Association between days post conception and lactation persistency in dairy cattle.

Determining the optimal insemination moment for individual cows is complex, particularly when considering the impact of pregnancy on milk production. The effect of pregnancy on the absolute milk yield has already been reported in several studies. Currently, there is limited quantitative knowledge about the association between days post conception (DPC) and lactation persistency, based on a lactation curve model, and, specifically, how persistency changes during pregnancy and relates to the days in milk at conception (DIMc). Understanding this association might provide valuable insights to determine the optimal insemination moment. This study, therefore, aimed to investigate the association between DPC and lactation persistency, with an additional focus on the influence of DIMc. Available milk production data from 2005 to 2022 were available for 23,908 cows from 87 herds located throughout the Netherlands and Belgium. Persistency was measured by a lactation curve characteristic decay, representing the time taken to halve milk production after peak yield. Decay was calculated for 8 DPC (0, 30, 60, 90, 120, 150, 180 and 210 d after DIMc) and served as the dependent variable. Independent variables included DPC, DIMc (< = 60, 61-90, 91-120, 121-150, 151-180, 181-210, > 210), parity group, DPC × parity group, DPC × DIMc and variables from 30 d before DIMc as covariates. The results showed an increase in decay, i.e., a decrease in persistency, during pregnancy for both parity groups, albeit in different ways. Specifically, from DPC 150 to DPC 210, multiparous cows showed a higher decline in persistency compared with primiparous cows. Furthermore, a later DIMc (cows conceiving later) was associated with higher persistency. Except for the early DIMc groups (DIMc < 90), DIMc does not impact the change in persistency by gestation. The findings from this study contribute to a better understanding of how DPC and DIMc during lactation influence lactation persistency, enabling more informed decision-making by farmers who wish to take persistency into account in their reproduction management.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period.

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine ß-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas ß-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Prediction of first test day milk yield using historical records in dairy cows.

The transition between two lactations remains one of the most critical periods during the productive life of dairy cows. In this study, we aimed to develop a model that predicts the milk yield of dairy cows from test day milk yield data collected in the previous lactation. In the past, data routinely collected in the context of herd improvement programmes on dairy farms have been used to provide insights in the health status of animals or for genetic evaluations. Typically, only data from the current lactation is used, comparing expected (i.e., unperturbed) with realised milk yields. This approach cannot be used to monitor the transition period due to the lack of unperturbed milk yields at the start of a lactation. For multiparous cows, an opportunity lies in the use of data from the previous lactation to predict the expected production of the next one. We developed a methodology to predict the first test day milk yield after calving using information from the previous lactation. To this end, three random forest models (nextMILKFULL, nextMILKPH, and nextMILKP) were trained with three different feature sets to forecast the milk yield on the first test day of the next lactation. To evaluate the added value of using a machine-learning approach against simple models based on contemporary animals or production in the previous lactation, we compared the nextMILK models with four benchmark models. The nextMILK models had an RMSE ranging from 6.08 to 6.24 kg of milk. In conclusion, the nextMILK models had a better prediction performance compared to the benchmark models. Application-wise, the proposed methodology could be part of a monitoring tool tailored towards the transition period. Future research should focus on validation of the developed methodology within such tool.

Unaffected semen quality parameters in Neospora caninum seropositive Belgian Blue bulls.

Neospora caninum is a protozoan parasite that causes abortion, perinatal mortality, and subfertility in cattle worldwide. Despite the presence of the DNA of the parasite in semen of infected bulls, the effect on semen quality has not been extensively studied. This study aimed to investigate the effect of a natural Neospora caninum infection on fresh and frozen semen quality parameters in Belgian Blue bulls. Two hundred and fourteen bulls were serologically screened with an indirect ELISA-test specific for anti-Neospora caninum antibodies, every two months during one year. In addition to serological screening, semen was collected twice weekly using an artificial vagina. The following semen quality parameters were assessed: ejaculate volume, concentration, total motility of fresh semen samples, as well as morphology, total and progressive motility for frozen/thawed semen samples. Bulls were semen sampled throughout the whole year, but only semen samples of bulls that had six consecutive positive or negative ELISA-test results were included in our dataset (n = 98). Generalized linear and binomial mixed models were used for statistical analysis of each outcome variable. In these models the explanatory variables were defined as: age, barn location, mean Temperature Humidity Index (THI) during sperm production (14-42 days before sampling), maximum daily THI at collection, season of sperm production, season at collection and the Neospora caninum antibody test results. Initially, individual explanatory variables were tested in univariable models for each outcome variable. Akaike information criterion (AIC) values were used to select explanatory variables to build a multivariable model, where the Neospora caninum test result was forced in all models. The present study reveals an overall apparent seroprevalence of Neospora caninum of 9,2% in the study population. No significant associations were detected between natural neosporosis, substantiated by ELISA-antibody levels, and any of our tested outcome variables on fresh and frozen/thawed semen samples. Based on the results of the present study, we conclude that Neospora caninum seropositive bulls do not have lower semen quality parameters compared with seronegative bulls.

Heat stress in a temperate climate leads to adapted sensor-based behavioral patterns of dairy cows.

Most research on heat stress has focused on (sub)tropical climates. The effects of higher ambient temperatures on the daily behavior of dairy cows in a maritime and temperate climate are less studied. With this retrospective observational study, we address that gap by associating the daily time budgets of dairy cows in the Netherlands with daily temperature and temperature-humidity index (THI) variables. During a period of 4 years, cows on 8 commercial dairy farms in the Netherlands were equipped with neck and leg sensors to collect data from 4,345 cow lactations regarding their daily time budget. The time spent eating, ruminating, lying, standing, and walking was recorded. Individual cow data were divided into 3 data sets: (1) lactating cows from 5 farms with a conventional milking system (CMS) and pasture access, (2) lactating cows from 3 farms with an automatic milking system (AMS) without pasture access, and (3) dry cows from all 8 farms. Hourly environment temperature and relative humidity data from the nearest weather station of the Dutch National Weather Service was used for THI calculation for each farm. Based on heat stress thresholds from previous studies, daily mean temperatures were grouped into 7 categories: 0 = (<0°C), 1 = (0-12°C, reference category), 2 = (12-16°C), 3 = (16-20°C), 4 = (20-24°C), 5 = (24-28°C), and 6 = (≥28°C). Temperature-humidity index values were grouped as follows: 0 = (THI <30), 1 = (THI 30-56, reference category), 2 = (THI 56-60), 3 = (THI 60-64), 4 = (THI 64-68), 5 = (THI 68-72) and 6 = (THI ≥72). To associate daily mean temperature and THI with sensor-based behavioral parameters of dry cows and of lactating cows from AMS and CMS farms, we used generalized linear mixed models. In addition, associations between sensor data and other climate variables, such as daily maximum and minimum temperature, and THI were analyzed. On the warmest days, eating time decreased in the CMS group by 92 min/d, in the AMS group by 87 min/d, and in the dry group by 75 min/d compared with the reference category. Lying time decreased in the CMS group by 36 min/d, in the AMS group by 56 min/d, and in the dry group by 33 min/d. Adaptation to daily temperature and THI was already noticeable from a mean temperature of 12°C or a mean THI of 56 or above, when dairy cows started spending less time lying and eating and spent more time standing. Further, rumination time decreased, although only in dry cows and cows on AMS farms. With higher values for daily mean THI and temperature, walking time decreased as well. These patterns were very similar for temperature and THI variables. These results show that dairy cows in temperate climates begin to adapt their behavior at a relatively low mean environmental temperature or THI. In the temperate maritime climate of the Netherlands, our results indicate that daily mean temperature suffices to study the effects of behavioral adaptation to heat stress in dairy cows.

Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours.

Cows from 8 commercial Dutch dairy farms were equipped with 2 sensors to study their complete time budgets of eating, rumination, lying, standing and walking times as derived from a neck and a leg sensor. Daily sensor data of 1074 cows with 3201 lactations was used from 1 month prepartum until 10 months postpartum. Farms provided data over a 5 year period. The final models (lactational time budget and 24h time budget) showed significant effects of parity, farm and calving season. When primiparous cows were introduced in the lactational herd, they showed a decrease in lying time of 215 min (95% CI: 187-242) and an increase in standing time of 159 min (95% CI: 138-179), walking time of 23 min (95% CI: 20-26) and rumination time of 69 min (95% CI: 57-82). Eating time in primiparous cows increased from 1 month prepartum until 9 months in lactation with 88 min (95% CI: 76-101) and then remained stable until the end of lactation. Parity 2 and parity 3+ cows decreased in eating time by 30 min (95% CI: 20-40) and 26 min (95% CI: 18-33), respectively, from 1 month before to 1 month after calving. Until month 6, eating time increased 11 min (95% CI: 1-22) for parity 2, and 24 min (95% CI: 16-32) for parity 3+. From 1 month before calving to 1 month after calving, they showed an increase in ruminating of 17 min (95% CI: 6-28) and 28 min (95% CI: 21-35), an increase in standing time of 117 min (95% CI: 100-135) and 133 min (95% CI: 121-146), while lying time decreased with 113 min (95% CI: 91-136) and 130 min (95% CI: 114-146), for parity 2 and 3+, respectively. After month 1 in milk to the end of lactation, lying time increased 67 min (95% CI: 49-85) for parity 2, and 77 min (95% CI: 53-100) for parity 3+. Lactational time budget patterns are comparable between all 8 farms, but cows on conventional milking system (CMS) farms with pasture access appear to show higher standing and walking time, and spent less time lying compared to cows on automatic milking system (AMS) farms without pasture access. Every behavioral parameter presented a 24h pattern. Cows eat, stand and walk during the day and lie down and ruminate during the night. Daily patterns in time budgets on all farms are comparable except for walking time. During the day, cows on CMS farms with pasture access spent more time walking than cows on AMS farms without pasture access. The average 24h pattern between parities is comparable, but primiparous cows spent more time walking during daytime compared to older cows. These results indicate a specific behavioral pattern per parameter from the last month prepartum until 10 months postpartum with different patterns between parities but comparable patterns across farms. Furthermore, cows appear to have a circadian rhythm with varying time budgets in the transition period and during lactation.

Associations between body condition score, locomotion score, and sensor-based time budgets of dairy cattle during the dry period and early lactation.

Lameness, one of the most important disorders in the dairy industry, is related to postpartum diseases and has an effect on dairy cow welfare, leading to changes in cows' daily behavioral variables. This study quantified the effect of lameness on the daily time budget of dairy cows in the transition period. In total, 784 multiparous dairy cows from 8 commercial Dutch dairy farms were visually scored on their locomotion (score of 1-5) and body condition (score of 1-5). Each cow was scored in the early and late dry period as well as in wk 4 and 8 postpartum. Cows with locomotion scores 1 and 2 were grouped together as nonlame, cows with score 3 were considered moderately lame, and cows with scores 4 and 5 were grouped together as severely lame. Cows were equipped with 2 types of sensors that measured behavioral parameters. The leg sensor provided number of steps, number of stand-ups (moving from lying to standing), lying time, number of lying bouts, and lying bout length. The neck sensor provided eating time, number of eating bouts, eating bout length, rumination time, number of rumination bouts, and rumination bout length. Sensor data for each behavioral parameter were averaged between 2 d before and 2 d after locomotion scoring. The percentage of nonlame cows decreased from 63% in the early dry period to 46% at 8 wk in lactation; this decrease was more severe for cows with higher parity. Cows that calved in autumn had the highest odds for lameness. Body condition score loss of >0.75 point in early lactation was associated with lameness in wk 4 postpartum. Moderately lame cows had a reduction of daily eating time of around 20 min, whereas severely lame cows had a reduction of almost 40 min. Similarly, moderately and severely lame dry cows showed a reduction of 200 steps/d, and severely lame cows in lactation showed a reduction of 600 steps/d. Daily lying time increased by 26 min and lying bout length increased by 8 min in severely lame cows compared with nonlame cows. These results indicate a high prevalence of lameness on Dutch dairy farms, with an increase in higher locomotion scores from the dry period into early lactation. Time budgets for multiparous dairy cows differed between the dry period and the lactating period, with a higher locomotion score (increased lameness) having an effect on cows' complete behavioral profile. Body condition score loss in early lactation was associated with poor locomotion postpartum, whereas lameness resulted in less eating time in the dry period and early lactation, creating a harmful cycle.

Relationships between metabolic profiles and gene expression in liver and leukocytes of dairy cows in early lactation.

Homeorhetic mechanisms assist dairy cows in the transition from pregnancy to lactation. Less successful cows develop severe negative energy balance (NEB), placing them at risk of metabolic and infectious diseases and reduced fertility. We have previously placed multiparous Holstein Friesian cows from 4 herds into metabolic clusters, using as biomarkers measurements of plasma nonesterified fatty acids, ß-hydroxybutyrate, glucose and IGF-1 collected at 14 and 35 d in milk (DIM). This study characterized the global transcriptomic profiles of liver and circulating leukocytes from the same animals to determine underlying mechanisms associated with their metabolic and immune function. Liver biopsy and whole-blood samples were collected around 14 DIM for RNA sequencing. All cows with available RNA sequencing data were placed into balanced (BAL, n = 44), intermediate (n = 44), or imbalanced (IMBAL, n = 19) metabolic cluster groups. Differential gene expression was compared between the 3 groups using ANOVA, but only the comparison between BAL and IMBAL cows is reported. Pathway analysis was undertaken using DAVID Bioinformatic Resources (https://david.ncifcrf.gov/). Milk yields did not differ between BAL and IMBAL cows but dry matter intake was less in IMBAL cows and they were in greater energy deficit at 14 DIM (-4.48 v -11.70 MJ/d for BAL and IMBAL cows). Significantly differentially expressed pathways in hepatic tissue included AMPK signaling, glucagon signaling, adipocytokine signaling, and insulin resistance. Genes involved in lipid metabolism and cholesterol transport were more highly expressed in IMBAL cows but IGF1 and IGFALS were downregulated. Leukocytes from BAL cows had greater expression of histones and genes involved in nucleosomes and cell division. Leukocyte expression of heat shock proteins increased in IMBAL cows, suggesting an unfolded protein response, and several key genes involved in immune responses to pathogens were upregulated (e.g., DEFB13, HP, OAS1Z, PTX3, and TLR4). Differentially expressed genes upregulated in IMBAL cows in both tissues included CD36, CPT1, KFL11, and PDK4, all central regulators of energy metabolism. The IMBAL cows therefore had greater difficulty maintaining glucose homeostasis and had dysregulated hepatic lipid metabolism. Their energy deficit was associated with a reduced capacity for cell division and greater evidence of stress responses in the leukocyte population, likely contributing to an increased risk of infectious disease.

Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows.

The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was carried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and ß-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week until 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no difference in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.

Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation.

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.

Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms.

This study aimed to evaluate the associations between transition cow conditions and diseases TD with fertility in Holstein cows, and to compare analytic methods for doing so. Kaplan-Meier, Cox proportional hazard, and decision tree models were used to analyze the associations of TD with the pregnancy risk at 120 and 210 DIM from a 1-year cohort with 1946 calvings from one farm. The association between TD and fertility was evaluated as follows: 1 cows with TD whether complicated with another TD or not TD-all, versus healthy cows, and 2 cows with uncomplicated TD TD-single, versus cows with multiple TD TD+; complicated cases, versus healthy cows. The occurrence of twins, milk fever, retained placenta, metritis, ketosis, displaced abomasum, and clinical mastitis were recorded. Using Kaplan-Meier models, in primiparous cows the 120 DIM pregnancy risk was 62% (95% CI: 57-67 %) for healthy animals. This was not significantly different for TD-single (58%; 95% CI: 51-66 %) but was reduced for TD+ (45%; 95% CI: 33-60 %). Among healthy primiparous cows, 80% (95% CI: 75-84 %) were pregnant by 210 DIM, but pregnancy risk at that time was reduced for primiparous cows with TD-single (72%; 95% CI: 65-79 %) and TD+ (62%; 95% CI: 49-75 %). In healthy multiparous cows, the 120 DIM pregnancy risk was 53% (95% CI: 49-56 %), which was reduced for TD-single (36%; 95% CI: 31-42 %) and TD+ (30%; 95% CI: 24-38 %). The 210 DIM pregnancy risk for healthy multiparous cows was 70% (95% CI: 67-72 %), being higher than the 210 DIM pregnancy risk for multiparous cows with TD-single (47%; 95% CI: 42-53 %) or TD+ (46%; 95% CI: 38-54 %). Cows with TD-all presented similar pregnancy risk estimates as for TD + . Cox proportional hazards regressions provided similar magnitudes of effects as the Kaplan-Meier estimates. Survival analysis and decision tree models identified parity as the most influential variable affecting fertility. Both modeling techniques concurred that TD + had a greater effect than TD-single on the probability of pregnancy at 120 and 210 DIM. Decision trees for individual TD identified that displaced abomasum affected fertility at 120 DIM in primiparous while metritis was the most influential TD at 120 and 210 DIM for multiparous cows. The data were too sparse to assess multiple interactions in multivariable Cox proportional hazard models for individual TD. Machine learning helped to explore interactions between individual TD to study their hierarchical effect on fertility, identifying conditional relationships that merit further investigation.

Between- and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation.

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (ß-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-ß-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites' levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.

Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers.

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and ß-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-ß-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.

Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and ß-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R 2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R 2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

Association between metabolic diseases and the culling risk of high-yielding dairy cows in a transition management facility using survival and decision tree analysis.

The objective of this study was to assess the association between individual metabolic diseases (MD) and multiple MD (MD+) in the transition period (±3 wk relative to calving) and the culling risk in the first 120 d in milk (DIM) in Holstein-Friesian dairy cows. Health records from a transition management facility in Germany with 1,946 calvings were analyzed in a 1-yr cohort via survival analysis and a decision tree model. The recorded MD were milk fever (MF), retained placenta (RP), metritis (METR), ketosis (KET), displaced abomasum (DA), twinning (TWIN), and clinical mastitis (MAST). The overall culling within 120 DIM was 18%. The 120 DIM culling risk for healthy cows (64.8% of the total) was 13%, whereas it was 25% for MD (24.5%) and 33% for MD+ (10.7%) cows. The 120 DIM culling risk (%) for each MD and MD+, respectively, was 34.6 and 48 for MF and MF+, 15 and 31.5 for RP and RP+, 9.4 and 22.2 for METR and METR+, 30.7 and 37.3 for KET and KET+, 56.1 and 46.8 for DA and DA+, 30.3 and 34 for TWIN and TWIN+, and 36.6 and 27.8 for MAST and MAST+. Moreover, the incidence risk (%) for each MD and MD+, respectively, was 4 and 2.6 for MF and MF+, 1 and 2.8 for RP and RP+, 8.7 and 6 for METR and METR+, 4.5 and 6.1 for KET and KET+, 0.8 and 2.4 for DA and DA+, 1.7 and 2.7 for TWIN and TWIN+, and 3.6 and 1.8 for MAST and MAST+. Setting the healthy cows as the referent, the 120 DIM hazard ratios (HR) for culling were MD 2.1, MD+ 2.9, MF 3.3, MF+ 4.6, RP+ 2.7, METR+ 1.8, KET 2.6, KET+ 3.3, DA 5.5, DA+ 4.5, TWIN 2.8, TWIN+ 3.0, MAST 3.1, and MAST+ 2.3. According to both decision tree and random forest analyses, MF was the most significant disease influencing survival, followed by DA, MAST, METR, and TWIN. In conclusion, the presence of MD or MD+ during the transition period was associated with increased culling risk in the first 120 DIM. The culling hazard was greater when an MD was complicated with another MD. In this study performed in a well-managed large farm, uncomplicated cases of RP (HR = 1.2) and METR (HR = 0.7) did not have an influence on the 120 DIM culling risk. Interestingly, both decision tree and random forest analyses pointed to MF and DA as main culling reasons in the first 120 DIM in the present dairy herd.

Influence of adipocyte size and adipose depot on the number of adipose tissue macrophages and the expression of adipokines in dairy cows at the end of pregnancy.

The aim of this study was to determine the number of adipose tissue macrophages (ATM) and the mRNA expression of adipokines [adiponectin (ADIPOQ), leptin (LEP), interleukin 6 (IL6), tumor necrosis factor (TNF), and interleukin 10 (IL10)] in different adipose depots from cows with a variable body condition score (BCS) at the end of the dry period. We hypothesized that the number of ATM and the expression of these adipokines depend on adipocyte size and the anatomical location of the adipose depot. Subcutaneous, omental, mesenteric, perirenal, and intrapelvic adipose tissue samples were taken immediately after euthanasia of 10 Holstein Friesian dairy cows (upcoming parity 2 to 5, age 3.9 ± 1.4 yr; mean ± standard deviation) at the end of pregnancy (actual days of pregnancy at the moment of euthanasia: 269 ± 5 d). During the dry period, all animals received similar diets to meet but not exceed requirements. Five animals were considered to have a normal BCS (2.5-3.5) and 5 animals were considered to be over-conditioned (BCS = 3.75-5). Body weight of the animals at the moment of euthanasia was 717 ± 77 kg. Expression of the different genes was determined by reverse transcription quantitative real-time PCR. Adipocyte size was determined by measuring the area of 100 adipocytes on histological sections. Average adipocyte area was 10,475 ± 1,019, 8,500 ± 780, 10,383 ± 1,227, 11,466 ± 1,039, and 11,087 ± 1,632 µm2 for the subcutaneous, mesenteric, omental, intrapelvic, and perirenal adipose depot, respectively. Immunohistochemistry using anti-bovine CD172a antibodies was performed to determine the proportion of ATM (the number of CD172a-positive cells per 100 adipocytes, given as a percentage). Expression of LEP, IL6, and TNF was positively associated with adipocyte size, whereas no association could be detected between ADIPOQ and IL10 with the size of the adipocytes. The omental adipose depot was especially infiltrated with ATM (1.92 ± 0.55, 1.10 ± 0.33, and 8.28 ± 2.24% for the subcutaneous, mesenteric, and omental adipose depot, respectively). The proportion of ATM was positively associated with the size of the adipocytes in the omental and mesenteric adipose depot. Expression of ADIPOQ, LEP, IL6, TNF, and IL10 differed among depots, which suggests differences in inflammatory characteristics depending on the anatomical location of depots. In conclusion, the results of the present study confirm the adipose tissue as a potential source of inflammatory mediators and demonstrate ATM infiltration, especially in the omental adipose depot.

Cytological endometritis diagnosed at artificial insemination in repeat breeder dairy cows.

This study aimed to (i) assess the prevalence of cytological endometritis (CYTO) diagnosed at artificial insemination (AI); (ii) evaluate the effect of CYTO on the pregnancy outcome of the same AI sample; and (iii) determine the risk factors associated with CYTO diagnosed at AI in repeat breeder (RB) dairy cows. We analysed the productive and reproductive performances of 146 RB Holstein-Friesian cows. To obtain a CYTO sample at AI, we used the cytotape technique. Generalized mixed effect models were computed to find the risk factors associated with the pregnancy and CYTO outcome. Based on ≥1% PMN cut-off point, the CYTO prevalence at AI in RB cows was 25.3%. The overall pregnancy at AI was 44.2%. The conception rate in CYTO-positive (n = 37) RB cows was 29.7% versus 49.5% for CYTO-negative (n = 109) cows. A RB cow diagnosed CYTO positive at AI had 0.47 [odds ratio (OR)] odds to become pregnant in comparison with a CYTO-negative cow. Cows that produced more milk than their counterparts in this study had increased odds (OR = 1.01) to be CYTO positive at AI. A novel risk factor positively associated with CYTO diagnosed at AI in RB cows was the level of daily milk urea (OR = 1.11). To conclude, CYTO at the moment of AI had a significantly negative effect on the pregnancy outcome in RB dairy cows. However, as only one of fourth of RB cows is affected with CYTO at AI, it may not be considered a key element associated with the RB syndrome.

Novel approaches to assess the quality of fertility data stored in dairy herd management software.

Scientific journals and popular press magazines are littered with articles in which the authors use data from dairy herd management software. Almost none of such papers include data cleaning and data quality assessment in their study design despite this being a very critical step during data mining. This paper presents 2 novel data cleaning methods that permit identification of animals with good and bad data quality. The first method is a deterministic or rule-based data cleaning method. Reproduction and mutation or life-changing events such as birth and death were converted to a symbolic (alphabetical letter) representation and split into triplets (3-letter code). The triplets were manually labeled as physiologically correct, suspicious, or impossible. The deterministic data cleaning method was applied to assess the quality of data stored in dairy herd management from 26 farms enrolled in the herd health management program from the Faculty of Veterinary Medicine Ghent University, Belgium. In total, 150,443 triplets were created, 65.4% were labeled as correct, 17.4% as suspicious, and 17.2% as impossible. The second method, a probabilistic method, uses a machine learning algorithm (random forests) to predict the correctness of fertility and mutation events in an early stage of data cleaning. The prediction accuracy of the random forests algorithm was compared with a classical linear statistical method (penalized logistic regression), outperforming the latter substantially, with a superior receiver operating characteristic curve and a higher accuracy (89 vs. 72%). From those results, we conclude that the triplet method can be used to assess the quality of reproduction data stored in dairy herd management software and that a machine learning technique such as random forests is capable of predicting the correctness of fertility data.