Genetic analyses of novel temperament and milkability traits in Norwegian Red cattle based on data from automatic milking systems

Anatomy and Physiology of Water Buffalo Mammary Glands: An Anatomofunctional Comparison with Dairy Cattle

The present review aims to analyze the anatomical and physiological characteristics of the mammary gland and udders of water buffalo by making an anatomofunctional comparison with dairy cattle. It will also discuss the knowledge generated around the physiological regulation of milk ejection in the water buffalo. It was found that buffalo's average udder depth and width is approximately 20 cm smaller than Bos cattle. One of the main differences with dairy cattle is a longer teat canal length (around 8.25-11.56 cm), which highly influences buffalo milking. In this sense, a narrower teat canal (2.71 ± 0.10 cm) and thicker sphincter muscle are associated with needing higher vacuum levels when using machine milking in buffalo. Moreover, the predominant alveolar fraction of water buffalo storing 90-95% of the entire milk production is another element that can be related to the lower milk yields in buffalo (when compared to Bos cattle) and the requirements for prolonged prestimulation in this species. Considering the anatomical characteristics of water buffalo's udder could help improve bubaline dairy systems.

Genomic Selection for Dairy Cattle Behaviour Considering Novel Traits in a Changing Technical Production Environment

Cow behaviour is a major factor influencing dairy herd profitability and is an indicator of animal welfare and disease. Behaviour is a complex network of behavioural patterns in response to environmental and social stimuli and human handling. Advances in agricultural technology have led to changes in dairy cow husbandry systems worldwide. Increasing herd sizes, less time availability to take care of the animals and modern technology such as automatic milking systems (AMSs) imply limited human-cow interactions. On the other hand, cow behaviour responses to the technical environment (cow-AMS interactions) simultaneously improve production efficiency and welfare and contribute to simplified "cow handling" and reduced labour time. Automatic milking systems generate objective behaviour traits linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of the genetic mechanisms influencing cow learning and social behaviour, in turn affecting herd management, productivity and welfare. Moreover, physiological and molecular biomarkers such as heart rate, neurotransmitters and hormones might be useful indicators and predictors of cow behaviour. This review gives an overview of published behaviour studies in dairy cows in the context of genetics and genomics and discusses possibilities for breeding approaches to achieve desired behaviour in a technical production environment.

Contemporary Knowledge on the Assessment of Temperament in Cattle and Its Impact on Production and Reproduction Including Some Immunological, Genetic and Metabolic Parameters

Temperament is associated with the well-being, health, production and reproduction of cattle. In order to increase the population of individuals with the desired temperament, its evaluation should be standardized and be made one of the obligatory elements of breeding and veterinary examination. A number of different tests are used for temperament assessment. In this article, the importance of temperament correlation with some metabolic, genetic, immunological, production and reproductive parameters have been shown, pointing at its influence on the economy and cattle handling. The most common methods for assessing the temperament of cattle are presented, including long-time scales of temperament assessment. At the same time, the relationship of the temperament of cattle with production efficiency, immunity and reproductive indicators has been shown, indicating that its correct assessment is an important aspect of the proper development of the herd and the associated economic growth.

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.

Dairy Cows' Temperament and Milking Performance during the Adaptation to an Automatic Milking System

Adaptative responses of cows to an automatic milking system (AMS) could depend on their temperament, i.e., cows with certain temperament profiles could be able to cope more successfully with the AMS. The relationships between dairy cows' temperament, behaviour, and productive parameters during the changeover from a conventional milking system (CMS) to an AMS were investigated. Thirty-three multiparous cows were classified as 'calm' or 'reactive' based on each of the temperament tests conducted: race time, flight speed (FS), and flight distance, at 5, 25, and 45 days in milk at CMS, then the cows were moved from the CMS to the AMS. During the first five milkings in AMS, the number of steps and kicks during each milking were recorded. The daily milk yield was automatically recorded. The number of steps did not vary by temperament classification, but the number of kicks per milking was greater for calm (0.45 ± 0.14) than for reactive cows (0.05 ± 0.03) when they were classified by FS (p < 0.01). During the first seven days in the AMS, reactive cows for the FS test produced more milk than calm cows (36.5 ± 1.8 vs. 33.2 ± 1.6 L/day; p = 0.05). In conclusion, behavioural and productive parameters were influenced by cows´ temperament during the milking system changeover since the calm cows kicked more and produced less than the reactive ones.

Estimation of milkability breeding values and variance components for Italian Holstein

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The national genetic evaluation for milkability in Italian Holstein was revised.

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A stricter data editing and a different approach to the phenotype, from ratio to single observations, were applied.

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A different statistical model was used, changing from a linear to a threshold model.

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Higher genomic reliability was achieved with the revised model.

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The revised model provides more reliable breeding values for decision-making at the farm level.

The importance of milkability as a trait is growing because of the need to efficiently use labor and machinery; therefore, it is crucial to update the statistical model for the trait to improve the accuracy of the estimated breeding values, and thus provide a more accurate tool for decision-making at the farm level. In the Italian Holstein Friesian cattle population, milkability is recorded twice a year by the milk recording system as a binary trait (slow, coded as 2, or not slow, coded as 1). Data consisted of 7,862,371 records from 2,945,249 cows collected between 2004 and 2021. A single-trait threshold animal model with repeated measures was used, with parity, days in milk class, calving season, and regression of production (fat + protein grams) within days in milk class as fixed effects and herd-year-season of recording, permanent environment, and animal as random effects. The results for heritability and repeatability were 0.275 and 0.5, estimated with the Gibbs sampler THRGIBBS1F90. Genomic validation, carried out using genotyped proven bulls born before 2009 as the training set, gave a result of 0.386 for reliability. The genetic correlations of this trait confirmed that both extremes of the estimated breeding value must be treated cautiously, because correlations with important traits such as mastitis resistance, body condition score, and teat length are unfavorable.

Genetic Parameters of Workability Traits in the Population of Polish Holstein-Friesian Cows Based on Conventional and Genomic Data

Heritabilities of workability (WT) traits-milking speed (MS) and temperament (MT)-as well as genetic and phenotypic correlations between these traits in the population of Polish Holstein-Friesian (PHF) cows were estimated. The estimation of genetic parameters was performed twice: first with the use of pedigree data; and second with the use of pedigree and genomic data. Phenotypic data from routinely conducted MS and MT evaluations for 1,045,511 cows born from 2004 to 2013 were available; the cows were evaluated from 2011 to 2015. The main dataset was reduced based on imposed restrictions (e.g., on age of calving, stage of lactation and day of first trial milking). The dataset prepared in this manner comprised 391,615 cows. It was then reduced to daughters of 10% randomly selected sires for computational reasons. Finally, for genetic parameter estimation, 13,280 records of cows were used. The linear observation model included additive random effects of animal, fixed effects of herd-year-season of calving subclass (HYS) and lactation phase, fixed regressions on cow age at calving and the percent of HF breed genes in the cow genotype. Heritabilities estimated based on pedigree data were 0.12 (±0.0067) for MS and 0.08 (±0.0063) for MT, the genetic correlation between MS and MT was estimated at 0.05 (±0.0002) and the phenotypic correlation coefficient was estimated at 0.14 (±0.0004). The inclusion of genomic information of sire bulls had no clear effect on the size of the estimated WT genetic parameters. The heritabilities of MS and MT were 0.11 (±0.0065) and 0.09 (±0.0012), respectively. The genetic and phenotypic correlation coefficients were 0.07 (±0.0003) and 0.12 (±0.0005), respectively. The sizes of the obtained heritabilities of WT and of the genetic and phenotypic correlation between these traits indicate the possibility of effective population improvement for both WT traits.

Genetic Parameters Estimation of Milking Traits in Polish Holstein-Friesians Based on Automatic Milking System Data

The automatic milking system (AMS) provides a large amount of information characterizing the course of each milking cow, which is not available in the conventional system. The aim of our study was to estimate heritability and genetic correlations for milk yield (MY), milking frequency (MF), and speed (MS) for 1713 Polish Holstein-Friesian primiparous cows milked in barns with an AMS. Daily heritability indicators estimated using second-order Legendre polynomials and Random Regression Models showed high variation during lactation, ranging 0.131-0.345 for MY, 0.153-0.322 for MF, and 0.336-0.493 for MS. The rates of genetic correlation between traits ranged: 0.561-0.929 for MY-MF, (-0.255)-0.090 for MF-MS, (-0.174)-0.020 for MY-MS. It is possible to carry out effective selection for milking speed, which provides an opportunity to increase the number of cows per milking robot, and thus increase the profitability of production in the herd. The results proved that selection for milk yield and daily milking frequency is also feasible. The research showed a high, positive genetic correlation between milking frequency and milk yield, which allows us to conclude that preferring breeding cows with a natural tendency to frequent visits to the milking robot should indirectly improve the genetic basis of milking.

Relationship between Temperament and Stage of Lactation, Productivity and Milk Composition of Dairy Cows

Simple Summary

Cattle temperament can be described as a response to changes in the environment and is crucial for successful herd management using innovative technologies. Despite the economic aspects of animal productivity and welfare, there is still a lack of objective evidence for a wider use of temperament in dairy cattle breeding programmes. The aim of this study was to evaluate the relationship between cow temperament and milk indices describing cow productivity, metabolic status and mastitis resistance. The coefficient of heritability of temperament was determined. Only a small part of the phenotypic changes in this indicator in the analysed population was associated with genetic factors; however, the correlation of cow temperament with milk lactose and somatic cells suggests that temperament could be used in sustainable breeding programmes, giving priority to animal welfare and health. A statistically significant decrease in temperament scores with increasing lactation periods was only found in primiparous cows. It is also argued that changes in milk production, milk composition and quality associated with mastitis and a cow’s metabolic status should be taken into account when assessing the cow’s temperament, as these factors can affect the welfare and behaviour of an animal, and therefore the expression and intensity of their reaction to their environment.

Abstract

The aim of this study was to assess the relationship between temperament and milk performance in cows at different stages of lactation, describing their productivity, metabolic status and resistance to mastitis. This study showed that with increasing lactation, cows’ temperament indicators decreased (p < 0.001) and they became calmer. The highest temperament score on a five-point scale was found in cows between 45 and 100 days of lactation. In the group of pregnant cows, we found more cows (p = 0.005) with a temperament score of 1–2 compared with non-pregnant cows A normal temperament was usually detected in cows with lactose levels in milk of 4.60% or more and when the somatic cell count (SCC) values in cow milk were <100,000/mL and 100,000–200,000/mL, with a milk fat-to-protein ratio of 1.2. A larger number of more sensitive and highly aggressive cows was detected at a low milk urea level. In contrast to a positive phenotypic correlation (p < 0.05), this study showed a negative genetic correlation between the temperament of cows and milk yield (p < 0.001). Positive genetic correlations between temperament scores and milk somatic cells (p < 0.001) and milk fat-to-protein ratio (p < 0.05) were found to indicate a lower genetic predisposition in cows with a calmer temperament to subclinical mastitis and ketosis. On the other hand, the heritability of temperament (h² = 0.044–0.100) showed that only a small part of the phenotypic changes in this indicator is associated with genetic factors.

Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.

Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period

Simple Summary

Barns equipped with the automatic milking system (AMS) record huge amounts of data on milk flow rate, milk yield and composition, milk temperature, amount of concentrate intake and rumination time. Our study attempted to use this information, recorded during the periparturient period (divided into subperiods: second (14–8 days) and first (7–1 days) week before calving; 1–4, 5–7, 8–14, 15–21 and 22–28 days of lactation), to predict lactation milk yield in Polish Holstein–Friesian cows. In the first stage of statistical analysis, coefficients of simple correlation between lactation milk yield and AMS parameters were calculated. We found that prediction of lactation milk yield based on individual pieces of data may be ineffective—the calculated coefficients of correlation were low or moderate. In the next step of data analysis, we used a modern data mining technique in the form of decision trees. Based on the graphic, easy-to-interpret decision tree, we concluded that the highest lactation yield is to be expected for cows with completed lactations (survived until the next lactation), which were milked 4.07 times per day on average in the 4th week of lactation.

Abstract

Early prediction of lactation milk yield enables more efficient herd management. Therefore, this study attempted to predict lactation milk yield (LMY) in 524 Polish Holstein–Friesian cows, based on information recorded by the automatic milking system (AMS) in the periparturient period. The cows calved in 2016 and/or 2017 and were used in 3 herds equipped with milking robots. In the first stage of data analysis, calculations were made of the coefficients of simple correlation between rumination time (expressed as mean time per cow during the periparturient period: second (14–8 days) and first (7–1 days) week before calving, 1–4, 5–7, 8–14, 15–21 and 22–28 days of lactation), electrical conductivity and temperature of milk (expressed as means per cow on days 1–4, 5–7, 8–14, 15–21 and 22–28), amount of concentrate intake, number of milkings/day, milking time/visit, milk speed and lactation milk yield. In the next step of the statistical analysis, a decision tree technique was employed to determine factors responsible for LMY. The study showed that the correlation coefficients between LMY and AMS traits recorded during the periparturient period were low or moderate, ranging from 0.002 to 0.312. Prediction of LMY from the constructed decision tree model was found to be possible. The employed Classification and Regression Trees (CART) algorithm demonstrated that the highest lactation yield is to be expected for cows with completed lactations (survived until the next lactation), which were milked 4.07 times per day on average in the 4th week of lactation. We proved that the application of the decision tree method could allow breeders to select, already in the postparturient period, appropriate levels of AMS milking variables, which will ensure high milk yield per lactation.

Genetic relationship among somatic cell score and some milking traits in Holstein-Friesian primiparous cows milked by an automated milking system

The automated milking system provides breeders with a large amount of automatically collected information about each cow in herd that cannot be easily obtained in non-robotised systems. This knowledge can be used in breeding programs improving somatic cell count (SCC) level. The objective of this study was to estimate heritabilities and genetic correlations among test-day (TD) somatic cell score (SCS) and selected milking traits, such as daily milk yield (MY), milking frequency (MF), milking time (MT) and milking speed (MS), attachment time (AT) to single teat cups, electrical conductivity (EC) and milk temperature (MTEMP). Data were collected for 1899 Polish Holstein-Friesian primiparous cows milked in an automatic milking system. Genetic parameters of the studied traits were estimated using Bayesian method via Gibbs sampling and two-trait random regression animal model with fixed effect of herd x TD, fixed regressions on days in milk (DIM) nested within age at calving by season of calving and RR for additive genetic and permanent environmental effects. Both fixed and RR were fitted with fourth-order Legendre polynomials on DIM. The estimated daily heritabilities were in the following ranges: MY - 0.162-0.338, MF - 0.156-0.444, MT - 0.090-0.320, MS - 0.252-0.665, AT - 0.105-0.394, EC - 0.269-0.466, MTEMP - 0.135-0.304 and SCS - 0.155-0.321. The heritabilities for traits expressed on a 305-d basis were moderate to high: 0.460 for MY, 0.514 for MF, 0.315 for MT, 0.431 for MS, 0.256 for AT, 0.386 for EC, 0.407 for MTEMP and 0.359 for SCS. Genetic correlations between traits on a 305-d basis showed that SCS was most strongly genetically correlated with MTEMP (0.572) and MS (0.480), whereas genetic relationships of SCS with MT (0.221) and EC (-0.216) were moderate. Phenotypic correlations between traits on a 305-d basis were moderate or low. Somatic cell score was negatively phenotypically correlated with MY, MF and MT, with the highest relationship with MT (-0.302). The largest positive phenotypic correlations were observed between SCS and MS (0.311) and with MTEMP (0.286). In summary, it is concluded that there is a chance to carry out effective selection for lower SCS and for some other traits, in particular MS and MTEMP. The obtained results are promising enough to conduct further research to evaluate how these traits can be used both to increase the accuracy of genetic evaluations of SCC and to improve udder health.

Genetic, farm, and lactation effects on behavior and performance of US Holsteins in automated milking systems

Selecting for favorable behavior and performance could enhance the efficiency of production in automated milking systems (AMS). The objectives of this study were to describe AMS behavior and performance in Holsteins, estimate genetic parameters among AMS traits, and determine genetic relationships of AMS traits with other routinely recorded traits. The edited data included 1,101,651 individual milking records and 394,636 daily records from 2,531 lactations and 1,714 cows that resided on 3 farms; data were obtained from the Dairy Data Warehouse (Assen, Netherlands) cloud. Traits considered were individual milking and daily totals for milk yield, milking time, milk harvest rate (the ratio of milk yield to milking time), milk flow rate, electrical conductivity, machine kickoffs, incomplete milkings, and blood in milk; the number of milkings per day and 305-d mature-equivalent milk yield (305ME) were also evaluated. Individual milkings were evaluated with mixed models that included fixed effects of week of lactation, lactation group (1, 2, ≥3), hour of day, and farm; random effects included cow within lactation, lactation group by week of lactation, and interactions of farm with date, hour, week of lactation, and year-season of calving. Daily records were evaluated with 3-trait animal models that included 305ME and 2 AMS traits with random additive genetic and permanent environment effects. Estimated breeding values were extracted and correlated with yield, conformation, and udder health genetic evaluations. Farm specific robot access policies had notable effects on week of lactation patterns for milk yield and number of milkings. Mature cows had higher milk harvest rates (2.05 kg/min) than first-lactation cows (1.73 kg/min) with larger differences in early lactation. First-lactation cows were more likely to kick off the machine (15.04%) than mature cows (8.62%), particularly in early lactation. Heritability estimates were generally lower for behavior traits (0.03 for incomplete milkings and 0.08 for kickoffs) than for milk harvest rate (0.30) and flow rate (0.55). Udder conformation traits did not have favorable genetic correlations with AMS traits, with the exception that longer teats were correlated with fewer kickoffs (-0.34) and incomplete milkings (-0.49); increased milk harvest rate and flow rate were unfavorably associated with genetic merit for udder health. There is genetic variation for milking efficiency and behavioral traits, suggesting genetic selection to enhance efficiency in AMS systems is possible. Genetic associations with udder conformation indicate that selection for udder morphology is unlikely to substantially improve milking efficiency. This calls for more direct selection of traits related to AMS efficiency.

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

A genetic study of new udder health indicator traits with data from automatic milking systems

The current study aimed to investigate new udder health traits based on data from automatic milking systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each milking, whereas OCC was measured per milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11-0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.