Lely (Netherlands)

Automatic Milking Systems (AMSs) enable the continuous recording of production, milkability, behavioral, and physiological traits, offering new opportunities for genetic evaluation in dairy cattle. This study aimed to estimate variance components and genetic parameters for milk yield-related traits, milking efficiency traits, rumination time (RT), and daily average milk temperature (MTEMP) using AMS-derived data from 1252 Holstein cows. 65,475 weekly records from a single commercial herd were analyzed using repeatability animal models fitted by restricted maximum likelihood. Heritability estimates were moderate to high for milking time (MT) (0.31), milking speed (MS) (0.38), RT (0.30), and MTEMP (0.28), whereas behavioral traits such as number of milking (NoM) (0.26) and number of refused (NoREF) (0.11) showed lower but meaningful heritabilities. Repeatability was highest for MT and MS (0.77 and 0.79), indicating consistent milking performance across repeated records. MTEMP demonstrated clear seasonal variation, increasing in warmer periods and decreasing during colder months, indicating sensitivity to environmental conditions. Genetic correlations among traits revealed both favorable and unfavorable associations; however, several estimates were associated with relatively large standard errors and should therefore be interpreted with caution. The inclusion of MTEMP as a proxy physiological trait derived from AMS data showed measurable genetic variation, although its biological interpretation requires careful consideration. Overall, the results suggest that AMS-derived phenotypes may contribute useful information for genetic studies of functional traits, but the single-herd structure, limited pedigree depth, and data aggregation procedures restrict the generalizability of the findings. Further multi-herd and genomics-based studies are required to validate these results and assess their applicability in breeding programs.

Targeted reproductive management (TRM) aims to improve the fertility efficiency of the dairy herd by applying group-level management strategies based on expected reproductive performance. Key to the utility of TRM is the accuracy with which an animal's reproductive performance can be predicted. Automatic milking systems (AMS) allow for the collection of data relating to milk quantity, quality, and robot visit behavior throughout the transition period. In addition to this, auxiliary data sources, such as rumination and activity monitors, as well as historical cow-level data, are often readily available. The utility of this data for the prediction of fertility has not been previously explored. The first objective of this study was to assess the accuracy with which the likelihood of expression of estrus between 22 and 65 DIM and conception to first insemination between 22 and 80 DIM could be predicted using data collected by AMS from 1 to 21 DIM. Our second objective was to assess the change in model performance following the addition of 2 auxiliary data sources. Using data derived solely from the AMS (RBT dataset) a binary random forest classification model was constructed for both outcomes of interest. The performance of these models was compared with models constructed using AMS data in conjunction with 2 auxiliary sources (RBT+ dataset). Expression of estrus was classified with an area under the receiver operator curve (AUC-ROC) of 0.6 and 0.65, conception to first insemination with an AUC-ROC of 0.56 and 0.62 for the RBT and RBT+ datasets, respectively. No statistically significant improvement in classification accuracy was achieved by the addition of auxiliary data sources. This is the first study to report the utility of data collected by AMS for the prediction of reproductive performance. Though the performance described is comparable with previously reported models, their utility for the implementation of TRM is limited by poor classification accuracy within key subgroups. Of note within this study is the failure of the addition of auxiliary data sources to increase the accuracy of prediction over models built using AMS data alone. We discuss the advantages and limitations the integration of additional data sources imposes on model training and deployment and suggest alternative methods to improve performance while preserving model parsimony.

Objective: To compare growth and body composition in preterm infants with bronchopulmonary dysplasia (BPD) with normal healthy term infants during the first year of life. Design: Twenty nine preterm infants with BPD (mean (SD) gestational age 27.1 (1.6) weeks; birth weight 852 (173) g) were followed prospectively. Anthropometry and body composition determined by total body electrical conductivity were measured and compared with those of healthy term infants at the same post-term age. Results: In infants with BPD, the mean weight standard deviation scores (SD scores) 6 weeks after term were significantly lower (−1.44 and −2.68, boys and girls respectively) than in healthy term infants of the same age and did not improve during the first year. The mean length SD score was significantly lower in infants with BPD 6 weeks after term than in healthy term infants of the same age, and, although it improved significantly during the first year, the mean length SD score in girls with BPD was significantly below 0 12 months after term. In infants with BPD, the mean free fat mass (FFM) SD score and the mean total body fat (TBF) SD score at 6 weeks post-term age were significantly below 0. The mean FFM SD scores (−1.01 and −2.56, boys and girls respectively) and the mean TBF SD scores (−1.14 and −2.40, boys and girls respectively) 12 months after term were significantly lower than in healthy term infants of the same age. Conclusions: Preterm infants with BPD have impaired growth, with a deficit in TBF and FFM already 6 weeks after term; FFM and TBF remain low compared with healthy term infants during the first year of life. Nutritional intervention studies in infants with BPD are needed to evaluate if nutrition is the major determinant of growth and body composition or if this pattern of growth in preterm infants with BPD is the result of disturbed endocrine control.