Effects of temperature-humidity index on blood metabolites of German dairy cows and their female calves.

Heat stress (HS) impairs productivity, health, and welfare in dairy cows, and additionally causes metabolic changes. Hence, specific metabolites could be used as HS biomarkers. Consequently, the aim of the present study was to compare blood metabolite concentrations of German Holstein dairy cows and of their female calves suffering from high temperature-humidity index (THI) during late gestation (cows) or during their first week of life (calves) or not. According to the mean daily THI (mTHI) at the day before blood sampling, animals were classified into 2 groups: high mTHI ≥60 (hmTHI) and low mTHI <60 (lmTHI). To perform a standard cross-sectional 2-group study, cow groups (n = 48) and calf groups (n = 47) were compared separately. Differences in metabolite concentrations between hmTHI and lmTHI animals were inferred based on a targeted metabolomics approach. In the first step, processed metabolomics data were evaluated by multivariate data analysis techniques, and were visualized using the web-based platform MetaboAnalyst V5.0. The most important metabolites with pronounced differences between groups were further analyzed in a second step using linear mixed models. We identified 9 thermally sensitive metabolites for the cows [dodecanedioic acid; 3-indolepropionic acid; sarcosine; triglycerides (14:0_34:0), (16:0_38:7), (18:0_32:1), and (18:0_36:2); phosphatidylcholine aa C38:1; and lysophosphatidylcholine a C20:3] and for the calves [phosphatidylcholines aa C38:1, ae C38:3, ae C36:0, and ae C36:2; cholesteryl esters (17:1) and (20:3); sphingomyelins C18:0 and C18:1; and p-cresol sulfate], most of them related to lipid metabolism. Apart from 2 metabolites (3-indolepropionic acid and sarcosine) in cows, the metabolite plasma concentrations were lower in hmTHI than in lmTHI groups. In our heat-stressed dry cows, results indicate an altered lipid metabolism compared with lactating heat-stressed cows, due to the missing antilipolytic effect of HS. The results also indicate alterations in lipid metabolism of calves due to high mTHI in the first week of life. From a cross-generation perspective, high mTHI directly before calving seems to reduce colostrum quality, with detrimental effects on metabolite concentrations in offspring.

Genotype by heat stress interactions for production and functional traits in dairy cows from an across-generation perspective.

The aim of this study was to analyze time-lagged heat stress (HS) effects during late gestation on genetic co(variance) components in dairy cattle across generations for production, female fertility, and health traits. The data set for production and female fertility traits considered 162,492 Holstein Friesian cows from calving years 2003 to 2012, kept in medium-sized family farms. The health data set included 69,986 cows from calving years 2008 to 2016, kept in participating large-scale co-operator herds. Production traits were milk yield (MKG), fat percentage (fat%), and somatic cell score (SCS) from the first official test-day in first lactation. Female fertility traits were the nonreturn rate after 56 d (NRR56) in heifers and the interval from calving to first insemination (ICFI) in first-parity cows. Health traits included clinical mastitis (MAST), digital dermatitis (DD), and endometritis (EM) in the early lactation period in first-parity cows. Meteorological data included temperature and humidity from public weather stations in closest herd distance. The HS indicator was the temperature-humidity index (THI) during dams' late gestation, also defined as in utero HS. For the genetic analyses of production, female fertility, and health traits in the offspring generation, a sire-maternal grandsire random regression model with Legendre polynomials of order 3 for the production and of order 2 for the fertility and health traits on prenatal THI, was applied. All statistical models additionally considered a random maternal effect. THI from late gestation (i.e., prenatal climate conditions), influenced genetic parameter estimates in the offspring generation. For MKG, heritabilities and additive genetic variances decreased in a wave-like pattern with increasing THI. Especially for THI >58, the decrease was very obvious with a minimal heritability of 0.08. For fat% and SCS, heritabilities increased slightly subjected to prenatal HS conditions at THI >67. The ICFI heritabilities differed marginally across THI [heritability (h2) = 0.02-0.04]. For NRR56, MAST, and DD, curves for heritabilities and genetic variances were U-shaped, with largest estimates at the extreme ends of the THI scale. For EM, heritability increased from THI 25 (h2 = 0.13) to THI 71 (h2 = 0.39). The trait-specific alterations of genetic parameters along the THI gradient indicate pronounced genetic differentiation due to intrauterine HS for NRR56, MAST, DD, and EM, but decreasing genetic variation for MKG and ICFI. Genetic correlations smaller than 0.80 for NRR56, MAST, DD, and EM between THI 65 with corresponding traits at remaining THI indicated genotype by environment interactions. The lowest genetic correlations were identified when considering the most distant THI. For MKG, fat%, SCS, and ICFI, genetic correlations throughout were larger than 0.80, disproving concerns for any genotype by environment interactions. Variations in genetic (co)variance components across prenatal THI may be due to epigenetic modifications in the offspring genome, triggered by in utero HS. Epigenetic modifications have a persistent effect on phenotypic responses, even for traits recorded late in life. However, it is imperative to infer the underlying epigenetic mechanisms in ongoing molecular experiments.

Associations between minerals and metabolic indicators in maternal blood pre- and postpartum with ewe body condition, methane emissions, and lamb body weight development.

In sheep production, economic efficiency strongly depends on the maternal health and feed efficiency status and on weaning performances of their offspring. Accordingly, an optimal level for the supply with macro- and microelements and the ewe energy status has impact on the fetal development during gestation and on maternal milk production during lactation. Furthermore, this study addressed intergenerational aspects, i.e., on associations between maternal energy metabolism profiles considering the macro- and microelement status, metabolic indicators (e.g. ß-hydroxybutyrate (BHB)), body condition and methane (CH4) emissions with lamb BW (LBW) in two sheep breeds. Traits were recorded at the beginning of gestation (ewe traits), at lambing, three weeks postpartum, and at weaning (ewe and lamb traits). Trait recording included CH4 emissions (recorded via laser methane detector (LMD)), ewe BW (EBW), backfat thickness (BFT), and body condition score (BCS) from 46 ewes (24 Merinoland- (ML), 22 Rhönsheep (RH)), and LBW of their 87 (35 ML, 52 RH) purebred lambs. Serum levels of the following ewe blood parameters were determined: calcium (Ca), sodium (Na), potassium (K), phosphate (P), nonesterified fatty acids (NEFA), BHB, glutamate dehydrogenase (GLDH), selenium (Se), copper (Cu), iron (Fe), zinc (Zn), and magnesium (Mg). Mixed models were applied to infer associations between ewe blood parameters with EBW, BFT, BCS, and CH4 and with LBW recorded in offspring. At weaning, a maternal serum Mg level > 1.0 mmol/L was significantly associated with an increase of 13% in LBW in ML, compared to offspring from ML ewes with a serum Mg concentration within the lower reference range (0.8 mmol/L). Furthermore, higher Cu levels were favorably associated with ewe BCS and BFT at weaning in both breeds. In RH ewes, a Se level > 2.4 µmol/l was significantly associated with increased BCS. In the ML breed, high Zn levels during lactation were associated with reduced CH4 emissions. Ewe EBW was significantly larger for ML ewes representing low Ca levels. A low BHB level was associated with decreasing CH4 emissions in RH and ML. Serum levels for Na, K, P, GLDH, and Fe did not significantly affect the traits of interest. Trait associations from the present study indicate the importance of the mineral supply and metabolic status of the ewe with regard to body condition, CH4 emissions, and LBW development, but depending on the breed. Identified associations might contribute to energy efficiency in sheep production systems.

Phenotypic relationships, genetic parameters, genome-wide associations, and identification of potential candidate genes for ketosis and fat-to-protein ratio in German Holstein cows.

Energy demand for milk production in early lactation exceeds energy intake, especially in high-yielding Holstein cows. Energy deficiency causes increasing susceptibility to metabolic disorders. In addition to several blood parameters, the fat-to-protein ratio (FPR) is suggested as an indicator for ketosis, because a FPR >1.5 refers to high lipolysis. The aim of this study was to analyze phenotypic, quantitative genetic, and genomic associations between FPR and ketosis. In this regard, 8,912 first-lactation Holstein cows were phenotyped for ketosis according to a veterinarian diagnosis key. Ketosis was diagnosed if the cow showed an abnormal carbohydrate metabolism with increased content of ketone bodies in the blood or urine. At least one entry for ketosis in the first 6 wk after calving implied a score = 1 (diseased); otherwise, a score = 0 (healthy) was assigned. The FPR from the first test-day was defined as a Gaussian distributed trait (FPRgauss), and also as a binary response trait (FPRbin), considering a threshold of FPR = 1.5. After imputation and quality controls, 45,613 SNP markers from the 8,912 genotyped cows were used for genomic studies. Phenotypically, an increasing ketosis incidence was associated with significantly higher FPR, and vice versa. Hence, from a practical trait recording perspective, first test-day FPR is suggested as an indicator for ketosis. The ketosis heritability was slightly larger when modeling the pedigree-based relationship matrix (pedigree-based: 0.17; SNP-based: 0.11). For FPRbin, heritabilities were larger when modeling the genomic relationship matrix (pedigree-based: 0.09; SNP-based: 0.15). For FPRgauss, heritabilities were almost identical for both pedigree and genomic relationship matrices (pedigree-based: 0.14; SNP-based: 0.15). Genetic correlations between ketosis with FPRbin and FPRgauss using either pedigree- or genomic-based relationship matrices were in a moderate range from 0.39 to 0.71. Applying genome-wide association studies, we identified the specific SNP rs109896020 (BTA 5, position: 115,456,438 bp) significantly contributing to ketosis. The identified potential candidate gene PARVB in close chromosomal distance is associated with nonalcoholic fatty liver disease in humans. The most important SNP contributing to FPRbin was located within the DGAT1 gene. Different SNP significantly contributed to ketosis and FPRbin, indicating different mechanisms for both traits genomically.

Variance heterogeneity and genotype by environment interactions in native Black and White dual-purpose cattle for different herd allocation schemes.

Black and White dual-purpose cattle (DSN) are kept in diverse production systems, but the same set of genetic parameters is used for official national genetic evaluations, neglecting the herd or production system characteristics. The aim of the present study was to infer genetic (co)variance components within and across defined herd descriptor groups or clusters, considering only herds keeping the local and endangered DSN breed. The study considered 3659 DSN and 2324 Holstein Friesian (HF) cows from parities one to three. The 46 herds always kept DSN cows, but in most cases, herds were 'mixed' herds (Mixed), including both genetic lines HF and DSN. In order to study environmental sensitivity, we had a focus on the naturally occurring negative energy balance in the early lactation period. In consequence, traits were records from the 1st official test-day after calving for milk yield (Milk-kg), somatic cell score (SCS) and fat-to-protein ratio (FPR). Genetic parameters were estimated in bivariate runs (separate runs for the three genetic lines Mixed, HF and DSN), defining the same trait from different herd groups or clusters as different traits. Additive-genetic variances and heritabilities were larger in herd groups that indicated superior herd management, implying that cow records from these herds allow a better genetic differentiation. Superior herd management included larger herds, low calving age, high herd production levels and low intra-herd somatic cell count. Herd descriptor group differences in additive-genetic variances for Milk-kg were stronger in HF than in DSN, indicating environmental sensitivity for DSN. Similar variance components and heritabilities across groups, clusters and genetic lines were found for data stratification according to geographical descriptors altitude and latitude. Considering 72 bivariate herd group runs, 29 genetic correlations were very close to 1 (mostly for Milk-kg). Somatic cell score was the trait showing the smallest genetic correlations, especially in the DSN analyses, and when stratifying herds according to genetic line compositions (rg=0.11), or according to the percentage of natural service sires (rg=0.08). For estimations based on the results of a cluster analysis considering several herd descriptors simultaneously, indications for genotype × environment interactions could be found for SCS, but genetic correlations were larger than 0.80 for Milk-kg and FPR. In conclusion, we suggest multiple-trait animal model applications in genetic evaluations, in order to select the best sires for specific herd environments or herd clusters.

Genome-wide pleiotropy and shared biological pathways for resistance to bovine pathogens.

Host genetic architecture is a major factor in resistance to pathogens and parasites. The collection and analysis of sufficient data on both disease resistance and host genetics has, however, been a major obstacle to dissection the genetics of resistance to single or multiple pathogens. A severe challenge in the estimation of heritabilities and genetic correlations from pedigree-based studies has been the confounding effects of the common environment shared among relatives which are difficult to model in pedigree analyses, especially for health traits with low incidence rates. To circumvent this problem we used genome-wide single-nucleotide polymorphism data and implemented the Genomic-Restricted Maximum Likelihood (G-REML) method to estimate the heritabilities and genetic correlations for resistance to 23 different infectious pathogens in calves and cows in populations undergoing natural pathogen challenge. Furthermore, we conducted gene-based analysis and generalized gene-set analysis to understand the biological background of resistance to infectious diseases. The results showed relatively higher heritabilities of resistance in calves than in cows and significant pleiotropy (both positive and negative) among some calf and cow resistance traits. We also found significant pleiotropy between resistance and performance in both calves and cows. Finally, we confirmed the role of the B-lymphocyte pathway as one of the most important biological pathways associated with resistance to all pathogens. These results both illustrate the potential power of these approaches to illuminate the genetics of pathogen resistance in cattle and provide foundational information for future genomic selection aimed at improving the overall production fitness of cattle.

Phenotypic, genetic, and single nucleotide polymorphism marker associations between calf diseases and subsequent performance and disease occurrences of first-lactation German Holstein cows.

A total of 31,396 females born from 2010 to 2013 in 43 large-scale Holstein-Friesian herds were phenotyped for calf and cow disease traits using a veterinarian diagnosis key. Calf diseases were general disease status (cGDS), calf diarrhea (cDIA), and calf respiratory disease (cRD) recorded from birth to 2 mo of age. Incidences were 0.48 for cGDS, 0.28 for cRD, and 0.21 for cDIA. Cow disease trait recording focused on the early period directly after calving in first parity, including the interval from 10 d before calving to 200 d in lactation. For cows, at least one entry for the respective disease implied a score = 1 (sick); otherwise, score = 0 (healthy). Corresponding cow diseases were first-lactation general disease status (flGDS), first-lactation diarrhea (flDIA), and first-lactation respiratory disease (flRD). Additional cow disease categories included mastitis (flMAST), claw disorders (flCLAW), female fertility disorders (flFF), and metabolic disorders (flMET). A further cow trait category considered first-lactation test-day production traits from official test-days 1 and 2 after calving. The genotype data set included 41,256 single nucleotide polymorphisms (SNP) from 9,388 females with phenotypes. Linear and generalized linear mixed models with a logit link-function were applied to Gaussian and categorical cow traits, respectively, considering the calf disease as a fixed effect. Most of the calf diseases were not significantly associated with the occurrence of any cow disease. By trend, increasing risks for the occurrence of cow diseases were observed for healthy calves, indicating mechanisms of disease resistance with aging. Also by trend, occurrence of calf diseases was associated with decreasing milk, protein, and fat yields. Univariate linear and threshold animal models were used to estimate heritabilities and breeding values (EBV) for all calf and cow traits. Heritabilities for cGDS and cRD were 0.06 and 0.07 for cDIA. Genetic correlations among all traits were estimated using linear-linear animal models in a series of bivariate runs. The genetic correlation between cDIA and cRD was 0.29. Apart from the genetic correlation between flRD with cGDS (-0.38), EBV correlations and genetic correlations between calf diseases with all cow traits were close to zero. Genome-wide association studies were applied to estimate SNP effects for cRD and cDIA, and for the corresponding traits observed in cows (flRD and flDIA). Different significant SNP markers contributed to cDIA and flDIA, or to cRD and flRD. The average correlation coefficient between cRD and flRD considering SNP effects from all chromosomes was 0.01, and between cDIA and flDIA was -0.04. In conclusion, calf diseases are not appropriate early predictors for cow traits during the early lactation stage in parity 1.

Alternative strategies for genetic analyses of milk flow in dairy cattle.

Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components using a variety of statistical models. For bivariate linear-threshold model applications, Gaussian-distributed AMF (linear sire model) was categorized into 2 distinct classes (threshold sire model) by setting arbitrary thresholds for extremely slow or extremely fast milking cows. In different bivariate runs with the 2 traits, Gaussian AMF and binary AMF, within a Bayesian framework, thresholds for the binary trait were 1.2, 1.6, 2.6, and 2.8 kg/min. Posterior heritabilities for AMF from the linear and the threshold models in all runs were in a narrow range and close to 0.26, and the posterior genetic correlation between AMF, defined as either a Gaussian or binary trait, was 0.99. A data subset was used to infer genetic and phenotypic relationships between AMF with test-day traits milk yield, fat percentage, protein percentage, somatic cell score (SCS), fat-to-protein ratio, and energy-corrected milk using recursive linear sire models, standard multiple trait linear sire models, and multiple trait linear sire models accounting for the effect of a trait 1 on a trait 2, and of trait 2 on trait 3, via linear regressions. The time-lagged 3-trait system focused on the first test-day trait after calving (trait 1), on AMF (trait 2), and on the test-day trait (trait 3) after the AMF measurement. Posterior means for heritabilities for AMF from linear and recursive linear models used for the reduced data set ranged between 0.29 and 0.38, and were slightly higher than heritabilities from the threshold models applied to the full data set. Genetic correlations from the recursive linear model and the linear model were similar for identical trait combinations including AMF and test-day traits 1 and 3. The largest difference was found for the genetic correlation between AMF and fat percentage from the first test day (i.e., -0.31 from the recursive linear model vs. -0.26 from the linear model). Genetic correlations from the linear model, including an additional regression coefficient, partly differed, especially when comparing correlations between AMF and SCS and between AMF and fat-to-protein ratio recorded after the AMF measurement data. Structural equation coefficients from the recursive linear model and corresponding regression coefficients from the linear model with additional regression, both depicting associations on the phenotypic scale, were quite similar. From a physiological perspective, all models confirmed the antagonistic relationship between SCS with AMF on genetic and phenotypic scales. A pronounced recursive relationship was also noted between productivity (milk yield and energy-corrected milk) and AMF, suggesting further research using physiological parameters as indicators for cow stress response (e.g., level of hormones) should be conducted.

Simulation, prediction, and genetic analyses of daily methane emissions in dairy cattle.

This study presents an approach combining phenotypes from novel traits, deterministic equations from cattle nutrition, and stochastic simulation techniques from animal breeding to generate test-day methane emissions (MEm) of dairy cows. Data included test-day production traits (milk yield, fat percentage, protein percentage, milk urea nitrogen), conformation traits (wither height, hip width, body condition score), female fertility traits (days open, calving interval, stillbirth), and health traits (clinical mastitis) from 961 first lactation Brown Swiss cows kept on 41 low-input farms in Switzerland. Test-day MEm were predicted based on the traits from the current data set and 2 deterministic prediction equations, resulting in the traits labeled MEm1 and MEm2. Stochastic simulations were used to assign individual concentrate intake in dependency of farm-type specifications (requirement when calculating MEm2). Genetic parameters for MEm1 and MEm2 were estimated using random regression models. Predicted MEm had moderate heritabilities over lactation and ranged from 0.15 to 0.37, with highest heritabilities around DIM 100. Genetic correlations between MEm1 and MEm2 ranged between 0.91 and 0.94. Antagonistic genetic correlations in the range from 0.70 to 0.92 were found for the associations between MEm2 and milk yield. Genetic correlations between MEm with days open and with calving interval increased from 0.10 at the beginning to 0.90 at the end of lactation. Genetic relationships between MEm2 and stillbirth were negative (0 to -0.24) from the beginning to the peak phase of lactation. Positive genetic relationships in the range from 0.02 to 0.49 were found between MEm2 with clinical mastitis. Interpretation of genetic (co)variance components should also consider the limitations when using data generated by prediction equations. Prediction functions only describe that part of MEm which is dependent on the factors and effects included in the function. With high probability, there are more important effects contributing to variations of MEm that are not explained or are independent from these functions. Furthermore, autocorrelations exist between indicator traits and predicted MEm. Nevertheless, this integrative approach, combining information from dairy cattle nutrition with dairy cattle genetics, generated novel traits which are difficult to record on a large scale. The simulated data basis for MEm was used to determine the size of a cow calibration group for genomic selection. A calibration group including 2,581 cows with MEm phenotypes was competitive with conventional breeding strategies.

Future consequences and challenges for dairy cow production systems arising from climate change in Central Europe - a review.

It is well documented that global warming is unequivocal. Dairy production systems are considered as important sources of greenhouse gas emissions; however, little is known about the sensitivity and vulnerability of these production systems themselves to climate warming. This review brings different aspects of dairy cow production in Central Europe into focus, with a holistic approach to emphasize potential future consequences and challenges arising from climate change. With the current understanding of the effects of climate change, it is expected that yield of forage per hectare will be influenced positively, whereas quality will mainly depend on water availability and soil characteristics. Thus, the botanical composition of future grassland should include species that are able to withstand the changing conditions (e.g. lucerne and bird's foot trefoil). Changes in nutrient concentration of forage plants, elevated heat loads and altered feeding patterns of animals may influence rumen physiology. Several promising nutritional strategies are available to lower potential negative impacts of climate change on dairy cow nutrition and performance. Adjustment of feeding and drinking regimes, diet composition and additive supplementation can contribute to the maintenance of adequate dairy cow nutrition and performance. Provision of adequate shade and cooling will reduce the direct effects of heat stress. As estimated genetic parameters are promising, heat stress tolerance as a functional trait may be included into breeding programmes. Indirect effects of global warming on the health and welfare of animals seem to be more complicated and thus are less predictable. As the epidemiology of certain gastrointestinal nematodes and liver fluke is favourably influenced by increased temperature and humidity, relations between climate change and disease dynamics should be followed closely. Under current conditions, climate change associated economic impacts are estimated to be neutral if some form of adaptation is integrated. Therefore, it is essential to establish and adopt mitigation strategies covering available tools from management, nutrition, health and plant and animal breeding to cope with the future consequences of climate change on dairy farming.

Flow cytometric differential cell counts in milk for the evaluation of inflammatory reactions in clinically healthy and subclinically infected bovine mammary glands.

Somatic cell counts (SCC) are generally used as an indicator of udder health. In Germany, a cutoff value of 100,000 cells/mL is currently used to differentiate between healthy and diseased mammary glands. In addition to SCC, differential cell counts (DCC) can be applied for a more detailed evaluation of the udder health status. The aim of this study was to differentiate immune cells in milk of udder quarters classified as healthy based on SCC values of <100,000 cells/mL. Twenty cows were selected and 65 healthy udder quarters were compared with a control group of 15 diseased udder quarters (SCC>100,000 cells/mL). Cells were isolated from milk of all quarters to measure simultaneously percentages of lymphocytes, macrophages, and polymorphonuclear neutrophilic leukocytes (PMNL) by flow cytometric analysis. The bacteriological status of all 80 quarters was also determined. Differential cell count patterns of milk samples (n = 15) with extreme low SCC values of ≤ 6,250 cells/mL revealed high lymphocyte proportions of up to 88%. Milk cell populations in samples (n = 42) with SCC values from >6,250 to ≤ 25,000 cells/mL were also dominated by lymphocytes, whereas DCC patterns of 6 out of 41 milk samples with SCC values from ≥ 9,000 to ≤ 46,000 cells/mL indicated already inflammatory reactions based on the predominance of PMNL (56-75%). In 13 of 15 milk samples of the diseased udder quarters (SCC >100,000 cells/mL), PMNL were categorically found as dominant cell population with proportions of ≥ 49%. Macrophages were the second predominant cell population in almost all samples tested in relation to lymphocytes and PMNL. Further analysis of the data demonstrated significant differences of the cellular components between udder quarters infected by major pathogens (e.g., Staphylococcus aureus; n = 5) and culture-negative udder quarters (n = 56). Even the percentages of immune cells in milk from quarters infected by minor pathogens (e.g., coagulase-negative staphylococci; n = 19) differed significantly from those in milk of culture-negative quarters. Our flow cytometric analysis of immune cells in milk of udder quarters classified as healthy by SCC <100,000 cells/mL revealed inflammatory reactions based on DCC.

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates.

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates.

Somatic cell counts and bacteriological status in quarter foremilk samples of cows in Hesse, Germany--a longitudinal study.

Somatic cell counts (SCC) are generally used as an indicator of udder health. Currently in Germany, 100,000 cells/mL is the threshold differentiating infected and noninfected mammary glands. The aim of our study was the detailed analysis of udder health in a representative part of the dairy cow population in Hesse, Germany. Between 2000 and 2008, 615,187 quarter foremilk samples were analyzed. In addition to evaluation of distribution of SCC and prevalence of mastitis pathogens, pathogen prevalence was also calculated depending on SCC. The data indicated that 38% of all samples had SCC >100,000 cells/mL and 62% showed SCC ≤ 100,000 cells/mL; 31% of all samples revealed SCC ≤ 25,000 cells/mL. Coagulase-negative staphylococci were the dominant pathogens in the Hessian quarter foremilk samples (17.17% of all samples) followed by Corynebacterium spp. (13.56%), Streptococcus uberis (8.7%), and Staphylococcus aureus (5.01%). Mastitis pathogens were detected in 83% of all samples with SCC >100,000 cells/mL. However, the prevalence of mastitis pathogens in the SCC range from 1,000 to ≤ 100,000 cells/mL was 8.5% (5.51% minor pathogens, 2.01% major pathogens, and 0.98% other pathogens). For farms producing high quality milk, exceptional hygiene management is compulsory. One of the farms randomly selected showed clearly different results from the Hessian survey. Fifteen percent more samples lay in the SCC range ≤ 100,000 cells/mL with a lower prevalence of mastitis pathogens of 1.91% (1.03% minor pathogens, 0.83% major pathogens, and 0.05% other pathogens). Based on these results, inflammatory processes can obviously be detected in mammary glands of udder quarters healthy according to the current definitions. However, we argue that such inflammation can be detected by examination of the relationship of immune cells in milk.