Martin Gehringer from LKV NÖ met with Martin Huber and Katharina Eiblhuber from DeLaval at the D4Dairy farm of Norbert Friedl to discuss the data collection for the pilot study.
by Kristina Linke (comments: 0)
TV channel ORF sent a Newton Wissenschaft contribution about digitalization in agriculture, where the PhD from project 1.2 Lena Lemmens and the D4Dairy Partner Höhere Bundeslehr- und Forschungsanstalt Francisco Josephinum participated.
by Kristina Linke (comments: 0)
On October 9th, 2019, Prof. Martin Wagner was awarded a prize by the Province of Lower Austria at the Science Gala in Grafenegg.
D4Dairy congratulates cordially!
by Kristina Linke (comments: 0)
An interface between smaXtec and the Rinderdatenverbund RDV has just been successfully set up. Thus already existing data of the farms do not have to be entered again by the farmers. The networking can also be expected to make alarms more accurate and to generate correspondingly improved evaluations for herd management (e.g. in the LKV herd manager) and valuable parameters for the genetic improvement of animal health.
The aim would be to be able to generate new knowledge from this data diversity in the interaction of the most varied characteristics, which could subsequently be of great benefit for Austrian cattle breeding.
Since I use an automatic milking system in my farm, I use the associated herd management program (Lely). Currently, the herd management program and the LKV Herd Manager exchange data for performance control. The areas of birth reports and inseminations are not covered. Especially in this area I would like to see more intensive networking with the Rinderdatenverbund RDV via the LKV Herd Manager. Currently I have to more or less get into two different systems and make entries.
This network will strengthen not only the family farms, but also the business partners involved and Austria as a business location as a whole.
Thanks to all project partners and especially to the project team that managed to bundle the large number of partners for a joint project that ultimately benefits the farmer.
The application of new BigData methods to the extensive data set of the cattle industry holds enormous potential for the early detection and development of preventive models for diseases.
The simple, efficient and effective use of data for dairy farms is a major concern of our company. Our customers should spend as little time as possible on using our monitoring system. That is why we sought this cooperation through the D4Dairy project in order to create the greatest possible added value for our customers by combining data.
D4Dairy's overall goal is to provide digital support to dairy management via a data-driven, networked information system, exploiting the potential of advanced technologies (mid-infra-red spectra, genome information, ...) and advanced data analysis to further improve animal health, nutrition, animal welfare and product quality.
Building on the COMET project ADDA, the existing network along the milk value chain was expanded to include technology providers and scientific partners with a focus on new technologies to form the D4Dairy consortium.
The D4Dairy subprojects are divided into two areas:
Area 1: Data and Decision - Digitalisation, data integration and decision support
In recent years, there has been a dramatic increase in the generation, processing and storage of data on dairy farms. To help to utilize this data in a sensible way, the focus in Area 1 lies on integration of the data generated on farms (data from health-related sensors, feed automation, antibiotic use, housing climate data,…) and interoperability of the systems used in the farms and on process development and optimization, with the aim of generating decision support tools. The projects in this area include work on aspects of quality assurance, data exchange, data protection and research on the interrelationships between different characteristics. Based on the results and findings from the various research projects, tools for farmers and veterinarians to support decision-making will be developed. Different strategies to reduce the risk of antimicrobial resistance are an important part in this area as well. Furthermore, Area 1 brings together all results from D4Dairy, since it is here that the information and knowledge transfer takes place, and studies in the social context (knowledge transfer, acceptance) are carried out.
P1.1 tackles data exchange management and data networking between project partners. The aim is to create a database for D4Dairy research and for the development of applications for farmers. P1.1 describes the state of the art and the goals of various stakeholders (farmers/technology suppliers/organisations/…) dealing with automation systems applied in dairying. A focus is put on work organisation and process quality. A work package is dedicated to legal aspects of protection of data and intellectual property. Thus, newly developed applications are in line with the wishes of users and the current legal requirements. P1.1 develops the technical basis for an automated data exchange between the partners. This covers the routine data exchange between the central cattle database and the involved public and industry partners as well as the exchange of extended datasets for research purposes. Novel IT concepts for the quality assurance of sensor data and the efficient management of data usage and sharing rights will be investigated. New applications will be intensively tested on pilot farms. The insights of D4Dairy will be incorporated in concepts for the further development of novel traits and services.
The availability of integrated and novel data combined with advanced analytical methodologies allows earlier detection of the onset of potential health problems within dairy herds and thus enables timelier intervention. D4Dairy aims to develop possibilities for data-driven strategies for improving health and to support farmers in utilizing such strategies and tools so they can prevent or react to emerging health and welfare issues in a timely manner.
Data from digital technologies such as data from AMS (automated milking systems), from sensors and feeding systems will be used to derive new traits. The project will work on trait definition and their relationship to other traits of interest as well as sensor data evaluation for early detection of disease with focus on lameness, but also ketosis and others. Concepts for optimisation of feeding processes is another focus of work within this project. Outcome of the other projects within D4Dairy will incorporated in digital decision support tools developed within D4Dairy.
Project 1.3 comprises three separate subprojects, namely the harmonisation of antimicrobial susceptibility testing (AST) for mastitis pathogens (1.3.1), using data to drive recommendations for drying off strategies (1.3.2), and strategies to improve calf health and beef quality (1.3.3).
Working with laboratories throughout Austria, subproject 1.3.1 aims to standardise AST, improve comparability of resistance data as well as integrating the results of such testing into the central cattle database. AST data will then be more accessible to and evaluable for farmers and veterinarians. Subproject 1.3.2, together with a dairy and leading milk processor, will develop a decision-making tool for veterinarians to advise farmers on the best drying off strategy for their farm. Subproject 1.3.3. will analyse the effects of feeding calves milk containing antibiotic drug residues (“waste milk“) on the development of antimicrobial resistant bacteria on farm, and to suggest alternatives to this practice.
The use of digital tools, sensor technology and automation in modern agriculture is rapidly advancing and expanding worldwide. The application of digital technology changes the way farms are operated and managed and provides new perspectives and challenges for consultants and veterinarians. Therefore, it is essential for these groups to acquire knowledge in this field. Besides, consumers are becoming more concerned about animal health, animal welfare, food safety and the nutritional aspects of food. Although trust in the food produced can be strengthened through the use of modern technologies, social acceptance of dairying 4.0 is of great importance and should be taken into account. In this project we want to carry out quantitative and qualitative studies on the acceptance and assessment of new technologies among farmers, consultants, veterinarians and consumers. Furthermore, we will disseminate results via various communications channels to meet training needs of the farmers, consultants and veterinarians.
Area 2: Data and Detection - Data driven detection of risk factors and early predictors for improved health
Functional traits, in particular health (related) traits, have gained importance in livestock breeding during the last years. In this context, data from automation and sensors or other novel traits in combination with advanced analysis methodology offer new possibilities for herd management and breeding. Area 2 of D4Dairy covers the broad range of recording and validation of novel traits, the combination of novel and already available traits as well as detection of different risk factors for animal health by using novel methodology up to implementation in optimisation, mitigation and breeding strategies. This enables the improvement of animal health and welfare in dairy cattle farms. Both reduction of costs and increase of returns by improving both functional and performance traits will result in a higher profitability of the dairy cattle sector.
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.
In order to tackle these complex and interdisciplinary challenges, D4Dairy has established an internationally competitive, transdisciplinary network of universities, competence centres and research institutes in Austria and abroad, as well as companies along the milk value chain (farmers, breeding organisations, milk processors, animal health services, interest groups, etc.) and - last but not least - national and international technology providers (sensors, feeding, climate measurement, data processing). The consortium consists of 31 company partners and 13 scientific partners.