Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Abstract

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 10fold 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-byherd 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 ...

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

“…The authors concluded that pregnant versus open cows post insemination could be discriminated with promising accuracy using milk spectra, parity, and days in milk. Ho et al (2019) reported that milk spectra from early lactation cow together with other on-farm data (e.g., days in milk, days from calving to insemination, calving age, milk yield, genotypes, among others) could be used to classify cows that conceived at first insemination or did not conceive within the breading season with reasonable accuracy, based on the area under the curve (0.75), in herd-by-herd external validation. Delhez et al (2020) observed that milk spectra recorded after 150 days of pregnancy was promising to predict the pregnancy status in Holstein, with the area under the curve around 0.76 in cowindependent external validation.…”

Predicting the likelihood of conception to first insemination of dairy cows using milk mid-infrared spectroscopy

“…We followed the same approach as Ho et al (2019), but applied to additional data that were added to the data set used by Ho et al (2019) specifically to address the question of whether the model could be validated in a commercial setting where the outcome of mating is unknown. Between 2016 and 2018 inclusive, commercial farmer records, collected by several milk recording organizations, of insemination date, calving date, DIM at herd-test, DAI, age at calving (i.e., interval between birth date and calving date), herd-test day milk yield (MY), fat, protein, and lactose percentages, SCC, calving season (i.e., spring, summer, autumn, and winter), and MIR spectroscopy were obtained from DataGene (https: / / www .datagene .com .au/ ) for 9,850 lactating cows (33,483 records) from 29 commercial dairy herds located in Victoria, Tasmania, and New South Wales of Australia.…”

“…The models currently available for predicting fertility of dairy cows use data from different sources, ranging from comparatively difficult to measure (e.g., BW and BCS), to moderately easy to measure (e.g., milk progesterone) or easy to measure [e.g., milk production, milk composition, and milk mid-infrared (MIR) spectroscopy] (Shahinfar et al, 2014;Hempstalk et al, 2015;Cook and Green, 2016;Blavy et al, 2018;Ho et al, 2019). Among these models, some use early-lactation milk-recording data (e.g., milk production, milk composition, and MIR spectroscopy), which is a pragmatic approach because this information is readily accessible for most dairy farms, and therefore, it would not add too much cost.…”

“…The objective of this study was to extend the concept of Ho et al (2019) to develop a tool that can be used to identify cows with either a high or low likelihood of conception to first insemination, using data from commercial dairy herds in Australia. The tool could be used to provide farmers with a list of animals that might be inseminated with premium semen (i.e., if predicted to have a good likelihood of conception-fertile animals) or those that potentially need specific breeding or management (i.e., if predicted to have a poor likelihood of conception-subfertile animals).…”

Relationship Between Blood Metabolic Profiles and Milk Yield Associated With Parity and Stage of Lactation in Crossbred Dairy Cows in Bangladesh

“…55,56,60 More recently, mid-infrared (MIR) spectroscopy of milk has shown promise for assessing more complex animal health traits. 27,[61][62][63] It is established that MIR spectral data can be used to screen for subclinical ketosis through identification of ketone bodies in milk 64 and to estimate energy balance in early lactation. 65…”