Genetic associations of test-day fat:protein ratio with milk yield, fertility, and udder health traits in Nordic Red cattle

Developmental programming of production and reproduction in dairy cows: IV. Association of maternal milk fat and protein percentage and milk fat to protein ratio with offspring's birth weight, survival, productive and reproductive performance and AMH concentration from birth to the first lactation period

Although the association of maternal milk production with developmental programming of offspring has been investigated, there is limited information available on the relationship of maternal milk components with productive and reproductive performance of the offspring. Therefore, the present study was conducted to analyze the association of maternal milk fat and protein percentage and milk fat to protein ratio with birth weight, survival, productive and reproductive performance and AMH concentration in the offspring. In study I, data of birth weight, milk yield and reproductive variables of offspring born to lactating dams (n = 14,582) and data associated with average maternal milk fat percentage (MFP), protein percentage (MPP) and fat to protein ratio (MFPR) during 305-day lactation were retrieved. Afterwards, offspring were classified in various categories of MFP, MPP and MFPR. In study II, blood samples (n = 339) were collected from offspring in various categories of MFP, MPP and MFPR for measurement of serum AMH. Maternal milk fat percentage was positively associated with birth weight and average percentage of milk fat (APMF) and protein (APMP) and milk fat to protein ratio (FPR) during the first lactation, but negatively associated with culling rate during nulliparity in the offspring (P < 0.05). Maternal milk protein percentage was positively associated with birth weight, APMF, APMP, FPR and culling rate, but negatively associated with milk yield and fertility in the offspring (P < 0.05). Maternal FPR was positively associated with APMF and FPR, but negatively associated with culling rate, APMP and fertility in the offspring (P < 0.05). However, concentration of AMH in the offspring was not associated with MFP, MPP and MFPR (P > 0.05). In conclusion, the present study revealed that maternal milk fat and protein percentage and their ratio were associated with birth weight, survival, production and reproduction of the offspring. Yet it was a preliminary research and further studies are required to elucidate the mechanisms underlying these associations.

Developmental programming of reproduction in the female animal

Successful reproduction is a cornerstone in food animal industry in order to sustain food production for human. Therefore, various methods focusing on genetics and postnatal environment have been identified and applied to improve fertility in livestock. Yet there is evidence indicating that environmental factors during prenatal and/or neonatal life can also impact the function of reproductive system and fertility in the animals during adulthood, which is called the developmental programming of reproduction. The current review summarizes data associated with the developmental origins of reproduction in the female animals. In this regard, this review focuses on the effect of plane of nutrition, maternal body condition, hypoxia, litter size, maternal age, parity, level of milk production and milk components, lactocrine signaling, stress, thermal stress, exposure to androgens, endocrine disrupting chemicals, mycotoxins and pollutants, affliction with infection and inflammation, and maternal gut microbiota during prenatal and neonatal periods on the neuroendocrine system, puberty, health of reproductive organs and fertility in the female offspring. It is noteworthy that these prenatal and neonatal factors do not always exert their effects on the reproductive performance of the female by compromising the development of organs directly related to reproductive function such as hypothalamus, pituitary, ovary, oviduct and uterus. Since they can impair the development of non-reproductive organs and systems modulating reproductive function as well (e.g., metabolic system and level of milk yield in dairy animals). Furthermore, when these factors affect the epigenetics of the offspring, their adverse effects will not be limited to one generation and can transfer transgenerationally. Hence, pinpointing the factors influencing developmental programming of reproduction and considering them in management of livestock operations could be a potential strategy to help improve fertility in food animals.

Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil

The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90 days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.

In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = -0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Association between Days Open and Parity, Calving Season or Milk Spectral Data

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle

Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (-0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.

Udder health, conceptual construct, and uses of the term: A systematic review from 1962 to 2019

Background and Aim:

Udder health management is essential for the further development of milk production systems and public health. This process depends on the generation of knowledge regarding control, prevention, and promotion of health. In scientific literature, it is impossible to find a synthesis of the categories that would allow comprehension of the complex phenomenon udder health. Different research approaches have allowed this polysemic concept, described by some researchers as multifactorial and by philosophical perspectives as a social phenomenon, to be further studied. Thus, the objective of this systematic review was to systematize the conceptual categories of udder health and the use of the term in the original articles published in the scientific literature from the period 1962 to 2019.

Materials and Methods:

A systematic review with a broad approach was designed by applying the phases of identification, screening, selection, and inclusion criteria described in the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes guide. An exhaustive search of original articles by specificity was carried out in the Science Direct, PubMed, Scielo, LILACS, and Google Scholar databases. The investigation was carried out on November 22, 2019. According to the inclusion criteria established, articles needed to be original studies, to be publications on bovine livestock, written in English, Spanish and Portuguese. Furthermore, the articles considered needed to tackle the term udder health so that its conceptual categorization could be extracted. Google Scholar patents and citations and articles removed from databases or not available were excluded from the study and those that, based on the reading of the complete text, considered the farming of animal species other than bovine. A qualitative synthesis of the year of publication, continent, approach, type of study, and conceptual category of udder health was carried out by calculating frequencies (Statistical Package for the Social Sciences version 24).

Results:

In total, 165 articles were included in the study. Eight conceptual categories, consolidated over time, were systematized, showing that udder health is not a static problem, and that science has been responding through the generation of new knowledge around conceptual categories as different udder health problems emerge.

Conclusion:

Culture and politics were two categories, related to all the others, that stood out in the results. These two categories were of great interest in countries advanced in milk production and in the implementation of udder health policies, which acknowledge the producer and other actors of the production chain as fundamental political actors for policies, decision-making processes, and public health care to be effective. The lack of synonyms for the term udder health (e.g., mastitis) may have led to the exclusion of important articles in each category. However, the constriction to the term udder health was intentional and aimed at constructing the concept. Udder health is hereby understood as a health-disease process, different from the term mastitis, which from its semantic origin, refers only to the disease process. According to this study, the concept can be understood through the categories of traditional epidemiology based on risk factors and disease; microbiology; genetics, resistance, and immunity; animal welfare; nutrition; organic production; culture; and politics.

Ketone bodies - causes and effects of their increased presence in cows' body fluids: A review

Ketosis is the most common metabolic disease in high-performance dairy cows during the first 6-8 weeks of lactation. Its main symptoms include an excessive amount of so-called ketone bodies in a cow's body fluids. Ketone bodies consist of β-hydroxybutyric acid (βHBA), acetoacetic acid, and acetone. βHBA is the main component with its share of the total volume of ketone bodies in the blood of about 70%. Clinical symptoms of ketosis in cows include loss of appetite, preference for forage to concentrated feed, and acetone odor in their mouth and urine. Those symptoms are accompanied by a production drop, an increase of concurrent illness (mastitis, metritis, and displaced abomasum), and poor reproductive performance. One of the ketosis characteristic effects is an increase in the level of fat in milk (>5%), while protein levels decrease (<2.9%). In the case of subclinical ketosis (SCK), the fat-protein ratio in milk is increased to above 1.4:1. The current consensus for SCK is to consider a cutoff point of βHBA to be at least 1.2 mmol/L in blood plasma. Ketosis prevention is based on keeping perinatal cows in good condition, that is, with around 3.5 points in the five-point body condition scoring, carefully balancing food doses during the first 2 months of lactation with the correct energy-protein ratio. Glucose precursor products should be administered orally, in particular to at-risk herds. Ketosis occurs in 7-14% on average of the total number of cows in a herd. In general, data on the prevalence of SCK vary considerably, depending on their source. Moreover, the problem is mostly observed in poorly-fed animals with high milk production potential. The objectives of this review are to reveal the current situation of ketosis prevalence, the possibility of diagnosis, consequences in dairy cows and to provide some recommendations for ketosis treatment and prevention.

Bulk milk quality as affected by cattle breed composition of the herd in mountain area

The aim of this study was to investigate the variation of gross composition, somatic cell count, urea content, and fatty acids (FA) composition of bulk milk from single-breed and multi-breed farms in alpine area, keeping either Brown Swiss (BS), Holstein Friesian (HF), Simmental (SI), or their combinations. Gross milk composition, urea content, and FA composition were predicted using mid-infrared spectroscopy. Observations were grouped in 7 combinations consisting of 3 singlebreed and 4 multi-breed types of herd. A mixed linear model was used for data analysis, accounting for the fixed effects of herd composition (7 combinations), month of sampling, year of sampling, and the interactions between herd composition and month of sampling, and between herd composition and year of sampling. Farm was included as random effect. Results highlighted that about two thirds of South Tyrolean farms were single-breed and herds with more than 20 lactating cows changed herd structure over time, switching from multi- to single-breed. Single-breed BS farms produced milk with greater fat, protein, casein, lactose, and FA content than single-breed HF and SI farms. Further, multi-breed herds including BS cows produced milk with greater fat, protein, casein, and polyunsaturated FA content than multi-breed HF+SI herds. Overall, single-breed SI farms produced milk with lower somatic cell count than other herd combinations. Despite the number of BS cows in South Tyrol has decreased in favor of SI in the last years, this breed is still the most interesting for alpine dairy farming to achieve optimal milk quality in both single- and multi-breed scenarios. The tendency to move to SI is mainly related to good milk performance of SI cows coupled with their robustness, high carcass value, high market value of calves, and adaptability to mountain farming system.

Association between days open and milk spectral data in dairy cows

Data on 19,489 Brown Swiss cows reared in northeastern Italy were used to associate absorbances of individual wavenumbers within the mid-infrared range with days open (DO). Different postcalving days in milk (DIM) intervals were studied to determine the most informative milk sampling periods for predicting DO. Milk samples were analyzed using a MilkoScan (Foss Electric, Hillerød, Denmark) Fourier-transform infrared (FTIR) spectrometer for 1,060 wavenumbers (wn) ranging from 5,011 to 925 cm^-1. To determine DO, we considered an insemination to lead to conception when there was no return of heat (i.e., no successive insemination) and the cow had a subsequent calving date whereby gestation length was required to be within ±30 d of 290 d. Only milk records within the first 90 DIM were considered. Associations were inferred by (1) fitting linear regression models between the DO and each individual wavenumber or milk component, and (2) fitting a Bayesian regression model that included the complete FTIR spectral data. The effects of including systematic effects (parity number, year-season, herd) in the model on these associations were also studied. These analyses were performed for the complete data (5-90 DIM) and for data stratified by DIM period (5 to 30, 31 to 60, and 61 to 90 DIM). Overall, regions of wavenumbers of the milk FTIR spectra that were associated with DO included wn 2,973 to 2,830 cm^-1 [related to fat-B (C-H stretch)], wn 2,217 to 1,769 cm^-1 [related to fat-A (C = O stretch)], wn 1,546 cm^-1 (related to protein), wn 1,465 cm^-1 (related to urea and fat), wn 1,399 to 1,245 cm^-1 (related to acetone), and wn 1,110 cm^-1 (related to lactose). Estimated effects depended on the DIM period, with milk samples drawn during DIM intervals 31 to 60 d and 61 to 90 d being most strongly associated with DO. These DIM intervals are also typically most associated with negative energy balance and peak lactation.

Estimation of genetic parameters for dairy traits and somatic cell score in the first 3 parities using a random regression test-day model in French Alpine goats

Lactation curve shape can affect an animal's health, feed requirements, and milk production throughout the year. We implemented a random regression model for the genetic evaluation of lactation curve shapes of dairy traits in French Alpine goats for their first 3 parities. Milk, fat, and protein yields, fat and protein contents, somatic cell score, and fat/protein ratio were considered. The data consisted of test-day records from 49,849 first lactation Alpine goats during their first 3 lactations. The reference model used a Legendre polynomial of order 2 for each parity to describe the genetic and permanent environmental effects, and was compared with a model that combined the second and third parities. A rank reduction of the variance-covariance matrix was also performed using an eigenvalue decomposition for each parity from the 2 models. Genetic parameters were consistent between the models tested. With a reduction to rank 2 and combining the second and third parities, the first 2 principal components correctly summarized the genetic variability of milk yield level and persistency, with a near-nil correlation between the 2, and with a much shorter computation time than the reference model. A favorable correlation of +0.43 between milk yield persistency and fat/protein ratio persistency at the beginning of the lactation was found from buck estimated breeding values.

Genetic correlations between energy status indicator traits and female fertility in primiparous Nordic Red Dairy cattle

Inclusion of feed efficiency traits into the dairy cattle breeding programmes will require considering early lactation energy status to avoid deterioration in health and fertility of dairy cows. In this regard, energy status indicator (ESI) traits, for example, blood metabolites or milk fatty acids (FAs), are of interest. These indicators can be predicted from routine milk samples by mid-IR reflectance spectroscopy (MIR). In this study, we estimated genetic variation in ESI traits and their genetic correlation with female fertility in early lactation. The data consisted of 37 424 primiparous Nordic Red Dairy cows with milk test-day records between 8 and 91 days in milk (DIM). Routine test-day milk samples were analysed by MIR using previously developed calibration equations for blood plasma non-esterified FA (NEFA), milk FAs, milk beta-hydroxybutyrate (BHB) and milk acetone concentrations. Six ESI traits were considered and included: plasma NEFA concentration (mmol/l) either predicted by multiple linear regression including DIM, milk fat to protein ratio (FPR) and FAs C10:0, C14:0, C18:1 cis-9, C14:0 * C18:1 cis-9 (NEFAFA) or directly from milk MIR spectra (NEFAMIR), C18:1 cis-9 (g/100 ml milk), FPR, BHB (mmol/l milk) and acetone (mmol/l milk). The interval from calving to first insemination (ICF) was considered as the fertility trait. Data were analysed using linear mixed models. Heritability estimates varied during the first three lactation months from 0.13 to 0.19, 0.10 to 0.17, 0.09 to 0.14, 0.07 to 0.10, 0.13 to 0.17 and 0.13 to 0.18 for NEFAMIR, NEFAFA, C18:1 cis-9, FPR, milk BHB and acetone, respectively. Genetic correlations between all ESI traits and ICF were from 0.18 to 0.40 in the first lactation period (8 to 35 DIM), in general somewhat lower (0.03 to 0.43) in the second period (36 to 63 DIM) and decreased clearly (-0.02 to 0.19) in the third period (64 to 91 DIM). Our results indicate that genetic variation in energy status of cows in early lactation can be determined using MIR-predicted indicators. In addition, the markedly lower genetic correlation between ESI traits and fertility in the third lactation month indicated that energy status should be determined from the first test-day milk samples during the first 2 months of lactation.

Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model

BACKGROUND

Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats.

RESULTS

The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them.

CONCLUSIONS

A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation.

Genetic aspects of some reproductive, udder health and energy status traits in Holstein cows

The objective of this study was to estimate heritability as well as genetic and environmental relationships between days to first heat (DFH), days to first service (DFS), interval from calving to conception (ICC), calving interval (CI) and number of inseminations per conception (NIS) with mastitis (Mast), number of mastitis occurrence (NumMast), different measures of somatic cell count (SCC) and fat to protein ratio (F:P) in the first lactation of Holstein cows in Iran using linear and threshold animal and sire models. The 33851 first lactation records of Holstein cows from five large dairy herds with calving dates from March 2002 to September 2014 were analyzed with univariate and bivariate linear and threshold animal and sire models using Gibbs sampling methodology. Data from parity one to nine comprising 62483 records were used to conduct repeatability model analysis for reproductive traits. Heritabilities of the reproduction traits varied from 0.067 (for ICC) to 0.105 (for DFH) using linear animal models. Also, the heritabilities of udder health traits varied from 0.005 to 0.102 using different models. The repeatabilities of reproductive traits ranged from 0.110 to 0.307. In general, the genetic correlations (r_g) between reproduction traits were positive and high (with the exception of r_g between DFH-NIS). The r_g between reproduction traits with udder health traits ranged from -0.029 to 0.359 and 0.151 to 0.584 using linear-linear and threshold-linear animal models, respectively. Generally, there was favorable r_g between reproduction traits with udder health traits; therefore, selection for one set of these traits would improve the correlated traits. However, due to different (co)variance components and economic weights in each country/region, it can be recommended to investigate inclusion of both sets of these traits in breeding objectives.

Genetic correlation estimates between milk production traits, mastitis and different measures of somatic cells in Holstein cows

The objective of this study was to estimate genetic parameters including repeatability, heritability as well as genetic and environmental relationships between 305-day milk yield, milk fat and protein yield (Fat and Pro), milk fat and protein percentages (Fat% and Pro%), mastitis (Mast), number of mastitis occurrence and different measurements of somatic cell counts using linear and threshold animal as well as linear and threshold sire models in Holstein cows of Iran. Records of 33851 first lactation Holstein cows from five large dairy herds with calving dates from March 2002 to September 2014 were analysed, using Gibbs sampling methodology. Heritabilities of production traits estimated by linear animal model ranged from 0.14 (Fat%) to 0.29 (Pro%). Generally, udder health traits had low heritability (ranged from 0.005 to 0.10). Estimates of heritability for Mast using linear models were higher than those obtained with threshold models. However, in general estimates of heritabilities using threshold models were higher than those from linear models. There were unfavourable genetic correlations between production traits and Mast, which implies that breeding programs with emphasis on 305-day milk yield will experience deterioration in udder health. Despite low heritability of udder health traits, genetic variability exists for these traits that allow selecting superior animals and increasing resistance to Mast and animal welfare. Considering relatively high ratio of permanent environmental variance for Mast, culling decisions can be made with higher accuracy in order to reduce Mast incidence phenotypically over time.

The dynamic of the ruminal content pH change and its relationship to milk composition

The main goal of this study was to evaluate the health condition of dairy cows in relationship to milk production and milk composition using continuous monitoring boluses. In total, seven Holstein cows had boluses implemented for monitoring rumen pH and temperature every 15 min with an accuracy of ± 0.1 pH/ °C. Milk production test day records were noted by Breeding Services of Slovakia, s.e. (state-owned enterprise) 5 × per each cow with a bolus over 27 weeks of lactation. Dairy cows were divided into three groups according to their mean daily pH. After that, the test day records with the selected group were paired. Only two cows had pH values within the normal pH intervals during the control days. Consequently, there was found to be a 6.8% (P < 0.05) decrease in daily milk production in cows with lowered pH compared to cows with normal pH. Furthermore, there was found to be a 14.08% (P < 0.05) decrease in daily milk production in cows with an increased pH compared to cows with normal pH. Narrower fat to protein ratio and lactose content was found in cows with decreased and increased ruminal pH. The lowest milk fat concentration (P < 0.05) but the highest somatic cell count and urea content were determined in cows with decreased pH. These results show that continuous monitoring of rumen environment is a suitable method for nutrition and health management in dairy herds and allows the nutritionist to make nutritional interventions for pH stabilization within normal range in order to keep good milk production and high milk quality.

Modelling lactation curve for milk fat to protein ratio in Iranian buffaloes (Bubalus bubalis) using non-linear mixed models

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (-2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

Genetic associations between milk fat-to-protein ratio, milk production and fertility in the first two lactations of Thai Holsteins dairy cattle

The aims of this study were to estimate, simultaneously, the genetic parameters of test-day milk fat-to-protein ratio (FPR), test-day milk yield (MY), and days-open (DO) in the first two lactations of Thai Holsteins. A total of 76 194 test-day production records collected from 8874 cows with 8674 DO records between 2001 and 2011 from different lactations were treated as separated traits. The estimates of heritability for test-day FPR in the first lactation showed an increasing trend, whereas the estimates in the second lactation showed a U-shape trend. Genetic correlations for FPR-DO and MY-DO showed a decreasing trend along days in milk (DIM) in both lactations, whereas genetic correlations for FPR-MY increased along DIM in the first lactation but decreased in the second lactation. Genetic correlations of FPR between consecutive DIM were moderate to high, which showed the effectiveness of simultaneous analyses. Selection of FPR in the early stage has no adverse effect on MY and DO for the first lactation but has a negative effect on MY and positive effect on DO for the second lactation. This study showed that genetic improvement of the energy balance using FPR, MY and DO with multi-trait test day model could be applied in a Thailand dairy cattle breeding program.

Genetic relationships of fertility traits with test-day milk yield and fat-to-protein ratio in tropical smallholder dairy farms

The test-day milk fat-to-protein ratio (TD-FPR) could serve as a measure of energy balance status and might be used as a criterion to improve metabolic stability and fertility through genetic selection. Therefore, genetic parameters for fertility traits, test-day milk yield (TD-MY) and TD-FPR, as well as, their relationships during different stages of lactation, were estimated on data collected from 25 968 primiparous Thai dairy crossbred cows. Gibbs sampling algorithms were implemented to obtain (co)variance components using both univariate linear and threshold animal models and bivariate linear-linear and linear-threshold animal models with random regression. Average TD-MY and TD-FPR were 12.60 and 1.15. Heritability estimates for TD-MY, TD-FPR and selected fertility traits ranged from 0.31 to 0.58, 0.17 to 0.19 and 0.02 to 0.05, respectively. Genetic correlations among TD-FPR and TD-MY, TD-FPR and fertility traits, and TD-MY and fertility traits ranged from 0.05 to -0.44, from -0.98 to 0.98 and -0.22 to 0.79, respectively. Selection for lower TD-FPR would decrease numbers of inseminations per conception and increase conception at first service and pregnancy within 90 days. In addition, cow selection based only on high milk production has strong effects to prolong days to first service, days open and calving interval.

Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits

For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.

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.