Energy profiling of dairy cows from routine milk mid-infrared analysis

Combined Metabolomics and Biochemical Analyses of Serum and Milk Revealed Parity-Related Metabolic Differences in Sanhe Dairy Cattle

The production performance of dairy cattle is closely related to their metabolic state. This study aims to provide a comprehensive understanding of the production performance and metabolic features of Sanhe dairy cattle across different parities, with a specific focus on evaluating variations in milk traits and metabolites in both milk and serum. Sanhe dairy cattle from parities 1 to 4 (S1, n = 10; S2, n = 9; S3, n = 10; and S4, n = 10) at mid-lactation were maintained under the same feeding and management conditions. The milk traits, hydrolyzed milk amino acid levels, serum biochemical parameters, and serum free amino acid levels of the Sanhe dairy cattle were determined. Multiparous Sanhe dairy cattle (S2, S3, and S4) had a greater milk protein content, lower milk lactose content, and lower solids-not-fat content than primiparous Sanhe dairy cattle (S1). Moreover, S1 had a higher ratio of essential to total amino acids (EAAs/TAAs) in both the serum and milk. The serum biochemical results showed the lower glucose and total protein levels in S1 cattle were associated with milk quality. Furthermore, ultra-high-resolution high-performance liquid chromatography with tandem MS analysis (UPLC-MS/MS) identified 86 and 105 differential metabolites in the serum and milk, respectively, and these were mainly involved in amino acid, carbohydrate, and lipid metabolism. S1 and S2/S3/S4 had significantly different metabolic patterns in the serum and milk, and more vitamin B-related metabolites were significantly higher identified in S1 than in multiparous cattle. Among 36 shared differential metabolites in the serum and milk, 10 and 7 metabolites were significantly and strongly correlated with differential physiological indices, respectively. The differential metabolites identified were enriched in key metabolic pathways, illustrating the metabolic characteristics of the serum and milk from Sanhe dairy cattle of different parities. L-phenylalanine, dehydroepiandrosterone, and linoleic acid in the milk and N-acetylornithine in the serum could be used as potential marker metabolites to distinguish between Sanhe dairy cattle with parities of 1-4. In addition, a metabolic map of the serum and milk from the three aspects of carbohydrates, amino acids, and lipids was created for the further analysis and exploration of their relationships. These results reveal significant variations in milk traits and metabolites across different parities of Sanhe dairy cattle, highlighting the influence of parity on the metabolic profiles and production performance. Tailored nutritional strategies based on parity-specific metabolic profiles are recommended to optimize milk production and quality in Sanhe cattle.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

Metabolic Changes in Serum and Milk of Holstein Cows in Their First to Fourth Parity Revealed by Biochemical Analysis and Untargeted Metabolomics

The performance of dairy cows is closely tied to the metabolic state, and this performance varies depending on the number of times the cows have given birth. However, there is still a lack of research on the relationship between the metabolic state of Holstein cows and the performance of lactation across multiple parities. In this study, biochemical analyses and metabolomics studies were performed on the serum and milk from Holstein cows of parities 1-4 (H1, N = 10; H2, N = 7; H3, N = 9; H4, N = 9) in mid-lactation (DIM of 141 ± 4 days) to investigate the link between performance and metabolic changes. The results of the milk quality analysis showed that the lactose levels were highest in H1 (p = 0.036). The total protein content in the serum increased with increasing parity (p = 0.013). Additionally, the lipase activity was found to be lowest in H1 (p = 0.022). There was no difference in the composition of the hydrolyzed amino acids in the milk among H1 to H4. However, the free amino acids histidine and glutamate in the serum were lowest in H1 and highest in H3 (p < 0.001), while glycine was higher in H4 (p = 0.031). The metabolomics analysis revealed that 53 and 118 differential metabolites were identified in the milk and serum, respectively. The differential metabolites in the cows' milk were classified into seven categories based on KEGG. Most of the differential metabolites in the cows' milk were found to be more abundant in H1, and these metabolites were enriched in two impact pathways. The differential metabolites in the serum could be classified into nine categories and enriched in six metabolic pathways. A total of six shared metabolites were identified in the serum and milk, among which cholesterol and citric acid were closely related to amino acid metabolism in the serum. These findings indicate a significant influence of blood metabolites on the energy and amino acid metabolism during the milk production process in the Holstein cows across 1-4 lactations, and that an in-depth understanding of the metabolic changes that occur in Holstein cows during different lactations is essential for precision farming, and that it is worthwhile to further investigate these key metabolites that have an impact through controlled experiments.

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.

Association between Days Open and Parity, Calving Season or Milk Spectral Data

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

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.

A history of lameness and low body condition score is associated with reduced digital cushion volume, measured by magnetic resonance imaging, in dairy cattle

Claw horn lesions (CHL) are the result of a failing of the functional anatomy of the hoof in dairy cows. The digital cushion is understood to be a vital structure in the prevention of CHL. Claw horn lesions have previously been shown to lead to pathological change to the pedal bone; however, their effects on the digital cushion are unknown. The primary aim of this study was to examine associations between the history of CHL through an animal's life and the structure of the digital cushion at slaughter using magnetic resonance imaging. The retrospective cohort study resulted in the scanning of 102 pairs of hindfeet, collected from adult Holstein dairy cows culled from a research herd, using a 3-Tesla research-grade magnetic resonance imaging scanner. Volume and fat measurements were calculated for each digital cushion within each claw from a modified Dixon Quant sequence. Animal-level variables were constructed around the animals' lactating lifetime, with lameness scores and body condition score collected at least every 2 wk. The combined volume of digital cushion in the lateral claws was used as the outcome variable in multivariable linear models. The volume of the digital cushion was negatively associated with the number of lameness events or CHL recorded. Furthermore, animals with body condition score >3, culled later in lactation, or of a greater body weight were more likely to have a higher volume of digital cushion in the lateral claws. We propose that the observations made in the current study are the effects of a range of factors broadly associated with genetic, developmental, and disease-related inputs. Our understanding of how we can select for genetically more robust animals and how we can precondition the hoof before first calving needs to be improved to reduce the risk of future CHL in adult dairy cattle. Furthermore, understanding optimal treatment regimens and their effect on hoof anatomy may reduce the recurrence of CHL in the current lactation and future lactations.

Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle

Background

Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV).

Results

Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV.

Conclusions

Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

Fatty Acid Profiles from Routine Milk Recording as a Decision Tool for Body Weight Change of Dairy Cows after Calving

Cows mobilize body reserves during early lactation, which is reflected in the milk fatty acid (FA) profile. Milk FA can be routinely predicted by Fourier-transform infrared (FTIR) spectroscopy, and be, thus, used to develop an early indicator for bodyweight change (BWC) in early lactating cows in commercial dairy farms. Cow records from 165 herds in Denmark between 2015 and 2017 were used with bodyweight (BW) records at each milking from floor scales in automatic milking systems. Milk FA in monthly test-day samples was predicted by FTIR. Predictions of BWC were based on a random forest model and included parity, stage of lactation, and test day milk production and components (fat, protein, and FA). Bodyweight loss was mainly explained by decreased short-chain FA (C4:0-C10:0) and increased C18:0 FA. The root mean square error (RMSE) of prediction after cross-validation was 1.79 g/kg of BW (R2 of 0.94). Model evaluation with previously unseen BWC records resulted in reduced prediction performance (RMSE of 2.33 g/kg of BW; R2 of 0.31). An early warning system may be implemented for cows with a large BW loss during early lactation based on milk FA profiles, but model performance should be improved, ideally by using the full FTIR milk spectra.

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB.

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

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions

Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The second objective is to emphasize methods and indicators to evaluate the quality of models and the quality of predictions generated under routine conditions. The last objective is to outline the issues and the solutions linked with the use and transfer of models on large number of instruments. Based on partial least square regression and 10 datasets including milk MIR spectra and reference quantitative values for 57 traits of interest, the impact of the different factors is illustrated by evaluating the influence on the validation root mean square error of prediction (RMSEP). In the displayed examples, all factors, when well set up, increase the quality of predictions, with an improvement of the RMSEP ranging from 12% to 43%. This work also aims to underline the need for and the complementarity between different validation procedures, statistical parameters and quality assurance methods. Finally, when using and transferring models, the impact of the spectral standardization on the prediction reproducibility is highlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions.

The use of milk Fourier transform mid-infrared spectra and milk yield to estimate heat production as a measure of efficiency of dairy cows

Background

Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions, fermentation gases and heat. Heat production may differ among dairy cows despite comparable milk yield and body weight. Therefore, heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers. The latter is an accurate but time-consuming technique. In contrast, milk Fourier transform mid-infrared (FTIR) spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows. Thus, this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers, milk FTIR spectra and milk yield measurements from dairy cows.

Methods

Heat production was computed based on the animal’s consumed oxygen, and produced carbon dioxide and methane in respiration chambers. Heat production data included 168 24-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows. Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers. Milk yield was determined to predict heat production using a linear regression. Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000. The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum. A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares (PLS) regression.

Results

Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW^0.75. The coefficient of determination for the linear regression between milk yield and heat production was 0.46, whereas it was 0.23 for the FTIR spectra-based PLS model. The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57% and a root mean square error of prediction of 86.5 kJ/kg BW^0.75. The ratio of performance to deviation (RPD) was 1.56.

Conclusion

The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.