00. Very low SCS (<-1.00) had detrimental effects on milk yield and quality traits similar to or even stronger than high SCS (>4.00). Moreover, cows with SCS lower than -1.00 on a test day were about 7 times more likely to present high SCS within the following 90 ± 5 d than cows with SCS between -1.00 and 0.00. Breeds responded similarly to the increase of SCS, but the overall loss of fat and crude protein yields, and several minerals among Holstein Friesian were lower with increasing SCS. The best milk yield and quality were observed between SCS 0.00 and 1.00, but milk quality of Holstein Friesians started to decrease at lower SCS compared with milk quality of Brown Swiss and Simmental cows. Results suggest a breed-dependent optimum of SCS, and highlighted that very low SCS can be an indicator of udder health problems and, thus, may be used for early detection of mastitis.

Comparing dairy farm milk yield and components, somatic cell score, and reproductive performance among United States regions using summer to winter ratios

Heat stress abatement is a challenge for dairy producers in the United States, especially in the southern states. Thus, managing heat stress is critical to maintain dairy cow performance in the summer. The ability to employ a metric to measure heat stress and evaluate abatement strategies may benefit dairy producers by providing meaningful feedback on the effectiveness of current and future management strategies with the goal of improving heat stress management. Therefore, this study aimed to explore the use of the summer to winter performance ratio metric to quantify and compare farm performance variables among regions of the United States. Monthly performance data recorded by the Dairy Herd Improvement Association from 2007 to 2016, for all US Dairy Herd Improvement Association herds processing records through Dairy Records Management Systems (Raleigh, NC), were obtained. Season dates were based on the astronomical definition of the Northern Hemisphere with summer as June 21 to September 21 and winter as December 21 to March 19. States were grouped into regions based on climate zone classification. Performance records included a total of 16,573 herds [Northeast (n = 7,955), Midwest (n = 6,555), Northern Plains (n = 305), Southeast (n = 1,370), and Southern Plains (n = 388) regions]. Herd test day performance variables energy-corrected milk, somatic cell score, milk fat and protein percentage, conception rate, heat detection rate, and pregnancy rate in summer and winter were used to calculate summer to winter ratios for each region. The MIXED procedure of SAS 9.4 (SAS Institute Inc., Cary, NC) was used to compare test day performance variables. The effects of year, mean days in milk, mean 150-d milk, mean herd size, and number of milkings per day were included as covariates in the models. Dairy cattle performance in all climate regions was negatively affected by summer heat stress, but some regions greater than others. A difference was also observed among regions when comparing summer to winter ratios for each performance parameter. This indicates that summer performance varies by climate region identified by the summer to winter ratio and demonstrates usefulness of the metric to monitor degree of heat stress based on performance.

Phenotypic analysis of milk composition, milk urea nitrogen and somatic cell score of Italian Jersey cattle breed

The present study aimed to assess the phenotypic variation of milk yield (MY) and quality traits in Italian Jersey (IJ) breed. Sources of variation were investigated through a linear mixed model, including the fixed effects of days in milk (DIM), parity, calving season, milking frequency, recording type, the interaction between DIM and parity, and the random effects of herd-test-day (HTD), cow and the residual. Results highlighted the high contents of milk fat (5.18%), protein (4.08%) and casein (3.16%) of IJ cows. Somatic cell score, averaging 3.35 units, should be lowered through specific managerial actions. Phenotypic variances of MY and milk quality traits were mainly due to cow effect, whereas phenotypic variance of milk urea nitrogen (MUN) content was mainly due to HTD effect, meaning that managerial conditions, especially feeding, are very important to explain the variation of MUN. In conclusion, the present study allowed to characterise milk of IJ cows at population level and to identify environmental factors associated with variation of MY and quality traits, which will be useful to adjust phenotypic records in genetic evaluation of Jersey breed.HighlightsFactors affecting milk quality traits of Italian Jersey cows have been investigated.Phenotypic variance of milk composition and SCS was mainly due to cow effect, and that of MUN was mainly driven by herd-test-day effect.Significant environmental factors identified in the present study will be used to adjust phenotypic records in genetic analysis.

Effects of a combination of plant bioactive lipid compounds and biotin compared with monensin on body condition, energy metabolism and milk performance in transition dairy cows.

The aim of this study was to test whether a combination of plant bioactive lipid compounds (also termed ‘essential oils’) and biotin (PBLC+B) could decrease the mobilization of body reserves and ketosis incidence in postpartum dairy cows. We compared non-supplemented control (CON) cows with cows receiving monensin (MON) as a controlled-release capsule at d -21, and with cows receiving PBLC+B from day (d) -21 before calving until calving (Phase 1) and further until d 37 after calving (Phase 2), followed by PBLC+B discontinuation from d 38 to d 58 (Phase 3). The PBLC+B cows had higher body weight and higher back fat thickness than CON cows and lesser body weight change than MON and CON cows in Phase 3. Body condition score was not different among groups. Milk protein concentration tended to be higher on the first herd test day in PBLC+B vs. CON cows. Milk fat concentration tended to be highest in PBLC+B cows throughout Phases 2 and 3, with significantly higher values in PBLC+B vs. MON cows on the second herd test day. Yields of energy-corrected milk were higher in PBLC+B vs. CON and MON cows in Phase 2 and higher in PBLC+B and MON cows vs. CON cows in Phase 3. The incidence of subclinical ketosis was 83%, 61% and 50% in CON, PBLC+B and MON cows, respectively, with lower mean β-hydroxybutyrate values in MON than in PBLC+B cows in Phase 1 prepartum. The serum triglyceride concentration was higher in PBLC+B vs. CON cows on d 37. No differences were observed in serum glucose, urea, non-esterified fatty acids, cholesterol and bilirubin concentrations. Aspartate transaminase and γ-glutamyltranspeptidase but not glutamate dehydrogenase activities tended to be highest in MON and lowest in PBLC+B in Phase 2. We conclude that PBLC+B prevent body weight loss after parturition and are associated with similar ketosis incidence and partly higher yields of energy-corrected milk compared to MON supplementation of dairy cows.

Milk protein composition in purebred Holsteins and in first/second-generation crossbred cows from Swedish Red, Montbeliarde and Brown Swiss bulls

The aim of this study was to analyze milk protein composition in purebred and crossbred dairy cattle and estimate the effects of individual sources of variation on the investigated traits. Milk samples were collected from 505 cows from three commercial farms located in Northern Italy, some of which had originated from crossbreeding programs, although most were purebred Holsteins (HO). The basic crossbreeding scheme was a three-breed rotational system using Swedish Red (SR) semen on HO cows (SR×HO), Montbeliarde (MO) semen on SR×HO cows (MO×(SR×HO)) and HO semen again on MO×(SR×HO) cows. A smaller number of purebred HO from each of the herds were mated inverting the breed order (MO×HO and SR×(MO×HO)) or using Brown Swiss (BS) bulls (BS×HO) then MO bulls (MO×(BS×HO)). Milk samples were analyzed by reverse-phase HPLC to obtain protein fraction amounts (g/l) and proportions (% of total true protein). Traits were analyzed using a linear model, which included the fixed effects of herd-test-day (HTD), parity, days in milk and breed combination. Results showed that milk protein fractions were influenced by HTD, stage of lactation, parity and breed combination. The increase in protein concentration during lactation was due in particular to β-casein (β-CN), αS1-CN and β-lactoglobulin (β-LG). The higher protein content of primiparous milk was mainly due to higher concentrations of all casein fractions. The milk from crossbred cows had higher contents and proportions of κ-CN and α-lactalbumin (α-LA), lower proportions of β-LG and greater proportion of caseins/smaller in whey proteins on milk true protein than purebred HO. The three-way crossbreds differed from two-way crossbreds only in having greater proportions of α-LA in their milk. Of the three-way crossbreds, the SR sired cows yielded milk with a smaller content and proportion of β-LG than the MO sired cows, and, consequently, a higher proportion of caseins than whey proteins. Results from this study support the feasibility of using crossbreeding programs to alter milk protein profiles with the aim of improving milk quality and cheese-making properties.

Genetic parameters estimation for milking speed in Croatian Holstein cattle

Milking speed (MS) has a growing importance from a dairy management standpoint. Cows with slow MS require more labour, while cows with fast MS could be in greater risk for udder diseases. Although MS is considered as a trait of importance, little attention has been given to estimate genetic components for MS and its relationship with other traits. The objectives of this study were: 1) to estimate genetic parameters for MS in Croatian Holstein cattle; 2) to use them for the prediction of breeding values; and 3) to estimate proof correlations of MS with production and conformation traits in order to understand the interrelationships among traits. Data included 129,723 test-day records for 35,908 first calving cows taken from the central database of the Croatian Agricultural Agency. Pedigree file consisted of 85,605 animals. In order to improve the normality, logarithmic transformation for MS was used. Variance components where estimated by REML method using VCE 6 program. Statistical model included calving season, milking time, and milk yield class as fixed class effects, while age at first calving and days in milk were fitted as covariates. Random effects were: common herd-test day, permanent environmental and direct additive genetic effect. Common herdtest day and permanent environmental effect accounted 27 % and 15 % of variability. Direct additive genetic effect explained another 14 % of phenotypic variation for MS. Analysis of proof correlations between bulls and cows BV for MS with production and conformation traits showed low to mediate relationships. Most of these proof correlations were positive with an exception of teats length. Genetic evaluation for MS provide useful tool for breeding decisions due to moderate heritability of MS and the trait should be included in the total merit index in the future. In order to determine the appropriate economic weights for MS in the overall index, genetic correlations among MS and production and conformation traits should be estimated.

Factors affecting milking speed in Murciano-Granadina breed goats.

Milk flow during the first minute of milking was analyzed using data from 1,132 Murciano-Granadina breed goats belonging to 17 herds. During the individual lactations, 2 test days were scheduled for recording several milk flow traits, total milk, milk composition (fat and protein percentages), and somatic cell count. Average lag time from teatcup attachment to arrival of milk at the milk claw (T0) was 4.9 s and at the milk meter (T1) was 15.8 s. Average milk flow after 30 s (MF0.5) was 0.29kg/30 s (0 to 1.1kg/30 s) and milk flow at 60 s or milking speed (MF1) was 0.67kg/min (0.1 to 2.1kg/min). Repeatabilities of T0, T1, MF0.5, and MF1 were 0.45, 0.58, 0.62, and 0.68, respectively. The MF1 showed high phenotypic correlation withT1(-0.63) and MF0.5 (0.90), medium values withT0(-0.42) and total milk (0.22), and very low values (-0.04 to -0.12) with fat, protein, and somatic cell count. We found no differences between flows during the first 3 lactations, with a reduction as the lactation number increased. Months in milk since parturition affected MF1, being highest in the first 3mo (0.67-0.71kg/min) and decreasing until the end of lactation (0.58kg/min). The effect of herd-test day was significant for all traits. Inclusion of all these effects for the analysis of milk flow traits is considered necessary.

Fatty acid synthase (FASN) gene polymorphism and early lactation milk fat composition in Xinong Saanen goats

Fatty Acid Synthase (FASN) is a multifunctional protein, catalyzing the de novo fatty acid (FA) synthesis, its high mRNA expression in goat mammary epithelial cells (GMECs) substantiated by the medium to high heritability of milk fat (MF), makes it a candidate gene for association analysis with milk fat profile. The study aimed to develop markers of Capra hircus FASN to improve healthfulness of goat milk FAs and to investigate early lactation MF profile. 300 milk samples were collected, 30 days postpartum, from 300 Saanen does, from 2 herds sired by 73 bucks, and were analyzed with gas-chromatography. Linear mixed models, that considered the effects of herd, herd test day (HTD), parity, doe, sire and allele and haplotype effects were used to investigate the association of 3 FASN SNPs (−911 C/T intron1 SNP1, 852 A/G intron2 SNP2 and 14420 T/C exon37 SNP3) or two haplotypes (H1, C-A-T and H2, T-G-C, constructed from the three SNPs) in separate models, with 31 individual FAs, 8 FA groups, and 7 FA indices, by SPSS 20, REML function. A single test per SNP and haplotype was performed to avoid the effect of multiple testing. H1, SNP1 and SNP2 were most desirable for milk healthfulness because they were significantly associated with lower concentrations of myristic acid (C14:0) and palmitic acid (C16:0) and higher concentrations of linoleic acid (C18:2 n-6, cis). Herd followed by HTD and parity were the predominant factors affecting FA levels, indicating an effect of nutrition and management. Lower levels of de novo synthesized FAs but higher levels of C18:1 cis-9 were observed, indicating mobilization of body fat reserves. Although considered as de novo, butyric acid levels were consistent throughout lactation and showed negative correlations with other de novo FAs, suggesting other sources of origin. Majority of the odd chain fatty acids (OCFAs) originate from rumen bacteria, hence they may reflect rumen microbial activity. In our study this was confirmed by the observed positive correlations of all OCFAs with rumenic CLA (through its precursor C18:1 trans-11) and other trans-FAs, which are end products of biohydrogenation. The SNP markers developed will assist in marker assisted selection, and the early lactation milk FA analysis will help in deciphering factors of variation.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle.

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3–L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

Investigation of the association between the test day milk fat–protein ratio and clinical mastitis using a Poisson regression approach for analysis of time-to-event data

The objective of the present study was to investigate the association between the milk fat–protein ratio and the incidence rate of clinical mastitis including repeated cases of clinical mastitis to determine the usefulness of this association to monitor metabolic disorders as risk factors for udder health. Herd records from 10 dairy herds of Holstein cows in Saxony, Germany, from September 2005–2011 (36,827 lactations of 17,657 cows) were used for statistical analysis. A mixed Poisson regression model with the weekly incidence rate of clinical mastitis as outcome variable was fitted. The model included repeated events of the outcome, time-varying covariates and multilevel clustering. Because the recording of clinical mastitis might have been imperfect, a probabilistic bias analysis was conducted to assess the impact of the misclassification of clinical mastitis on the conventional results.The lactational incidence of clinical mastitis was 38.2%. In 36.2% and 34.9% of the lactations, there was at least one dairy herd test day with a fat–protein ratio of <1.0 or >1.5, respectively. Misclassification of clinical mastitis was assumed to have resulted in bias towards the null. A clinical mastitis case increased the incidence rate of following cases of the same cow. Fat–protein ratios of <1.0 and >1.5 were associated with higher incidence rates of clinical mastitis depending on week in milk. The effect of a fat–protein ratio >1.5 on the incidence rate of clinical mastitis increased considerably over the course of lactation, whereas the effect of a fat–protein ratio <1.0 decreased. Fat–protein ratios <1.0 or >1.5 on the precedent test days of all cows irrespective of their time in milk seemed to be better predictors for clinical mastitis than the first test day results per lactation.

GENETIC AND PHENOTYPIC TRENDS FOR TEST DAY MILK, FAT AND PROTIN YIELDS APPLYING RANDOM REGRESSION MODEL IN EGYPTIAN BUFFALOES

SUMMARY The aim of this study was to detect genetic and phenotypic trends for test day milk, fat and protein yields in Egyptian buffalo applying the random regression model (RRM) and determining the genetic and phenotypic trend. Data of 4971 test days (TD) milk yield traits were recorded for 691 Egyptian buffalo cows, daughters of 120 sires and 532 dams from four herds belonging to the Animal Production Research Institute, Egypt. Ten-month classes of lactation days were considered for the test-day yields. The model included the random effects of direct additive genetic, permanent environment and error, while the fixed effects were herd-test day, year and season of calving and parity as well as days in milk as a covariable, which was modeled by orthogonal Legendre polynomials. The additive genetic variance estimates at first test day for milk, fat and protein yields were respectively 0.035kg, 2.26g, 0.80g, increased until the fourth (0.807kg, 30.52g, 12.52g), decreased thereafter, reaching the lowest value at the ninth test day for milk and protein yields (0.238kg, 0.97g) and at the tenth test day for fat yield (7.28g).Heritability estimated at first test day was 0.05, increased until the fourth test day (0.30), and decreased thereafter and reached the lowest value at the tenth test day (0.06). The highest heritabilities were found to be 0.29 and 0.31 for fourth test day in fat and protein yields, respectively. The range in phenotypic values change decreased from 7.99 to 5.66kg, 53.37 to 35.07g and 30.86 to 21.54g, while the respective genetic values change increased from -0.22 to 0.17kg, -1.41 to 1.36g and 0.82 to 0.70g for milk, fat and protein yields, respectively. The genetic trends were slightly positive for all traits indicating that the selection program performs correctly. For all traits, the phenotypic trends showing deteriorating trends indicating the presence of some environmental inadequacies especially for nutritional level.

Estimation of genetic parameters for milk yield across lactations in mixed-breed dairy goats

Currently, breeding values for dairy goats in the United Kingdom are not estimated and selection is based only on phenotypes. Several studies from other countries have applied various methodologies to estimate breeding values for milk yield in dairy goats. However, most of the previous analyses were based on relatively small data sets, which might have affected the accuracy of the parameter estimates. The objective of this study was to estimate genetic parameters for milk yield in crossbred dairy goats in lactations 1 to 4. The research was based on data provided by 2 commercial goat farms in the United Kingdom comprising 390,482 milk yield records on 13,591 dairy goats kidding between 1987 and 2012. The population was created by crossing 3 breeds: Alpine, Saanen, and Toggenburg. In each generation, the best-performing animals were selected for breeding and, as a result, a synthetic breed was created. The pedigree file contained 28,184 individuals, of which 2,414 were founders. The data set contained test-day records of milk yield, lactation number, farm, age at kidding, and year and season of kidding. Data on milk composition was unavailable. Covariance components were estimated with the average information REML algorithm in the ASReml package (VSN International Ltd., Hemel Hempstead, UK). A random regression animal model for milk yield with fixed effects of herd test day, year-season, and age at kidding was used. Heritability was the highest at 200 and 250d in milk (DIM), reaching 0.45 in the first lactation and between 0.34 and 0.25 in subsequent lactations. After 300 DIM, the heritability started decreasing to 0.23 and 0.10 at 400 DIM in the first and subsequent lactations, respectively. Genetic correlation between milk yield in the first and subsequent lactations was between 0.16 and 0.88. This study found that milk yields in first and subsequent lactations are highly correlated, both at the genetic and phenotypic level. Estimates of heritability for milk yield were higher than most of the values reported in the literature, although they were in the range reported in this species. This should facilitate genetic improvement for the population studied as part of a broader multi-trait breeding program.

Modelling and estimation of genotype by environment interactions for production traits in French dairy cattle

BackgroundGenotype by environment interactions are currently ignored in national genetic evaluations of dairy cattle. However, this is often questioned, especially when environment or herd management is wide-ranging. The aim of this study was to assess genotype by environment interactions for production traits (milk, protein, fat yields and fat and protein contents) in French dairy cattle using an original approach to characterize the environments.MethodsGenetic parameters of production traits were estimated for three breeds (Holstein, Normande and Montbéliarde) using multiple-trait and reaction norm models. Variables derived from Herd Test Day profiles obtained after a test day model evaluation were used to define herd environment.ResultsMultiple-trait and reaction norm models gave similar results. Genetic correlations were very close to unity for all traits, except between some extreme environments. However, a relatively wide range of heritabilities by trait and breed was found across environments. This was more the case for milk, protein and fat yields than for protein and fat contents.ConclusionsNo real reranking of animals was observed across environments. However, a significant scale effect exists: the more intensive the herd management for milk yield, the larger the heritability.

Characterization of French dairy farm environments from herd-test-day profiles

Genotype by environment interactions are ignored in national genetic evaluations of dairy cattle. However, some breeders consider that the genetic merit of top sires is not correctly estimated for use in their own particular environment or with their own herd management. With the objective of later investigating genotype by environment interactions at the national level, we studied the relationship between herd management (e.g., feeding system, herd size, production, workforce) and herd-test-day (HTD) profiles for milk yield and contents of fat and protein using a database of 934 herds. Herd-test-day profiles, estimated using a test-day model, are thought to reflect herd management because they represent the part of production due only to month-to-month variations in environmental conditions of production; that is, those related mainly to feeding and climate conditions. Herd clustering based on results from a factor analysis of descriptors of HTD profiles was performed. Each cluster of herds was then characterized in terms of herd management. Three herd clusters were identified. Cluster 1 was characterized by low HTD profiles for milk yield and protein and fat contents as well as a low level of intensification. Cluster 2 was defined by a high milk yield HTD profile and a high level of intensification. Cluster 3 consisted of herds with HTD profiles showing high protein and fat contents but it was difficult to link this cluster to any specific herd management. The use of clusters based on HTD profiles as descriptors of distinct herd management systems will be used in a genotype by environment interaction study in France.

Reaction norms and genotype‐by‐environment interaction in the German Holstein dairy cattle

Reaction norm random regression sire models were used to study genotype-by-environment interactions (G × E) in the German Holstein dairy cattle population. Around 2300 sires with a minimum of 50 daughters per sire and seven first-lactation test day observations per daughter were analysed. Corrected test day records for milk yield, protein yield, fat yield and somatic cell score (SCS) were used. Herd test day solutions for milk traits, milk energy yield or SCS were used as environmental descriptors. Second-order orthogonal polynomial regressions were applied to the sire effects. The results revealed significant slope variances of the reaction norms, which caused a non-constant additive genetic variance across the environmental ranges considered. This pointed to the presence of minor G × E effects. The additive genetic variance increased when the environment improved, that is, higher (lower) herd test day solutions for milk traits (SCS). This was also influenced by pure scaling effects, because the non-genetic variance increased in an improved environment and the heritability was less influenced by the environment. The G × E effects caused very little reranking of the sires for the environmental range considered in this study.