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

Farm-level risk factors associated with increased milk β-hydroxybutyrate and hyperketolactia prevalence on farms with automated milking systems

The objectives of this study were to determine the farm-level hyperketolactia (HKL) prevalence, as diagnosed from milk β-hydroxybutyrate (BHB) concentration, on dairy farms milking with an automatic milking system (AMS) and to describe the farm-level housing, management, and nutritional risk factors associated with increased farm-average milk BHB and the within-herd HKL prevalence in the first 45 DIM. Canadian AMS farms (n = 162; eastern Canada n = 8, Quebec n = 23, Ontario n = 75, western Canada n = 55) were visited once between April to September 2019 to record housing and herd management practices. The first test milk data for each cow under 45 DIM were collected, along with the final test of the previous lactations for all multiparous cows, from April 1, 2019 to September 30, 2020. The first test milk BHB was then used to classify each individual cow as having HKL (milk BHB ≥ 0.15 mmol/L) at the time of testing. Milk fat and protein content, milk BHB, and HKL prevalence were summarized by farm and lactation group (all, primiparous, and multiparous). During this same time period, formulated diets for dry and lactating cows, including ingredients and nutrient composition, and AMS milking data were collected. Data from the AMS were used to determine milking behaviors and milk production of each herd during the first 45 DIM. Multivariable regression models were used to associate herd-level housing, feeding management practices, and formulated nutrient composition with first test milk BHB concentrations and within-herd HKL levels separately for primiparous and multiparous cows. The within-herd HKL prevalence for all cows was 21.8%, with primiparous cows having a lower mean prevalence (12.2 ± 9.2%) than multiparous cows (26.6 ± 11.3%). Milk BHB concentration (0.095 ± 0.018 mmol/L) and HKL prevalence for primiparous cows were positively associated with formulated prepartum DMI and forage content of the dry cow diet while being negatively associated with formulated postpartum DMI, the major ingredient in the concentrate supplemented through the AMS, and the PMR-to-AMS concentrate ratio. However, multiparous cows' milk BHB concentration (0.12 ± 0.023 mmol/L) and HKL prevalence were positively associated with the length of the previous lactation, milk BHB at dry off, prepartum diet nonfiber carbohydrate content, and the major forage fed on farm, while tending to be negatively associated with feed bunk space during lactation. This is the first study to determine the farm-level risk factors associated with herd-level prevalence of HKL in AMS dairy herds, thus helping optimize management and guide diet formulation to promote the reduction of HKL prevalence.

Validation of an on-farm portable blood analyzer for quantifying blood analytes in dairy cows

The periparturient period for dairy cows is a metabolically dynamic time period where the cow is adjusting from gestation to the onset of lactation. Metabolic disorders such as ketosis, hypocalcemia, and fatty liver occur during this time; however, tools to diagnose these diseases on-farm is limited. The need for compact metabolite quantification devices that can quantify metabolites on farm from whole blood samples is warranted. The purpose of this study was to validate a portable blood analyzer (PBA) by analyzing metabolites on privately owned dairy farms in southcentral Wisconsin. Additional tests were completed to determine if plasma metabolite quantification was similar to whole-blood quantification. Two phases were conducted on two separate farms to complete these analyses and data were analyzed by Bland-Altman plot and correlations. Metabolites quantified from whole blood samples included albumin, alanine and aspartate aminotransferases, β-hydroxybutyrate, blood urea nitrogen, total calcium, cholesterol, creatinine kinase, γ-glutamyl transferase, glucose, magnesium, nonesterified fatty acids, phosphorous, and total protein and were analyzed in the lab after plasma separation to determine gold-standard laboratory concentrations. Across Phase 1 and 2, whole-blood PBA metabolite concentrations resulted in similar results compared to the laboratory assays. For plasma analyzed on the PBA, overall results were positively correlated, but robustness was dependent upon initial validation results indicating some metabolites are suitable for plasma quantification on the device. These results indicate that the PBA is a viable on-farm metabolite quantification tool that will be valuable for on-farm diagnosis of metabolic stress and dysfunction in transition dairy cows.

Rumen-Protected Choline Improves Metabolism and Lactation Performance in Dairy Cows

Choline is required for the synthesis of phosphatidylcholine, an important constituent of lipoproteins. Early lactation cows presumably synthesize insufficient phosphatidylcholine, and choline supplementation in a rumen-protected form might benefit metabolism and lactation. The objectives of this study were to determine the effects of feeding rumen-protected choline (RPC) on lactation and metabolism in dairy cows. In experiment 1, 369 nulliparous and parous Holstein cows housed in four pens per treatment were fed 12.9 g/day of choline as RPC from 25 days prepartum until 80 days postpartum. In experiment 2, 578 nulliparous cows housed in five pens/treatment were fed 12.9 g/day of choline as RPC in the last 22 days of gestation only. In both experiments, blood was sampled and analyzed for concentrations of nonesterified fatty acids (NEFAs) and glucose at 1, 14, and 21 days postpartum and of choline at 1 and 14 days postpartum. Blood from all cows was sampled and analyzed for concentrations of β-OH butyrate (BHB) at 1 and 14 days postpartum. Cows with BHB > 1.2 mmol/L were classified as having hyperketonemia. Hepatic tissue was collected from 46 cows from the eight pens in experiment 1 at 9 days postpartum and analyzed for concentrations of glycogen and triacylglycerol. Milk yield and components were measured for 80 days postpartum in experiment 1, whereas only milk yield was measured in experiment 2. The pen was the experimental unit of analysis. Supplementing RPC tended to increase dry matter intake (DMI) prepartum in experiments 1 and 2 and postpartum in experiment 1. Feeding cows with RPC increased yields of 3.5% fat-corrected milk (42.8 vs. 44.8 kg/day), energy-corrected milk (38.5 vs. 40.3 kg/day), milk fat (1.52 vs. 1.61 kg/day), and true protein (1.16 vs. 1.21 kg/day) in experiment 1. Milk yield tended to be greater with RPC (26.4 vs. 27.4 kg/day) in experiment 2. Supplementing RPC increased plasma choline concentrations on day 14 postpartum in experiment 1 (3.32 ± 0.27 vs. 4.34 ± 0.28 µM) and on day 1 in experiment 2 (3.35 ± 0.16 and 13.73 ± 0.15 µM). Treatment did not affect the concentrations of glucose, NEFAs, or BHB in plasma, but the incidence of hyperketonemia was less in multiparous cows fed RPC than those fed the control in experiment 1. Feeding cows with RPC reduced hepatic triacylglycerol content and tended to reduce the ratio of triacylglycerol to glycogen and the risk of hepatic lipidosis in cows in experiment 1. The concentrations of hepatic triacylglycerol on day 9 postpartum were inversely related to those of choline in plasma on day 1 postpartum. Feeding cows with RPC improved lactation and metabolism, but more benefits were noted when it was fed before and after calving.

Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.

Gene association analysis of an osteopontin polymorphism and ketosis resistance in dairy cattle

The aim of this study was to identify the c.495C > T polymorphism within exon 1 of the osteopontin gene (OPN), and to analyze its association with susceptibility to ketosis in Polish Holstein-Friesian (HF) cows. The study utilized blood samples from 977 HF cows, for the determination of β-hydroxybutyric acid (BHB) and for DNA isolation. The c.495C > T polymorphism of the bovine osteopontin gene was determined by PCR-RFLP. The CT genotype (0.50) was deemed the most common, while TT (0.08) was the rarest genotype. Cows with ketosis most often had the CC genotype, while cows with the TT genotype had the lowest incidence of ketosis. To confirm the relationship between the genotype and ketosis in cows, a weight of evidence (WoE) was generated. A very strong effect of the TT genotype on resistance to ketosis was demonstrated. The distribution of the ROC curve shows that the probability of resistance to ketosis is > 75% if cows have the TT genotype of the OPN gene (cutoff value is 0.758). Results suggest that TT genotype at the c.495C > T locus of the OPN gene might be effective way to detect the cows with risk of ketosis.

Association between hyperketolactia and production in early-lactating dairy cows

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

Prediction of metabolic status of dairy cows in early lactation using milk fatty acids and test-day variables

Early lactation metabolic imbalance is an important physiological change affecting the health, production, and reproduction of dairy cows. The aims of this study were (1) to evaluate the potential of test-day (TD) variables with or without milk fatty acids (FA) content to classify metabolically imbalanced cows and (2) to evaluate the robustness of the metabolic classification with external data. A data set was compiled from 3 experiments containing plasma β-hydroxybutyrate, nonesterified FA, glucose, insulin-like growth factor-I, FA proportions in milk fat, and TD variables collected from 244 lactations in wk 2 after calving. Based on the plasma metabolites, 3 metabolic clusters were identified using fuzzy c-means clustering and the probabilistic membership value of each cow to the 3 clusters was determined. Comparing the mean concentration of the plasma metabolites, the clusters were differentiated into metabolically imbalanced, moderately impacted, and balanced. Following this, the 2 metabolic status groups identified were imbalanced cows (n = 42), which were separated from what we refer to as "others" (n = 202) based on the membership value of each cow for the imbalanced cluster using a threshold of 0.5. The following 2 FA data sets were composed: (1) FA (groups) having high prediction accuracy by Fourier-transform infrared spectroscopy and, thus, have practical significance, and (2) FA (groups) formerly identified as associated with metabolic changes in early lactation. Metabolic status prediction models were built using FA alone or combined with TD variables as predictors of metabolic groups. Comparison was made among models and external evaluations were performed using an independent data set of 115 lactations. The area under the receiver operating characteristics curve of the models was between 75 and 91%, indicating their moderate to high accuracy as a diagnostic test for metabolic imbalance. The addition of FA groups to the TD models enhanced the accuracy of the models. Models with FA and TD variables showed high sensitivities (80-88%). Specificities of these models (73-79%) were also moderate and acceptable. The accuracy of the FA models on the external data set was high (area under the receiver operating characteristics curve between 76 and 84). The persistently good performance of models with Fourier-transform infrared spectroscopy-quantifiable FA on the external data set showed their robustness and potential for routine screening of metabolically imbalanced cows in early lactation.

Hyperketonemia: A Marker of Disease, a Sign of a High-Producing Dairy Cow, or Both?

This review covers the history and nomenclature of ketosis, the source and use of ketones in transition cows, and the controversial role of hyperketonemia's association with health and production outcomes in dairy cows. With the goal of assisting veterinarians with on-farm diagnostic and treatment methods, the authors present current and evolving means of direct and indirect hyperketonemia detection as well as a summary of treatment modalities and their efficacy. They encourage veterinarians to include hyperketonemia testing as part of their routine physical examinations and contemplate day in milk at hyperketonemia diagnosis when designing treatment and management strategies.

Factors Affecting Fatty Acid Composition of Holstein Cow's Milk

Milk fatty acid composition has gained the interest of both manufacturers and consumers during recent years. The present paper aimed to perform an analysis of C14:0, C16:0, C18:0, C18:1, saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA) and short chain fatty acid (SCFA) concentration in cow's milk in relation to the type of ration, parity, lactation phase and season. Cows' milk from animals being fed total mixed rations, including corn silage, had higher C14:0, C16:0 and SFA concentrations than those being fed pasture-based rations but lower concentrations of C18:0 and PUFA. Comparing to 1st parity cows, 2nd and 3rd parity animals had higher SFA and SCFA concentrations in milk. With respect to spring, C14:0, C16:0 and SFA concentrations increased in summer, autumn and winter while MUFA, PUFA and SCFA concentrations decreased. Considering the lactation phase, C14:0, C16:0 and SFA concentrations decreased in fresh cows with ketosis comparing to healthy fresh cows and increased in peak, mid and late lactation. C18:0, C18:1 and MUFA follow the opposite trend. The milk fatty acid profile varies significantly through the studied effects. The fact that the fatty acid profile is associated with animal health, organoleptic properties of milk or even methane production highlights the importance of studying factors that affect its variation.

Prediction of Liver Triglyceride Content in Early Lactation Multiparous Holstein Cows Using Blood Metabolite, Mineral, and Protein Biomarker Concentrations

Bovine fatty liver syndrome (bFLS) is difficult to diagnose because a liver tissue biopsy is required to assess liver triglyceride (TG) content. We hypothesized that a blood biomarker panel could be a convenient alternative method of liver TG content assessment and bFLS diagnosis. Our objectives were to predict liver TG using blood biomarker concentrations across days in milk (DIM; longitudinal, LT) or at a single timepoint (ST; 3, 7, or 14 DIM), as well as different biomarker combination based on their perceived accessibility. Data from two separate experiments (n = 65 cows) was used for model training and validation. Response variables were based on the maximum liver TG observed in 1 and 14 DIM liver biopsies: Max TG (continuous), Low TG (TG > 13.3% dry matter; DM), Median TG (TG > 17.1% DM), and High TG (TG > 22.0% DM). Model performance varied but High TG was well predicted by sparse partial least squares—discriminate analysis models using LT and ST data, achieving balanced error rates ≤ 15.4% for several model variations during cross-validation. In conclusion, blood biomarker panels using 7 DIM, 14 DIM, or LT data may be a useful diagnostic tool for bFLS in research and field settings.

The Effect of Supplementation of Rumen-Protected Choline on Reproductive and Productive Performances of Dairy Cows

We aimed to evaluate the effects of organic herbal preparations containing rumen-protected choline (RPC) in dairy cow milk’s BHB and progesterone (P4) concentration changes, reproduction, and production performances. Cows were divided into the following two groups: The CHOL (n = 60) cow diet was supplemented with 10 g/day RPC from 20 days pre-calving to 20 days post-calving, and CONT (n = 60) were fed a conventional diet. BHB and P4 concentrations were measured at 5−64 DIM and 21−64 DIM, respectively, with DelPro 4.2. BHB was lower in the CHOL group at 5−64 DIM than CONT p > 0.05. The first post-calving P4 peak, p < 0.001, was determined earlier in the CHOL group, and the P4 profile during 21−64 DIM was similar, p > 0.05. The insemination rate was lower, and the interval between calvings was shorter. The first insemination time was earlier in the CHOL group, p < 0.05. Milk yield was higher in the CHOL group at 21−64 DIM, p > 0.05. The CHOL group had more fat in their milk at 31−60 DIM, p < 0.05. There were no significant differences in protein and SCC between the groups, p > 0.05. Based on our results, we concluded that the supplementation of RPC pre- and post-calving had statistically significant effects on the first peak of P4, and benefited the reproduction performances, milk yield, and milk fat during the early postpartum period.

Diurnal variation of milk fatty acids in early-lactation Holstein cows with and without hyperketonemia

Estimates of milk constituents by Fourier-transform mid-infrared (FTIR) analysis have been shown to be a useful tool in monitoring energy deficit in early-lactation dairy cows. Our objectives were to describe the diurnal variation in milk fatty acids (FAs) and estimate the association of hyperketonemia with concentrations and diurnal patterns of FTIR estimates of milk FA. Blood samples were collected via jugular catheters bihourly for 5 d from multiparous Holstein cows (n = 28) enrolled between 3 and 9 days in milk. Milk samples were collected thrice daily at 0600, 1400, and 2200 h for d 2, 3, and 4 of the study period. Cows were retrospectively classified as hyperketonemic (HYK; n = 13) or non-HYK (n = 15) based on blood beta-hydroxybutyrate (bBHB) concentrations analyzed during the study period. Cows were classified as HYK if bBHB was ≥ 1.2 mmol/l for ≥ 50% (22/44) of bihourly timepoints; cows were classified as non-HYK if bBHB was ≥ 1.2 mmol/l for < 50% of bihourly timepoints. The HYK cows had bBHB ≥ 1.2 mmol/l for 31.4 ± 6.8 timepoints while the non-HYK cows had bBHB ≥ 1.2 mmol/l for 8.0 ± 3.9 timepoints. We used generalized linear mixed models to analyze concentrations of milk FA over time and differences between HYK groups. The relative percentage of de novo, mixed, and preformed FAs all followed diurnal patterns, however only the yield of preformed FA diurnally cycled, reaching a nadir at 0600 h and peaking at 1400 h. The yield per milking of preformed FA was also greater in the HYK cows than in the non-HYK cows. Oleic acid in milk followed a similar diurnal pattern to the yield of preformed FA, likely driving the cyclical nature of preformed FA. Finally, stearic acid was greater in HYK cows. Our results suggest that FTIR estimates of milk FA offer the potential to provide insight on the energy status of early-lactation cows, and when interested in understanding the absolute concentrations and yields of milk FA, diurnal variation should be considered.

The Association between Blood Β-Hydroxybutyric Acid Concentration in the Second Week of Lactation and Reproduction Performance of Lithuanian Black and White Cows

Simple Summary

Determination of BHB concentration in the second week of lactation (WK 2) may allow us to predict the fertility properties of cows and help better manage farms. BHB concentration can be considered as a predictor trait of reproduction success. High BHB concentration requires a higher amount of insemination. The season in which the cows calve and the parity must be considered in the assessment as these factors affect BHB concentration in WK 2.

Abstract

Hyperketonemia is a very common metabolic state in dairy cows, which result in lower milk production, impaired fertility, and increased frequency of other diseases. In this study, we aimed to determine the influence of season, parity, and milk yield of cows on beta-hydroxybutyrate (BHB) concentration in the second week of lactation (WK 2) and establish the relationship between BHB concentration in WK 2 and reproduction performance traits such as insemination rate and first insemination day of Lithuanian Black and White dairy cows. The study included clinically healthy Lithuanian Black and White cows (n = 692). Blood BHB concentration was measured using capillary blood samples collected after morning milking when cows were 7–10 DIM. The impact of WK 2 blood BHB concentration on the insemination rate and first insemination day were investigated. The effect of BHB was evaluated according to the season, parity, and milk yield per lactation (305 DIM). Significant differences were observed in BHB concentration in WK 2 due to season and parity, but no statistically significant differences were observed for milk yields (305 d). Increased blood BHB concentration in WK 2 negatively affected insemination rate (p < 0.001) and first insemination day (p < 0.001). The study findings indicate that BHB concentration in WK 2 depends on season and parity, while the milk yield is not associated with BHB concentration. High BHB concentration in WK 2 increases insemination rate and delays the first insemination day for high milk-yielding Lithuanian Black and White dairy cows.

The Use of Multilayer Perceptron Artificial Neural Networks to Detect Dairy Cows at Risk of Ketosis

Simple Summary

Ketosis is a serious metabolic disease in high-yield dairy cows, that affects productive herds throughout the world. Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during early lactation, so early detection and prevention are important for both economic and animal welfare reasons. Neural networks, which offer a high degree of accuracy in predicting various phenomena and processes where there is no clear causal correlation or there are no rules that allow the establishment of a logical cause-and-effect relationship, can be used to address problems related to prediction, classification, or control. A Multi-Layer perceptron (MLP) is a feedforward artificial neural network model that takes input data for a set of proper output. This study investigated the performance of four algorithms used to train MLP networks. The experimental results demonstrate that the MLP network model improved the accuracy of process recognition of subclinical ketosis in dairy cows. The received artificial model’s results were saved in the predictive model markup language (PMML) and can be used to describe the learning set, the algorithm used in the data mining application and related information.

Abstract

Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as animal welfare reasons. Recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. ANNs offer a modeling method that enables the mapping of highly complex functional relationships. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows, using feedforward multilayer perceptron (MLP) artificial neural networks. The results were verified based on the estimated sensitivity and specificity of selected network models, an optimum cut-off point was identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The study demonstrated that BHB, ACE and lactose (LAC) levels, as well as the fat-to-protein ratio in milk, were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, variables such as BHB and ACE levels in milk were of particular relevance, with a sensitivity and specificity of 0.84 and 0.61, respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool.

Diagnostic performance of the BHBCheck β-hydroxybutyrate meter for hyperketonaemia in Indian cows and buffaloes

The objective of the study was to evaluate the diagnostic performance of the electronic handheld BHBCheck meter (BHM) (PortaCheck, Inc., USA) to determine whole blood, plasma and serum β-hydroxybutyrate (BHB) against serum BHB determined using reference laboratory method of Randox D-3 Hydroxybutyrate Ranbut assay (RSM) in Indian dairy cows and buffaloes. Blood samples were collected by puncturing coccygeal vessels for determining whole blood, serum and plasma BHB using BHM and serum BHB using RSM from 217 cows (Gir breed; median 42 DIM and 3rd lactation) and 223 buffaloes (non-descript; median 39 DIM and 3rd lactation) from nearby herds. The Pearson's correlation between whole blood (0.988; 0.987), plasma (0.985; 0.983) and serum (0.985; 0.983) BHB determined using the BHM and serum BHB determined with the RSM in Indian cows and buffaloes, respectively, were significant. Bland-Altman plot demonstrated an excellent agreement between whole blood, plasma and serum BHB determined with BHM, against the serum BHB determined with RSM in Indian cows and buffaloes, respectively. For hyperketonaemia with reference serum BHB cut-off values ≥ 1.2 and 1.4 mmol/L determined with RSM, it recorded optimized BHB thresholds, sensitivity and specificity for whole blood (≥ 0.9 to 1.0 mmol/L; 91 to 95% and 88 to 98%), plasma (≥ 0.9 to 1.0 mmol/L; 91 and 100%) and serum (≥ 0.9 to 1.0 mmol/L; 92 to 100% and 85 to 94%) with BHM in cows and buffaloes, respectively. In conclusion, BHB determined with BHM demonstrated an excellent correlation, agreement and test characteristics with BHB determined with RSM and hence can accurately determine whole blood, plasma and serum BHB in cows and buffaloes.

Characterization of ketolactia in dairy cows during early lactation

Fourier transform infrared spectroscopy (FTIR) allows for the determination of milk acetone (mACE) and β-hydroxybutyrate (mBHB) concentrations, providing a potential herd monitoring tool for hyperketolactia, defined as elevated milk ketone bodies. The study aim was to characterize mACE and mBHB concentration dynamics during early lactation in Polish Holstein-Friesian cows. Milk samples (n = 3,867,390) were collected within 6 to 60 days in milk (DIM) over a 4-yr period (April 1, 2013 to March 31, 2017) from approximately 21,300 dairy herds (average 38.7 cows/herd). Fixed effects of parity, DIM, and their interaction on mACE and mBHB concentrations were determined using a mixed model with a herd-year-season fixed effect and random cow effect. Published hyperketolactic mACE (≥0.15 mmol/L) and mBHB (≥0.10 mmol/L) threshold concentrations were used to classify study milk samples into ketolactia groups of normal (mACE <0.15 mmol/L and mBHB <0.10 mmol/L) and hyperketolactic (HYKL; either mACE ≥0.15 mmol/L or mBHB ≥0.10 mmol/L). Additionally, HYKL samples were categorized into subpopulations as having elevated mBHB and mACE (HYKL_ACEBHB, mACE ≥0.15 mmol/L and mBHB ≥0.10 mmol/L), only elevated mBHB (HYKL_BHB; mACE <0.15 mmol/L and mBHB ≥0.10 mmol/L), or only elevated mACE (HYKL_ACE; mACE ≥0.15 mmol/L and mBHB <0.10 mmol/L). Effects of parity, DIM, ketolactia group or subpopulation, and their interactions on mACE and mBHB concentrations were also determined using the mixed model that included ketolactia group or subpopulation as an independent variable. Across all data, mACE and mBHB concentrations were influenced by effects of parity, DIM, and their interaction as well as parity, DIM, ketolactia group or subpopulation, and their interactions. For all samples, mACE and mBHB concentrations decreased with increasing DIM, with mACE concentration declining more rapidly compared with mBHB. In the data set, 68% and 32% of all samples were defined as normal or HYKL, respectively. Among HYKL samples, mACE was elevated soon after calving and declined over time. In contrast, mBHB started lower after calving and increased reaching peak concentrations around 30 DIM, and then decreased. Within HYKL samples, 50.8, 41.3, and 7.9% were categorized as HYKL_ACEBHB, HYKL_BHB, and HYKL_ACE respectively. Between 6 and 21 DIM, 11.3% of HYKL were classified as HYKL_ACE. Primiparous cows had greater (14.8%) HYKL_ACE samples in this time period. In conclusion, this study has characterized mACE and mBHB concentrations during early lactation and determined effects of parity, DIM, and their interaction. Using published criteria interpreting mACE and mBHB concentrations, it was intriguing to identify a unique population of samples having elevated mACE without mBHB in early lactation, especially in primiparous cows. Further research is needed to determine if this sample population represents an unhealthy metabolic status that adversely affects cow health and performance.

Menopausal Women's Health Care Method Based on Computer Nursing Diagnosis Intelligent System

Taking into account the current feature extraction speed and recognition effect of intelligent diagnosis of menopausal women's health care behavior, this paper proposes to use a cross-layer convolutional neural network to extract behavior features autonomously and use support vector machine multiclass behavior classifier to classify behavior. Compared with the feature images extracted by traditional methods, the behavioral features extracted in this paper are related to the individual menopausal women and have better semantic information, and the feature description ability in the time domain and the space domain has been enhanced. Through Matlab software, using the database established in this paper to compare its feature extraction time, test classification time, and final recognition accuracy with ordinary convolutional neural networks, it is concluded that the cross-layer CNN-SVM model can ensure the speed of feature extraction. It proves that the method in this paper can be applied to the behavioral intelligent diagnosis system for intelligently nursing menopausal women and has good practical value. This paper designs a home care bed intelligent monitoring system, which can automatically detect the posture of the care bed, and not only can change the posture of the bed under the control of personnel, but also can automatically complete the posture conversion according to the setting. At the same time, the system has the function of monitoring the physical condition of the person being cared for and can detect the heart rate, blood oxygen, and other physiological indicators of the bedridden person. In addition, the system can also provide a remote diagnosis function, allowing nursing staff to remotely view the current state of the nursing bed and the physical condition of the person. After testing, the system works stably, improves the automation and safety of the nursing bed control, and enriches the functions of the nursing bed.

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.

Invited Review: Ketosis Diagnosis and Monitoring in High-Producing Dairy Cows

This work reviews the current impact and manifestation of ketosis (hyperketonemia) in dairy cattle, emphasizing the practical use of laboratory methods, field tests, and milk data to monitoring this disease. Ketosis is a major issue in high-producing cows, easily reaching a prevalence of 20% during early postpartum when the negative energy balance is well established. Its economic losses, mainly related to decreasing milk yield, fertility, and treatment costs, have been estimated up to €250 per case of ketosis/year, which can double if associated diseases are considered. A deep relationship between subclinical or clinical ketosis and negative energy balance and related production diseases can be observed mainly in the first two months postpartum. Fourier transform infrared spectrometry methods gradually take place in laboratory routine to evaluates body ketones (e.g., beta-hydroxybutyrate) and probably will accurately substitute cowside blood and milk tests at a farm in avenir. Fat to protein ratio and urea in milk are largely evaluated each month in dairy farms indicating animals at risk of hyperketonemia. At preventive levels, other than periodical evaluation of body condition score and controlling modifiable or identifying non-modifiable risk factors, the ruminatory activity assessment during the peripartum seems to be a valuable tool at farms. We conclude that a technological advance progressively takes place to mitigate the effects of these metabolic diseases, which challenge the high-yielding cows.

Assessment of the Relationship between Postpartum Health and Mid-Lactation Performance, Behavior, and Feed Efficiency in Holstein Dairy Cows

The objective of this study was to investigate the relationships between postpartum health disorders and mid-lactation performance, feed efficiency, and sensor-derived behavioral traits. Multiparous cows (n = 179) were monitored for health disorders for 21 days postpartum and enrolled in a 45-day trial between 50 to 200 days in milk, wherein feed intake, milk yield and components, body weight, body condition score, and activity, lying, and feeding behaviors were recorded. Feed efficiency was measured as residual feed intake and the ratio of fat- or energy-corrected milk to dry matter intake. Cows were classified as either having hyperketonemia (HYK; n = 72) or not (n = 107) and grouped by frequency of postpartum health disorders: none (HLT; n = 94), one (DIS; n = 63), or ≥2 (DIS+; n = 22). Cows that were diagnosed with HYK had higher mid-lactation yields of fat- and energy-corrected milk. No differences in feed efficiency were detected between HYK or health status groups. Highly active mid-lactation time was higher in healthy animals, and rumination time was lower in ≥4th lactation cows compared with HYK or DIS and DIS+ cows. Differences in mid-lactation behaviors between HYK and health status groups may reflect the long-term impacts of health disorders. The lack of a relationship between postpartum health and mid-lactation feed efficiency indicates that health disorders do not have long-lasting impacts on feed efficiency.

Hyperketonemia Predictions Provide an On-Farm Management Tool with Epidemiological Insights

Simple Summary

In dairy cows, the transition to lactation period is metabolically challenging. Elevated blood ketone bodies, known as hyperketonemia or ketosis, is a postpartum metabolic disorder that is associated with negative energy balance, greater comorbidity risk, and decreased milk production. Research to understand the etiology of hyperketonemia has highlighted risk factors and unfavorable outcomes; however, analysis of real-world data is valuable for determining the outcomes across a region. Dairy herd improvement data from herds with diverse size and production were analyzed to determine potential risk factors for and production outcomes of hyperketonemia in the Midwest region (US). Cows predicted to have hyperketonemia had greater previous lactation dry period length, somatic cell count, and dystocia, which may represent risk factors for ketosis. Cows with predicted hyperketonemia had lower milk yield and milk protein but greater milk fat and somatic cell count in the current lactation. Culling rate within 60d of calving, days open, and artificial inseminations were all greater in cows predicted to have hyperketonemia. Prevalence of hyperketonemia decreased linearly in herds with greater rolling herd average milk yield. This work demonstrates the impact of hyperketonemia on production variables which underscores the importance on continued work to reduce hyperketonemia prevalence.

Abstract

Prediction of hyperketonemia (HYK), a postpartum metabolic disorder in dairy cows, through use of cow and milk data has allowed for high-throughput detection and monitoring during monthly milk sampling. The objective of this study was to determine associations between predicted HYK (pHYK) and production parameters in a dataset generated from routine milk analysis samples. Data from 240,714 lactations across 335 farms were analyzed with multiple linear regression models to determine HYK status. Data on HYK or disease treatment was not solicited. Consistent with past research, pHYK cows had greater previous lactation dry period length, somatic cell count, and dystocia. Cows identified as pHYK had lower milk yield and protein percent but greater milk fat, specifically greater mixed and preformed fatty acids (FA), and greater somatic cell count (SCC). Differential somatic cell count was greater in second and fourth parity pHYK cows. Culling (60d), days open, and number of artificial inseminations were greater in pHYK cows. Hyperketonemia prevalence decreased linearly in herds with greater rolling herd average milk yield. This research confirms previously identified risk factors and negative outcomes associated with pHYK and highlights novel associations with differential SCC, mixed FA, and preformed FA across farm sizes and production levels.

Effects of parity and days in milk on milk composition in correlation with β-hydroxybutyrate in tropic dairy cows

The current study was conducted to evaluate the effect of parity and days in milk on milk yield and milk production traits and their correlation with β-hydroxybutyrate (BHB) concentrations in milk of Chinese tropic Holstein dairy cows which are adapted to a humid subtropical climate in central China. About 3055 milking records of Holstein cows were obtained from three farms in the hot region in the center of China. The records were classified according to parity to 4 categories: first parity, second parity, third parity, and greater than third parity. According to days in milk, there were 4 groups, first group from (1-100 days), second group from (101-200 days), third group from (201-305 days), and fourth group (>305 days). Milk samples collected between April and November 2019 from the three farms were routinely checked for milk components including BHB using mid-infrared spectroscopy a MilkoScan FT+ (Foss, Hillerød, Denmark). Data were analyzed by multivariate analysis of variance (generalized linear model, GLM). Pearson's correlation coefficients were used to measure the correlation between SCC and BHB with milk yield and milk production traits. Results showed the significant effect of parity and days in milk on milk yield and milk production traits. There was a negative effect of parity and days in milk on milk quality, with increasing parity and days in milk being associated with higher somatic cell count (SCC) (P <0.001). Days in milk significantly affected (P=0.001) BHB. It was concluded that with increasing parity and prolonged days in milk, there was a negative effect on milk quality and udder health of the tropic dairy cows in central China. Based on the results of the current study, sampling milk for specific metabolites, somatic cell count, and quality are sufficient to asses herd health.

Monitoring and Improving the Metabolic Health of Dairy Cows during the Transition Period

Simple Summary

The transition from late gestation to early lactation is a challenging period for dairy cows. A successful transition period depends on metabolic adaptation to the new physiological state in early lactation and proper management in order to support the cow’s requirements. This review paper will discuss various aspects of routine and consistent approaches to collect and analyze herd records, to detect unintended disruptions in performance. In addition, we discuss how to incorporate methods to assess health, production, nutrition, and welfare information to monitor cows during the transition period. Lastly, we discuss management strategies that can be implemented to improve the metabolic health and performance of transition dairy cows.

Abstract

The peripartum period of a dairy cow is characterized by several physiological and behavioral changes in response to a rapid increase in nutrient demands, to support the final stages of fetal growth and the production of colostrum and milk. Traditionally, the transition period is defined as the period 3 weeks before and 3 weeks after parturition. However, several researchers have argued that the transition period begins at the time of dry-off (~60–50 days prior to calving) and extends beyond the first month post-calving in high producing dairy cows. Independent of the definition used, adequate adaptation to the physiological demands of this period is paramount for a successful lactation. Nonetheless, not all cows are successful in transitioning from late gestation to early lactation, leading to approximately one third of dairy cows having at least one clinical disease (metabolic and/or infectious) and more than half of the cows having at least one subclinical case of disease within the first 90 days of lactation. Thus, monitoring dairy cows during this period is essential to detect early disease signs, diagnose clinical and subclinical diseases, and initiate targeted health management to avoid health and production impairment. In this review, we discuss different strategies to monitor dairy cows to detected unintended disruptions in performance and management strategies that can be implemented to improve the metabolic health and performance of dairy cows during the transition period.

Genetic and nongenetic profiling of milk β-hydroxybutyrate and acetone and their associations with ketosis in Holstein cows

Ketosis is a metabolic disorder of increasing importance in high-yielding dairy cows, but accurate population-wide binary health trait recording is difficult to implement. Against this background, proper Gaussian indicator traits, which can be routinely measured in milk, are needed. Consequently, we focused on the ketone bodies acetone and β-hydroxybutyrate (BHB), measured via Fourier-transform infrared spectroscopy (FTIR) in milk. In the present study, 62,568 Holstein cows from large-scale German co-operator herds were phenotyped for clinical ketosis (KET) according to a veterinarian diagnosis key. A sub-sample of 16,861 cows additionally had first test-day observations for FTIR acetone and BHB. Associations between FTIR acetone and BHB with KET and with test-day traits were studied phenotypically and quantitative genetically. Furthermore, we estimated SNP marker effects for acetone and BHB (application of genome-wide association studies) based on 40,828 SNP markers from 4,384 genotyped cows, and studied potential candidate genes influencing body fat mobilization. Generalized linear mixed models were applied to infer the influence of binary KET on Gaussian-distributed acetone and BHB (definition of an identity link function), and vice versa, such as the influence of acetone and BHB on KET (definition of a logit link function). Additionally, linear models were applied to study associations between BHB, acetone and test-day traits (milk yield, fat percentage, protein percentage, fat-to-protein ratio and somatic cell score) from the first test-day after calving. An increasing KET incidence was statistically significant associated with increasing FTIR acetone and BHB milk concentrations. Acetone and BHB concentrations were positively associated with fat percentage, fat-to-protein ratio and somatic cell score. Bivariate linear animal models were applied to estimate genetic (co)variance components for KET, acetone, BHB and test-day traits within parities 1 to 3, and considering all parities simultaneously in repeatability models. Pedigree-based heritabilities were quite small (i.e., in the range from 0.01 in parity 3 to 0.07 in parity 1 for acetone, and from 0.03-0.04 for BHB). Heritabilites from repeatability models were 0.05 for acetone, and 0.03 for BHB. Genetic correlations between acetone and BHB were moderate to large within parities and considering all parities simultaneously (0.69-0.98). Genetic correlations between acetone and BHB with KET from different parities ranged from 0.71 to 0.99. Genetic correlations between acetone across parities, and between BHB across parities, ranged from 0.55 to 0.66. Genetic correlations between KET, acetone, and BHB with fat-to-protein ratio and with fat percentage were large and positive, but negative with milk yield. In genome-wide association studies, we identified SNP on BTA 4, 10, 11, and 29 significantly influencing acetone, and on BTA 1 and 16 significantly influencing BHB. The identified potential candidate genes NRXN3, ACOXL, BCL2L11, HIBADH, KCNJ1, and PRG4 are involved in lipid and glucose metabolism pathways.

Hyperketonemia GWAS and parity-dependent SNP associations in Holstein dairy cows intensively sampled for blood β-hydroxybutyrate concentration

Hyperketonemia (HYK) is a metabolic disorder that affects early postpartum dairy cows; however, there has been limited success in identifying genomic variants contributing to HYK susceptibility. We conducted a genome-wide association study (GWAS) using HYK phenotypes based on an intensive screening protocol, interrogated genotype interactions with parity group (GWIS), and evaluated the enrichment of annotated metabolic pathways. Holstein cows were enrolled into the experiment after parturition, and blood samples were collected at four timepoints between 5 and 18 days postpartum. Concentration of blood β-hydroxybutyrate (BHB) was quantified cow-side via a handheld BHB meter. Cows were labeled as a HYK case when at least one blood sample had BHB ≥ 1.2 mmol/L, and all other cows were considered non-HYK controls. After quality control procedures, 1,710 cows and 58,699 genotypes were available for further analysis. The GWAS and GWIS were performed using the forward feature select linear mixed model method. There was evidence for an association between ARS-BFGL-NGS-91238 and HYK susceptibility, as well as parity-dependent associations to HYK for BovineHD0600024247 and BovineHD1400023753. Candidate genes annotated to these single nuclear polymorphism associations have been previously associated with obesity, diabetes, insulin resistance, and fatty liver in humans and rodent models. Enrichment analysis revealed focal adhesion and axon guidance as metabolic pathways contributing to HYK etiology, while genetic variation in pathways related to insulin secretion and sensitivity may affect HYK susceptibility in a parity-dependent matter. In conclusion, the present work proposes several novel marker associations and metabolic pathways contributing to genetic risk for HYK susceptibility.

Opportunities and limitations of milk mid-infrared spectra-based estimation of acetone and β-hydroxybutyrate for the prediction of metabolic stress and ketosis in dairy cows

Subclinical (SCK) and clinical (CK) ketosis are metabolic disorders responsible for big losses in dairy production. Although Fourier-transform mid-infrared spectrometry (FTIR) to predict ketosis in cows exposed to great metabolic stress was studied extensively, little is known about its suitability in predicting hyperketonemia using individual samples, e.g. in small dairy herds or when only few animals are at risk of ketosis. The objective of the present research was to determine the applicability of milk metabolites predicted by FTIR spectrometry in the individual screening for ketosis. In experiment 1, blood and milk samples were taken every two weeks after calving from Holstein (n = 80), Brown Swiss (n = 72) and Swiss Fleckvieh (n = 58) cows. In experiment 2, cows diagnosed with CK (n = 474) and 420 samples with blood β-hydroxybutyrate [BHB] <1.0 mmol/l were used to investigate if CK could be detected by FTIR-predicted BHB and acetone from a preceding milk control. In experiment 3, correlations between data from an in farm automatic milk analyser and FTIR-predicted BHB and acetone from the monthly milk controls were evaluated. Hyperketonemia occurred in majority during the first eight weeks of lactation. Correlations between blood BHB and FTIR-predicted BHB and acetone were low (r = 0.37 and 0.12, respectively, P < 0.0001), as well as the percentage of true positive values (11.9 and 16.6%, respectively). No association of FTIR predicted ketone bodies with the interval of milk sampling relative to CK diagnosis was found. Data obtained from the automatic milk analyser were moderately correlated with the same day FTIR-predicted BHB analysis (r = 0.61). In conclusion, the low correlations with blood BHB and the small number of true positive samples discourage the use of milk mid-infrared spectrometry analyses as the only method to predict hyperketonemia at the individual cow level.

Relationship Between Content of Ketone Bodies in Milk and Milk Freezing Point of Polish Holstein-Friesian Cows in Early Lactation

The objective of this study was to determine the relationship between milk β-hydroxybutyrate (BHB), acetone (ACE) as well as parity and lactation stage and milk freezing point (MFP) in Polish Holstein-Friesian cows in early lactation. Additionally, we studied the relationship between milk ketone bodies and daily milk yield (DMY), fat (MF) and protein (MP) content in milk. The data obtained from the Polish Federation of Cattle Breeders and Dairy Farmers, comprised 749,894 test day milk samples, collected between 6 and 60 days in milk (DIM) from 521,049 lactations of 514,066 cows. Milk BHB and ACE were determined using the Fourier transform infrared (FTIR) technology. Four classes of parities were created: first, second, third, and fourth to seventh and two classes of lactation stage: 5–21 and 22–60 DIM. BHB was grouped into five classes: ≤0.05, 0.06–0.10, 0.11–0.20, 0.21–0.50 and >0.50 mmol/L, and ACE was also classified into five classes: ≤0.05, 0.06–0.10, 0.11–0.15, 0.16–0.30 and >0.30 mmol/L. Data on MFP, DMY, and MF and MP content were analyzed using the MIXED procedure of SAS and a linear model in which effects of parity, lactation stage, BHB and ACE classes were included, together with interactions between lactation stage and BHB classes, parity and BHB classes, lactation stage and ACE classes, and parity and ACE classes. The differences among parity, lactation stages, BHB and ACE classes in MFP, DMY, MF and MP were highly significant. There was a clear tendency for decreasing of MFP with increasing of BHB. Such a trend did not occur in case of ACE. DMY and MP decreased and MF increased with increasing BHB or ACE. In conclusion, since MFP can be measured relatively easily and is well related to milk BHB content, it may be used in the prediction of diagnostic models of ketosis based on milk composition.

Predictive ability of host genetics and rumen microbiome for subclinical ketosis

Subclinical metabolic disorders such as ketosis cause substantial economic losses for dairy farmers in addition to the serious welfare issues they pose for dairy cows. Major hurdles in genetic improvement against metabolic disorders such as ketosis include difficulties in large-scale phenotype recording and low heritability of traits. Milk concentrations of ketone bodies, such as acetone and β-hydroxybutyric acid (BHB), might be useful indicators to select cows for low susceptibility to ketosis. However, heritability estimates reported for milk BHB and acetone in several dairy cattle breeds were low. The rumen microbial community has been reported to play a significant role in host energy homeostasis and metabolic and physiologic adaptations. The current study aims at investigating the effects of cows' genome and rumen microbial composition on concentrations of acetone and BHB in milk, and identifying specific rumen microbial taxa associated with variation in milk acetone and BHB concentrations. We determined the concentrations of acetone and BHB in milk using nuclear magnetic resonance spectroscopy on morning milk samples collected from 277 Danish Holstein cows. Imputed high-density genotype data were available for these cows. Using genomic and microbial prediction models with a 10-fold resampling strategy, we found that rumen microbial composition explains a larger proportion of the variation in milk concentrations of acetone and BHB than do host genetics. Moreover, we identified associations between milk acetone and BHB with some specific bacterial and archaeal operational taxonomic units previously reported to have low to moderate heritability, presenting an opportunity for genetic improvement. However, higher covariation between specific microbial taxa and milk acetone and BHB concentrations might not necessarily indicate a causal relationship; therefore further validation is needed before considering implementation in selection programs.

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

Simple Summary

Population-level phenotyping of blood metabolites is hardly achievable due to the limitation of reference analyses. Mid-infrared spectroscopy has recently been used to develop prediction models for major blood metabolites, allowing their determination on a large scale. The current study investigated the variation of blood β-hydroxybutyrate, non-esterified fatty acids, and urea nitrogen predicted from a large milk mid-infrared spectra database of Brown Swiss, Holstein-Friesian, and Simmental cows. Holstein-Friesian cows had the greatest concentrations of β-hydroxybutyrate and non-esterified fatty acids, and the lowest urea nitrogen in blood, which may underline an altered energy and nutritional status.

Abstract

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

Symposium review: Big data, big predictions: Utilizing milk Fourier-transform infrared and genomics to improve hyperketonemia management

Negative animal health and performance outcomes are associated with disease incidences that can be labor-intensive, costly, and cumbersome for many farms. Amelioration of unfavorable outcomes through early detection and treatment of disease has emphasized the value of improving health monitoring. Although the value is recognized, detecting hyperketonemia (HYK) is still difficult for many farms to do practically and efficiently. Increasing data streams available to farms presents opportunities to use data to better monitor cow and herd health; however, challenges remain with regard to validating, integrating, and interpreting data. During the transition to lactation period, useful data are presented in the form of milk production and composition, milk Fourier-transform infrared (FTIR) wavelength absorbance, cow management records, and genomics, which have been employed to monitor postpartum onset of HYK. Attempts to predict postpartum HYK from test-day milk and performance variables incorporated into multiple linear regression models have demonstrated sufficient accuracy to monitor monthly herd prevalence; however, they lacked the sensitivity and specificity for individual cow diagnostics. Subsequent artificial neural network prediction models employing FTIR data and milk composition variables achieved 83 and 81% sensitivity and specificity for individual cow diagnostics. Although these results fail to reach the diagnostic goals of 90%, they are achieved without individual cow blood samples, which may justify acceptance of lower performance. The caveat is that these models depend on milk analysis, which is traditionally done every 4 weeks. This infrequent sampling allows for a single diagnostic sample for about half of the fresh cows. Benefits to farms are greatly improved if postpartum cows can be milk-tested weekly. Additionally, this allows for close monitoring of somatic cell count and may open the door for use of other herd health monitoring tools. Future improvements in these models may be achievable by maximizing sensitivity at the expense of specificity and may be most economical in disorders for which the cost of treatment is less than that of mistreating (e.g., HYK). Genomic predictions for HYK may be improved by incorporating genome-wide associated SNP and further utilized for precision management of HYK risk groups. Development and validation of HYK prediction models may provide producers with individual cow and herd-level management tools.

Fasciola hepatica seroprevalence in Northern German dairy herds and associations with milk production parameters and milk ketone bodies

Infections with the liver fluke Fasciola hepatica remain a serious problem in dairy herds causing significant production losses. In sheep, a strong relationship between F. hepatica infections and an increase in serum ketone bodies due to reduced feed intake and liver damage was demonstrated. We hypothesized that F. hepatica infections might contribute to an increase in milk ketone bodies in dairy herds. Thus, the objective of the study was to estimate the association between F. hepatica bulk tank milk (BTM) antibodies and milk production parameters (milk yield, milk protein, fat yield), somatic cell count (SCC) and the milk ketone bodies ß-hydroxybutyrate (BHB) and acetone, inferred from Fourier transform infrared (FTIR) spectrometry, via linear mixed model analysis. A further aim was to follow up the F. hepatica seroprevalence in dairy herds in the northern German region East Frisia. We collected BTM samples between October and December from 1022 herds in 2017 and 1318 herds in 2018. Overall, 33.1 % of the herds tested positive in 2017 and 37.0 % in 2018, showing decreased F. hepatica seroprevalences compared to prior seroprevalence studies in the same region in 2010, 2008 and 2006 (> 45 % positive herds). We estimated a significant negative association (P < 0.001) between herd F. hepatica infection category and average milk yield with a loss of -1.62 kg per cow per day in strongly infected herds compared to BTM ELISA negative herds. Moreover, F. hepatica infection category had a significant effect on herd average milk protein and fat yield (P < 0.001), showing a decrease of 0.06 kg for both parameters from BTM ELISA negative herds to strongly infected herds. No significant association with milk SCC was found (P = 0.664). Regarding ketone bodies, we estimated significant higher average BHB values in strongly infected herds compared to the other three infection categories in the model analysis (P = 0.002). The association between F. hepatica infection category and acetone values was not significant (P = 0.079). Besides primary ketosis, fasciolosis should be considered as differential diagnosis in dairy herds with increased BHB values.

Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows

Dairy cows commonly experience an unbalanced energy status in early lactation, and this condition can lead to the onset of several metabolic disorders. Blood metabolic profile testing is a valid tool to monitor and detect the most common early lactation disorders, but blood sampling and analysis are time-consuming and expensive, and the procedure is invasive and stressful for the cows. Mid-infrared (MIR) spectroscopy is routinely used to analyze milk composition, being a cost-effective and nondestructive method. The present study aimed to assess the feasibility of using routine milk MIR spectra for the prediction of main blood metabolites in dairy cows, and to investigate associations between measured blood metabolites and milk traits. Twenty herds of Holstein Friesian, Brown Swiss, or Simmental cows located in Northeast Italy were visited 1 to 4 times between December 2017 and June 2018, and blood and milk samples were collected from all lactating cows within 35 d in milk. Concentrations of main blood metabolites and milk MIR spectra were recorded from 295 blood and milk samples and used to develop prediction models for blood metabolic traits through backward interval partial least squares analysis. Blood β-hydroxybutyrate (BHB), urea, and nonesterified fatty acids were the most predictable traits, with coefficients of determination of 0.63, 0.58, and 0.52, respectively. On the contrary, predictive performance for blood glucose, triglycerides, cholesterol, glutamic oxaloacetic transaminase, and glutamic pyruvic transaminase were not accurate. Associations of blood BHB and urea with their respective contents in milk were moderate to strong, whereas all other correlations were weak. Predicted blood BHB showed an improved performance in detecting cows with hyperketonemia (blood BHB ≥ 1.2 mmol/L), compared with commercial calibration equation for milk BHB. Results highlighted the opportunity of using milk MIR spectra to predict blood metabolites and thus to collect routine information on the metabolic status of early-lactation cows at a population level.

Association of mid-infrared-predicted milk and blood constituents with early-lactation disease, removal, and production outcomes in Holstein cows

Partial least squares regression estimates of milk and blood constituents using Fourier-transform mid-infrared (FTIR) analysis have shown promise as a tool for monitoring early-lactation excessive energy deficit in dairy herds. Our objective was to analyze milk via FTIR to determine the association of early-lactation predicted milk β-hydroxybutyrate (BHB) concentrations, predicted blood nonesterified fatty acid (NEFA) concentrations, and predicted milk de novo fatty acid (FA) percentages relative to total FA concentrations, with the risk of disease or removal in early lactation (hyperketonemia, displaced abomasum, metritis, culling, or death) and average daily milk yield during the first 15 wk of lactation. We enrolled 517 multiparous Holstein cows from 2 dairy farms in New York. Composite milk samples were collected twice weekly from 3 to 18 DIM for a total of 4 timepoints (T1, T2, T3, T4) and analyzed using FTIR spectrometry for milk BHB and FA composition and predicted blood NEFA. Blood samples were collected for hyperketonemia determination (BHB ≥ 1.2 mmol/L) using a handheld meter, and farm-diagnosed occurrence of disease or removal during the first 30 DIM and average daily milk yield during the first 15 wk of lactation were collected from herd management software. The incidence of disease or removal between 3 and 18 DIM was 20.2%. Explanatory models for disease or removal were developed for each predicted constituent of interest at each timepoint using fixed-effect multivariable Poisson regression. Repeated measures ANOVA models were developed for each predicted constituent to assess differences in average daily milk yield. For all timepoints, increased risk of disease or removal was associated with higher predicted milk BHB [relative risk (RR)_T1 = 2.0; RR_T2 = 3.4; RR_T3 = 5.2; RR_T4 = 9.1], higher predicted blood NEFA (RR_T1 = 2.7; RR_T2 = 2.5; RR_T3 = 3.8; RR_T4 = 10.0), and lower predicted milk de novo FA relative percentages (RR_T1 = 2.9; RR_T2 = 3.3; RR_T3 = 5.8; RR_T4 = 7.2). Average daily milk yield was increased for cows above the cut point for predicted milk BHB (2.1 kg/d) and predicted blood NEFA (3.5 kg/d) and below the cut point for de novo FA relative percentages (2.3 kg/d). Our results suggest that FTIR-predicted milk BHB, blood NEFA, and milk de novo FA relative percentages are promising indicators of subsequent disease or removal in early lactation; their positive relationship with milk yield warrants further exploration.

Invited review: β-hydroxybutyrate concentration in blood and milk and its associations with cow performance

Hyperketonemia (HYK) is one of the most frequent and costly metabolic disorders in high-producing dairy cows and its diagnosis is based on β-hydroxybutyrate (BHB) concentration in blood. In the last 10 years, the number of papers that have dealt with the impact of elevated BHB levels in dairy cattle has increased. Therefore, this paper reviewed the recent literature on BHB concentration in blood and milk, and its relationships with dairy cow health and performance, and farm profitability. Most studies applied the threshold of 1.2 mmol/l of BHB concentration in blood to indicate HYK; several authors considered BHB concentrations between 1.2 and 2.9 mmol/l as subclinical ketosis, and values ⩾3.0 mmol/l as clinical ketosis. Results on HYK frequency (prevalence and incidence) and cow performance varied according to parity and days in milk, being greater in multiparous than in primiparous cows, and in the first 2 weeks of lactation than in later stages. Hyperketonemia has been associated with greater milk fat content, fat-to-protein ratio and energy-corrected milk, and lower protein and urea nitrogen in milk. The relationships with milk yield and somatic cell count are still controversial. In general, HYK impairs health of dairy cows by increasing the risk of the onset of other early lactation diseases, and it negatively affects reproductive performance. The economic cost of HYK is mainly due to impaired reproductive performance and milk loss. From a genetic point of view, results from the literature suggested the feasibility of selecting cows with low susceptibility to HYK. The present review highlights that milk is the most promising matrix to identify HYK, because it is easy to sample and allows a complete screening of the herd through BHB concentration predicted using mid-IR spectroscopy during routine milk recording. Further research is needed to validate accurate and convenient methods to discriminate between cows in risk of HYK and healthy animals in field conditions and to support farmers to achieve an early detection and minimise the economic losses.

Postpartum supplementation of fermented ammoniated condensed whey improved feed efficiency and plasma metabolite profile

Postpartum dietary supplementation of gluconeogenic precursors may improve the plasma metabolite profile of dairy cows, reducing metabolic disorders and improving lactation performance. The objective of this trial was to examine the effects of supplementation with fermented ammoniated condensed whey (FACW) postpartum on lactation performance and on profile of plasma metabolites and hormones in transition dairy cows. Individually fed multiparous Holstein cows were blocked by calving date and randomly assigned to control (2.9% dry matter of diet as soybean meal; n = 20) or FACW (2.9% dry matter of diet as liquid GlucoBoost, Fermented Nutrition, Luxemburg, WI; n = 19) dietary treatments. Treatments were offered from 1 to 45 d in milk (DIM). Cows were milked twice a day. Dry matter intake and milk yield were recorded daily and averaged weekly. Individual milk samples from 2 consecutive milkings were obtained once a week for component analysis. Rumen fluid was collected (n = 3 cows/treatment) at 4 time points per day at 7 and 21 DIM. Blood samples were collected within 1 h before feeding time for metabolite analysis and hyperketonemia diagnosis. Supplementation of FACW improved feed efficiency relative to control; this effect may be partially explained by a marginally significant reduction in dry matter intake from wk 3 to 7 for FACW-supplemented cows with no detected FACW-driven changes in milk yield, milk protein yield, and milk energy output compared with control. Also, there was no evidence for differences in intake of net energy for lactation, efficiency of energy use, energy balance, or body weight or body condition score change from calving to 45 DIM between treatments. Supplementation of FACW shifted rumen measures toward greater molar proportions of propionate and butyrate, and lesser molar proportions of acetate and valerate. Cows supplemented with FACW had greater plasma glucose concentrations in the period from 3 to 7 DIM and greater plasma insulin concentrations compared with control. Plasma nonesterified fatty acid and β-hydroxybutyrate concentrations were decreased in cows supplemented with FACW compared with control cows in the period from 3 to 7 DIM. These findings indicate that FACW may have improved the plasma metabolite profile immediately postpartum in dairy cows. Additionally, supplementation of FACW resulted in improved feed efficiency as accessed by measures of milk output relative to feed intake.

Predicting blood β-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network

Prediction of postpartum hyperketonemia (HYK) using Fourier transform infrared (FTIR) spectrometry analysis could be a practical diagnostic option for farms because these data are now available from routine milk analysis during Dairy Herd Improvement testing. The objectives of this study were to (1) develop and evaluate blood β-hydroxybutyrate (BHB) prediction models using multivariate linear regression (MLR), partial least squares regression (PLS), and artificial neural network (ANN) methods and (2) evaluate whether milk FTIR spectrum (mFTIR)-based models are improved with the inclusion of test-day variables (mTest; milk composition and producer-reported data). Paired blood and milk samples were collected from multiparous cows 5 to 18 d postpartum at 3 Wisconsin farms (3,629 observations from 1,013 cows). Blood BHB concentration was determined by a Precision Xtra meter (Abbot Diabetes Care, Alameda, CA), and milk samples were analyzed by a privately owned laboratory (AgSource, Menomonie, WI) for components and FTIR spectrum absorbance. Producer-recorded variables were extracted from farm management software. A blood BHB ≥1.2 mmol/L was considered HYK. The data set was divided into a training set (n = 3,020) and an external testing set (n = 609). Model fitting was implemented with JMP 12 (SAS Institute, Cary, NC). A 5-fold cross-validation was performed on the training data set for the MLR, PLS, and ANN prediction methods, with square root of blood BHB as the dependent variable. Each method was fitted using 3 combinations of variables: mFTIR, mTest, or mTest + mFTIR variables. Models were evaluated based on coefficient of determination, root mean squared error, and area under the receiver operating characteristic curve. Four models (PLS-mTest + mFTIR, ANN-mFTIR, ANN-mTest, and ANN-mTest + mFTIR) were chosen for further evaluation in the testing set after fitting to the full training set. In the cross-validation analysis, model fit was greatest for ANN, followed by PLS and MLR. Diagnostic strength after cross-validation was poorest for MLR and was similar for ANN and PLS. Models that used mTest + mFTIR variables performed marginally better than models that used only mFTIR or mTest variables. These results suggest that blood BHB prediction models that use mFTIR + mTest variables may be useful additions to existing HYK diagnostic and management programs.