Correlations of feed intake predicted with milk infrared spectra and breeding values in the Dutch Holstein population.

Feed is a major cost in dairy production, and substantial genetic variation in feed efficiency exists between cows. Therefore, breeders aim to improve feed efficiency of dairy cattle. However, phenotypic data on individual feed intake on commercial farms is scarce, and accurate measurements are very costly. Several studies have shown that information from Fourier-transformed infrared spectra of milk samples (milk infrared, milk IR) can be used to predict phenotypes such as energy balance and energy intake, but this is usually based on small data sets obtained under experimental circumstances. The added value of information from milk IR spectra for estimation of breeding values is unknown. The objectives of this study were (1) to develop prediction equations for dry matter intake (DMI) and residual DMI (rDMI) from milk IR spectra; (2) to apply these for a data set of milk IR spectra from commercial Dutch dairy farms; (3) to estimate genetic parameters for these traits; and (4) to estimate correlations between these predictions and other traits in the breeding goal. We used data from feeding trials where individual feed intake was recorded daily and for which milk IR spectra were determined weekly to develop prediction equations for DMI and rDMI with partial least squares regression. This data set contained over 7,600 weekly averaged DMI records linked with milk IR spectra from 271 cows. The equations were applied for a data set with test day information from 676 Dutch dairy herds with 621,567 records of 78,488 cows. Both milk IR-predicted DMI and rDMI were analyzed with an animal model to obtain genetic parameters and sire effect estimates that could be correlated with breeding values. A partial least squares regression model with 10 components from the milk IR spectra explained around 25% of DMI variation and less than 10% of rDMI variation in the validation set. Nearly all variation in the milk IR spectra was captured by 7 components; additional components contributed marginally to the spectral variation but decreased prediction errors for both traits. Accuracies of predictions of DMI and rDMI from milk IR spectra for a large feeding experiment were 0.47 and 0.26 on average, respectively, with small differences between ration treatments (ranging from 0.43 to 0.55 and from 0.21 to 0.34, respectively) and among lactation stages (ranging from 0.24 to 0.59 and from 0.13 to 0.36, respectively), with the highest prediction accuracies in early lactation. The estimated heritabilities for predicted DMI and rDMI were 0.3 and 0.4, respectively, which suggests genetic potential for both predicted traits. The correlations of sire estimates for milk IR-predicted DMI with official Dutch breeding values were strongest with milk production (0.33), longevity (0.26), and fertility (-0.27), indicating that cows that eat more produce more, live longer, and have poorer fertility. The correlations of sire estimates for predicted DMI and rDMI with the official breeding values for DMI were low (0.14 and 0.03, respectively). This implies that the added value of including milk IR-predicted DMI information in the estimation procedure of breeding values for DMI would be considered insufficient for practical application.

Animal Health in Compost-Bedded Pack and Cubicle Dairy Barns in Six European Countries.

The purpose of this study was to compare animal health in compost-bedded pack (CBP) and cubicle housing (CH) systems using data from dairy herd improvement associations. Thirty-two commercial dairy farms located in Austria, Germany, Italy, The Netherlands, Slovenia, and Sweden were included in the study. A matching design (pairing CBP and CH within country) according to herd selection criteria was used. We explored the following health indicators: somatic cell counts (SCC), high SCC, new high SCC, ketosis risk, prolonged calving intervals, dystocia, and stillbirth. Traits for culling and culling-related issues, such as length of life and length of productive life, were also included. We used multivariable (mixed) linear and logistic regression models to evaluate differences between the systems. Udder health, as measured by SCC, was inferior in CBP, although the geometric means were low in both systems. The incidence of stillbirths was higher in CBP, while prolonged calving intervals were fewer, indicating that there were fewer reproductive disorders. There were no differences in longevity between the systems, although CBP had lower proportions of first calvers. Overall, we conclude that there were few and minor differences in health and longevity between the CBP and CH systems in the European context.

A data-driven prediction of lifetime resilience of dairy cows using commercial sensor data collected during first lactation.

Reliable prediction of lifetime resilience early in life can contribute to improved management decisions of dairy farmers. Several studies have shown that time series sensor data can be used to predict lifetime resilience rankings. However, such predictions generally require the translation of sensor data into biologically meaningful sensor features, which involve proper feature definitions and a lot of preprocessing. The objective of this study was to investigate the hypothesis that data-driven random forest algorithms can equal or improve the prediction of lifetime resilience scores compared with ordinal logistic regression, and that these algorithms require considerably less effort for data preprocessing. We studied this by developing prediction models that forecast lifetime resilience of a cow early in her productive life using sensor data from the first lactation. We used an existing data set from a Dutch experimental herd, with data of culled cows for which birth dates, insemination dates, calving dates, culling dates, and health treatments were available to calculate lifetime resilience scores. Moreover, 4 types of first-lactation sensor data, converted to daily aggregated values, were available: milk yield, body weight, activity, and rumination. For each sensor, 14 sensor features were calculated, of which part were based on absolute daily values and part on relative to herd average values. First, we predicted lifetime resilience rank with stepwise logistic regression using sensor features as predictors and a P-value of <0.2 as the cut-off. Next, we applied a random forest with the 6 features that remained in the final logistic regression model. We then applied a random forest with all sensor features, and finally applied a random forest with daily aggregated values as features. All models were validated with stratified 10-fold cross-validation with 90% of the records in the training set and 10% in the validation set. Model performances expressed in percentage of correctly classified cows (accuracy) and percentage of cows being critically misclassified (i.e., high as low and vice versa) ± standard deviation were 45.1 ± 8.1% and 10.8% with the ordinal logistic regression model, 45.7 ± 8.4% and 16.0% with the random forest using the same 6 features as the logistic regression model, 48.4 ± 6.7% and 10.0% for the random forest with all sensor features, and 50.5 ± 6.3% and 8.4% for the random forest with daily sensor values. This random forest also revealed that data collected in early and late stages of first lactation seem to be of particular importance in the prediction compared with that in mid lactation. Accuracies of the models were not significantly different, but the percentage of critically misclassified cows was significantly higher for the second model than for the other models. We concluded that a data-driven random forest algorithm with daily aggregated sensor data as input can be used for the prediction of lifetime resilience classification with an overall accuracy of ~50%, and provides at least as good prediction as models with sensor features as input.

Symposium review: Animal welfare in free-walk systems in Europe.

Providing more space per animal, soft bedding, and free roaming in animal housing systems is widely presumed to be beneficial for the welfare of the animals. This observational study aimed to investigate the basis of this assumption in free-walk housing systems (FWS) for dairy cows in Europe. The dairy cattle Welfare Quality assessment protocol was adapted for application to FWS, and the focus was on animal-based measures, from individual cow scoring to comfort around resting. The study was conducted on 41 farms [21 FWS and 20 cubicle housing (CH)] from 6 European countries (Germany, the Netherlands, Italy, Austria, Slovenia, and Sweden) displaying a variety of management systems. A total of 4,036 animals were scored. We found differences in animal welfare under different management conditions. The hindquarters and lower hind legs of cows from FWS were dirtier than those of cows in CH, but we found no difference in the dirtiness of udders or teats. Cows from FWS showed fewer hairless patches in all body areas except the neck; fewer lesions in the lower hind legs and hindquarters; and less swelling in the lower hind legs, flanks, and carpus than cows from CH. The prevalence of sound cows appeared to be higher in FWS, and moderate lameness prevalence was lower compared with CH. We found no difference in the prevalence of severe lameness between systems. We conducted a total of 684 observation sessions of comfort around resting, consisting of 830 lying down and 849 rising up movements. Cows in FWS took less time to lie down, had less difficulty rising up, and had fewer collisions with the environment during both behaviors than cows in CH. Cows lay partly or completely outside the supposed lying area less frequently in FWS than in CH. Cows in FWS adopted comfortable lying positions more often compared with CH, showing a higher occurrence of long and wide positions than cows in CH. Short positions were more common in FWS, and narrow positions were slightly more common in CH. We found large variations in animal-based measures between study herds and within housing systems. However, the observed patterns associated with each system demonstrated differences in cow scoring and comfort around resting. This study shows that a wide range of good and bad management practices exist in FWS, especially related to cow hygiene.