Risk factors associated with the incidence of ketosis in dairy cows

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 β-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 β-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.

Characteristics of Holstein cows predisposed to ketosis during the post-partum transition period

BACKGROUND

Ketosis is a common metabolic disorder during the post-partum transition period of dairy cattle. How the method of reproduction, parturition time, and calf birth weight affect the occurrence of ketosis on dairy herds remains elusive.

OBJECTIVES

This study investigated factors associated with the severity of ketosis.

METHODS

We divided 186 Holstein cows into three classifications based on the highest β-hydroxybutyrate (BHBA) concentration during the post-partum transition period, namely non-ketosis (<1.2 mmol/L, n = 94), subclinical ketosis (1.2-2.9 mmol/L, n = 58), and clinical ketosis (≥3.0 mmol/L, n = 34). We evaluated characteristics of cows associated with the severity of ketosis.

RESULTS

Ketosis was not associated with the method of reproduction, parturition time, pregnancy wastage, premature delivery, retained placenta, and type of calf. Cows calving in spring and especially summer were at higher risk of severe ketosis (p < 0.01). Cows with increased body condition score (BCS) at parturition, age, lactation number, and calving interval were more likely to develop severe ketosis (p < 0.05). Cows with clinical ketosis produced most milk (29.9 ± 1.0 kg) from days four to six, whereas cows without ketosis produced the least (21.3 ± 0.8 kg) (p < 0.001). Heavier calf birth weight resulted in high risk of severe ketosis (p < 0.01), due to increased milk yield during the early lactation.

CONCLUSIONS

The severity of ketosis is associated with the calving season, BCS at parturition, age, lactation number, calving interval, milk yield in the early lactation period, and calf birth weight. Nonetheless, it was not associated with the method of reproduction, parturition time, pregnancy wastage, premature delivery, retained placenta, and type of calf. This study is the first to investigate the associations between ketosis and calf birth weight. Our findings could help predict cows at risk of ketosis and take precautions.

Predicting ketosis during the transition period in Holstein Friesian cows using hematological and serum biochemical parameters on the calving date

Ketosis often occurs during the postpartum transition period in dairy cows, leading to economic and welfare problems. Previously, ketosis was reported to be associated with hematological and serum biochemical parameters. However, the association between the parameters on the calving date and ketosis during the postpartum transition period remains unclear. This study aimed to investigate this association. Blood samples were collected from the jugular vein of Holstein cows on the calving date and β-hydroxybutyrate was tested once every 3 days (8 times in 21 days). The cows were divided into three groups: non-ketosis, subclinical ketosis, and clinical ketosis. The clinical ketosis group significantly had the highest values of mean corpuscular volume, mean corpuscular hemoglobin, β-hydroxybutyrate, non-esterified fatty acids, and total bilirubin, but the lowest values of red cell distribution width, the counts of white blood cell, monocyte, and eosinophil, albumin, alanine transaminase, lactate dehydrogenase, and amylase. In contrast, the non-ketosis group showed the opposite results (p < 0.05). In conclusion, these parameters are associated with the development and severity of ketosis. Our findings suggest that these parameters on the calving date may be useful indicators to identify dairy Holstein cow susceptible to ketosis during the transition period.

Predicting Subclinical Ketosis in Dairy Cows Using Machine Learning Techniques

Simple Summary

The maintenance of cows in good health and physical condition is an important component of dairy cattle management. One of the major metabolic disorders in dairy cows is subclinical ketosis. Due to financial and organizational reasons it is often impossible to test all cows in a herd for ketosis using standard blood examination method. Using milk data from test-day records, obtained without additional costs for breeders, we found diagnostic models identifying cows-at-risk of subclinical ketosis. In addition, to select the best models, we present a general scoring approach for various machine learning models. With our models, breeders can identify dairy cows-at-risk of subclinical ketosis and implement appropriate management strategies and prevent losses in milk production.

Abstract

The diagnosis of subclinical ketosis in dairy cows based on blood ketone bodies is a challenging and costly procedure. Scientists are searching for tools based on results of milk performance assessment that would allow monitoring the risk of subclinical ketosis. The objective of the study was (1) to design a scoring system that would allow choosing the best machine learning models for the identification of cows-at-risk of subclinical ketosis, (2) to select the best performing models, and (3) to validate them using a testing dataset containing unseen data. The scoring system was developed using two machine learning modeling pipelines, one for regression and one for classification. As part of the system, different feature selections, outlier detection, data scaling and oversampling methods were used. Various linear and non-linear models were fit using training datasets and evaluated on holdout, testing the datasets. For the assessment of suitability of individual models for predicting subclinical ketosis, three β-hydroxybutyrate concentration in blood (bBHB) thresholds were defined: 1.0, 1.2 and 1.4 mmol/L. Considering the thresholds of 1.2 and 1.4, the logistic regression model was found to be the best fitted model, which included independent variables such as fat-to-protein ratio, acetone and β-hydroxybutyrate concentrations in milk, lactose percentage, lactation number and days in milk. In the cross-validation, this model showed an average sensitivity of 0.74 or 0.75 and specificity of 0.76 or 0.78, at the pre-defined bBHB threshold 1.2 or 1.4 mmol/L, respectively. The values of these metrics were also similar in the external validation on the testing dataset (0.72 or 0.74 for sensitivity and 0.80 or 0.81 for specificity). For the bBHB threshold at 1.0 mmol/L, the best classification model was the model based on the SVC (Support Vector Classification) machine learning method, for which the sensitivity in the cross-validation was 0.74 and the specificity was 0.73. These metrics had lower values for the testing dataset (0.57 and 0.72 respectively). Regression models were characterized by poor fitness to data (R² < 0.4). The study results suggest that the prediction of subclinical ketosis based on data from test-day records using classification methods and machine learning algorithms can be a useful tool for monitoring the incidence of this metabolic disorder in dairy cattle herds.

Aspects of transition cow metabolomics-Part I: Effects of a metaphylactic butaphosphan and cyanocobalamin treatment on the metabolome in liver, blood, and urine in cows with different liver metabotypes

Dairy cows in modern production systems are at risk to develop metabolic disorders during the transition period. Reasons for individual differences in susceptibility, as well as the underlying pathomechanisms, are still only partially understood. The development of metaphylactic treatment protocols is needed. In this context, an on-farm prospective 3-fold blinded randomized study involving 80 German Holstein cows was performed throughout 1 yr. The trial involved a thorough recording of the production and clinical traits, clinical chemistry, and liver biopsies and blood and urine sampling at d 14 (mean: 12 d, range: 1-26 d) antepartum (AP), and d 7 (7, 4-13) and 28 (28, 23-34) postpartum (PP) for metabolomics analyses. Two groups received a treatment with butaphosphan and cyanocobalamin (BCC) at either the dosage recommended by the manufacturer or the double dosage (5 or 10 mL/100 kg of body weight 10% butaphosphan and 0.005% cyanocobalamin (Catosal, Bayer Animal Health), n = 20 in each group, parity: 4.2 ± 2.0 and 3.4 ± 1.3, respectively (mean ± SD)] and one group a placebo treatment (NaCl 0.9%, n = 40, parity: 4.0 ± 1.9). The animals were treated at 6 time points (7, 6, and 5 d AP, and 1, 2, and 3 d PP) via intravenous injection. Mass spectroscopy-based targeted metabolomics analysis of blood plasma and liver samples were performed using the AbsoluteIDQ p180 kit (Biocrates Life Sciences), whereas the urine samples were analyzed by nuclear magnetic resonance spectroscopy. Statistical analysis was performed using multivariate [partial least squares discriminant analysis (PLS-DA)] and univariate methods (linear mixed model). Multivariate data analysis (PLS-DA plots) of the liver metabolome revealed 3 different metabotypes (A = medium, B = minor, C = large alterations in liver metabolome profile between AP and PP status). Metabotype B animals were characterized by higher PP lipomobilization (stronger PP body condition decrease and higher blood bilirubin, fatty acids, gamma-glutamyltransferase, and triglyceride levels) and a higher occurrence of transition cow diseases, compared with the animals in metabotype C. Analysis of the feeding data showed that the period of metabotype B animals (calving in a distinct time frame) was characterized by a decreased grass silage quality. The PP liver metabolome of the metabotype C animals was characterized by higher concentrations of AA, acylcarnitines, lysoPC and sphingomyelins compared with metabotype B. For the metaphylactic treatment with BCC a dose-dependent effect was confirmed, differing between the metabotypes. In all matrices and metabotypes at various time points significant treatment effects were observed, with different profiles in clinical chemistry and as well in metabolomics data. The most clear-cut treatment effect was observed in metabotype B in the liver at 7 d PP, characterized by an increase in several acylcarnitines and phosphatidylcholines, indicating a more efficient influx and oxidation of fatty acids in mitochondria and thereby an increase in energy supply and more efficient triglyceride export in the liver. The results from the liver metabolomics analysis support the application of an indication-based metaphylactic treatment with BCC.

Hepatic metabolomics and transcriptomics to study susceptibility to ketosis in response to prepartal nutritional management

Background

Ketosis in dairy cows is associated with body fat mobilization during the peripartal period. Sub-clinical and clinical ketosis arise more frequently in cows that are overfed energy during the entire dry (last 50 to 45 days prior to parturition) or close-up period (last ~ 28 days prepartum).

Methods

A retrospective analysis was performed on 12 cows from a larger cohort that were fed a higher-energy diet [1.54 Mcal/kg of dry matter (DM); 35.9% of DM corn silage and 13% of DM ground corn] during the close-up dry period, of which 6 did not develop clinical ketosis (OVE, 0.83 mmol/L plasma hydroxybutyrate; BHB) and 6 were diagnosed with clinical ketosis (KET, 1.4 mmol/L BHB) during the first week postpartum. A whole-transcriptome bovine microarray (Agilent Technologies) and metabolomics (GC-MS, LC-MS; Metabolon® Inc.) were used to perform transcript and metabolite profiling of liver tissue harvested at − 10 days relative to parturition which allowed to establish potential associations between prepartal transcriptome/metabolome profiles and susceptibility to clinical ketosis postpartum.

Results

Cows in KET had greater (P = 0.01) overall body weight between − 2 and 1 week around parturition, but similar body condition score than OVE. Although dry matter intake (DMI) did not differ prepartum, KET cows had lower (P < 0.01) DMI and similar milk yield as OVE cows during the first week postpartum. Transcriptome analysis revealed a total of 3065 differentially expressed genes (DEG; P ≤ 0.05) in KET. Metabolomics identified 15 out of 313 total biochemical compounds significantly affected (P ≤ 0.10) in KET. Among those, greater concentrations (P ≤ 0.06, + 2.3-fold) of glycochenodeoxycholate in KET cows also have been detected in humans developing non-alcoholic fatty liver disease. Bioinformatics analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database and the DEG revealed that, among the top 20 most-impacted metabolic pathway categories in KET, 65% were overall downregulated. Those included ‘Metabolism of cofactors and vitamins’, ‘Biosynthesis of other secondary metabolites’, ‘Lipid’, ‘Carbohydrate’, and ‘Glycan biosynthesis and metabolism’. The lower relative concentration of glucose-6-phosphate and marked downregulation of fructose-1,6-bisphosphatase 2 and pyruvate dehydrogenase kinase 4 support a strong impairment in gluconeogenesis in prepartal liver of cows developing KET postpartum. Among the top 20 most-impacted non-metabolic pathways, 85% were downregulated. Pathways such as ‘mTOR signalling’ and ‘Insulin signalling’ were among those. ‘Ribosome’, ‘Nucleotide excision repair’, and ‘Adherens junctions’ were the only upregulated pathways in cows with KET.

Conclusions

The combined data analyses revealed more extensive alterations of the prepartal liver transcriptome than metabolome in cows overfed energy and developing ketosis postpartum. The causative link between these tissue-level adaptations and onset of clinical ketosis needs to be studied further.

Effects of feeding management on disease incidence and blood metabolites in dairy herds in Iwate Prefecture, Japan

The objective of the present study was to identify the effect of feeding management on disease incidence and blood metabolite levels in dairy herds in Iwate Prefecture, Japan. A generalized linear model approach was used to identify the risk factors for ketosis and displaced abomasum (DA) in dairy herds (n=30), and metabolic profile test (MPT) results were compared to verify the involvement of the factors. Consequently, the proportion of corn silage (CS) with ≥30% of dry matter (DM) fed to cows during the lactation period was confirmed as the most reliable risk factor for ketosis, while no risk factor was identified for DA. Meanwhile, the incidence rates of ketosis and DA were significantly (P<0.05) higher in the herds that were fed CS (n=20) than in those fed a non-CS diet (n=10). When the MPT results of the herds fed with CS containing ≥30% of DM (HCS group, n=4; 76 cows), with CS containing <30% of DM (LCS group, n=14; 285 cows), and a non-CS diet (NCS group, n=12; 236 cows) were compared, the HCS group showed higher beta-hydroxybutyric and lower blood urea nitrogen concentrations for until 49 days after parturition. Overall, feeding cows with CS diets containing over 30% of DM might increase their risk of developing negative energy and protein balances, thereby resulting in increasing incidences of ketosis in the Iwate Prefecture.

Survey of ketolactia, determining the main predisposing management factors and consequences in Hungarian dairy herds by using a cow-side milk test

The aims of the survey were to determine the prevalence of ketosis in dairy herds by measuring the concentration of beta-hydroxybutyrate (BHBA) in milk by Keto-Test (Sanwa Kagaku Kenkyusho, Nagoya, Japan); risk factors and the relationship with postpartum diseases were investigated. 1667 early lactating (days in milk 0-75) cows were tested in 52 dairy herds in 2013 and 2014 years. In total, 29.3 per cent of samples were positive (BHBA_MILK ≥100 µmol/l), including 3.7 per cent high positives (BHBA_MILK ≥500 µmol/l). The prevalence was similar in herds with less than or more than 9000 kg milk yield (0.34 and 0.38, respectively, P=0.4); however, it was higher in the herds with more than 1000 cows than in smaller herds (<500 and 500-1000 cows) (0.46, P=0.03). The BHBA level in milk was in a non-linear positive relationship with parity (P=0.01), associated with retained placenta (P=0.0006), mastitis (P=0.02) and clinical ketosis (P<0.001). The results confirm the high prevalence of ketolactia in Hungarian dairy herds and its links to herd-related and cow-related risk factors and diseases occurring commonly in fresh cows.

Options to reduce vulnerability to metabolic stress by genetic selection

Genetic selection for milk production has been very successful. However to achieve high yields, the metabolic load on dairy cows is believed to be substantial. If the size of this load is large enough then the animal may become ‘metabolically stressed’. Signs of this may include some sort of distortion of normal physiological function. There is evidence from both population studies and research herds to suggest that intense selection for milk yield has led to a deterioration in some aspects of health and fertility. Genetic correlation estimates between production and measures of fertility are unfavourable. As an example, calving intervals of high merit animals from Langhill are on average 12 days longer than those of average genetic merit, which is mostly due to a delay in days to first heat. It is suggested that some aspects of health and fertility problems in high genetic merit animals are a consequence, in part, of so-called metabolic stress. Future breeding goals should be broadened to include a broad spectrum of traits related to efficient milk production, in addition to either health and fertility traits themselves, or traits believed to be precursors of them, such as those related to metabolic stress. The complexity and subjectivity of metabolic stress and its components makes it very difficult to include in future breeding goals. However, traits related to energy balance, such as some measures of condition score, dry-matter intake and live weight may be useful in breeding programmes where one of the goals is to alleviate metabolic stress.

Predicting hyperketonemia by logistic and linear regression using test-day milk and performance variables in early-lactation Holstein and Jersey cows

Although cowside testing strategies for diagnosing hyperketonemia (HYK) are available, many are labor intensive and costly, and some lack sufficient accuracy. Predicting milk ketone bodies by Fourier transform infrared spectrometry during routine milk sampling may offer a more practical monitoring strategy. The objectives of this study were to (1) develop linear and logistic regression models using all available test-day milk and performance variables for predicting HYK and (2) compare prediction methods (Fourier transform infrared milk ketone bodies, linear regression models, and logistic regression models) to determine which is the most predictive of HYK. Given the data available, a secondary objective was to evaluate differences in test-day milk and performance variables (continuous measurements) between Holsteins and Jerseys and between cows with or without HYK within breed. Blood samples were collected on the same day as milk sampling from 658 Holstein and 468 Jersey cows between 5 and 20 d in milk (DIM). Diagnosis of HYK was at a serum β-hydroxybutyrate (BHB) concentration ≥1.2 mmol/L. Concentrations of milk BHB and acetone were predicted by Fourier transform infrared spectrometry (Foss Analytical, Hillerød, Denmark). Thresholds of milk BHB and acetone were tested for diagnostic accuracy, and logistic models were built from continuous variables to predict HYK in primiparous and multiparous cows within breed. Linear models were constructed from continuous variables for primiparous and multiparous cows within breed that were 5 to 11 DIM or 12 to 20 DIM. Milk ketone body thresholds diagnosed HYK with 64.0 to 92.9% accuracy in Holsteins and 59.1 to 86.6% accuracy in Jerseys. Logistic models predicted HYK with 82.6 to 97.3% accuracy. Internally cross-validated multiple linear regression models diagnosed HYK of Holstein cows with 97.8% accuracy for primiparous and 83.3% accuracy for multiparous cows. Accuracy of Jersey models was 81.3% in primiparous and 83.4% in multiparous cows. These results suggest that predicting serum BHB from continuous test-day milk and performance variables could serve as a valuable diagnostic tool for monitoring HYK in Holstein and Jersey herds.

Effects of a combination butaphosphan and cyanocobalamin product and insulin on ketosis resolution and milk production

The objective of this study was to determine the effects of butaphosphan-cyanocobalamin (B+C), glargine insulin, and propylene glycol on resolution of ketosis and average daily milk yield after treatment. Cows from 16 herds in Ontario, Canada, and 1 herd in Michigan were tested at weekly intervals between 3 and 16 DIM. Ketosis was defined as blood β-hydroxybutyrate (BHB) ≥1.2 mmol/L. All ketotic cows were given a baseline treatment of 3 d of 300 g of propylene glycol orally. Animals were then randomly assigned to treatment with 3 doses of either 25 mL of B+C or 25 mL of saline placebo and 1 dose of either 2 mL (200 IU) of glargine insulin or 2 mL of saline placebo in a 2 × 2 factorial arrangement. Outcomes of interest on all farms were ketosis cure (blood BHB <1.2 mmol/L 1 wk postenrollment), maintenance of ketosis cure (blood BHB <1.2 mmol/L 1 and 2 wk postenrollment), and blood BHB concentrations at 1 and 2 wk postenrollment. Milk weights were collected daily in 1 large freestall herd. Repeated measures ANOVA was used to evaluate blood BHB concentrations 2 wk after treatment and milk production for 30 d after treatment. Poisson regression was used to examine the effect of treatment on cure and maintenance of cure. Due to a regulatory delay causing temporary unavailability of B+C in Canada, data were analyzed in 2 sets of models: one for insulin and the corresponding placebo (n = 620) and one for the full trial (n = 380). Animals with blood glucose concentrations ≤2.2 mmol/L at the time of ketosis diagnosis were 2.1 times more likely (95% CI = 1.2 to 3.7) to be cured if treated with B+C. Animals in lactation 3 or higher that had blood glucose concentrations <2.2 mmol/L at enrollment produced 4.2 kg/d (95% CI = 1.4 to 7.1) more milk if treated with insulin versus placebo and 2.8 kg/d (95% CI = 0.9 to 4.7) more milk if treated with B+C versus placebo. Animals in lactation 3 or higher with blood glucose ≥2.2 mmol/L that were treated with insulin produced 2.3 kg/d (95% CI = 0.3 to 4.4) less milk than untreated controls. No interaction was observed between treatments. This evidence suggests that B+C and insulin may be beneficial for ketosis treatment in animals with blood glucose <2.2 mmol/L at ketosis diagnosis. It also suggests that blood glucose concentration may be an important predictor of success of ketosis treatment.

A time series model of daily milk yields and its possible use for detection of a disease (ketosis)

A time series model was used to describe the daily milk yield of healthy cows in the first 48 days of lactation. A moving average (MA) model of order 1 on the first-order differences of the data was selected — the same chosen in a previous study by Deluyker et al. (1990). When the model was used to generate predicted daily yields in a second data set, for cows in which clinical ketosis had been diagnosed, it was found that significant deviations of actual yield below the daily forecast occurred from 3 days before the day of diagnosis. The model appeared to be transportable to healthy cows from another herd. Threshold values were defined to identify ailing animals by their deviation from predicted yield. However, the thresholds were not very sensitive, and required that a fairly high level of false positives be accepted (25% of a healthy herd over a 5-day period).

Incidence and clinical vital parameters in primary ketosis of Murrah buffaloes

AIM

The present study was undertaken to ascertain the incidence and clinical vital parameters in cases of primary ketosis in Murrah buffaloes brought to teaching veterinary clinical complex, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar and from adjoining villages of the district Hisar, Haryana, India.

MATERIALS AND METHODS

The investigation was conducted on 24 clinical cases (out of total 145 screened) of primary ketosis. The diagnosis was confirmed on the basis of clinical signs and significantly positive two tests for ketone bodies in urine (Rothera's and Keto-Diastix strip test). Data collected were statistically analyzed using independent Student's t-test.

RESULTS

Overall incidence of disease in these areas was found to be 16.55% and all the animals were recently parturited (mean: 1.42±0.14 month), on an average in their third lactation (mean: 2.38±0.30) and exhibited clinical signs such as selective anorexia (refusal to feed on concentrate diet), drastic reduction in milk yield (mean: 64.4±5.35%), ketotic odor from urine, breath, and milk and rapid loss of body condition. All the clinical vital parameters in ketotic buffaloes (body temperature, heart rate, respiration rate, and rumen movements) were within normal range.

CONCLUSION

Primary ketosis in Murrah buffaloes was the most common seen in the third lactation, within the first 2 months after parturition with characteristics clinical signs and no variability in vital parameters. The disease has severe effect on the production status of affected animal.

Predicting Risk of Ketosis in Dairy Cows Using In-Line Measurements of β-Hydroxybutyrate: A Biological Model

Automated monitoring of individual cows to determine health status is a potentially valuable management tool, especially in large dairy herds. Herein is described the rationale, structure, and functionality of a biological model to predict risk of ketosis in individual cows using in-line measurements of the ketone body beta-hydroxybutyrate (BHBA) in milk. The model also uses acceleration in milk yield, body fatness at calving, diseases in current lactation, and incidences of ketosis in earlier lactations as additional risk factors for ketosis. However, the model is designed to function merely on the basis of milk BHBA in the absence of other data. Values of milk BHBA are smoothed using a state space model before these are used in calculations in the biological part of the model. The model is designed to be updated each time a new BHBA measurement or a disease occurrence is available and then uses previous and current data. Outputs of the model are the risk of ketosis (value between 0 and 1, where 0 = no risk and 1 = clinical ketosis) and how many days until the next milk sample should be taken and analyzed for BHBA. At higher risks for ketosis, more frequent milk sampling is the recommended output. Test examples from cows for which BHBA has been measured extensively were used to show the functionality of the model. The model performed equally well when reductions in sampling frequency were applied, and it was also relatively robust to the addition of up to +/- 2 residual SD of random noise in the BHBA values. This model has the potential to provide the basis for a useful disease monitoring and management tool. However, thorough validation awaits a much larger dataset and testing of the model under a variety of on-farm situations.

The relationship between milk yield and the incidence of some diseases in dairy cows

Logistic regression models were used to examine the relationship between milk yield and incidence of certain disorders. Lactations (n = 2197) of 1074 Holstein-Friesian cows from 10 dairies (25 to 146 cows per dairy) in Lower Saxony were studied. The 305-d yield from the previous and current lactations served as the standards for milk yield. Eight disorder complexes were considered: retained placenta, metritis, ovarian cysts, mastitis, claw diseases, milk fever, ketosis, and displaced abomasum. Each disorder complex was modeled separately. In addition to milk yield, the influences of the lactation number, the calving season and the other disorder complexes were examined with the "herd" factor taken into account. A correlation between retained placenta, mastitis, and milk fever to milk yield during the previous lactation was found to be probable and for ketosis and displaced abomasum such a correlation was found to be possible. A connection to the yield in the current lactation was shown for ovarian cysts, claw diseases, and milk fever. No relationship to milk yield existed for metritis. An influence of the lactation number was also demonstrated in various models. Single models allowed a demonstration of the influences of both milk yield and lactation number. Limitations of the model types are discussed.