This study is part of a larger project whose overall objective was to evaluate the possibilities for genetic improvement of efficiency in Austrian dairy cattle. In 2014, a 1-yr data collection was carried out. Data from 6,519 cows kept on 161 farms were recorded. In addition to routinely recorded data (e.g., milk yield, fertility, disease data), data of novel traits [e.g., body weight (BW), body condition score (BCS), lameness score, body measurements] and individual feeding information and feed quality were recorded on each test-day. The specific objective of this study was to estimate genetic parameters for efficiency (related) traits and to investigate their relationships with BCS and lameness in Austrian Fleckvieh, Brown Swiss, and Holstein cows. The following efficiency (related) traits were considered: energy-corrected milk (ECM), BW, dry matter intake (DMI), energy intake (INEL), ratio of milk output to metabolic BW (ECM/BW), ratio of milk output to DMI (ECM/DMI), and ratio of milk energy output to total energy intake (LE/INEL, LE = energy in milk). For Fleckvieh, the heritability estimates of the efficiency (related) traits ranged from 0.11 for LE/INEL to 0.44 for BW. Heritabilities for BCS and lameness were 0.19 and 0.07, respectively. Repeatabilities were high and ranged from 0.30 for LE/INEL to 0.83 for BW. Heritability estimates were generally lower for Brown Swiss and Holstein, but repeatabilities were in the same range as for Fleckvieh. In all 3 breeds, more-efficient cows were found to have a higher milk yield, lower BW, slightly higher DMI, and lower BCS. Higher efficiency was associated with slightly fewer lameness problems, most likely due to the lower BW (especially in Fleckvieh) and higher DMI of the more-efficient cows. Body weight and BCS were positively correlated. Therefore, when selecting for a lower BW, BCS is required as additional information because, otherwise, no distinction between large animals with low BCS and smaller animals with normal BCS would be possible.

Abstract. In this study records of 58 925 litters of Austrian Large White and 17 846 litters of Austrian Landrace pigs were analysed. Regression models were used to determine the effects of litter, dam and sire inbreeding on total number of born, born alive and weaned piglets in Large White and Landrace. In both populations, litter and dam inbreeding showed a negative effect on all traits. Sire inbreeding had no effect in Large White, whereas a significant positive effect was observed in Landrace. On average, inbred sires with an inbreeding coefficient of 10 % had 0.45 more piglets born total and 0.43 more piglets born alive in comparison to non-inbred sires. In a further analysis the total inbreeding coefficients of the animals were divided into two parts: »new« and »old« inbreeding. »New« inbreeding was defined as the period of the first five generations. It was shown that the observed inbreeding effects were not only caused by recent inbreeding. Reproductive performance was also affected by »old« inbreeding. Finally partial inbreeding coefficients of four important ancestors in each population were calculated to investigate if inbreeding effects are similar among these ancestors. The results revealed a varation of inbreeding effects among the four ancestors. Alleles contibuting to inbreeding depression were descendent from specific ancestors.

Mid-infrared (MIR) spectroscopy is the method of choice for the standard milk recording system, to determine milk components including fat, protein, lactose and urea. Since milk composition is related to health and metabolic status of a cow, MIR spectra could be potentially used for disease detection. In dairy production, mastitis is one of the most prevalent diseases. The aim of this study was to develop a calibration equation to predict mastitis events from routinely recorded MIR spectra data. A further aim was to evaluate the use of test day somatic cell score (SCS) as covariate on the accuracy of the prediction model. The data for this study is from the Austrian milk recording system and its health monitoring system (GMON). Test day data including MIR spectra data was merged with diagnosis data of Fleckvieh, Brown Swiss and Holstein Friesian cows. As prediction variables, MIR absorbance data after first derivatives and selection of wavenumbers, corrected for days in milk, were used. The data set contained roughly 600,000 records and was split into calibration and validation sets by farm. Calibration sets were made to be balanced (as many healthy as mastitis cases), while the validation set was kept large and realistic. Prediction was done with Partial Least Squares Discriminant Analysis, key indicators of model fit were sensitivity and specificity. Results were extracted for association between spectra and diagnosis with different time windows (days between diagnosis and test days) in validation. The comparison of different sets of predictor variables (MIR, SCS, MIR + SCS) showed an advantage in prediction for MIR + SCS. For this prediction model, specificity was 0.79 and sensitivity was 0.68 in time window -7 to +7 days (calibration and validation). Corresponding values for MIR were 0.71 and 0.61, for SCS they were 0.81 and 0.62. In general, prediction of mastitis performed better with a shorter distance between test day and mastitis event, yet even for time windows of -21 to +21 days, prediction accuracies were still reasonable, with sensitivities ranging from 0.50 to 0.57 and specificities remaining unchanged (0.71 to 0.85). Additional research to further improve prediction equation, and studies on genetic correlations among clinical mastitis, SCS and MIR predicted mastitis are planned.

The modernization and intensification of the dairy industry has led to larger herd sizes and higher milk production, both globally and in Austria. Benchmarking allows the monitoring of animal health and welfare as well as the identification of potential for improvement by comparing certain parameters with other farms with similar management environments. Using data from the Austrian routine recording system of various traits of milk production, fertility, and health, farmers and their veterinarians (with the consent of the farmer) can compare farm parameters with detailed data available from their district or state and ensure more efficient herd management. The aim of the present study was to provide an overview of dairy milk production in Austria based on the annual herd health reports and to examine the effects of herd size and milk production on fertility and health parameters. Annual herd health reports from all farms participating in the health monitoring system were considered, and analyses were conducted across breeds. A large variation between farms was observed. The results showed that, based on parameters of milk yield and herd size for the range of farms within this study, it cannot be concluded that these circumstances automatically lead to poor animal health. Farms with very small herd sizes differed significantly from those with larger herd sizes. Overall herd size effects were however small in Austria. Higher milk production based on a single farm does not necessarily cause more health and fertility problems; however, we detected a tendency for an increased risk of fertility, udder, and metabolic diagnoses. An active health management program might result in higher incidence rates for fertility or udder diagnoses, as a veterinary treatment might be economically superior if, for example, the calving interval can be shortened or the somatic cell count can be reduced. The results of the present study showed that it is advisable to use different benchmarks in combination for monitoring health, as well as for deciding on strategies to improve overall herd health management. Animal health reports on Austrian dairy cows are continuously being developed and new parameters integrated.

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