Prediction of key milk biomarkers in dairy cows through milk mid-infrared spectra and international collaborations.

At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (ß-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-ß-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows.

A routine monitoring for subacute ruminal acidosis (SARA) on the individual level could support the minimization of economic losses and the ensuring of animal welfare in dairy cows. The objectives of this study were (1) to develop a SARA risk score (SRS) by combining information from different data acquisition systems to generate an integrative indicator trait, (2) the investigation of associations of the SRS with feed analysis data, blood characteristics, performance data, and milk composition, including the fatty acid (FA) profile, (3) the development of a milk mid-infrared (MIR) spectra-based prediction equation for this novel reference trait SRS, and (4) its application to an external data set consisting of MIR data of test day records to investigate the association between the MIR-based predictions of the SRS and the milk FA profile. The primary data set, which was used for the objectives (1) to (3), consisted of data collected from 10 commercial farms with a total of 100 Holstein cows in early lactation. The data comprised barn climate parameters, pH and temperature logging from intrareticular measurement boluses, as well as jaw movement and locomotion behavior recordings of noseband-sensor halters and pedometers. Further sampling and data collection included feed samples, blood samples, milk performance, and milk samples, whereof the latter were used to get the milk MIR spectra and to estimate the main milk components, the milk FA profile, and the lactoferrin content. Because all measurements were characterized by different temporal resolutions, the data preparation consisted of an aggregation into values on a daily basis and merging it into one data set. For the development of the SRS, a total of 7 traits were selected, which were derived from measurements of pH and temperature in the reticulum, chewing behavior, and milk yield. After adjustment for fixed effects and standardization, these 7 traits were combined into the SRS using a linear combination and directional weights based on current knowledge derived from literature studies. The secondary data set was used for objective (4) and consisted of test day records of the entire herds, including performance data, milk MIR spectra and MIR-predicted FA. At farm level, it could be shown that diets with higher proportions of concentrated feed resulted in both lower daily mean pH and higher SRS values. On the individual level, an increased SRS could be associated with a modified FA profile (e.g., lower levels of short- and medium-chain FA, higher levels of C17:0, odd- and branched-chain FA). Furthermore, a milk MIR-based partial least squares regression model with a moderate predictability was established for the SRS. This work provides the basis for the development of routine SARA monitoring and demonstrates the high potential of milk composition-based assessment of the health status of lactating cows.

Modeling reticular and ventral ruminal pH of lactating dairy cows using ingestion and rumination behavior.

The prevention and control of metabolic and digestive diseases is an enormous challenge in dairy farming. Subacute ruminal acidosis (SARA) is assumed to be the most severe feed-related disorder and it impairs both animal health and economic efficiency. Currently, ruminal pH as well as variables derived from the daily pH curve are the main indicators for SARA. The objective of this study was to explain the daily pH course in the ventral rumen and reticulum of dairy cows using ingestion pattern and rumination behavior data gathered by automated data recording systems. The data of 13 ruminally fistulated lactating cows were collected at the experimental station of the Friedrich-Loeffler-Institut (Brunswick, Germany). The data included continuous pH measurements, which were recorded simultaneously in the reticulum by pH-measuring boluses and in the ventral rumen by a separate data logger. In addition, rumination behavior was measured using jaw movement sensors, and feed and water intakes were recorded by transponder-assisted systems. Milk yield and body weight were determined during and after each milking, respectively. For statistical evaluation, the data were analyzed using time-series modeling with multiple linear mixed regressions. Before applying the developed mathematical statistical modeling, we performed a plausibility assessment to ensure data quality. The major part of the mathematical statistical modeling consisted of data preparation, where all variables were transformed into a uniform 1-min resolution. Signal transformations were used to model individual feed and water intakes as well as rumination behavior events over time. Our results indicated that diurnal pH curves of both the reticulum and ventral rumen could be predicted by the transformed feed and water intake rates. Rumination events were associated with a marginal temporal increase in pH. We observed that the pH of the ventral rumen was delayed by approximately 37 min compared with that of the reticulum, which was therefore considered in the modeling. With the models developed in this study, 67.0% of the variance of the reticular pH curves and 37.8% of the variance of the ruminal pH curves could be explained by fixed effects. We deduced that the diurnal pH course is, to a large extent, associated with the animal's individual feed intake and rumination behavior.

Statistical modeling of ruminal pH parameters from dairy cows based on a meta-analysis.

Adequate feeding of high-performance dairy cows is extremely important to avoid the digestive disorder subacute ruminal acidosis. Subacute ruminal acidosis is defined as a status with a below-average ruminal pH that does not cause direct clinical symptoms at the individual level but is relevant for animal welfare due to a higher risk of secondary health problems at the herd level. The main objective of this study was to apply meta-analytical methods in an exploratory approach to investigate the association between pH parameters of the ventral rumen with milk and diet parameters. Data from 32 studies using continuous pH measurement in the ventral rumen of lactating cows were included in the meta-analysis. Available information extracted from all studies was categorized into parameters associated with management, cow, diet, milk, and pH. The statistical analysis was divided into 4 sections. First, a multiple imputation procedure based on a principal component model was applied, since approximately 19% of the data set consisted of missing values due to heterogeneity in provided information between the studies included in the analysis. In a second step, all potential predictors for the pH parameters, including the daily mean pH, the time with a pH below 5.8, and the pH range, were examined for their prediction suitability using multi-level mixed effects meta-regression models. These analyses were performed on the raw and the imputed data. Because the results of both approaches were consistent, the imputing procedure was considered to be appropriate. Third, automated variable selection was applied to all 3 pH parameters separately for the predictor groups milk and diet using the imputed data set. Thereby, multi-model inference was used to estimate the relative importance of the selected variables. Finally, a functional relationship between the 3 pH parameters was established. The fat to protein ratio of milk, milk fat, and milk protein showed significant associations in meta-regression analysis for all 3 pH parameters when used as a single predictor. Out of the group of diet-specific variables, the acid detergent fiber, neutral detergent fiber, nonfiber carbohydrate, starch content, as well as the forage to concentrate ratio, showed the highest significance in the models. In particular, the multi-model inference showed that the protein, fat, and lactose content of the milk can best quantify the association to the daily mean pH and the time with a pH below 5.8 in a multiple regression model.