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The objective of this study was to evaluate the ability of milk infrared spectra to predict cow lameness score (LMS) for use as an indicator of cow health on Australian dairy farms, or as an indicator trait for genetic evaluation purposes. The study involved 3,771 cows from 10 farms in Australia. Milk infrared spectra collected during the monthly herd testing were available in all the farms involved in the study. Lameness score was measured once in each herd, within 72 h from a test day, and merged to the closest spectra records. Lameness score was expressed on a scale from 0 to 3, where 0 is assigned to sound cows and scores 1 to 3 are assigned to cows with increased lameness severity. Partial least squares discriminant analysis was used to develop prediction models for classifying sound (score 0) and not-sound cows (i.e., cows walking unevenly, score greater than 0). Discriminant models were tested in a 10-fold random cross-validation process. Milk infrared spectra correctly classified only 57% of the cows walking unevenly and only 59% of the sound cows. When additional predictors (parity, age at calving, days in milk, and milk yield) were included in the prediction model, the model correctly classified 57% of the cows walking unevenly and 62% of the sound cows. The same model applied only to the cows in the first third of lactation correctly classified 66% of the cows walking unevenly and 57% of the sound cows. When the prediction model was used to identify lame cows (scores 2 and 3), only 49% of them were classified as such. These results are considered to be too poor to envisage a practical application of these models in the near future as on-farm tools to provide an indication of LMS. To investigate whether, at this stage, predictions of the LMS could be useful as large-scale phenotypes for animal breeding purposes, we estimated (co)variance components for actual and predicted LMS using 2,670 and 24,560 records, respectively. As the genetic correlation between actual and predicted LMS was not significantly different from zero, predictions of lameness from milk spectra and additional on-farm variables cannot be used, at this stage, as an indicator trait for actual LMS. More research is needed to find better strategies to predict lameness.

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.

The objective of this study was to evaluate the ability of milk infrared spectra to predict blood β-hydroxybutyrate (BHB) concentration for use as a management tool for cow metabolic health on pasture-grazed dairy farms and for large-scale phenotyping for genetic evaluation purposes. The study involved 542 cows (Holstein-Friesian and Holstein-Friesian × Jersey crossbreds), from 2 farms located in the Waikato and Taranaki regions of New Zealand that operated under a seasonal-calving, pasture-based dairy system. Milk infrared spectra were collected once a week during the first 5 wk of lactation. A blood "prick" sample was taken from the ventral labial vein of each cow 3 times a week for the first 5 wk of lactation. The content of BHB in blood was measured immediately using a handheld device. After outlier elimination, 1,910 spectra records and corresponding BHB measures were used for prediction model development. Partial least square regression and partial least squares discriminant analysis were used to develop prediction models for quantitative determination of blood BHB content and for identifying cows with hyperketonemia (HYK). Both quantitative and discriminant predictions were developed using the phenotypes and infrared spectra from two-thirds of the cows (randomly assigned to the calibration set) and tested using the remaining one-third (validation set). A moderate accuracy was obtained for prediction of blood BHB. The coefficient of determination (R2) of the prediction model in calibration was 0.56, with a root mean squared error of prediction of 0.28 mmol/L and a ratio of performance to deviation, calculated as the ratio of the standard deviation of the partial least squares model calibration set to the standard error of prediction, of 1.50. In the validation set, the R2 was 0.50, with root mean squared error of prediction values of 0.32 mmol/L, which resulted in a ratio of performance to deviation of 1.39. When the reference test for HYK was defined as blood concentration of BHB ≥1.2 mmol/L, discriminant models indicated that milk infrared spectra correctly classified 76% of the HYK-positive cows and 82% of the HYK-negative cows. The quantitative models were not able to provide accurate estimates, but they could differentiate between high and low BHB concentrations. Furthermore, the discriminant models allowed the classification of cows with reasonable accuracy. This study indicates that the prediction of blood BHB content or occurrence of HYK from milk spectra is possible with moderate accuracy in pasture-grazed cows and could be used during routine milk testing. Applicability of infrared spectroscopy is not likely suited for obtaining accurate BHB measurements at an individual cow level, but discriminant models might be used in the future as herd-level management tools for classification of cows that are at risk of HYK, whereas quantitative models might provide large-scale phenotypes to be used as an indicator trait for breeding cows with improved metabolic health.