Body and milk traits as indicators of dairy cow energy status in early lactation

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (ΔBW), change in body condition score (ΔBCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30, 0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; all means ± standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW, and days in milk. The model resulted in a cross-validation coefficient of determination (R²cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R²cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 × C14:0, and days in milk (R²cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R²cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.     

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.     

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002–0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.     

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

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

Effect of dietary starch level and high rumen-undegradable protein on endocrine-metabolic status,milk yield,and milk composition in dairy cows during early and late lactation

Diet composition defines the amount and type of nutrients absorbed by dairy cows. Endocrine-metabolic interactions can influence these parameters, and so nutrient availability for the mammary gland can significantly vary and affect milk yield and its composition. Six dairy cows in early and then late lactation received, for 28 d in a changeover design, 2 diets designed to provide, within the same stage of lactation, similar amounts of rumen fermentable material but either high starch plus sugar (HS) content or low starch plus sugar content (LS). All diets had similar dietary crude protein and calculated supply of essential amino acids. Dry matter intake within each stage of lactation was similar between groups. Milk yield was similar between groups in early lactation, whereas a higher milk yield was observed in late lactation when feeding HS. At the metabolic level, the main difference observed between the diets in both stages of lactation was lower blood glucose in cows fed LS. The lower glucose availability during consumption of LS caused substantial modifications in the circulating and postprandial pattern of metabolic hormones. Feeding LS versus HS resulted in an increase in the ratio of bovine somatotropin to insulin. This increased mobilization of lipid reserves resulted in higher blood concentrations of nonesterified fatty acids and β-hydroxybutyrate, which contributed to the higher milk fat content in both stages of lactation in the LS group. This greater recourse to body fat stores was confirmed by the greater loss of body weight during early lactation and the slower recovery of body weight in late lactation in cows fed LS. The lower insulin to glucagon ratio observed in cows fed LS in early and late lactation likely caused an increase in hepatic uptake and catabolism of amino acids, as confirmed by the higher blood urea concentrations. Despite the higher catabolism of amino acids in LS in early lactation, similar milk protein output was observed for both diets, suggesting similar availability of amino acids for peripheral tissue and mammary gland. The latter could be the result of sparing of amino acids at the gut level due to starch that escaped from the rumen, and to the balanced amino acid profile of digestible protein. This last aspect appears worthy of further research, with the aim to enhance the efficiency of protein metabolism of dairy cows, reducing environmental nitrogen pollution without affecting milk yield potential.     

Lead excretion in milk of accidentally exposed dairy cattle

Lead (Pb) exposure in dairy cattle is associated with economic losses due to mortality and treatment costs, but with production animals there is also risk to the human food chain. The first objective of this study was to quantify the Pb concentration in milk from Pb-exposed cattle. The second objective was to correlate blood and milk Pb concentrations from individual cows. The third objective was long-term monitoring to determine the duration of milk contamination after exposure ceased. A dairy herd of more than 100 cows was accidentally exposed to Pb-contaminated feed. Milk and blood were collected for Pb analysis. Serial collection of milk samples continued for 2.5 years. The initial concentration of Pb in bulk tank milk was 0.0999 mg l^–1. The highest milk Pb concentration from an individual cow was 0.4657 mg l^–1 and the highest blood Pb concentration was 1.216 mg l^–1. One milk sample collected at the end of the study (day 922) contained 0.0117 mg Pb l^–1 of Pb. The calculated relationship between milk (y) and blood (x) Pb concentration was ln(y) = 3.4(x) – 2.21 (R² = 0.98).     

Effect of lactation stage and energy status on milk fat composition of Holstein-Friesian cows

The effects of lactation stage, negative energy balance (NEB), and milk fat depression (MFD) were estimated on detailed milk fat composition in primiparous Holstein-Friesian cows. One morning milk sample was collected from each of 1,933 cows from 398 commercial Dutch herds in winter 2005. Milk fat composition was measured using gas chromatography, and fat and protein percentage were measured using infrared spectrometry. Each fatty acid changed 0.5 to 1 phenotypic standard deviation over lactation, except odd-chain C5:0 to C15:0, branched-chain fatty acids, and trans-10, cis-12 conjugated linoleic acid (CLA). The greatest change was an increase from 31.2 to 33.3% (wt/wt) for C16:0 from d 80 to 150 of lactation. Energy status was estimated for each cow as the deviation from each average lactation fat-to-protein ratio (FPdev). A high FPdev (>0.12) indicated NEB. Negative energy balance was associated with an increase in C16:0 (0.696 ± 0.178) and C18:0 (0.467 ± 0.093), which suggested mobilization of body fat reserves. Furthermore, NEB was associated with a decrease in odd-chain C5:0 to C15:0 (−0.084 ± 0.020), which might reflect a reduced allocation of C3 components to milk fat synthesis. A low FPdev indicated MFD (<−0.12) and was associated with a decrease in C16:0 (−0.681 ± 0.255) and C18:0 (−0.128 ± 0.135) and an increase in total unsaturated fatty acids (0.523 ± 0.227). The study showed that both lactation stage and energy balance significantly contribute to variation in milk fat composition and alter the activity of different fatty acid pathways.     

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.     

The effect of nutritional management in early lactation and dairy cow genotype on milk production,metabolic status,and uterine recovery in a pasture-based system

High levels of milk production coupled with low feed intake cause negative energy balance in early lactation, especially in the first month postpartum (PP). Therefore, specific nutritional management at this time may improve nutritional and metabolic status with the possibility of contrasting genotypes responding differently. Thus, the objective of this study was to compare the effects of nutritional management strategies and dairy cow genotype on milk production, metabolic status, and some fertility parameters during early lactation in a pasture-based system. Sixty Holstein Friesian cows were blocked on parity and genotype [low-fertility high-milk (LFHM) and high-fertility low-milk (HFLM)] and were randomly assigned to 1 of 2 treatments in a 2 × 2 factorial arrangement, in a randomized complete block design based on calving date, previous 305-d milk yield, and precalving body condition score (BCS). The nutritional management treatments were: (1) ad libitum access to fresh pasture plus an allowance of 3 kg of concentrates per day (CTR, n = 30); and (2) ab libitum access to a tailored total mixed ration (TMR, n = 30). These diets were offered for the first 30 d PP. Following the first 30 d PP, cows fed TMR joined the CTR treatment and were managed similarly until 100 d PP. Blood samples were taken at d 7, 14, 21, and 28 PP to determine metabolic status. Milk samples for composition analysis were collected weekly and BCS assessed every 2 wk. Genotype had a significant effect on milk output, whereas LFHM had increased fat (+0.28 kg/d) and fat-plus-protein (+0.17 kg/d) yield in the first 30 d PP compared with HFLM cows. The LFHM group also exhibited higher protein and lactose yields over the first 100 d PP. Nutritional management did create significant differences in milk composition in the first 30 d: TMR cows had lower protein, milk urea nitrogen, and casein concentration and higher lactose concentration than CTR cows. Over the first 100 d PP, TMR cows had higher fat-plus-protein and lactose yields. Feeding TMR reduced concentrations of nonesterified fatty acids (?0.12 mmol/L) and β-hydroxybutyric acid (?0.10 mmol/L) compared with the CTR group. Cows fed TMR had smaller BCS losses from calving to 60 d PP. There was no effect of any treatment on uterine recovery. Cows in the LFHM group demonstrated greater milk production in the first 30 and 100 d in milk. These results demonstrate that feeding cows a TMR for the first month of lactation has positive effects on milk output, metabolic status, and BCS profile.     

Effects of pre- and postfresh transition diets varying in dietary energy density on metabolic status of periparturient dairy cows

Effects of dietary energy density during late gestation and early lactation on metabolic status of periparturient cows were studied. Four weeks before expected calving, animals were fed a low (DL; 1.58 Mcal of NE_L/kg) or high energy density diet (DH; 1.70 Mcal of NE_L/kg). After calving, half of the cows from each prepartum treatment were assigned to a low (L; 1.57 Mcal of NE_L/kg) or high energy density diet (H; 1.63 Mcal of NE_L/kg) until d 20 postpartum. After d 20, all animals were fed H until d 70. Animals fed DH had a more positive energy balance during the prepartum period. Animals fed DH had higher plasma concentrations of glucose and insulin and lower concentrations of plasma nonesterified fatty acid (NEFA) on d −7 relative to calving compared with animals fed DL. No differences in blood concentrations of metabolites, insulin and liver triglycerides (TG) content were observed on d 1. Liver TG content at d 1 and 21 were more related to magnitude of change in energy intake prepartum than to energy intake in the last week of gestation. Cows fed H had higher concentrations of plasma glucose and insulin, but similar plasma NEFA during the postpartum period compared with cows fed L. Plasma concentrations of β-hydroxybutyrate (BHBA) and liver TG content on d 21 were 46 and 30% lower, respectively, for cows fed H compared with cows fed L. Interactions between prepartum and postpartum treatments indicated that negative effects of delaying higher concentrate feeding until d 21 postpartum can be partially offset by increasing concentrate in the diet before calving. Cows fed L had a higher increase in white line hemorrhage scores between prepartum and 10 wk postpartum compared with cows fed H. Energy density of prepartum diets had a minor influence on metabolic status of cows postpartum. A more favorable metabolic profile occurs when increasing the concentrate content of the diet immediately postpartum compared with delaying the increase until d 21 postpartum.     

Improved milk production efficiency in early lactation dairy cattle with dietary addition of a developmental fibrolytic enzyme additive

A 3-part study was conducted to evaluate the effect of a developmental fibrolytic enzyme additive on the digestibility of selected forages and the production performance of early-lactation dairy cows. In part 1, 4 replicate 24-h batch culture in vitro incubations were conducted with alfalfa hay, alfalfa silage, and barley silage as substrates and ruminal fluid as the inoculum. A developmental fibrolytic enzyme additive (AB Vista, Marlborough, UK) was added at 5 doses: 0, 0.5, 1.0, 1.5, and 2.0 μL/g of forage dry matter (DM). After the 24-h incubation, DM, neutral detergent fiber (NDF), and acid detergent fiber (ADF) disappearance were determined. For alfalfa hay, DM, NDF, and ADF disappearance was greater at the highest dosage compared with no enzyme addition. Barley silage NDF and ADF and alfalfa silage NDF disappearance tended to be greater for the highest enzyme dosage compared with no enzyme addition. In part 2, 6 ruminally cannulated, lactating Holstein dairy cows were used to determine in situ degradation of alfalfa and barley silage, with (1.0 mL/kg of silage DM) and without added enzyme. Three cows received a control diet (no enzyme added) and the other 3 received an enzyme-supplemented (1.0 mL/kg of diet DM) diet. Enzyme addition after the 24 h in situ incubation did not affect the disappearance of barley silage or alfalfa silage. In part 3, 60 early-lactation Holstein dairy cows were fed 1 of 3 diets for a 10-wk period: (1) control (CTL; no enzyme), (2) low enzyme (CTL treated with 0.5 mL of enzyme/kg of diet DM), and (3) high enzyme (CTL treated with 1.0 mL of enzyme/kg of diet DM). Adding enzyme to the diet had no effect on milk yield, but dry matter intake was lower for the high enzyme treatment and tended to be lower for the low enzyme treatment compared with CTL. Consequently, milk production efficiency (kg of 3.5% fat-corrected milk/kg of DM intake) linearly increased with increasing enzyme addition. Cows fed the low and high enzyme diets were 5.3 (not statistically significant) and 11.3% more efficient, respectively, compared with CTL cows. This developmental fibrolytic enzyme additive has the potential to increase fiber digestibility of forages, which could lead to greater milk production efficiency for dairy cows in early lactation.     

Identification of potential markers in blood for the development of subclinical and clinical mastitis in dairy cattle at parturition and during early lactation

Our objective was to identify specific blood markers as risk factors for the development of mastitis during early lactation. We used a subset of cows from a larger experiment that consisted of a total of 634 lactations from 317 cows. Cows were of 3 breeds and ranged from parity 1 to 4. Blood samples were collected weekly from 56 d before expected calving date through 90 d in milk (DIM). Blood was analyzed for several hormones, metabolites, and enzymes, and energy intake and energy balance were calculated. Veterinary treatment records and daily composite milk somatic cell counts were analyzed and used to determine incidence and severity of mastitis in early lactation. Cows were separated into 2 groups: 1) WK0, consisting of cows that developed clinical mastitis (CM), cows that developed subclinical mastitis (SM), or cows that were healthy (H) during the first 7 DIM; and 2) EL, consisting of CM, SM, or H cows during wk 2 through 13 of lactation. Data were adjusted for numerous fixed effects (e.g., parity, breed, season, and DIM) before statistical analysis. The time of mastitis (TOM) was recorded as the DIM in which the first rise in somatic cell count was observed and was recorded as TOM = 0. The time before and after TOM was distinguished as ± n wk relative to TOM = 0. Healthy cows were paired with either a SM or CM cow and the TOM for each H cow was equal to the TOM for its paired SM or CM cow. Data from wk −1 and −2 relative to TOM were analyzed for group WK0 and EL, respectively. For all parameters, SM cows did not differ from H cows from either group. The CM cows had higher nonesterified fatty acid levels and a tendency toward higher β-hydroxybutyrate levels than H cows before mastitis for both groups. For group WK0, glucose was higher −1 wk relative to calving in CM than H cows. For group EL, aspartate aminotransferase was higher −2 wk relative to mastitis in CM than H cows during 8 to 90 DIM. All other variables were similar among CM, SM, and H cows for both groups. Our results indicate that substances in blood, especially nonesterified fatty acids and aspartate aminotransferase, may be potential markers for the risk of mastitis in early lactation.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Risk factors associated with milk fever occurrence in grazing dairy cattle

The aim of this study was to determine risk factors associated with milk fever (MF) occurrence in Costa Rican grazing dairy cattle. A total of 69,870 cows from 126 dairy herds were included in the study. Data were collected in the Veterinary Automated Management and Production Control Program software by the Population Medicine Research Program of the Veterinary Medicine School, National University of Costa Rica, from 1985 to 2014. To determine the risk factors for MF, 2 logistic regression mixed models were evaluated. The first model used breed, month of calving, ecological life zone, herd nested within ecological life zone, and parity as fixed effects. The second model excluded first-lactation animals and cows without production information, had the same fixed effects of the first model, and added previous MF case, previous lactation length, previous dry period length, previous corrected 305-d milk yield, and calving interval length as fixed effects. Both models used animal and year as random effects. Of the 235,971 recorded lactations, 4,312 (1.83%) reported MF event. The significantly associated risk factors for MF occurrence, ranked by their highest odds ratio (OR), were parity (OR = 52.59), previous dry period length (OR = 4.21), ecological life zone (OR = 3.20), breed (OR = 3.04), previous corrected 305-d milk yield (OR = 2.39), previous MF case (OR = 2.35), and month of calving (OR = 1.36). The findings of this study are the first data reported using an epidemiological approach to study risk factors for MF in Costa Rican dairy cattle. Some of these results might be used to improve preventive management practices at the farms to reduce the incidence of this metabolic disease in grazing dairy herds.     

Relationships between milk protein composition, milk protein variants, and cow fertility traits in Dutch Holstein-Friesian cattle

Selective breeding can change milk protein composition to improve the manufacturing properties of milk. However, the effects of such breeding strategies on other economically important traits should be investigated before implementation. The objectives of this study were to examine the association between cow fertility traits and (1) milk protein composition and (2) milk protein variants (β-lactoglobulin, β-casein, κ-casein, and β-κ-casein) in commercial Dutch Holstein-Friesian cattle. Data on 1,644 first-lactation cows were analyzed by fitting linear mixed models. Greater relative concentration of α_S1-casein within total milk protein had a positive phenotypic relationship with nonreturn rates and calving rate after first insemination. Furthermore, results showed virtually no significant relationship between cow fertility and concentration of other milk proteins or milk protein variants. Results of this study can be used to assess the correlated effects of breeding for improved milk protein composition on reproduction, thereby allowing for better evaluation of breeding programs before implementation. Our findings suggest that selecting cows based on milk protein composition or milk protein variants for improved manufacturing properties would have no negative influence on reproductive performance.     

Comparison of peripartum metabolic status and postpartum health of Holstein and Montbéliarde-sired crossbred dairy cows

Objectives of the current experiment were to evaluate plasma concentrations of metabolites and haptoglobin peripartum, uterine health and involution, and follicle growth and resumption of cyclicity of Holstein (HO) and Montbéliarde-sired crossbred cows. Cows (52 HO and 52 crossbred) were enrolled in the study 45 d before expected calving date. Cows had body weight and body condition score recorded on d −45, −14, 0, 1, 28, and 56 relative to calving. Dry matter intake was calculated for a subgroup of cows (25 HO and 38 crossbred) from 6 wk before to 6 wk after calving. Blood was sampled weekly from d −14 to 56 relative to calving for determination of glucose, nonesterified fatty acid, and β-hydroxybutyrate concentrations; from d −7 to 21 relative to calving for determination of haptoglobin concentration; and from d 14 to 56 postpartum for determination of progesterone concentration. Cows were examined at calving and on d 4, 7, 10, and 14 postpartum for diagnosis of postparturient diseases, on d 24 postpartum for diagnosis of purulent vaginal discharge, and on d 42 postpartum for diagnosis of subclinical endometritis. Uteri and ovaries were examined by ultrasonography every 3 d from d 14 to 41 postpartum. Milk yield and composition were measured monthly and yield of milk, fat, protein, and energy-corrected milk were recorded for the first 90 d postpartum. Body weight was not different between Holstein and crossbred cows, but HO cows had reduced body condition score compared with crossbred cows. Even though DMI from 6 wk before to 6 wk after calving tended to be greater for HO cows (16.8 ± 0.7 vs. 15.3 ± 0.5 kg/d), HO cows tended to have more pronounced decline in dry matter intake, expressed in percentage of body weight from d −15 to 0 relative to calving. Energy-corrected milk and nonesterified fatty acid and β-hydroxybutyrate concentrations were not different between breeds. No differences were observed in incidence of retained fetal membranes, metritis, and subclinical endometritis, but HO cows tended to be more likely to have pyrexia from d 0 to 15 postpartum (50.0 vs. 31.4%) and to have greater incidence of purulent vaginal discharge (44.2 vs. 26.5%) than crossbred cows. Holstein cows were more likely to have at least 1 uterine disorder postpartum than crossbred cows (63.5 vs. 36.7%). No differences between breeds were observed in uterine involution. Holstein cows had larger subordinate follicles (10.1 ± 0.4 vs. 8.9 ± 0.5) and a greater number of class III follicles (1.6 ± 0.1 vs. 1.2 ± 0.1) than crossbred cows. Furthermore, the first corpus luteum postpartum of HO cows was diagnosed at a slower rate compared with crossbred cows. Crossbred cows had improved uterine health compared with HO cows and this may have been a consequence of heterosis and (or) breed complementarity and less pronounced decrease in DMI during the last days of gestation.     

Milk coagulation ability of five dairy cattle breeds

Samples of herd milk (506) were analyzed to assess sources of variation for milk coagulation properties (MCP) for 5 different dairy cattle breeds. Data were recorded in 55 single-breed dairy herds in the Trento province, a mountain area in northeast Italy. The 5 cattle breeds were Holstein-Friesian (8 herds), Brown Swiss (16 herds), Simmental (10 herds), Rendena (13 herds), and Alpine Gray (8 herds). Herd milk samples were analyzed for the MCP traits, milk rennet coagulation time (RCT), curd-firming time, and curd firmness (a₃₀), as well as protein and fat percentages, somatic cell count, Soxhlet-Henkel acidity, and bacterial count. An ANOVA was performed to study the effect of breed, herd within breed, DIM, month of lactation, protein and fat percentages, somatic cell score, titratable acidity, and log bacterial count within breed on MCP. Breed was the most important source of variation. In particular, the Rendena breed showed the best MCP traits at 13.5 min and 27.0 mm for RCT and a₃₀, respectively. The Holstein-Friesian breed had the worst coagulation properties at 18.0 min and 17.5 mm for RCT and a₃₀, respectively. The other 3 breeds showed intermediate coagulation properties. The RCT values were better at the beginning of lactation, whereas RCT and a₃₀ values were better in September and October (14.3 min and 25.7 mm, respectively). Among the composition traits, only the titratable acidity affected MCP traits of herd milk positively.     

The effect of body condition score at calving and supplementation with Saccharomyces cerevisiae on milk production, metabolic status, and rumen fermentation of dairy cows in early lactation

The objective of this study was to examine the effects of live yeast (LY) supplementation and body condition score (BCS, 1-5 scale) at calving on milk production, metabolic status, and rumen physiology of postpartum (PP) dairy cows. Forty Holstein-Friesian dairy cows were randomly allocated to a 2 × 2 factorial design and blocked by yield, parity, BCS, and predicted calving date. Treatments were body condition at calving (low for BCS ≤3.5 or high for BCS ≥3.75; n = 20) and supplementation with LY (2.5 and 10 g of LY/d per cow for pre- and postcalving, respectively; control, no LY supplementation; n = 20). The supplement contained 10⁹ cfu of Saccharomyces cerevisiae/g (Yea-Sacc¹⁰²⁶ TS, Alltech Inc., Nashville, TN). Daily milk yield, dry matter intake, milk composition, BCS, body weight, and backfat thickness were recorded. Blood samples were harvested for metabolite analysis on d 1, 5, 15, 25, and 35 PP. Liver samples were harvested by biopsy for triacylglycerol (TAG) and glycogen analysis on d 7 precalving, and on d 7 and 21 PP. Rumen fluid was sampled by rumenocentesis for all cows on d 7 and 21 PP. Supplementation with LY had no effect on milk yield, dry matter intake, rumen fluid pH, or blood metabolites concentration of dairy cows with high or low BCS at calving. Feeding LY increased rumen acetate proportion and protozoal population, tended to increase liver glycogen, and decreased rumen ammonia nitrogen during early lactation. Over-conditioned cows at calving had greater body reserve mobilization and milk production and lower feed intake, whereas cows with a moderate BCS at calving had greater feed intake, lower concentrations of nonesterified fatty acids and β-hydroxybutyrate, lower liver TAG and TAG:glycogen ratio, and faster recovery from body condition loss. Additionally, the data suggest that concentrations of liver enzymes in blood might be used as an indicator for liver TAG:glycogen ratio. Results indicate that in the case of this experiment, where the control treatment was associated with an acceptable rumen pH, feeding yeast did not significantly improve indicators of energy status in dairy cows.     

Effects of substitution of different levels of steam-flaked corn for finely ground corn on lactation and digestion in early lactation dairy cows

Eight multiparous Holstein cows, 4 of them fitted with rumen cannulas, were used to test the effects of substitution of steam-flaked corn (SFC) for equal amounts of finely ground corn (FGC) in diets on feed intake and digestion, blood metabolites, and lactation performance in early lactation dairy cows. Cows were fed 4 diets in a replicated 4 × 4 Latin square design. The fistulated cows formed 1 replicate. Each experimental period lasted for 3 wk. The 4 diets contained 0, 10, 20, or 40% SFC and 40, 30, 20, or 0% FGC (dry matter basis), respectively. The milk protein content and yield, milk solid nonfat content and yield, plasma glucose concentration, and dry matter intake increased as the proportion of SFC increased in diets. Apparent total tract digestibilities of dry matter, organic matter, neutral detergent fiber, acid detergent fiber, and average ruminal fluid NH₃-N concentration decreased with increasing levels of SFC. The ruminal fluid pH was not affected by the substitution of SFC for FGC. The 20% SFC substitution improved digestion of crude protein, yield of fat-corrected milk, milk lactose content, fat, and fat yield. The 40% SFC substitution increased urea concentration in both plasma and milk. It was concluded that 20% of SFC substitution for FGC appeared to be an appropriate level in diet for early lactation dairy cows.