Many animal disorders arise from the combined interplay of genetic and environmental risk factors. Early detection and improved monitoring of these risk factors can contribute significantly to animal health and welfare. This project will study the risk factors for the development of diseases and develop parameters for early detection utilizing novel scientific approaches such as big data analyses.

Our aim is to develop a quantitative and predictive framework that enables us to disentangle how different genetic and environmental factors, as well as their interactions, contribute to animal health and welfare, based on a comprehensive large-scale database that contains time-resolved phenotypic and environmental information on as well as genetic information for animals. The project will work on the identification of early prognostic disease markers. Expected results are more meaningful parameters for herd management and breeding.

Midinfrared spectroscopy (MIR) is the established routine procedure of dairy milk analysis. Beeing quite recently mainly used for the analysis of the standard milk components fat, protein and lactose, it is now possible to determin the content of low concentration components like e.g. fatty acids, ketone bodies and indicators like lactoferrin  with decent acuracy. Recent research has been able show that the status of a dairy cow with regard to health and nutrition can be strongly linked to the biochemical composition of milk. So milk MIR spectroscopy is a  convenient method for early detection of health and nutrition problems in dairy  herds. The goal of the MIR project is the development and evaluation of new robust milk MIR prediction models by using a large number of routine milk samples and closely linked diagnosis made by veterinarians. Here participating research partners can resort to the huge expertise and data collected by the milkrecording organisations which make up the EEIG EMR (European Milk Recording). Furthermore new reference analysis will help to improve existing models of health related milk components.

The intention ist to supply dairy farmers with new reliable tools for their herd management which help them to cope with growing requirements concerning performance, economic efficiency and animal welfare in a sustainable way.

The overall aim of this project is to enhance animal health, welfare, production efficiency and potentially improve product quality by improving environmental data collection and data usage on farms. This aim will be achieved by (1) initially deploying commercially available sensors and communication networks to establish an integrated on-farm monitoring system for detecting various environmental variables. Then (2) the research team will collaborate with farm managers to collect production related data and generate optimisation strategies based on the collected information. In addition, the project is aimed at (3) developing practical/affordable sensor applications in dairy production and (4) modelling environmental stressors and animal responses to develop forecasting algorithms in relation to production loss associated with sub-optimal environment. To ensure that the project is applied; the study is executed in close collaboration with producers, commercial companies (Pessl Instruments and AgHiTech), academic institutions (BOKU, Vetmeduni) and industry bodies (ZuchtData, LKV).

The project uses Big Data approaches to generate easily interpretable management tools from complex data sources. Besides management purposes, these data sources can also be used for breeding.

For breeding applications the ease of interpretation of the trait is less important than the repeatability and especially the correlation with target traits. Therefore, it might be required to establish specific phenotypes for this project.

New traits will be developed for the complexes metabolism, udder and claw health. The work packages include the estimation of heritabilities and genetic correlations, the establishment of conventional and genomic breeding value estimations and the search for interesting loci via genome-wide association studies.

Mycotoxins - secondary metabolites of moulds – can lead to adverse health effects in livestock and cause economic losses worldwide. Yet, the presence of mycotoxins in Austrian dairy feeds and its impact on dairy health and fertility is largely unknown.

In this project, we perform an extensive mycotoxin survey in Austrian dairy farms with feed components being analysed for more than 400 fungal metabolites. By matching mycotoxin occurrence with dairy health and performance data, potential interlinks will be revealed. The rumen microbiome might also interfere with mycotoxin contamination, wherefore we use next generation sequencing techniques to unravel the effects of relevant mycotoxins on the rumen microbiome in vitro (rumen simulation technique, RUSITEC).

Results will contribute to our understanding of mycotoxins as possible trigger factor for impaired dairy health. Since climate change effects mycotoxin contamination levels in feed, our findings will be of importance for dairying beyond the duration of this project.