Dairy Herd Improvement Program Research Articles

Exploring herd-level perinatal calf mortality risk factors in eastern Canadian dairy farms

This closed cohort study aimed to identify the associations between dairy calf management practices and herd-level perinatal calf mortality risk. From February 2020 to June 2021, predominantly Holstein dairy farms in Québec (n = 1,832) and New Brunswick (n = 52), Canada, that were registered in the dairy herd improvement program were visited once. A questionnaire covering all aspects of precalving, calving, and colostrum management was administered. Data regarding perinatal mortality were retrieved from the dairy herd improvement program database for each farm for 2021. Perinatal mortality was calculated for each farm as the proportion of calves dead at birth or dying within 24 h after birth. A multivariable negative binomial model was used to assess herd-level factors associated with the risk of perinatal mortality. The final model included the lying surface in the calving area, the typical time to first colostrum intake, typical cow-calf contact time, the proportion of males born, the proportion of assisted calvings, and herd size. Herd-level perinatal mortality risk ranged from 0% to 38.1% (mean ± SE = 7.6% ± 0.1%). A greater proportion of males born, a higher proportion of assisted calvings, and delayed colostrum feeding were associated with increased herd-level perinatal mortality. Factors associated with a decreased herd-level perinatal mortality risk were having a typical cow-calf contact time between 7 and 12 h after calving compared with reduced cow-calf contact time, soft lying surfaces in the calving area compared with concrete and mat-lying surfaces, and an increased number of calvings per year. Our results show that although some of the significant risk factors are not well understood (i.e., calving area lying surface, typical cow-calf contact time), Canadian farmers could focus on the factors under their control (i.e., time to first colostrum feeding, proportion of difficult calvings, males born, and calvings per year) to reduce the risk of perinatal mortality. Future work should focus on qualitative research to understand the dairy farmer motivations and limitations to implementing practices identified in this and other studies to reduce perinatal mortality.

PSV-2 Difference in Sire Pta Evaluated By Taiwan Dhi and the Original Countries

Abstract Taiwan is located in a region with a tropical/subtropical oceanic climate characterized by high temperatures and humidity, which significantly affects the milk production, health, and reproduction of dairy cows. Most dairy herds in Taiwan have utilized semen of superior foreign sires from countries in temperate climate, mainly from USA and Canada. However, the performance of daughters in Taiwan of those foreign sires may differ from those in temperate countries. Therefore, the objective of this study was to evaluate the performance of foreign sire semen in Taiwan's dairy herds, and to compare the PTAs of those sires in both Taiwan and their original countries. Data and pedigree from the Dairy Herd Improvement program (DHI) were collected between 2009 and 2021. In total, 108,348 lactation records from 54,269 cows and 76,024 animals in the pedigree were obtained from 120 herds. Traits to evaluate include milk yield (MY), milk fat percentage (MF%), milk fat yield (MFY), milk protein percentage (MP%), and milk protein yield (MPY) standardized to 305d-2X yield. The herd-year-season and lactation were considered as fixed effects in the model with additive genetic effect and permanent environmental effect as random effects. In addition, an extra fixed effect, physical situation of lactation (PSL), was included to account for abnormal physiology or subclinical mastitis in those dairy cows. The ASReml program was used to estimate the variance and covariance components as well as heritability and correlation with multiple-trait animal models. Not only the accuracy of predicted transmitting ability (PTATW) ≥ 75% of those superior foreign sires using DHI data and their PTAs obtained from the original countries (PTAInt) were compared, but also the rank correlation between PTATW and PTAInt was also calculated. The results indicated that only when the accuracy of PTATW increased to 90%, a moderate correlation was observed. Moreover, PTATW and PTAInt of all the sires with an accuracy of PTATW ≥ 75% were tracked to the lactation performance of their daughters in Taiwan using regression analysis. The results showed that selecting sires with an increase of 1 kg in PTATW and in PTAInt would increase the average production of their daughters in Taiwan's dairy herds by 1.620 kg and 0.213 kg in milk yield, 1.328 kg and 1.201 kg in fat yield, and 1.493 kg and 0.550 kg in protein yield, respectively. Therefore, selecting offspring through PTATW of sires is much more efficient, and increasing the number of daughters for each sire and their DHI records would improve the accuracy of PTATW prediction and ranking, making it an important indicator for selecting progeny in Taiwan dairy herds.

100 years of data sovereignty: Cooperative data governance and innovation in US dairy

AbstractWe discuss a little‐known but highly successful approach to innovation and data governance observed in the U.S. dairy sector. The National Cooperative Dairy Herd Improvement Program (NCDHIP) is a century‐old institution that coordinates farm data collection to support research on dairy cattle breeding and genetic selection. After discussing the program's history, we discuss how its evolution can inform data governance in agriculture today. We identify three key attributes that make the NCDHIP a successful model in agriculture: overcoming free‐riding with member benefits to data providers, ensuring data interoperability with uniform data standards, and controlling data access and use with cooperative governance.

Predicting dry matter intake in mid-lactation Holstein cows using point-in-time data streams available on dairy farms.

Quantifying dry matter intake (DMI) in lactating dairy cows is important for determining feed efficiency; however, there are no methods for economically quantifying individual cow DMI on dairy farms where cows are group-fed. Attempts have been made to model DMI using cow factors, milk production, milk infrared spectra, and behavioral sensors with reasonable success. Other data streams are available on the farm that may contribute to DMI predictions. In this study, our objective was to model DMI with multiple linear regression using data from a single point-in-time that can easily be accessed on-farm. Candidate predictor variables included cow descriptors, milk yield and composition, milk fatty acid profile, and production and efficiency predicting transmitting abilities (PTA). Observations of DMI were obtained from 350 cows across 6 cohorts using individual feed bunks. The cow to bunk ratio was 2:1, with an overall bunk occupation rate of 32% throughout the day. The following models were developed sequentially with milk data obtained from a single morning milking and other data from the same day: model B (production, metabolic body weight, body condition score, lactation category, and week of lactation), model BC [model B + fatty acid (FA) content], model BY (model B + FA yield), model BPE (model B + production and efficiency PTA), model BYP (model BY + production PTA), model BYE (model BY + efficiency PTA), and model BYPE (model BY + production and efficiency PTA). Outcome variables predicted in these models were the DMI on the previous day or current day relative to the morning milk sample. The predictions for DMI on the previous day outperformed current day DMI in every model for which they were both determined. Addition of milk FA and PTA as candidate predictor variable types to the models resulted in enhanced predictive ability, with incremental enhancements when combined. The most robust model (BYPE) included cow descriptors, protein and FA yields, and PTA for milk and residual feed intake. Model BYPE described 21 to 32% more of the variation in DMI (based on concordance correlation coefficient) than when other common DMI models were applied to the same data set. Overall, reasonable performance of models including single point-in-time cow descriptors, milk and FA production, and production and efficiency PTA commonly available to dairy farmers through dairy herd improvement programs offer an opportunity for on-farm prediction of DMI, yet further improvement may be possible.

Environmental factors affecting milk composition in Holstein cattle breed

The incidence of mastitis in dairy herds is one of the main difficulties faced by dairy farmers, with a negative effect on the productivity of the herd and the welfare of the animals. Somatic cell count in milk is an indicator of udder health and frequency of clinical and subclinical mastitis incidence in dairy herds, and it is also often used to determine quality payments to dairy producers. Milk urea can be an indicator of the nutritional status of the dairy cows. The interpretation of these parameters assists in making important management decisions with regards to the health status and nutrition of dairy cattle. The objective of this study was to identify and evaluate environmental factors (farm, season, parity and stage of lactation) which affect the milk production and composition of Holstein breed, using field data. The presented research included 25,460 individual milk samples which were analysed as part of the Dairy Herd Improvement program, from 11 Holstein dairy farms in the region of Vojvodina, Northern Serbia. Analyses of raw milk samples were carried out on the FOSS instruments - CombiFossTMFT+, a combination instrument consisting of the MilcoScanTMFT+ and the FossomaticTMFC. Statistical data processing was carried out by applying General Linear Model procedure, Statistics 13. Farm, season of milk control, parity and stage of lactation were included in the models as fixed effects. Significant differences in milk urea concentrations and somatic cell count were observed between farms (P<0.01), seasons (P<0.01), parity (P<0.01) and stage of lactation (P<0.01). Results showed that there were a highly significant (P<0.01) positive relationships between milk urea (MU) concentration and milk yield, MU and milk fat content, and between MU and solids non-fat (SNF), also between somatic cell count (SCC) and milk fat content, between SCC and protein content, as well as in between SCC and SNF. Highly significant negative relationships were found between milk urea and protein content and SCC, and between SCC and daily milk yield and lactose content. Proper analyses and interpretation of obtained results of milk samples obtained within the Dairy Herd Improvement program could contribute to better health management on the farms and it could have a positive impact on composition and nutritional value of milk, as well as on milk safety. It would be important to carry out further research in order to facilitate the detection of subclinical mastitis with MU as a potential indicator.

Mining data from milk mid-infrared spectroscopy and animal characteristics to improve the prediction of dairy cow's liveweight using feature selection algorithms based on partial least squares and Elastic Net regressions

Body weight (BW) of dairy cows is relevant for breeding programs and farm management to assess the maintenance requirements, reproduction performance, or health status of cow. Currently, it is still difficult to follow BW changes of individual cows routinely in large herds. Combined with animal characteristics, milk mid-infrared (MIR) spectrum was proposed as an additional source of information to predict BW under the framework of dairy herd improvement (DHI) programs. However, the presence of less informative variables in the prediction equation could impact negatively its robustness. This research aims to improve the robustness of BW regression models by applying a feature selection before modeling. A total of 5,920 BW records composed of animal characteristics and milk MIR spectrum were collected from Holstein cows. Three feature selection algorithms were applied to select the most informative variables: partial least squares regression (PLS) combined with sum of ranking difference (PLS-SRD), PLS combined with uninformative variables elimination (PLS-UVE), and the output of Elastic Net regression (EN). Four herd independent validation sets and the corresponding remained calibration datasets having on average 163 and 1,708 records, respectively, were used to develop models using PLS or EN approaches. Ten-fold cross-validation was conducted to parametrize each model. Parity, days in milk (DIM), milk yield (MY), and two MIR spectral points were selected as relevant variables to predict BW. PLS (root mean square error of validation, RMSEp = 60 kg) and EN (RMSEp = 60 kg) regressions employing these 5 predictors were more robust than the models developed without MIR or using MIR without feature selection. The EN models had a cross-validation root mean square error of around 53 kg. The 2 MIR points explained up to 4.20% variation in predicting BW. The RMSE of validation sets using another brand of spectrometer were around 64 kg. This study confirms the possibility to predict an indicator of BW from animal characteristics and MIR variables. The variable selection procedures improved the model's robustness and transferability. The accuracy of BW prediction seems to be sufficient to provide useful information for breeding program and farm management decisions under a DHI framework.

Invited review: The future of selection decisions and breeding programs: What are we breeding for, and who decides?

Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance.

PSVIII-36 Late-Breaking Abstract: Somatic cells count in dairy farms of Northern Kazakhstan

Abstract According to requirements of the Safety of Milk and Dairy Products in our republic somatic cells count should be less than 750,000 cells / mL. Achieving this level is possible with the implementation of somatic cell programs based on the experience of laboratories in Western Europe and Northern America (G.M. Jones). Somatic cells in milk are counted in the United States and Canada as part of the National Dairy Herd Improvement (DHI) program. The result was a significant improvement of the dairy herd by mastitis level (Barkema H.W., Schukken Y.H., Lam T.J., Beiboer M.L., Wilmink H. et al. 1998). Average somatic cell content was less than 200 thousand cells/mL. The purpose was to determine somatic cells count in the herds of Republic of Kazakhstan and to test the SCC program. Research work was carried out under project “Improving the breeding methods efficiency.” The chemical composition and somatic cell count were carried out on a CombiFossFT + infrared analyzer. The results of counting somatic cells in milk of dairy cows in the farms of the northern region, the Republic of Kazakhstan, showed that the quality of milk in most dairy farms meets the requirements of the technical regulation on the quality and safety of milk (table 1). According to the table, it can be said that livestock of dairy cattle by 16% or more are affected by clinical and subclinical mastitis. Moreover, each farm receives less than 6% or more of milk. To increase the efficiency of dairy cattle breeding in the Republic of Kazakhstan, it is necessary to introduce a program for somatic cell counting into the practice of dairy laboratories.

Distribution of non-aureus staphylococci from quarter milk, teat apices, and rectal feces of dairy cows, and their virulence potential

Non-aureus staphylococci (NAS) are predominantly isolated from bovine milk samples of quarters suffering from subclinical mastitis. They are also abundantly present on dairy cows' teat apices and can be recovered from bovine fecal samples, as recently described. Differences in ecology, epidemiology, effect on udder health, and virulence or protective traits have been reported among the species within this group. The objectives of this study were (1) to describe the species-specific distribution of NAS in 3 bovine-associated habitats, namely quarter milk, teat apices, and rectal feces, and (2) to evaluate the virulence potential of NAS by comparing their distribution in contrasting milk sample strata and the presence of selected virulence genes. A cross-sectional, systematic sampling procedure was followed in 8 dairy herds that participated in the local Dairy Herd Improvement program in Flanders, Belgium. Quarter milk samples (n = 573) were collected from 144 lactating cows in 8 herds. In 5 of the 8 herds, teat apex swabs (n = 192) were taken from 15 lactating cows, before and after milking, and from 18 dry cows. In the same 5 herds, rectal feces were sampled from 80 lactating cows (n = 80), taking into account that a cow could only serve as the source of one type of sample. In addition, milk samples of all clinical mastitis cases were continuously collected during the 1-yr study period from March 2017 to March 2018 in the 8 herds. In total, 1,676 Staphylococcus isolates were phenotypically identified and subjected to MALDI-TOF mass spectrometry. Thirty-three, 98, and 28% of all quarter milk, teat apex, and rectal fecal samples were NAS-positive, respectively, reaffirming the presence of NAS in rectal feces. The overall predominant species in the 3 habitats combined were Staphylococcus haemolyticus, Staphylococcus chromogenes, and Staphylococcus hominis. Four, 16, and 12% of the healthy quarters (quarter milk somatic cell count ≤50,000 cells/mL of milk), quarters with subclinical mastitis (quarter milk somatic cell count >50,000 cells/mL of milk), and quarters with clinical mastitis, respectively, were NAS-positive, suggesting that the potential to cause (mild) clinical mastitis is present among NAS. This was substantiated by comparing the presence of virulence genes of NAS isolates originating from contrasting milk sample strata (healthy quarters and quarters with clinical mastitis).

Associations between on-farm animal welfare indicators and productivity and profitability on Canadian dairies: I. On freestall farms

Motivating dairy producers to financially invest in the improvement of their animals' comfort and welfare can pose some challenges, especially when financial returns are uncertain. Economic advantages for dairy producers associated with increased animal welfare are likely to come from either a premium paid for the milk or increased productivity. The aim of the current study was to evaluate the associations between measures of herd productivity and farm profitability and animal-, management-, and resource-based indicators of cow welfare and comfort. The cow welfare measures were collected during a cow comfort assessment conducted on 130 Canadian freestall dairy farms, including 20 using an automatic milking system. Herd productivity and farm profitability measures were retrieved or calculated from data collected by the regional dairy herd improvement programs, and included milk production and quality, longevity, and economic margins over replacement costs. Univariable and multivariable linear regression models were used to assess the associations between welfare indicators and productivity and profitability measures. Increased yearly corrected milk production was associated with reduced prevalence of cows with knee lesions [β = 7.40; 95% confidence interval (CI): 2.6, 12.2], dirty flanks (β = 26.9; 95% CI: 7.4, 46.5), and lameness (β = 11.7; 95% CI: 3.3, 20.1). The farms' economic margin per cow, calculated over replacement costs, was associated with the within farm average lying time standard deviation (β = -7.2; 95% CI: -12.7, -1.7), percent of stalls with dry bedding (β = 6.4; 95% CI: 1.4, 11.4), and prevalence of cows with knee lesions (β = -5.1; 95% CI: -8.9, -1.3). Some of the relationships found were complex, including several interactions between the animal-, management-, and resource-based measures. Overall, the results suggest that improved cow comfort and welfare on freestall farms is associated with increased herd productivity and profitability, when the latest is calculated by the margins over the replacement costs.

Development of genetic evaluation for milk production traits of Holsteins in Japan

The procedure used for the genetic evaluation of dairy cattle in Japan has developed from a lactation sire–MGS model to a multiple‐lactation random regression test‐day animal model. Genetic evaluation of Holstein bulls in Japan began in 1989 with the use of field‐style progeny testing; dairy herd improvement program data from all over Japan were used, along with a sire and maternal grandsire model. In 1993, an animal model was introduced to estimate breeding values for yield and type traits. A random regression test‐day model was first applied in 2010. In the business of breeding dairy cattle, it is very important to users that estimated breeding values are reliable and stable among subsequent routine evaluations. With experience in the genetic evaluation of dairy cattle in Japan, Japanese researchers have found ways to improve the stability of estimated breeding values. These modifications involve changes in data editing, development of evaluation models, changes to the structures of unknown‐parent groups, awareness of the problems of predicting lactation yield from partial test‐day records, and adjustment for heterogeneity within herd variances. Here, I introduce developments in, and our experiences with, the genetic evaluation of yield traits of Holstein cattle in Japan.

Growth, productive performance, and udder health of crossbred Holstein x Simmental cows and purebred Holstein cows

The crossbreeding between dairy cow breeds has been used as an alternative to improve some characteristics such as milk composition, udder health, and reproductive aspects on dairy herds. The aim was to compare growth, milk yield and composition, and mammary gland health of crossbred Holstein x Simmental and purebred Holstein cows. Data were gathered from a farm located in Santa Catarina state, south of Brazil. Cow growth was assessed by weighing calves and heifers monthly. Yet for milk yield and udder health, data were taken from records of the official Dairy Herd Improvement Program carried out in the evaluated farm. These data were subjected to variance analysis using the MIXED procedure of SAS statistical software. Non-linear regressions were used to adjust the data to the growth curves. The crossbred Holstein x Simmental cows produced more milk than did purebreds (31.8 kg/day vs. 30.4 kg/day; p < 0.05), with higher contents of lactose (4.63% vs. 4.53%) and protein (3.14% vs. 3.03%), but no differences in fat content (3.00 vs. 2.96). Purebred cows had higher somatic cell score (SCS) 4.49 vs. 2.93 (P < 0.0001). Furthermore, no differences were found for the growth of calves and heifers. In conclusion, crossbred Holstein x Simmental cows have higher energy corrected milk as the Holstein ones, whit greater uncorrected milk yield only for cows with three or more lactations. Crossbreeds cows produce milk with better quality, indicated by lower SCS and higher solids content, besides showing a lower SCS and no differences regarding weight gain of calves and heifers.

Differential somatic cell count in milk before, during, and after lipopolysaccharide- and lipoteichoic-acid-induced mastitis in dairy cows

Intramammary infections induce the initiation of the inflammatory response, resulting in an increase in somatic cell count (SCC) in milk. The SCC includes several different types of cells but does not differentiate between them. On the contrary, the new differential somatic cell count (DSCC) parameter allows for the differentiation between 2 groups of cells: polymorphonuclear neutrophils (PMN) and lymphocytes versus macrophages. Therefore, the aim of this paper was to describe the changes of both DSCC and SCC during mastitis induced by cell wall components from typical mastitis-causing pathogens [lipopolysaccharide (LPS), Escherichia coli; lipoteichoic acid (LTA), Staphylococcus aureus] known to trigger different severities of mastitis. In addition, the effect the glucocorticoid prednisolone (PRED), which is known to attenuate the immune response in the mammary gland, was investigated. Twenty dairy cows were equally divided into 5 groups and treated with LPS, LTA, LPS+PRED, LTA+PRED, or a saline control. Milk samples were taken at the following time points: baseline (d -3, -2, and -1), right before treatment (d 0), 5 h after treatment (d 0.2), early cure phase (d 1 and 2), and late cure phase (d 3, 4, 5, 6, 7, and 14) and analyzed for DSCC and SCC. Mean DSCC values increased significantly from <60% at baseline and right before treatment to >81% 5 h after treatment and the early cure phase in all groups, except for the groups control and LTA+PRED. This increase clearly reflects a shift in cell populations to predominantly PMN. The SCC increased significantly following the stimulation, too, as expected. Interestingly, we observed cases where SCC increased moderately only whereas DSCC showed an evident increase, meaning that the shift in cell populations occurred even at low SCC levels. The PRED clearly lowered the cell migration in group LTA+PRED. This is the first ever study investigating DSCC during induced mastitis under controlled conditions. The combination of DSCC and SCC could be employed for the earlier detection of mastitis by revealing the shift in cell population independent from the SCC level. Furthermore, combining DSCC and SCC information could help to determine the stage of mastitis because we observed high DSCC and SCC results in the early stage of mastitis but evidently lower DSCC and high SCC in the cure phase. Hence, our results offer the first fundamental insights on how mastitis monitoring could be improved in the frame of dairy herd improvement programs.

A genome-wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle.

A genome-wide association study (GWAS) was conducted on 15 milk production traits in Chinese Holstein. The experimental population consisted of 445 cattle, each genotyped by the GGP (GeneSeek genomic profiling)-BovineLD V3 SNP chip, which had 26151 public SNPs in its manifest file. After data cleaning, 20326 SNPs were retained for the GWAS. The phenotypes were estimated breeding values of traits, provided by a public dairy herd improvement program center that had been collected once a month for 3years. Two statistical models, a fixed-effect linear regression model and a mixed-effect linear model, were used to estimate the association effects of SNPs on each of the phenotypes. Genome-wide significant and suggestive thresholds were set at 2.46E-06 and 4.95E-05 respectively. The two statistical models concurrently identified two genome-wide significant (P<0.05) SNPs on milk production traits in this Chinese Holstein population. The positional candidate genes, which were the ones closest to these two identified SNPs, were EEF2K (eukaryotic elongation factor 2 kinase) and KLHL1 (kelch like family member 1). These two genes could serve as new candidate genes for milk yield and lactation persistence, yet their roles need to be verified in further function studies.

Short communication: Microbiological quality of raw cow milk and its association with herd management practices in Northern China

Contamination of raw milk with bacterial pathogens is potentially hazardous to human health. The aim of this study was to evaluate the total bacteria count (TBC) and presence of pathogens in raw milk in Northern China along with the associated herd management practices. A total of 160 raw milk samples were collected from 80 dairy herds in Northern China. All raw milk samples were analyzed for TBC and pathogens by culturing. The results showed that the number of raw milk samples with TBC <2 × 106 cfu/mL and <1 × 105 cfu/mL was 146 (91.25%) and 70 (43.75%), respectively. A total of 84 (52.50%) raw milk samples were Staphylococcus aureus positive, 72 (45.00%) were Escherichia coli positive, 2 (1.25%) were Salmonella positive, 2 (1.25%) were Listeria monocytogenes positive, and 3 (1.88%) were Campylobacter positive. The prevalence of S. aureus was influenced by season, herd size, milking frequency, disinfection frequency, and use of a Dairy Herd Improvement program. The TBC was influenced by season and milk frequency. The correlation between TBC and prevalence of S. aureus or E. coli is significant. The effect size statistical analysis showed that season and herd (but not Dairy Herd Improvement, herd size, milking frequency, disinfection frequency, and area) were the most important factors affecting TBC in raw milk. In conclusion, the presence of bacteria in raw milk was associated with season and herd management practices, and further comprehensive study will be powerful for effectively characterizing various factors affecting milk microbial quality in bulk tanks in China.

Evaluation of the composite milk somatic cell count as a predictor of intramammary infection in dairy cattle

The objectives of this study were (1) to evaluate the test characteristics and predictive values of quarter-composite milk somatic cell count (quarter-cSCC) values based on either a single observation or the geometric mean of multiple recordings as a predictor of intramammary infection (IMI) in lactating dairy cows; and (2) to explore to what extent herd prevalence of IMI and cow factors such as parity and stage of lactation affect them. A total of 780 single-quarter milk samples were collected from 195 dairy cows for bacteriologic culture at a single cross-sectional herd screening performed at 21 different dairy herds as part of different research projects. Additionally, monthly quarter-cSCC milk samples at test day were available as part of the Dairy Herd Improvement program. Sensitivity (Se), specificity (Sp), positive predictive valu (PPV), and negative predictive value (NPV) were calculated to differentiate cows infected with any pathogen and cows infected with major pathogens from uninfected cows. Different threshold values for quarter-cSCC, ranging between 50,000 and 500,000 cells/mL, were evaluated for all animals in the study, as well as for high- and low-prevalence herds, heifers and multiparous cows, and cows in early, mid, and late lactation. The overall Se and Sp at a threshold of 200,000 cells/mL for a single quarter-cSCC observation obtained closest to the time of bacteriologic culture were 44.3 and 87.3%, respectively, for cows infected with any pathogen, and 65.1 and 73.0%, respectively, for cows infected with major pathogens. The overall PPV and NPV at a threshold of 200,000 cells/mL for a single quarter-cSCC observation obtained closest to the time of bacteriologic culture were 89.9 and 38.1%, respectively, for cows infected with any pathogen, and 40.6% and 88.1%, respectively, for cows infected with major pathogens. No major differences were observed between estimates of the test characteristics and predictive values of the quarter-cSCC criteria based on a single observation and the geometric mean of multiple observations. For IMI with any pathogen, the Se and PPV were higher in high-prevalence herds than in low-prevalence herds, particularly at thresholds of 50,000 and 100,000 cells/mL. For IMI with major pathogens, Sp was substantially higher in low-prevalence herds than in high-prevalence herds. Sensitivity was higher in multiparous cows than in heifers infected with any pathogen, more specifically at a threshold of 100,000 and 200,000 cells/mL. For cows in early and mid lactation infected with any pathogen, Sp was higher than for cows in late lactation using the single observation closest to the time of bacteriologic culture. The results suggest that the quarter-cSCC threshold value to select cows for bacteriologic culture to maximize the likelihood of finding the causative pathogen of IMI should depend on the group of pathogens one is interested in, the herd prevalence of subclinical mastitis, lactation stage, and the cow's parity.

Short communication: Influence of the sampling device on somatic cell count variation in cow milk samples (by official recording)

The objective of this study was to investigate the variability in cow´s milk somatic cell counts (SCC) depending on the type of milk meter used by dairy farms for official milk recording. The study was performed in 2011 and 2012 in the major cattle area of Spain. In total, 137,846 lactations of Holstein-Friesian cows were analysed at 1,912 farms. A generalised least squares regression model was used for data analysis. The model showed that the milk meter had a substantial effect on the SCC for individual milk samples obtained for official milk recording. The results suggested an overestimation of the SCC in milk samples from farms that had electronic devices in comparison with farms that used portable devices and underestimation when volumetric meters are used. A weak positive correlation was observed between the SCC and the percentage of fat in individual milk samples. The results underline the importance of considering this variable when using SCC data from milk recording in the dairy herd improvement program or in quality milk programs.

Heat stress related dairy cow mortality during heat waves and control periods in rural Southern Ontario from 2010-2012.

BackgroundHeat stress is a physiological response to extreme environmental heat such as heat waves. Heat stress can result in mortality in dairy cows when extreme heat is both rapidly changing and has a long duration. As a result of climate change, heat waves, which are defined as 3 days of temperatures of 32 °C or above, are an increasingly frequent extreme weather phenomenon in Southern Ontario. Heat waves are increasing the risk for on-farm dairy cow mortality in Southern Ontario. Heat stress indices (HSIs) are generally based on temperature and humidity and provide a relative measure of discomfort which can be used to predict increased risk of on-farm dairy cow mortality. In what follows, the heat stress distribution was described over space and presented with maps. Similarly, on-farm mortality was described and mapped. The goal of this study was to demonstrate that heat waves and related HSI increases during 2010–2012 were associated with increased on-farm dairy cow mortality in Southern Ontario.Mortality records and farm locations for all farms registered in the CanWest Dairy Herd Improvement Program in Southern Ontario were retrieved for 3 heat waves and 6 three-day control periods from 2010 to 2012. A random sample of controls (2:1) was taken from the data set to create a risk-based hybrid design. On-farm heat stress was estimated using data from 37 weather stations and subsequently interpolated across Southern Ontario by geostatistical kriging. A Poisson regression model was applied to assess the on-farm mortality in relation to varying levels of the HSI.ResultsFor every one unit increase in HSI the on-farm mortality rate across Southern Ontario increases by 1.03 times (CI95% (IRR) = (1.025,1.035); p = ≤ 0.001). With a typical 8.6 unit increase in HSI from a control period to a heat wave, mortality rates are predicted to increase by 1.27 times.ConclusionsSouthern Ontario was affected by heat waves, as demonstrated by high levels of heat stress and increased on-farm mortality. Farmers should be aware of these risks, and informed of appropriate methods to mitigate such risks.

Effect of Mycobacterium avium subsp. paratuberculosis infection status on culling and calving difficulty in dairy cattle

The present paper was designed to assess the effect of Mycobacterium avium subsp. paratuberculosis (MAP) infection status (antibody positive vs antibody negative, as measured by ELISA) on time to culling and calving difficulty in dairy cows. The study was carried out in 8 dairy farms in Galicia (north-west Spain). All of them were taking part in an ongoing paratuberculosis control program, as well as in a dairy herd improvement program. In order to estimate the relation between time to culling and MAP serological status of dairy cows, the present study followed the Andersen–Gill model for survival analysis. Similarly, in order to evaluate the influence on calving difficulty an ordinal logistic regression model was applied. The results indicated that seropositive cows were more likely to be culled due to death/urgent slaughter (hazard ratio=1.88), low productivity (hazard ratio=2.55), infertility (hazard ratio=4.64) and other causes (hazard ratio=1.67). Additionally, the probability of difficulties at calving time was 2.74 times higher for seropositive cows. The estimated effects could determine the economic benefits of a paratuberculosis control program.

Sensitivity and specificity of repeated test results from a commercial milk enzyme-linked immunosorbent assay for detection of Mycobacterium avium subspecies paratuberculosis in dairy cattle.

To investigate the sensitivity and specificity of results of initial and repeated milk ELISAs (at 6- or 12-month intervals) to detect cows that were shedding Mycobacterium avium subsp paratuberculosis (ie, were infectious) and to evaluate factors influencing the probability that the results of a repeated milk ELISA would be positive for an infectious cow if the results of the initial milk ELISA were negative. Prospective cohort study. 3,145 dairy cows from 32 herds. Herds from the 3 Maritime provinces in Canada (Prince Edward Island, New Brunswick, and Nova Scotia), participating in a Dairy Herd Improvement program, and that had undergone a prior Mycobacterium avium subsp paratuberculosis awareness project were selected for the study. Sample collection occurred between April 2009 and March 2011 with milk and fecal samples collected from all lactating cows in study herds every 6 months. Herds completing < 3 herd visits with collection of individual cow fecal or milk samples, within this sampling timeframe, were excluded from analyses. Fecal samples were cultured in liquid medium and a cow was defined as infectious if ≥ 1 sample was culture positive (reference test). A milk ELISA (index test) was completed with a commercial kit, following manufacturer's instructions. For a 6-month test interval, sensitivities of the milk ELISA to detect infectious cows were 22.0% and 32.6% for initial and combined initial and repeated tests (parallel interpretation), respectively. Specificity of the initial ELISA was 99.6% and was 99.2% for combined tests. For a 12-month test interval, sensitivities of the milk ELISA to detect infectious cows were 25.6% and 45.3% for initial and combined initial and repeated tests (parallel interpretation), respectively. Specificity of the initial ELISA was 99.6% and was 98.9% for combined tests. In infectious cows, magnitude of the initial negative ELISA result was a positive predictor for a positive repeated ELISA result. Results of a repeated milk ELISA improved detection of Mycobacterium avium subsp paratuberculosis infectious cows, with minimal loss of specificity. A 12-month test interval provided a greater increase in sensitivity, relative to an initial test, than did a 6-month interval. Infectious cows with an initial negative milk ELISA result close to the cutoff for a positive test were more likely to have positive results on a repeated ELISA. Repeated testing improved detection of infectious cows and reduced risk of misclassification compared with a single ELISA result.