Milk Recording System Research Articles

Prediction of First Lactation Milk Yield on The Basis of Test Day Yield Using Multiple Linear Regression in Gir Cows

The test-day model is a method of choice for the study of milk yield traits, and this method is very important in countries like India, where herd size is generally smaller and lacks a well-established milk recording system. The present investigation was carried out on 365 records of Gir cows maintained at a cattle breeding farm from 1986 to 2014 with the objective of predicting the first lactation milk yield using monthly test day milk records. MLR was used with the backward elimination method. The optimum equation had total five variables (test days) viz. TD1, TD2, TD3, TD4 and TD5. This equation gave an accuracy of prediction of 77.71%. Therefore, it is concluded that first lactation 305 day milk yield could be predicted as early as 5th month of lactation with higher degree of accuracy.

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

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

Genetic and phenotypic trends for female fertility traits derived from service records of South African Holstein cattle

The objective of this study was to estimate breeding values (EBV) and genetic and phenotypic trends for female fertility traits derived from artificial insemination (AI) records. Data were collected from automated milk recording systems: 64464 records from 18 South African (SA) Holstein herds. Breeding values were estimated for heifer traits, i.e., age at first service (AFS), age at first calving (AFC), number of services per conception (SPCh); and for cow traits, i.e., calving to first service (CFS), number of days open (DO), number of services per conception (SPC), first service within 80 days post-partum (FS80d), and whether cows were confirmed pregnant within 100 days post-partum (PD100d) or within 200 days post-partum (PD200d) using THRGIBBSF90. Trends were estimated for the period 1984 to 2011. There was no clear trend on average breeding values for heifer traits. However, phenotypic trends for heifer traits showed a desirable downward trend, with a decrease of 0.14 and 0.13 months/year for AFS and AFC, respectively. Phenotypic trends for cow fertility traits appeared to increase at the rate 0.16 and 0.83 days/year for CFS and DO, respectively, with animals requiring 0.02 more services per year for SPC. The average EBV for binary traits FS80d, PD100d, and PD200d, showed no distinct trend. In general, there was no clear genetic and phenotypic trend for all traits defined. The trends that appeared to follow trait improvement could have been coincidental. Thus, efforts have to be made towards the improvement of heifer and cow fertility traits explored in this study

How does a beef × dairy calving affect the dairy cow's following lactation?

For beef semen usage on dairy cows, much of the research has focused on the performance of the crossbred calves, yet little focus has been given to the subsequent performance of the cow herself. This study aimed to evaluate the performance of dairy cows for milk yield, fertility, and survival traits after giving birth to beef x dairy crossbred calves and compare this with the performance after giving birth to purebred dairy calves. Further, we aimed to study if the effect of a difficult calving was the same regardless of whether the calf was purebred dairy or beef x dairy crossbred. Phenotypic records from 587,288 calving events from 1997 to 2020 were collected from the Swedish milk recording system from cows of the dairy breeds Swedish Red (SR) and Swedish Holstein (SH). The sire beef breeds studied were Aberdeen Angus, Hereford (combined in category LIGHT), Charolais, Limousin, and Simmental (category HEAVY). Sixteen traits were defined and grouped in 3 categories: cumulative and 305-d milk, fat, and protein yield, daily milk yield, and 75-d milk yield as yield traits; calving to first insemination interval, calving to last insemination interval, first to last insemination interval, calving interval, and number of inseminations as fertility traits; and survival to 75 d or to next calving and lactation length as measures of survival. The data were analyzed for all traits for first and second parities separately using mixed linear models, with a focus on the estimates of cow breed by service sire breed combinations. All traits in parity 2 were adjusted for previous 305-d milk yield based on the expectation that low-yielding cows would more likely to be inseminated with beef semen. Overall, milk yield was lower after beef x dairy calvings compared with the purebred dairy calvings. The largest effects were found on cumulative yields and in second parity, with lower effects for yields early in lactation and yields in first parity. The largest decrease was 13-14 kg (0.12 phenotypic SD) for cumulative fat yield when breeding beef breed sires with purebred SR dams. For fertility traits, for most breed combinations, the effects were not large enough to be significant. Conversely, all beef x dairy crossbred combinations showed significantly lower results for survival to the next lactation, and mostly also for lactation length. There was some indication that dairy cows with beef x dairy calvings in parity 2 that were the result of maximum 2 inseminations in parity 1, had lower survival than corresponding calvings resulting from more than 2 inseminations. This could indicate that the former cows were marked for culling already when inseminated. There was generally an unfavorable effect of a difficult calving on all traits, however, there were almost no significant interactions between calving performance and dam by sire breed combination, and these interactions were never significant in first parity.

Using registry data to identify individual dairy cows with abnormal patterns in routinely recorded somatic cell counts

Data from the Danish milk recording system routinely enter the Danish Cattle Database, including somatic cell counts (SCC) for individual animals. Elevated SCC can signal intramammary inflammation, suggesting subclinical mastitis. Detecting mastitis is pivotal to limit severity, prevent pathogen spread, and target treatment or culling. This study aimed to differentiate normal and abnormal SCC patterns using recorded registry data.We used registry data from 2010 to 2020 for dairy cows in herds with 11 annual milk recordings. To create consistency across herds, we used data from 13,996 unique animals and eight different herds, selected based on the amount of data available, only selecting Holstein animals and conventional herds. We fitted log10-transformed SCC to days in milk (DIM) using the Wilmink and Wood’s curve functions, originally developed for milk yield over the lactation. We used Nonlinear Least Square and Nonlinear Mixed Effect models to fit the log10-transformed SCC observations to DIM at animal level.Using mean squared residuals (MSR), we found a consistently better fit using a Wood’s style function. Detection of MSR outliers in the model fitting process was used to identify animals with log10(SCC) curves deviating from the expected “normal” curve for that same animal. With this study, we propose a method to identify single animals with SCC patterns that indicate abnormalities, such as mastitis, based on registry data. This method could potentially lead to a registry data-based detection of mastitis cases in larger dairy herds.

The effect of genetic defects on pregnancy loss in Swedish dairy cattle

The effect of carrier status of 10 lethal recessive genetic defects on pregnancy maintenance in Swedish dairy cattle was examined. The genetic defects were Ayrshire Haplotype 1, Ayrshire Haplotype 2, BTA12, BTA23, and Brown Swiss Haplotype 2 in Red Dairy Cattle (RDC), and Holstein Haplotype 1, 3, 4, 6, and 7 (HH1-HH7) in Holstein. Effects of carrier status of BTA12 and HH3 on conception rate (CR), interval from first to last service (FLS), and milk production were also examined. Data were obtained for 1,429 herds in the Swedish milk recording system, while information on carrier status of genetic defects was obtained from the Nordic Cattle Genetic Evaluation. In total, data on 158,795 inseminations in 28,432 RDC and 22,018 Holstein females were available. Data permitted separate analyses of BTA12 and HH3, but carrier frequencies of other defects were too low to enable further analysis. Pregnancy loss was defined as failure to maintain pregnancy, where pregnancy status was confirmed with manual and chemical pregnancy diagnosis, insemination, calving, sales and culling data. Odds ratios (OR) and probabilities of pregnancy loss and CR were estimated using generalized linear mixed models, while pregnancy loss, CR, FLS, milk, protein, and fat yields were analyzed using linear mixed models. Pregnancy losses were reported on average within the first month post-AI. At-risk matings were more prone to suffer pregnancy loss in BTA12 (OR = 1.79) and HH3 carriers (OR = 1.77) than not-at-risk matings. At-risk matings also had lower CR (OR = 0.62 and 0.63 for BTA12 and HH3, respectively) than not-at-risk matings. Carrier females of BTA12 had longer FLS and higher milk production than noncarriers. Conception rate and pregnancy maintenance could be improved by avoiding at-risk matings. This finding could help reduce pregnancy loss due to genetic defects in the breeding program for improved fertility.

Investigating the potential for genetic selection of dairy calf disease traits using management data

Genetic selection could be a tool to help improve the health and welfare of calves, however, to date, there is limited research on the genetics of calfhood diseases. This study aimed to understand the current impact of calf diseases, by investigating incidence rates, estimating genetic parameters, and providing industry recommendations to improve calf disease recording practices on farms. Available calf disease data comprised of 69,695 Holstein calf disease records for respiratory problems (RESP) and diarrhea (DIAR), from 62,361 calves collected on 1,617 Canadian dairy herds from 2006 to 2021. Single and multiple trait analysis using both a threshold and linear animal model for each trait were evaluated. Furthermore, each trait was analyzed using 2 scenarios with respect to minimum disease incidence threshold criterion (herd-year incidence of at least 1% and 5%) to highlight the impact of different filtering thresholds on selection potential. Observed scale heritability estimates for RESP and DIAR ranged from 0.02 to 0.07 across analyses, while estimated genetic correlations between the traits ranged from 0.50 to 0.62. Sires were compared based on their estimated breeding value and their diseased daughter incidence rates. On average, calves born to the bottom 10% of sires were 1.8 times more likely to develop RESP and 1.9 times to develop DIAR compared with daughters born to the top 10% of sires. Results from the current study are promising for the inclusion of both DIAR and RESP in Canadian genetic evaluations. However, for effective genetic evaluation we require standardized approaches on data collection and industry outreach to highlight the importance of collecting and uploading this information to herd management software. In particular, it is important that the herd management software is accessible to the national milk recording system to allow for use in national genetic evaluation.

Association between hyperketolactia and production in early-lactating dairy cows

Study aims were to investigate associations of hyperketolactia (HYKL) status of Holstein dairy cows between 6 and 60 d in milk (DIM), defined by milk acetone (mACE) and β-hydroxybutyrate (mBHB) content, with daily milk yield and composition. Milk samples (∼5.0 million) were collected over a 5-yr period (2014-2019) within the milk recording system in Poland. Concentrations of mACE and mBHB determined by Fourier-transform infrared spectroscopy were used to categorize samples into 4 ketolactia groups. Based on threshold values of ≥0.15 mmol/L mACE and ≥0.10 mmol/L mBHB, ketolactia groups were normoketolactia (NKL; mACE <0.15 mmol/L and mBHB <0.10 mmol/L), BHB hyperketolactia (HYKLBHB; mACE <0.15 mmol/L and mBHB ≥0.10 mmol/L), ACE hyperketolactia (HYKLACE; mACE ≥0.15 mmol/L and mBHB <0.10 mmol/L), and ACE and BHB hyperketolactia (HYKLACEBHB; mACE ≥0.15 mmol/L and mBHB ≥0.10 mmol/L). To investigate ketolactia association with production outcomes, a linear model was developed, including ketolactia group, DIM, parity, their interactions, year-season as fixed effects, and random effects of herd and cow. Among all milk samples, 31.2% were classified as HYKL, and of these, 52.6%, 39.6%, and 7.8% were HYKLACEBHB, HYKLBHB, and HYKLACE, respectively. Ketolactia groups differed for all traits studied in all parities and DIM. Among HYKL groups, lowest milk yield was found in HYKLACEBHB cows, except for 6 to 30 DIM in first- and second-lactation cows. Milk yield of HYKLBHB cows was higher than that of NKL cows until 20 to 30 DIM, and then it was lower than NKL cows. Milk yield of HYKLACE cows was mostly lower than NKL cows. Energy-corrected milk (ECM) yield of HYKLACEBHB cows was higher than that of NKL cows until 30 to 35 DIM for second lactation and third lactation or greater, and in the whole study period for first lactation. The yield of ECM for HYKLBHB cows was mostly higher than that of NKL cows, whereas HYKLACE cows had higher ECM than NKL cows until 15 to 25 DIM and then was lower for the HYKLACE group. Milk composition differed among HYKL groups. Highest milk fat (MF) and lowest milk lactose (ML) contents were observed in HYKLACEBHB cows. Cows in HYKLACEBHB and HYKLBHB groups had higher MF and lower milk protein (MP; except in 6-8 DIM in first lactation) and ML content than NKL cows. Milk fat content was higher in HYKLACE than NKL cows in first lactation and during the first 30 to 40 DIM in older cows. Lactose content was lower in HYKLACE than in NKL cows within 30 to 40 DIM; afterward it was higher in NKL cows. Lower MP content was found in HYKLACE than in NKL cows, except during 6 to 9 DIM for cows in first lactation and third lactation or greater. In conclusion, HYKL is associated with altered milk production in all parities, but a range of these negative relations depends on ketone status addressing both ACE and BHB contents. Further research is needed to ascertain underpinning biochemical defects of HYKL from elevated ACE, alone or in combination with BHB, during early lactation.

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

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

Body and milk production traits as indicators of energy requirements and efficiency of purebred Holstein and 3-breed rotational crossbred cows from Viking Red, Montbéliarde, and Holstein sires

This study aimed to compare rotational 3-breed crossbred cows of Viking Red, Montbéliarde, and Holstein breeds with purebred Holstein cows for a range of body measurements, as well as different metrics of the cows' productivity and production efficiency. The study involved 791 cows (440 crossbreds and 351 purebreds), that were managed across 2 herds. Within each herd, crossbreds and purebreds were reared and milked together, fed the same diets, and managed as one group. The heart girth, height at withers, and body length were measured, and body condition score (BCS) was determined on all the cows on a single test day. The body weight (BW) of 225 cows were used to develop an equation to predict BW from body size traits, parity, and days in milk, which was then used to estimate the BW of all the cows. Equations from the literature were used to estimate body protein and lipid contents using the predicted BW and BCS. Evidence suggests that maintenance energy requirements may be closely related to body protein mass, and Holstein and crossbred cows may be different in body composition. Therefore, we computed the requirements of net energy for maintenance (NEM) on the basis either of the metabolic weight (NEM-MW: 0.418 MJ/kg of metabolic BW) or of the estimated body protein mass according to a coefficient (NEM-PM: 0.631 MJ/kg body protein mass) computed on the subset comprising the purebred Holstein. On the same day when body measurements were collected, individual test-day milk yield and fat and protein contents were retrieved once from the official Italian milk recording system, and milk was sampled to determine fresh cheese yield. Measures of NEM were used to scale the production traits. Statistical analyses of all variables included the fixed effects of herd, days in milk, parity, and genetic group (purebred Holstein and crossbred), and the herd × genetic group interaction. External validation of the equation predicting BW yielded a correlation coefficient of 0.94 and an average bias of -4.95 ± 36.81 kg. The crossbreds had similar predicted BW and NEM-MW compared with the Holsteins. However, NEM-PM of crossbreds was 3.8% lower than that of the Holsteins, due to their 11% greater BCS and different estimated body composition. The crossbred cows yielded 4.8% less milk and 3.4% less milk energy than the purebred Holsteins. However, the differences between genetic groups were no longer significant when the production traits were scaled on NEM-PM, suggesting that the crossbreds and purebreds have the same productive ability and efficiency per unit of body protein mass. In conclusion, measures of productivity and efficiency that combine the cows' production capability with traits related to body composition and the energy cost of production seem to be more effective criteria for comparing crossbred and purebred Holstein cows than just milk, fat, and protein yields.

A randomized study on the effect of extended voluntary waiting period in primiparous dairy cows on milk yield during first and second lactation

Extending the voluntary waiting period (VWP) for primiparous cows can have a positive impact on fertility without a negative impact on milk production per day in the calving interval (CInt). We investigated the effect of extended VWP during first lactation on milk yield (MY) during 2 consecutive lactations in primiparous cows. The study involved 16 commercial herds in southern Sweden. A total of 533 Holstein and Red dairy cattle (Swedish Red, Danish Red, Ayrshire) dairy cows were randomly assigned to a conventional 25 to 95 d VWP (n = 252) or extended 145 to 215 d VWP (n = 281). Data on calvings, inseminations, and test-day yields were retrieved from the Swedish Milk Recording System. Cows with VWP according to plan and completing 1 or 2 CInt with a second or third calving were included in the data analysis. Whole lactation and 305-d energy-corrected milk (ECM) yield were higher for the extended VWP group than the conventional VWP group in both the first lactation (12,307 vs. 9,587 and 9,653 vs. 9,127 kg ECM) and second lactation (12,817 vs. 11,986 and 11,957 vs. 11,304 kg ECM). We found no difference between the VWP groups in MY per day during the first CInt or during the first and second CInt combined, although MY per day during the second CInt was around 1.5 kg higher for cows with extended VWP than for cows with conventional VWP. Thus extended VWP for primiparous cows can be used as a management tool without compromising MY.

Use of differential somatic cell count, somatic cell score, and milk mid-infrared spectral analysis for monitoring mastitis in dairy cows during routine milk recording

Routine mastitis screening is crucial on dairy farms, and it usually involves analysis of somatic cell count (SCC), which serves as an indicator of mastitis. Milk mid-infrared (MIR) spectral analysis, the standard method to determine milk components, was evaluated as an additional tool for the prediction of mastitis, since health condition of a cow is affecting milk composition. Differential somatic cell count (DSCC), the percentage of SCC due to polymorphonuclear leukocytes (PMN) and lymphocytes, is another new indicator to detect cows having an infected udder mammary gland. In a previous study we developed mastitis prediction models based on somatic cell score (SCS) and MIR spectral data from routine milk recording. The present study compared various models to predict clinical mastitis based on the parameters DSCC, SCS and MIR spectral data, alone or in combination. Data on DSCC, SCC, MIR spectral data and mastitis diagnosis by veterinarians were extracted for 40,332 dairy cows of the breeds Fleckvieh, Brown Swiss and Holstein Friesian from the Austrian milk recording and health monitoring systems. Three data subsets were created based on whether test-day records were classified as “mastitis” when they occurred 21 days (subset 1), 14 days (subset 2), or 7 days (subset 3) before or after diagnosis of mastitis; all other records were classified as “healthy”. The performance of seven models (DSCC alone, SCS alone, MIR alone, DSCC+SCS, DSCC+MIR, MIR+SCS and DSCC+SCS+MIR) was assessed in these subsets using partial least squares discriminant analysis. In all subsets, the DSCC model showed the highest sensitivities (0.710 to 0.792) but lowest specificities (0.564 to 0.562) during external validation. The SCS model showed lower sensitivities (0.624 to 0.662) but higher specificities (0.725 to 0.769) than DSCC. The MIR model showed sensitivities from 0.598 to 0.617 and specificities from 0.668 to 0.681. The models combining multiple predictors showed the highest balanced accuracies: the MIR+SCS model in subset 1 (0.697), DSCC+SCS+MIR in subset 2 (0.712) and MIR+SCS in subset 3 (0.744). Taken together, our analyses suggest that DSCC, SCS and MIR spectral data are useful for routine mastitis screening, with SCS performing as the best single predictor and the MIR+SCS model performing best overall.

Estimation of Genetic Parameters for Heifer and Cow Fertility Traits Derived from On-Farm AI Service Records of South African Holstein Cattle.

Simple SummaryFemale fertility has gained significant attention and is incorporated in dairy cattle breeding objectives worldwide. Currently, age at first calving (AFC) and calving interval (CI) are the only traits used as indicators of fertility in the genetic evaluations of South African dairy cattle, but these traits are greatly affected by the breeder’s decisions and calving interval is available late in an animal’s life which delays selection decisions. This study investigated the possible use of additional fertility traits derived from artificial insemination records. The fertility traits examined in this study could be used in addition to age at first calving and calving interval to select for improved fertility in dairy cattle.This study aimed to derive additional fertility traits from service data and estimate their genetic parameters for their possible inclusion in the South African Holstein cattle breeding programs. Service records (n = 64,464) were collected from 18 South African Holstein herds using on-farm automated milk recording systems. Using a multivariate model, the data were used to estimate heritabilities and correlations among several fertility traits. The pedigree data consisted of information on 18,592 animals born between 1981 and 2013. Heritability estimates observed were low to moderate (0.02 ± 0.00 to 0.24 ± 0.00), indicating that there is some genetic basis for the explored fertility traits to warrant selection. The genetic correlations observed between fertility traits were generally favorable, with some high correlations between age at first service (AFS) and services per conception for heifers (SPCh) (0.73 ± 0.00) and between days from calving to first service (CFS) and services per conception for cows (SPC) (0.90 ± 0.01). Positive genetic correlations indicate that improvement in one trait is coupled with a correlated genetic increase in another trait. The studied fertility traits could be used in addition to AFC and CI to serve as a basis for the selection of reproduction in dairy cattle to minimize selection bias.

Prediction of Acute and Chronic Mastitis in Dairy Cows Based on Somatic Cell Score and Mid-Infrared Spectroscopy of Milk.

Simple SummaryMid-infrared (MIR) spectroscopy is the method of choice to determine milk components like fat, protein and urea. We examined the potential of MIR spectra analyses for the prediction of clinical mastitis events of dairy cows additionally, or alternatively, to somatic cell count, which is routinely used as an indicator for mastitis monitoring. Prediction models based on MIR spectra and a somatic cell count-derived score (SCS) were developed and compared. A model based on MIR spectra and SCS proved more accurate at predicting mastitis than models based on either indicator alone. Consequently, MIR spectra analyses add extra value in the prediction of clinical mastitis, making them potentially useful for dairy farm management and as an auxiliary trait for the genetic evaluation of udder health.Monitoring for mastitis on dairy farms is of particular importance, as it is one of the most prevalent bovine diseases. A commonly used indicator for mastitis monitoring is somatic cell count. A supplementary tool to predict mastitis risk may be mid-infrared (MIR) spectroscopy of milk. Because bovine health status can affect milk composition, this technique is already routinely used to determine standard milk components. The aim of the present study was to compare the performance of models to predict clinical mastitis based on MIR spectral data and/or somatic cell count score (SCS), and to explore differences of prediction accuracies for acute and chronic clinical mastitis diagnoses. Test-day data of the routine Austrian milk recording system and diagnosis data of its health monitoring, from 59,002 cows of the breeds Fleckvieh (dual purpose Simmental), Holstein Friesian and Brown Swiss, were used. Test-day records within 21 days before and 21 days after a mastitis diagnosis were defined as mastitis cases. Three different models (MIR, SCS, MIR + SCS) were compared, applying Partial Least Squares Discriminant Analysis. Results of external validation in the overall time window (−/+21 days) showed area under receiver operating characteristic curves (AUC) of 0.70 when based only on MIR, 0.72 when based only on SCS, and 0.76 when based on both. Considering as mastitis cases only the test-day records within 7 days after mastitis diagnosis, the corresponding areas under the curve were 0.77, 0.83 and 0.85. Hence, the model combining MIR spectral data and SCS was performing best. Mastitis probabilities derived from the prediction models are potentially valuable for routine mastitis monitoring for farmers, as well as for the genetic evaluation of the trait udder health.

Estimation of milkability breeding values and variance components for Italian Holstein

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 improvement of the dairy cattle in the tropical countries: A review

The principles of genetic improvement included the use of crossbreeding. An alternative method is the genetic improvement of the indigenous cattle by selection. The low reproductive performance and high calf mortality in the indigenous cattle caused a reduction in the selection intensity. Moreover, the generation interval is long in cattle because of the late sexual maturity and the long calving intervals. The most important problem is the absence of a milk recording system in many temperate countries. In such a case, the approach to improving the cattle through selection is limited and we will need to start with a single nucleus. Two types of selection can be adopted; without progeny testing which means the selection of bulls on pedigree information only and with progeny testing to identify the elite bulls according to the performance of their daughters.

Prediction of cow's fertility based on data recorded by automatic milking system during the periparturient period.

The results of most studies show the beneficial effect of milking automation on production parameters of dairy cows, but its effect on fertility traits is debatable. Therefore, a study was undertaken to predict cow fertility - services per conception (SC) and calving interval (CI) - based on automatic milking system (AMS) data collected in the periparturient period subdivided into the second and first week before calving, 1-4, 5-7, 8-14, 15-21 and 22-28days of lactation. SC and CI were predicted using daily indicators such as concentrate intake, number of milkings, cow box time, milking time, milking speed, colostrum and milk yield, composition, temperature and electrical conductivity. The study material was derived from the AMS management system and from the SYMLEK milk recording system. The analysis covered data for 16,329 milkings of 398 Polish Holstein-Friesian (PHF) cows, which were used in three AMS herds. The collected numerical data were statistically analysed by correlation analysis in parallel with decision tree technique (SAS statistical package). The present study showed that due to the low, mostly non-significant coefficients of correlation between AMS data collected between 2weeks before and 4weeks after calving, it is not possible to predict cow fertility based on single traits. It has been established that the decision tree method may help breeders, already during the postcalving period, to choose the level of factors associated with AMS milking, which will ensure good fertility of cows in a herd. The most favourable number of services per conception is to be expected from cows that were milked <1.6 times per day from 1 to 4days of lactation and electrical conductivity of their colostrum did not exceed 69 mS during that time. In turn, shortest CI (366days) will be characteristic of the cows whose average daily colostrum yield did not exceed 20.2kg and their daily concentrate intake from 8 to 14days of lactation was at least 5.0kg.

Survival of Polish Holstein-Friesian Cows to Second, Third and Fourth Lactation in Conventional and Automatic Milking Systems

Abstract The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility – infertility and reproductive disorders, locomotor diseases, low milk yield, other diseases – metabolic, digestive and respiratory diseases, accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example.

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed.

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows