Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Prediction of likelihood of conception in dairy cows using milk mid-infrared spectra collected before the first insemination and machine learning algorithms

Accurate and ex-ante prediction of cows' likelihood of conception (LC) based on milk composition information could improve reproduction management on dairy farms. Milk composition is already routinely measured by mid-infrared (MIR) spectra, which are known to change with advancing stages of pregnancy. For lactating cows, MIR spectra may also be used for predicting the LC. Our objectives were to classify the LC at first insemination using milk MIR spectra data collected from calving to first insemination and to identify the spectral regions that contribute the most to the prediction of LC at first insemination. After quality control, 4,866 MIR spectra, milk production, and reproduction records from 3,451 Holstein cows were used. The classification accuracy and area under the curve (AUC) of 6 models comprising different predictors and 3 machine learning methods were estimated and compared. The results showed that partial least square discriminant analysis (PLS-DA) and random forest had higher prediction accuracies than logistic regression. The classification accuracy of good and poor LC cows and AUC in herd-by-herd validation of the best model were 76.35 ± 10.60% and 0.77 ± 0.11, respectively. All wavenumbers with values of variable importance in the projection higher than 1.00 in PLS-DA belonged to 3 spectral regions, namely from 1,003 to 1,189, 1,794 to 2,260, and 2,300 to 2,660 cm-1. In conclusion, the model can predict LC in dairy cows from a high productive TMR system before insemination with a relatively good accuracy, allowing farmers to intervene in advance or adjust the insemination schedule for cows with a poor predicted LC.

Associations between serum health biomarker concentrations and reproductive performance, accounting for milk yield, in pasture-based Holstein cows in southeastern Australia

In this single cohort study, we investigated associations between the concentrations of a suite of serum biomarkers measured in the first 30 d of lactation and subsequent reproductive performance measured as mating start date to conception intervals, in pasture-based Holstein cows. A secondary objective was to examine associations between biomarker concentrations and 305-d milk yield to assess whether any positive associations between biomarker concentration and reproductive performance were explained by reduced milk production. The data used had been collected as part of an ongoing project from 2017 to 2020 to compile a data set from a large population of lactating dairy cows. Biomarkers measured were those associated with energy balance (β-hydroxybutyrate [BHB] and nonesterified fatty acids [NEFA]), protein nutritional status (urea and albumin), immune status (globulin, albumin to globulin ratio and haptoglobin), and macromineral status (calcium and magnesium). Associations between biomarker concentrations and mating start date to conception interval were investigated using Cox proportional hazard models, using between 634 and 1,121 lactations (varying by biomarker) from 632 to 1,103 cows and 11 to 17 mating periods from 10 to 13 herds. Based on hazard ratio (HR) estimates and associated 95% confidence intervals (CI), hazard of conception on any particular day of the herds' mating periods was positively associated with the concentrations of albumin (HR = 1.09; 95% CI: 1.05-1.12), albumin to globulin ratio (HR = 2.82; 95% CI: 1.66-4.79), calcium (HR = 2.01; 95% CI: 1.18-3.43), and magnesium (HR = 2.17; 95% CI: 1.01-4.66), and negatively associated with globulin concentration (HR = 0.98; 95% CI: 0.97 to 1.00). There was also some evidence that NEFA concentration was negatively associated (HR = 0.76; 95% CI: 0.57 to 1.01), and urea concentration positively associated (HR = 1.05; 95% CI: 0.99 to 1.11), with reproductive performance, but no evidence that BHB and haptoglobin concentrations were associated with reproductive performance. Except for NEFA, presence and direction of the associations between the biomarker and milk yield were not discordant with that for reproductive performance. Also, except for NEFA, we found no substantial evidence of nonlinear relationships between biomarker concentration and either reproductive performance or milk yield. Correlations between biomarker concentrations were generally weak, indicating that multibiomarker panels may collectively predict reproductive performance better than any single biomarker. We noted substantial variation in the concentrations of all biomarkers within, and for some biomarkers, between herd-year groups. Collectively, these results indicate that there may be scope to improve biomarker concentrations through nutritional, management, and genetic interventions, and by association, reproductive performance and milk yield may also improve.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Calcium dynamics and associated temporal patterns of milk constituents in early-lactation multiparous Holsteins

At the onset of lactation, calcium (Ca) homeostasis is challenged. For the transitioning dairy cow, inadequate responses to this challenge may result in subclinical hypocalcemia at some point in the postpartum period. It has been proposed that dynamics of blood Ca and the timing of subclinical hypocalcemia allow cows to be classified into 4 Ca dynamic groups by assessing serum total Ca concentrations (tCa) at 1 and 4 days in milk (DIM). These differing dynamics are associated with different risks of adverse health events and suboptimal production. Our prospective cohort study aimed to characterize the temporal patterns of milk constituents in cows with differing Ca dynamics to investigate the potential of Fourier-transform infrared spectroscopic (FTIR) analysis of milk as a diagnostic tool for identifying cows with unfavorable Ca dynamics. We sampled the blood of 343 multiparous Holsteins on a single dairy in Cayuga County, New York, at 1 and 4 DIM and classified these cows into Ca dynamic groups using threshold concentrations of tCa (1 DIM: tCa <1.98 mmol/L; 4 DIM: tCa <2.22 mmol/L) derived from receiver operating characteristic curve analysis based on epidemiologically relevant health and production outcomes. We also collected proportional milk samples from each of these cows from 3 to 10 DIM for FTIR analysis of milk constituents. Through this analysis we estimated the milk constituent levels of anhydrous lactose (g/100 g of milk and g/milking), true protein (g/100 g of milk and g/milking), fat (g/100 g of milk and g/milking), milk urea nitrogen (mg/100 g of milk), fatty acid (FA) groups including de novo, mixed origin, and preformed FA measured in grams/100 g of milk, by relative percentage, and grams/milking, as well as energy-related metabolites including ketone bodies and milk-predicted blood nonesterified FA. Individual milk constituents were compared among groups at each time point and over the entire sample period using linear regression models. Overall, we found differences among the constituent profiles of Ca dynamic groups at approximately every time point and over the entire sample period. The 2 at-risk groups of cows did not differ from each other at more than one time point for any constituent, however prominent differences existed between the milk of normocalcemic cows and the milk of the other Ca dynamic groups with respect to FA. Over the entire sample period, lactose and protein yield (g/milking) were lower in the milk of at-risk cows than in the milk of the other Ca dynamic groups. In addition, milk yield per milking followed patterns consistent with previous Ca dynamic group research. Though our use of a single farm does limit the general applicability of these findings, our conclusions provide evidence that FTIR may be a useful method for discriminating between cows with different Ca dynamics at time points that may be relevant in the optimization of management or development of clinical intervention strategies.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy

Metabolic diseases driven by negative energy balance in dairy cattle contribute to reduced milk production, increased disease incidence, culling, and death. Cow side tests for negative energy balance markers are available but are labor-intensive. Milk sample analysis using Fourier transform infrared spectroscopy (FTIR) allows for sampling numerous cows simultaneously. FTIR prediction models have moderate accuracy for hyperketonemia diagnosis (beta-hydroxybutyrate (BHB) ≥ 1.2 mmol/L). Most research using FTIR has focused on homogenous datasets and conventional prediction models, including partial least squares, linear discriminant analysis, and ElasticNet. Our objective was to evaluate more diverse modeling options, such as deep learning, gradient boosting machine models, and model ensembles for hyperketonemia classification. We compiled a sizable, heterogeneous dataset including milk FTIR and concurrent blood samples. Blood samples were tested for blood BHB, and wavenumber data was obtained from milk FTIR analysis. Using this dataset, we trained conventional prediction models and other options listed above. We demonstrate prediction model performance is similar for convolutional neural networks and ensemble models to simpler algorithm options. Results obtained from this study indicate that deep learning and model ensembles are potential algorithm options for predicting hyperketonemia in dairy cattle. Additionally, our results indicate hyperketonemia prediction models can be developed using heterogeneous datasets.

Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats

The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm^-1), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.

Determination of the lactose content in low-lactose milk using Fourier-transform infrared spectroscopy (FTIR) and convolutional neural network

Demand for low lactose milk and milk products has been increasing worldwide due to the high number of people with lactose intolerance. These low lactose dairy foods require fast, low-cost and efficient methods for sugar quantification. However, available methods do not meet all these requirements. In this work, we propose the association of FTIR (Fourier Transform Infrared) spectroscopy with artificial intelligence to identify and quantify residual lactose and other sugars in milk. Convolutional neural networks (CNN) were built from the infrared spectra without preprocessing the data using hyperparameter adjustment and saliency map. For the quantitative prediction of the sugars in milk, a regression model was proposed, while for the qualitative assessment, a classification model was used. Raw, pasteurized and ultra-high temperature (UHT) milk was added with lactose, glucose, and galactose in six concentrations (0.1-7.0 mg mL^-1) and, in total, 432 samples were submitted to convolutional neural network. Accuracy, precision, sensitivity, specificity, root mean square error, mean square error, mean absolute error, and coefficient of determination (R²) were used as evaluation parameters. The algorithms indicated a predictive capacity (accuracy) above 95% for classification, and R² of 81%, 86%, and 92% for respectively, lactose, glucose, and galactose quantification. Our results showed that the association of FTIR spectra with artificial intelligence tools, such as CNN, is an efficient, quick, and low-cost methodology for quantifying lactose and other sugars in milk.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein

The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals' performance under known environments, known individuals' performance under new environments, and new individuals' performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the 'environmental coordinates' of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information.

Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen

Blood urea nitrogen (BUN) is an indicator trait for urinary nitrogen excretion. Measuring BUN level requires a blood sample, which limits the number of records that can be obtained. Alternatively, BUN can be predicted using mid-infrared (MIR) spectroscopy of a milk sample and thus records become available on many more cows through routine milk recording processes. The genetic correlation between MIR predicted BUN (MBUN) and BUN is 0.90. Hence, genetically, BUN and MBUN can be considered as the same trait. The objective of our study was to perform genome-wide association studies (GWAS) for BUN and MBUN, compare these two GWAS and detect quantitative trait loci (QTL) for both traits, and compare the detected QTL with previously reported QTL for milk urea nitrogen (MUN). The dataset used for our analyses included 2098 and 18,120 phenotypes for BUN and MBUN, respectively, and imputed whole-genome sequence data. The GWAS for MBUN was carried out using either the full dataset, the 2098 cows with records for BUN, or 2000 randomly selected cows, so that the dataset size is comparable to that for BUN. The GWAS results for BUN and MBUN were very different, in spite of the strong genetic correlation between the two traits. We detected 12 QTL for MBUN, on bovine chromosomes 2, 3, 9, 11, 12, 14 and X, and one QTL for BUN on chromosome 13. The QTL detected on chromosomes 11, 14 and X overlapped with QTL detected for MUN. The GWAS results were highly sensitive to the subset of records used. Hence, caution is warranted when interpreting GWAS based on small datasets, such as for BUN. MBUN may provide an attractive alternative to perform a more powerful GWAS to detect QTL for BUN.

Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle

Simple Summary

Sensors, routinely collected on-farm tests, and other repeatable, high-throughput measurements can provide novel phenotype information on a frequent basis. Information from these sensors and high-throughput measurements could be harnessed to monitor or predict individual dairy cow feed intake. Predictive algorithms would allow for genetic selection of animals that consume less feed while producing the same amount of milk. Improved monitoring of feed intake could reduce the cost of milk production, improve animal health, and reduce the environmental impact of the dairy industry. Moreover, data from these information sources could aid in animal management (e.g., precision feeding and health detection). In order to implement tools, the relationship of measurements with feed intake needs to be established and prediction equations developed. Lastly, consideration should be given to the frequency of data collection, the need for standardization of data and other potential limitations of tools in the prediction of feed intake. This review summarizes measurements of feed efficiency, factors that may impact the efficiency and feed consumption of an animal, tools that have been researched and new traits that could be utilized for the prediction of feed intake and efficiency, and prediction equations for feed intake and efficiency presented in the literature to date.

Abstract

Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm.

Genetic parameters for mid-infrared spectroscopy-predicted fertility

Female fertility is a challenging trait to improve genetically because of its low heritability, its unfavorable genetic correlation with milk yield, and its relatively small number of records. The MFERT trait is the probability of conception to first insemination predicted using mid-infrared (MIR) spectroscopy of a milk sample collected during lactation as part of routine milk recording, age at calving, days in milk, and milk production. As such, MFERT could become available on many more cows than traditional fertility traits. Our objectives were (1) to estimate the heritability of MFERT; (2) to estimate genetic correlations between MFERT, traditional fertility traits, and milk production traits; and (3) to assess the potential of MFERT to be used as an indicator trait for fertility in a selection index. The MFERT trait had a heritability of 0.16, which was higher than that (0.05) obtained for traditional fertility traits. Genetic correlations between MFERT and traditional fertility traits were low to moderate. The weakest and strongest correlations (mean ± standard error) were with pregnancy at the end of the mating season (0.13 ± 0.05) and calving to first service (-0.61 ± 0.03), respectively. Based on our estimates, including MFERT in a fertility index will only substantially increase the accuracy of the index when there are many more records available for MFERT than for the traditional fertility traits. This is likely to be the case because the number of milk samples from commercial machines belonging to milk recording companies in Australia that are capable of generating MIR spectra is growing. Hence, the number of records for MFERT is expected to increase substantially in the near future.

Breeding Strategies for Weather Resilience in Small Ruminants in Atlantic and Mediterranean Climates

Many efforts are being made to cope with negative consequences of climate change (CC) on livestock. Among them, selective breeding of resilient animals to CC is presented as an opportunity to maintain high levels of performance regardless of variation in weather. In the present work, we proposed a set of breeding strategies to improve weather resilience in dairy goats raised in north-western European Atlantic conditions and dairy sheep raised in Mediterranean conditions while improving production efficiency at the same time. Breeding strategies differed in the selection emphasis placed on resilience traits, ranging from 0 to 40% in the index. Simulations were carried out mimicking real breeding programs including: milk yield, length of productive life, age at first kidding and mastitis incidence in dairy goats and milk, fat and protein yields, and fertility for dairy sheep. Considering the particular climatic conditions in the two regions, the predicted future climate scenarios, and genetic correlations among breeding objectives, resilience was defined as stability to weather changes for dairy goats and as the ability to improve performance under heat stress for dairy sheep. A strategy giving a selection weight of 10 and 20% for goat and sheep resilience, respectively, resulted in the best overall genetic response in terms of both, production and resilience ability. Not considering resilience in breeding programs could lead to a major production loss in future climate scenarios, whereas putting too much emphasis on resilience would result in a limited progress in milk production.

Genetic parameters of blood urea nitrogen and milk urea nitrogen concentration in dairy cattle managed in pasture-based production systems of New Zealand and Australia

Context Urinary nitrogen excretion by grazing cattle causes environmental pollution. Selecting for cows with a lower concentration of urinary nitrogen excretion may reduce the environmental impact. While urinary nitrogen excretion is difficult to measure, blood urea nitrogen (BUN), mid-infrared spectroscopy (MIR)-predicted BUN (MBUN), which is predicted from MIR spectra measured on milk samples, and milk urea nitrogen (MUN) are potential indicator traits. Australia and New Zealand have increasing datasets of cows with urea records, with 18 120 and 15 754 cows with urea records in Australia and New Zealand respectively. A collaboration between Australia and New Zealand could further increase the size of the dataset by sharing data. Aims Our aims were to estimate genetic parameters for urea traits within country, and genetic correlations between countries to gauge the benefit of having a joint reference population for genomic prediction of an indicator trait that is potentially suitable for selection to reduce urinary nitrogen excretion for both countries. Methods Genetic parameters were estimated within country (Australia and New Zealand) in Holstein, Jersey and a multibreed population, for BUN, MBUN and MUN in Australia and MUN in New Zealand, using high-density genotypes. Genetic correlations were also estimated between the urea traits recorded in Australia and MUN in New Zealand. Analyses used the first record available for each cow or within days-in-milk (DIM) intervals. Key results Heritabilities ranged from 0.08 to 0.32 for the various urea traits. Higher heritabilities were obtained for Jersey than for Holstein, and for the New Zealand cows than for the Australian cows. While urea traits were highly correlated within Australia (0.71–0.94), genetic correlations between Australia and New Zealand were small to moderate (0.08–0.58). Conclusions Our results showed that the heritability for urea traits differs among trait, breed, and country. While urea traits are highly correlated within country, genetic correlations between urea traits in Australia and MUN in New Zealand were only low to moderate. Implications Further study is required to identify the underlying causes of the difference in heritabilities observed, to compare the accuracies of different reference populations, and to estimate genetic correlations between urea traits and other traits such as fertility and feed intake. Larger datasets may help estimate genetic correlations more accurately between countries.