Test-day Record Research Articles

Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds

Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which the diagnostic remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds. A first data set composed of 488 milk samples of 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, else SARA-) was used for the development of machine learning models. Three sets of predictive variables: i) milk major components (MMC), ii) milk FA (MFA), and iii) MMC combined with MFA (MMCFA) were submitted to 3 different algorithms, namely Elastic net (EN), Extreme gradient boosting (XGB), and Partial least squares (PLS), and evaluated using 3 different scenarios of cross-validation. Accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including fatty acids estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, one model was selected (algorithm: EN; predictive variables: MMCFA; 60.4, 65.4, and 55.3% of accuracy, sensitivity, and specificity, respectively) and applied to a large data set comprising the first test-day record (milk major components and FA within the first 70 DIM of 211,972 Holstein cows (219,503 samples) collected from 3001 commercial dairy herds. Based on this analysis, the within-herd SARA prevalence of commercial farms was estimated at 6.6 ± 5.29% ranging from 0 to 38.3%. A subsequent linear mixed model was built to investigate the herd-level factors associated to higher within-herd SARA prevalence. Milking system, proportion of primiparous cows, herd size and seasons were all herd-level factors affecting SARA prevalence. Furthermore, milk production was positively, and milk fat yield negatively associated with SARA prevalence. Due to their moderate levels of accuracy, the SARA prediction models developed in our study, using data from continuous pH measurements on commercial farms, are not suitable for diagnostic purpose. However, these models can provide valuable information at the herd level.

Estimation of genotype by temperature-humidity index interactions on milk production and udder health traits in Montbeliarde cows

BackgroundHeat stress negatively influences cattle welfare, health and productivity. To cope with the forecasted increases in temperature and heat waves frequency, identifying high-producing animals that are tolerant to heat is of capital importance to maintain milk production. This study, based on the joint analysis of on-farm performance and weather data, had two objectives: (1) to determine the response in production performances (milk, fat and protein yields, fat and protein contents) and udder health (somatic cell score) to temperature-humidity index (THI) variations in Montbeliarde cows, and (2) to estimate the interactions between genotype and THI, to enable the identification of the most adapted animals for facing the expected increases in temperature.ResultsTest-day records from first and second lactations from 2016 to 2020 were associated with the average THI during the three days before the test-day record. In total, 446,717 test-day records from 55,650 cows in first lactation and 457,516 test-day records from 58,229 cows in second lactation were analysed. The optimal THI was below 55 (i.e. ~ 12–13 °C) for all traits. Individual responses to THI were estimated by random regression models, which also included individual responses to days in milk. Regardless of the stage of lactation, genetic correlations along the THI gradient were above 0.80, which suggests that genotype-by-THI interactions were weak for production and udder health traits. Nevertheless, a variability in the individual slope of decay could be highlighted at high THI. The genetic correlation between production level at moderate THI and the slope at high THI was negative, while for somatic cell score, it was positive, indicating that heat stress amplifies the susceptibility to mastitis.ConclusionsThe optimal THI for French Montbeliarde cows is below 55 for production and udder health traits. Genetic-by-THI interactions are weak in French Montbeliarde cows for production and udder health traits, but not all animals react in the same way to high temperatures. Even if there is little room for improvement, using a heat tolerance index in cattle selection would be relevant to anticipate the expected increases in temperature. Further investigations are needed to interpret this variability on production traits. However, the current selection for mastitis resistance seems appropriate to adapt cattle to rising temperatures.

Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes

Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

Hormone use for reproductive diseases and heat induction in relation to herd-level reproductive performance in Dutch dairy farms

This ecological study aimed to associate hormone use for reproductive diseases and heat induction with reproductive performance at herd level. Hormone use, herd characteristics, and test-day recording data were obtained from 754 representative Dutch dairy farms belonging to five large veterinary practices from 2017 to 2019 (1679 observations in total). Hormone use was classified into prostaglandin, gonadotropin-releasing hormone (GnRH), and progesterone, and was expressed at herd level as the annual number of hormone doses per 100 adult dairy cows. Hormone use was categorized into four levels (no usage, low, medium, and high use), following the 33rd and 66th percentiles of herds that applied them. Three herd-level reproductive performance indicators (calving interval, calving-to-1st insemination interval, number of inseminations per cow) were analyzed using multivariable General Estimating Equations models. The median annual total hormone use was 36.1 (mean=43.1; min=0.0; max=248.2) doses per 100 adult dairy cows in all herds while the median was 39.2 (mean=46.8; min=0.4; max=248.2) doses per 100 adult dairy cows among the user-herds. The median annual group-specific hormone use was 21.3 (mean=26.1; min 0.0; max=180.0), 11.0 (mean=15.3; min=0.0; max=127.0) and 0.0 (mean=1.8; min=0.0; max=40.3) doses per 100 adult dairy cows for prostaglandin, GnRH, and progesterone, respectively. The final statistical models identified that herds with a high hormone use had a calving interval and a calving-to-1st insemination interval that was 9.3 ± 2.6 and 16.4 ± 2.1 days shorter than that of non-user herds (424.0 ± 2.7 and 114.0 ± 2.1 days), respectively. Furthermore, high-user herds needed on average 0.3 ± 0.04 inseminations more to get their cows pregnant compared to non-user herds (1.83 ± 0.04 no. of inseminations per cow). Medium-user herds had a 6.5 ± 2.6 days shorter calving interval and a 12.0 ± 2.1 days shorter calving-to-1st insemination interval with 0.2 ± 0.04 additional inseminations per cow compared to non-user herds. Low-user herds had a 6.2 ± 2.7 days shorter calving interval and a 7.9 ± 2.2 days shorter calving-to-1st insemination interval compared to non-user herds. The model produced the same trend for prostaglandin and GnRH use, with the higher use being associated with a shorter calving interval, a shorter calving-to-1st insemination interval, and a higher insemination per cow number. For progesterone use the opposite effect was observed. In conclusion, using a large representative herd-level dataset, hormone use was associated with a better reproductive performance in terms of calving interval and calving-to-1st insemination interval but gave extra average number of inseminations per cow. It should be monitored how reproduction performance changes when striving for a more prudent hormone use.

Impact of different numbers of milk test-day records during lactation on the reliability of estimated breeding values.

The objective of this study was to evaluate the impact of the reduction in the number of test-day records per lactation on genetic parameters of test-day milk yield (TDMY) and the reliability of estimated breeding values for 305-day milk yield in Holstein cattle. Estimates of genetic parameters and breeding values were performed using the animal model of random regression and adjustment of the Legendre polynomial (fourth order). When comparing sires with the same number of daughters, greater reliability was found in the subpopulations with the highest number of milk test-day records per lactation per cow. It was also found that the elimination of at least one test-day record affected the reliability of estimated breeding value for 305-day milk yield in the sires, regardless of the class of number of daughters per sire. When selecting the 5% best sires and 20% best cows, the lowest order correlations were observed between the population with 10 test-day records per lactation (complete lactation) and the other subpopulations with incomplete lactations (4, 5, 6, 7, 8, 9 test-day records per lactation). The reduction in the number of test-day milk records per lactation interferes in the reliability of the estimated breeding value for sires and cows, negatively impacting the precision of selecting genetically superior animals.

Estimation of genetic parameters for subclinical mastitis using a threshold model in first parity dairy cows under pasture-based systems of Los Ríos Region in Chile

Somatic cell count (SCC) is an indirect measurement to estimate mammary gland health status. This trait provides information regarding the severity of the mammary tissue inflammation in each quarter. Milk samples coming from the farm milk storage vat containing 100,000 to 200,000 cell/mL are considered suspicious, while SCC over 200,000 cell/mL is an indication of subclinical mastitis. Chilean dairy processors penalise farmers monetarily when their bulk tank samples reach levels of 300,000 cell/ mL SCC. The objective of this study was to quantify the additive genetic component of the liability of cows to reach the 300,000 cell/mL threshold. A data set containing the highest SCC test-day record of 10,528 first lactation cows from 15 commercial dairy farms of Los Ríos Region in southern Chile was analysed. The unknown continuous underlying susceptibility of each cow to reach the 300,000 SCC threshold was modelled as a function of a contemporary group formed by the herd, year, and calving season, the regression coefficient of the unknown underlying susceptibility value of a cow on her daily milk yield (MY) and the additive animal genetic effect. Bayesian inference and Gibbs sampling were used to estimate additive and residual variances. The average daily MY and SCC were 17.84±5.25 kg and 125,327±236,297 cell/mL, respectively. The estimated heritability varied from 0.03 to 0.22 and the average was 0.10±0.03. It is concluded that the genetic variability for the susceptibility to reach the 300,000 SCC threshold could be exploited to improve resistance to subclinical mastitis.

Evaluation of Different Test-Day Milk Recording Protocols by Wood's Model Application for the Estimation of Dairy Goat Milk and Milk Constituent Yield.

Simple SummaryConsidering the seasonal trend of milk production due to application of estrus synchronization treatments in autumn or in spring season, as well as to meet meat production and market requirements for religious holidays, one may find it useful to simplify the milk and milk constituent yield quantification during the entire period of lactation. The present work aimed to verify the application of Wood’s model to a different number of test-day milk recording protocols in order to reduce the frequency of National Breeding Association test-day milking record protocol. Wood’s model fit well on goat milk production, showing high R2 values. Moreover, the milk yield, fat-corrected milk yield, fat yield, and protein yield estimation applying Wood’s model to four recordings (first, second, fourth, and sixth month records) showed results superimposable to International Committee of Animal Recording (ICAR) application and Wood’s model application on test-day records every 15 days.Goats have important social and economic roles in many countries because of their ability to survive and be productive in marginal areas. The overarching aim of this study was to compare the application of Wood’s model to different test-day milk recording protocols for estimation of total milk, fat, and protein yield in dairy goats. A total of 465 goats were used (Garganica, 78; Girgentana, 81; Jonica, 76; Maltese, 77; Red Mediterranean, 76; Saanen, 77). Milk yield was recorded every 15 days throughout lactation of 210 days, for a total of 14 collection days, during both morning and afternoon milking sessions. Milk samples were collected and analyzed for protein and fat. The fat-corrected milk was standardized at 35g fat/kg of milk. Wood models showed high R2 values, and thus good fitting, in all the considered breeds. Wood model applied to first, second, fourth, and sixth month recordings (C) and ICAR estimation showed total milk yield very close to Wood’s model applied to all 14 recordings (A) (p > 0.38). Differently, Wood’s model applied to the first, second, third, and fourth month recording (B) estimation showed great differences (p < 0.01). This could be applied for farms that had the necessity to synchronize flock groups for kidding in order to produce kid meat. In farms that apply the estrus induction and/or synchronization for kidding, it would be possible to perform only four test-day milk recordings and to apply the Wood’s model on them in order to obtain the estimation of total milk, fat, and protein yield during lactation for animals inscribed, or to be inscribed, to the genealogical book.

Monitoring udder health status using somatic cell counts in Holstein dairy herds located in north-east of Iran and effectiveness of 10-point mastitis control program.

The somatic cell count (SCC) of individual cow samples is a useful proxy for monitoring udder health status. The present study aimed to provide updated information about udder health in Iranian Holstein dairy cattle, and to quantify the effectiveness of the mastitis control program. A total of 17,990 monthly test-day records from 1,663 Holstein dairy cattle in 10 "regular" herds and 2,389 test-day records from 386 Holstein dairy cattle in 2 herds that were assigned to the 10-point mastitis control program ("controlled" herds) were included. Each test-day record comprised the date of recording, daily milk production (kg), fat and protein (%), days in milk, parity, and SCC. Median (Q1-Q3) SCC × 103 for "regular" and "controlled" herds were 136 (52-391) and 64 (24-204) cells/ml, respectively. Also, the percentage of records containing SCC >200,000 cells/ml (elevated SCC) for these groups were 40.3% and 25.5%, respectively. Mixed effects logistic analysis revealed that milk records from cows in the first lactation, early lactation, and with >40 kg daily milk yield had lower odds of elevated SCC. The odds of elevated SCC were lower in summer and autumn than in winter. Host and environmental characteristics influence SCC. This should be considered for the interpretation of SCC results. Mastitis control programs can support dairy producers to reach a standard level of udder health.

Evaluation of score for subclinical ketosis risk in Czech Holstein cows

Submitted 2020-07-17 | Accepted 2020-08-18 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.250-257 This study aimed to evaluate the score for subclinical ketosis risk, which is routinely monitored in Czech Holstein cows. The score is based on milk recording indicator traits which include fat-to-protein ratio, fat-to-lactose ratio, citric acid, β-hydroxybutyrate, and acetone concentrations. The score was significantly (P <0.001) affected by the age of cow at calving, days in milk (DIM) and season of test-day recording. Variance components were estimated with a univariate linear animal model for the score on the first test-day and with a multivariate linear animal model for the score in 3 successive test-days (6-40, 30-70, 60-100 DIM). The heritability estimate was lower at the beginning of lactation (0.08) and increased gradually to 0.11 at the end of the recorded period. Genetic correlations between the score at the first and the other two test-days were lower than 1 indicating that they are genetically different traits. Estimated breeding values were normally distributed with mean 0.20 and reliabilities up to 0.66 in females and 0.98 in males. Breeding values were negatively correlated with most of other routinely evaluated traits, with the strongest correlations with milk fat percentage (0.39), body condition score (-0.26) and fertility of cows (-0.25). The score for subclinical ketosis risk showed sufficient genetic variability and had the potential to be used in genetic improvement of resistance to (sub)clinical ketosis of Czech Holstein cows. Keywords: metabolic status, indicator trait, ketone body References Bastin, C. et al. (2016) On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs. Journal of Dairy Science, 99(5), 4080–4094. https://doi.org/10.3168/jds.2015-10087 Belay, T.K. et al. (2017) Genetic parameters of blood β-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian Red cows. Journal of Dairy Science, 100(8), 6298–6311. https://doi.org/10.3168/jds.2016-12458 Costa, A. et al. (2019) Genetic association of lactose and its ratios to other milk solids with health traits in Austrian Fleckvieh cows. Journal of Dairy Science, 102(5), 4238–4248. https://doi.org/10.3168/jds.2018-15883 Gebreyesus, G. et al. (2020) Predictive ability of host genetics and rumen microbiome for subclinical ketosis. Journal of Dairy Science, 103(5), 4557–4569. https://doi.org/10.3168/jds.2019-17824 Hanus, O. et al. (2017) Analyse of relationships between some milk indicators of cow energy metabolism and ketosis state. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(4), 1135–1147. Hanus O. et al. (2013) Identification of subclinical ketosis in early lactation of cows according to the results of milk yield and individual milk samples in milk recording scheme and interpretation of results. Certified methodology. Prague: Institute of Dairy Science. In Czech. Jamrozik, J. et al. (2016) Multiple-trait estimates of genetic parameters for metabolic disease traits, fertility disorders, and their predictors in Canadian Holsteins. Journal of Dairy Science, 99(3), 1990–1998. https://doi.org/10.3168/jds.2015-10505 Kasna, E. et al. (2020) Fat-to-protein ratio in milk of Holstein cows. Nas chov, 80(2), 32–36. In Czech. Kasna, E. et al. (2019) Genetic evaluation of reproductive and metabolic disorders and displaced abomasum in Czech Holstein cows. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(4), 939–946. Koeck, A. et al. (2014) Genetic analysis of milk β-hydroxybutyrate and its association with fat-to-protein ratio, body composition score, clinical ketosis, and displaced abomasum in early first lactation of Canadian Holstein. Journal of Dairy Science, 97(11), 7286–7292. https://doi.org/10.3168/jds.2014-8405 Madsen, P. & Jensen, J. (2013) A User’s Guide to DMU. Retrieved June 25, 2020 from https://dmu.ghpc.au.dk/DMU/Doc/Current/dmuv6_guide.5.2.pdf Martens, H. (2020) Transition period of the dairy cow revisited: I. Homeorhesis and its changes by selection and management. Journal of Agricultural Science, 12(3), 1–24. https://doi.org/10.5539/jas.v12n3pl Pryce, J. E. et al. (2016) Invited review: Opportunities for genetic improvement of metabolic diseases. Journal of Dairy Science, 99(9), 6855–6873. https://doi.org/10.3168/jds.2016-10854 SAS Institute Inc. (2017) Base SAS® 9.4 Procedures Guide, Seventh Edition. SAS Institute Inc., Cary, NC, USA. Slosarkova, S. et al. (2016) Monitoring of dairy cattle diseases in the Czech Republic. Veterinařstvi, 66(11), 859–866. In Czech. Van der Drift, S. G. A. et al. (2012) Genetic and non-genetic variation in plasma and milk β-hydroxybutyrate and milk acetone concentrations of early-lactation dairy cows. Journal of Dairy Science, 95(11), 6781–6787. https://doi.org/10.3168/jds.2012-5640 Vosman, J. J. et al. (2015) Genetic evaluation for ketosis in the Netherlands based on FTIR measurements. Interbull Bulletin No. 49: Proceedings of the 2015 Interbull Meeting, Orlando, Florida, 1–5.

Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows

At the beginning of lactation, high-producing cows commonly experience an unbalanced energy status that is often responsible for the onset of metabolic disorders and impaired health and performance. Blood β-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) are indicators of excessive fat mobilization and circulating ketone bodies. Recently, prediction models based on mid-infrared (MIR) spectroscopy have been developed to assess blood BHB and NEFA from routinely collected individual milk samples. This study aimed to estimate genetic parameters of blood BHB and NEFA predicted from milk MIR spectra and to assess their phenotypic and genetic correlations with milk production and composition traits in early-lactation Holstein cows. The data set comprised the first test-day record within lactation and spectra of individual milk samples (n = 22,718) of 13,106 Holstein cows collected from 5 to 35 d in milk (DIM). Blood BHB and NEFA were predicted from milk MIR spectra using previously developed prediction models. Genetic parameters of blood metabolites and milk traits were estimated for the whole observational period (5-35 DIM) and within 6 classes of DIM. Blood BHB and NEFA showed similar genetic variation across DIM, with the highest heritability in the first 10 d after calving (0.31 ± 0.06 and 0.19 ± 0.05 for BHB and NEFA, respectively). The genetic correlation between BHB and NEFA was moderate (0.51 ± 0.05). Genetic correlations of BHB with milk yield, SCS, protein percentage, lactose percentage, and urea nitrogen content were similar to, or at least in the same direction as, the correlations of NEFA with the same traits, whereas opposite correlations were observed with fat percentage and fat-to-protein ratio. Results of the current study suggest that blood BHB and NEFA predicted from milk MIR spectra have genetic variation that is potentially exploitable for breeding purposes. Therefore, they could be used as indicator traits of hyperketonemia in a selection index aimed to reduce the susceptibility of dairy cows to metabolic disorders in early lactation.

Milk somatic cell count and its relationship with milk yield and quality traits in Italian water buffaloes

In Southern Italy, buffalo (Bubalus bubalis) milk is mostly intended for the manufacture of Mozzarella di Bufala Campana Protected Denomination of Origin (PDO) cheese. Despite the economic boost of the last 2 decades, the buffalo farming system should be improved to maximize the efficiency of the dairy industry, improve yield and quality of milk and cheese, and work toward better animal welfare. Milk somatic cell count (SCC) is used worldwide as an indicator of udder health in individual milk and is useful for monitoring farm hygiene in bulk milk. Mastitis data are currently not available on a large scale in Italy; thus, SCC is essential for identifying animals with suspected udder infection and inflammation. Moreover, high milk SCC is associated with altered composition and acidity, and poor technological properties of milk. However, payment systems of the PDO area are based simply on the delivered volume of milk rather than on quality characteristics. Hence, currently there are no penalties for elevated SCC in bulk milk in the Italian buffalo dairy industry. In addition, SCC for buffalo milk is not mentioned by either the European Community regulations or the PDO protocol, evidencing a lack of rules for the maximum SCC limit. To provide a phenotypic characterization of SCC at the population level and to improve knowledge on buffalo milk quality, 876,299 test-day records of 70,156 buffaloes reared in the PDO area were analyzed. Data revealed that around 11% of herd-test-dates (≥5 animals sampled each) showed average milk SCC ≥400,000 cells/mL (i.e., above the threshold fixed by the European Community for bovine milk). This suggests that there is room to improve SCC at both the farm and individual level. Within first parity, more than 28 and 15% of lactations had average SCC ≥200,000 and ≥300,000 cells/mL, respectively. Both percentages increased with parity and were 39 and 25% in sixth parity, respectively. Supporting this, the proportion of lactations with average SCC ≥500,000 cells/mL increased from 6% in first parity to 12% in sixth parity. Milk yield and SCC were negatively correlated with each other, especially when SCC level was high. An ANOVA was carried out on test-day record milk yield and composition traits, with fixed effects of parity, lactation stage, class of somatic cell score (n = 6), month of calving, and their interactions; buffalo, herd-test-date, and residual were considered random effects. Significantly lower milk yield and lactose percentage were estimated in progressively higher classes of somatic cell score, whereas no significant differences were observed for fat and protein percentages. This is the first attempt to investigate milk SCC in a large data set of Italian dairy buffaloes. These findings may be helpful for defining reliable and effective SCC thresholds to be adopted whenever specific penalties for high SCC are included in milk payment systems. Finally, these results could be used in mastitis monitoring plans aiming to reduce SCC and udder issues at both the individual and farm levels in the Italian buffalo population.

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows.

Simple SummaryPopulation-level phenotyping of blood metabolites is hardly achievable due to the limitation of reference analyses. Mid-infrared spectroscopy has recently been used to develop prediction models for major blood metabolites, allowing their determination on a large scale. The current study investigated the variation of blood β-hydroxybutyrate, non-esterified fatty acids, and urea nitrogen predicted from a large milk mid-infrared spectra database of Brown Swiss, Holstein-Friesian, and Simmental cows. Holstein-Friesian cows had the greatest concentrations of β-hydroxybutyrate and non-esterified fatty acids, and the lowest urea nitrogen in blood, which may underline an altered energy and nutritional status.Serum metabolic profile is a common method to monitor health and nutritional status of dairy cows, but blood sampling and analysis are invasive, time-consuming, and expensive. Milk mid-infrared spectra have recently been used to develop prediction models for blood metabolites. The current study aimed to investigate factors affecting blood β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), and urea nitrogen (BUN) predicted from a large milk mid-infrared spectra database. Data consisted of the first test-day record of early-lactation cows in multi-breed herds. Holstein-Friesian cows had the greatest concentration of blood BHB and NEFA, followed by Simmental and Brown Swiss. The greatest and the lowest concentrations of BUN were detected for Brown Swiss and Holstein-Friesian, respectively. The greatest BHB concentration was observed in the first two weeks of lactation for Brown Swiss and Holstein-Friesian. Across the first month of lactation, NEFA decreased and BUN increased for all considered breeds. The greatest concentrations of blood BHB and NEFA were recorded in spring and early summer, whereas BUN peaked in December. Environmental effects identified in the present study can be included as adjusting factors in within-breed estimation of genetic parameters for major blood metabolites.

Intramammary antimicrobial treatment of subclinical mastitis and cow performance later in lactation

The aim of this study was to evaluate long-term therapeutic effects of antimicrobial treatment of recently acquired subclinical mastitis (RASCM) during lactation. Quarter-level clinical mastitis (CM) follow-up, composite somatic cell counts (SCC), and cow-level milk yield later in lactation were evaluated using follow-up data from 2 previously published linked randomized field trials. The first trial randomly assigned antimicrobial treatment with any intramammary product or negative control to culture-positive quarters of cows having a first elevated composite SCC after 2 consecutive low composite SCC measurements. Untreated cows that had a second elevated composite SCC at the next measurement and were staphylococci-positive (i.e., Staphylococcus aureus or non-aureus staphylococci) were randomly assigned to treatment or control. Quarter-level CM cases were reported by the participating herd personnel, and milk yield and composite SCC data were obtained from the regular test-day recording. Frailty survival models were used to evaluate the long-term therapeutic effects of antimicrobial treatment of RASCM on quarter-level CM follow-up. Mixed linear regression models were applied to quantify the effect on milk yield and composite SCC. Data of 638 quarters from 486 cows in 38 herds were available for statistical analyses, of which 229 quarters of 175 cows received antimicrobial treatment for RASCM. Antimicrobial treatment culminated in reduced composite SCC levels later in lactation but did not result in different milk yield levels or CM follow-up compared with control cows. Antimicrobial treatment of cows with RASCM should therefore only be considered in exceptional situations given the current focus on antimicrobial usage reduction in animal husbandry.

Evaluation of reference lactation length in Chios dairy sheep

Definition and establishment of a fixed reference lactation length could provide useful tools for on-farm comparison of ewes and flock management as well as genetic evaluations for the breeding programme. The objectives of this study were to (i) evaluate different reference lactation lengths for the Chios dairy sheep and (ii) define the most suitable reference length for the breed. A total of 260 042 test-day milk records from 24 474 ewes in 130 flocks collected between 2003 and 2014 were used; 15 different lactation lengths were evaluated ranging from 120 to 260 days, defined at 10-day intervals as reference for the Chios sheep. The evaluation criteria included: (a) heritability and repeatability of milk yield in each reference lactation, (b) genetic correlation of reference lactation milk yield with actual lactation milk yield and yield at first test-day record and (c) correlated response in reference lactation milk yield from selection based on first test-day milk yield. The latter emulates genetic gains achieved for milk yield based on early lactation selection. Heritability and repeatability estimates of reference lactation milk yield and genetic correlation with actual lactation yield favoured long reference lactations (180 to 230 days). On the contrary, correlation with first test-day record milk yield was higher for short lactations (120 to 170 days). Moreover, selection on first test-day record milk yield would lead to a correlated response in reference yield in 220 days equal to 85% of the highest estimate achieved in the maximum reference length of 260 days (190 days when only considering first lactation milk yield). Based on the results of the present study, an overall reference lactation length for the Chios breed of 220 days post-lambing and a first lactation reference length of 190 days post-lambing are recommended.

Genetic analysis of subclinical mastitis in early lactation of heifers using both linear and threshold models

Subclinical mastitis (SCM) causes economic losses for dairy producers by reducing milk production and leading to higher incidence of clinical mastitis and premature culling. The prevalence of SCM in first-lactation heifers is highest during early lactation. The objective of this study was to estimate genetic parameters for SCM in early lactation in first-parity Holsteins. Somatic cell count test-day records were collected monthly in 91 Canadian herds participating in the National Cohort of Dairy Farms of the Canadian Bovine Mastitis Research Network. Only the first test-day record available between 5 and 30 d in milk was considered for analysis. The final data set contained 8,518 records from first lactation Holstein heifers. Six alternative traits were defined as indicators of SCM, using various cutoff values of SCC, ranging from 150,000 to 400,000 cells/mL. Both linear and threshold animal models were used. Overall prevalence of SCM using the 6 traits ranged from 13 to 24%. Heritability estimates (standard error) from linear and threshold models ranged from 0.037 to 0.057 (0.015 to 0.018) and from 0.040 to 0.051 (0.017 to 0.020), respectively. We found strong genetic correlations (standard error) among alternative SCC traits, ranging from 0.90 to 0.99 (0.013 to 0.069), indicating that these 6 traits were genetically similar. Despite low heritability, based on estimated breeding values (EBV) predicted from both models, we noted exploitable genetic variation among sires. Higher EBV of SCM resistance corresponded to sires with a higher percentage of daughters without SCM. Based on a linear model (all 6 traits), percentage of daughters with SCM ranged from 5 to 13% and from 19 to 33% for the top 10% and worst 10% of 69 sires with minimum 20 daughters in at least 5 herds, respectively. Spearman's rank correlations among EBV of sires predicted from linear (from 0.75 to 0.95) and threshold (from 0.74 to 0.95) models were moderate to high, respectively. Very high rank correlations (0.98 to 0.99) between EBV predicted for the same trait from linear and threshold model indicated that reranking of sires based on model used was minimal. In conclusion, despite low heritability, we found utilizable genetic variation in early lactation of heifers. Hence, genetic selection to improve genetic resistance to SCM in early lactation of heifers was deemed possible.

Incorporation of observations with different residual error variances into existing complex test-day models

ABSTRACTAutomated milking systems (AMS) have become popular on dairy farms. Due to a different test-day recording scheme the variation of test-day observations differ from AMS differ from those of conventional milking system (CMS). An approach is presented for upgrading the genetic evaluation model for production traits milk, protein and fat yield by including residual covariance matrices for AMS and CMS test-day observations. Residual variances were found to be 16–37% smaller for milk and protein yields and 42–47% larger for fat yield when recorded under AMS herds compared to CMS herds. Daily heritability was higher for milk and protein yield and lower for fat yield when traits were recorded under AMS compared to recording under CMS. No difference was found between AMS and CMS in 305-day heritability for milk and protein yield except for second lactation milk yield. 305-day heritability for fat was lower for all lactations under AMS.

Patent gastrointestinal nematode infections in organically and conventionally pastured dairy cows and their impact on individual milk and fertility parameters

Infections with gastrointestinal nematodes (GIN) can lead to production losses and impacts on product quality in affected cows, which has mainly been demonstrated during deworming experiments or via herd-level measurements. Here, a field study was carried out to explore the association between GIN infection status and milk production as well as fertility parameters in individual dairy cows. Different selection lines of Black and White cows were included in the study, which were distributed among 17 small and medium-sized organic and conventional German grassland farms. Faecal samples of 1166 dairy cows were examined twice, in July and September 2015. Nematode eggs were found in the faeces of 473 (40.6%) cows. As expected, strongylid eggs (Trichostrongylidae or Oesophagostomum and Bunostomum spp., respectively) were the predominant morphotype, followed by Strongyloides papillosus and Capillaria spp. eggs. In July, cows kept under organic conditions had a significantly lower GIN prevalence in comparison to cows kept on conventional farms. Faecal egg counts were generally low, with the highest value in September and an arithmetic mean of 11.3 eggs per gram faeces (EPG) for all observations. The relationships between GIN infection status and milk yield (kg milk/cow/day), milk protein content (%) and milk fat content (%) for each first test-day record after parasitological assessment were estimated by using linear mixed models. Milk protein content was estimated 0.05% lower in GIN positive compared to GIN negative cows, whereas no significant effect on milk yield or milk fat content was observed. The impact of GIN infection status on success in first insemination (SFI) was estimated by using a threshold model. No significant association was demonstrated between GIN infection status and SFI. Unexpectedly, the fertility parameter days from calving-to-first-service (CTFS) showed a significantly shorter average interval in GIN positive cows. However, these data on reproductive performance need to be considered preliminary as long-term studies are needed to allow a firm prediction of the impact of GIN infection status on dairy cow fertility parameters.

No effects of homeopathy on somatic cell count, bovine milk yield and composition

This study aimed to evaluate the effect of a homeopathic complex, on the reduction of milk somatic cell count, production of milk, fat percentage and protein percentage of milk in lactating Holstein cows, over a period of three months. Twenty cows, in second parity, were randomly assigned in blocks with two treatments, after an initial period of adaptation in the experimental area for 15 days. The cows were distributed into a control group (that did not receive the homeopathic complex) and a treated (that received the homeopathic complex), evaluated for six test-day records. Laboratory analysis of fat and protein were made via the infrared absorption and somatic cell count by flow cytometry, wherein this latest has been transformed into logarithmic scale for statistical analysis. Analysis of variance to test the effects of treatment, test-day record and the interaction between these factors, was performed through repeated measures over time. For all traits, there was no significant interaction between treatment and test-day record (p > 0.05) and there was no statistical difference (p > 0.05) between the control and treated groups. The use of the homeopathic complex was not effective in reducing somatic cell count of dairy cows, for the trial period of three months. The use of homeopathy did not interfere significantly with milk yield or the protein percentage in Holstein cows.

Genomic selection for tolerance to heat stress in Australian dairy cattle

Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this study was to develop genomic estimated breeding values (GEBV) for heat tolerance in dairy cattle. Heat tolerance was defined as the rate of decline in production under heat stress. We combined herd test-day recording data from 366,835 Holstein and 76,852 Jersey cows with daily temperature and humidity measurements from weather stations closest to the tested herds for test days between 2003 and 2013. We used daily mean values of temperature-humidity index averaged for the day of test and the 4 previous days as the measure of heat stress. Tolerance to heat stress was estimated for each cow using a random regression model with a common threshold of temperature-humidity index=60 for all cows. The slope solutions for cows from this model were used to define the daughter trait deviations of their sires. Genomic best linear unbiased prediction was used to calculate GEBV for heat tolerance for milk, fat, and protein yield. Two reference populations were used, the first consisted of genotyped sires only (2,300 Holstein and 575 Jersey sires), and the other included genotyped sires and cows (2,189 Holstein and 1,188 Jersey cows). The remainder of the genotyped sires were used as a validation set. All animals had genotypes for 632,003 single nucleotide polymorphisms. When using only genotyped sires in the reference set and only the first parity data, the accuracy of GEBV for heat tolerance in relation to changes in milk, fat, and protein yield were 0.48, 0.50, and 0.49 in the Holstein validation sires and 0.44, 0.61, and 0.53 in the Jersey validation sires, respectively. Some slight improvement in the accuracy of prediction was achieved when cows were included in the reference population for Holsteins. No clear improvements in the accuracy of genomic prediction were observed when data from the second and third parities were included. Correlations of GEBV for heat tolerance with Australian Breeding Values for other traits suggested heat tolerance had a favorable genetic correlation with fertility (0.29–0.39 in Holsteins and 0.15–0.27 in Jerseys), but unfavorable correlations for some production traits. Options to improve heat tolerance with genomic selection in Australian dairy cattle are discussed.

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

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