Latest SCI publications
Development and implementation of genetic evaluations for longevity and conformation for sheep and goat breeds
Research project (§ 26 & § 27)
Duration : 2018-10-01 - 2021-09-30
A genetic evaluation for longevity will be developed for all sheep and goat breeds, which are already considered in the routine genetic evaluation and thus have a reasonable population size.For dairy sheep and goats, the longevity evaluation will be based on the routine genetic evaluation of dairy cattle. For meat, mountain and land sheep as well as for further goat breeds, different definitions of voluntary culling will however have to be elaborated. In the next step, a genetic evaluation for linear conformation traits will be developed for the dairy goat breeds Saanen and Chamois as well as for the sheep breeds Tyrol Mountain, Merinoland and Jura. The estimation of genetic correlations between functional longevity, linear conformation traits and further traits considered in the breeding goal will be based on the methodology developed in the project OptiGene (Project 100808). Both, de-regressed breeding values and yield deviations, will be used for the estimation. Selected linear conformation traits may be considered as auxiliary traits for functional longevity. In the last step of the project, possible genetic gains will be calculated to provide the basis for the new weights in the Fitness and the Total Merit Index. Among others, the following hypotheses are investigated: - the total merit index as the mathematical definition of the breeding goal may be enhanced by considering longevity in sheep and goat breeds - linearly described traits are more objective than traditionally scored conformation traits. They are thus beneficial with regard to genetic evaluation and may be utilized as auxiliary traits for longevity but potentially also for other traits, e.g. udder health.
Research project (§ 26 & § 27)
Duration : 2018-10-01 - 2022-09-30
The era of digitally enhanced farming with rapid advances in genomics, sensor technologies, Internet of Things with the occurrence of vast numbers of other structured and unstructured data combined with new possibilities for aggregating and analyzing the data offer both potentials for a second green revolution and, at the same time, challenges for dairy farmers and industry. Roles are going to change. There is the risk that Austrian dairying can no longer determine the direction in the breeding goal and thus positioning their products because access to relevant data is not ensured in the future. D4Dairy project will address the challenges of the stakeholders along the dairy value chain, in particular of the farmers and the economic partners contributing to this project. The overall goal of D4Dairy is the generation of added value for herd management as well as the improvement of animal health, animal welfare and product quality by creating a well-developed (data) network and by exploiting the opportunities offered by new (digital) technologies and analytical methods. The specific objectives of D4Dairy therefore are a) to capture the enormous amounts of diverse data (theoretically) available on the farm and from other partners along the milk chain; b) to aggregate these data into one central database, assess different data communication methods in compliance with legal requirements and develop a concept of interoperability; (c) to perform complex and advanced analyses in order to detect risk factors and identify early predictors of health problems using big data approaches, mid-infrared spectra, genetic and genomic studies, mycotoxin detection and information about the impact of housing climate on animal health and welfare; (d) to develop data-based strategies to reduce the use of antimicrobials and implement quality assurance programs and (e) to provide the information obtained from the analyses for decision support using newly developed complex and innovative tools that are easy to apply, operate in real-time in an automated fashion, and whose results are easy to interpret. Crucial for successful implementation is trust in the data integration framework, which will need to address questions related to data “ownership” and data security, especially as regards cloud-based storage solutions. Farmers will only use an integrated system if they are confident that they can entrust their proprietary data to the system. To address and master these complex and interdisciplinary challenges, D4Dairy has assembled an internationally competitive, transdisciplinary Austrian science hub, uniting experts and researchers from universities, centres of excellence, other research organisations, domestic and international, professionals from national and international company partners along the dairy value chain (dairy farms and cattle breeders, dairies and milk processors, performance recorders, laboratories, animal health services, marketing and quality assurance organisations); stakeholder organisations and most importantly, national and international technology providers and most importantly, national and international technology providers (sensors, feeding, in-house climate, dairy equipment and instrumentation, data processing and ICT).
Research project (§ 26 & § 27)
Duration : 2017-10-01 - 2020-09-30
Claw disorders and lameness are considered one of the most important welfare problems ion dairy cattle farming. The partners in this project acknowledge this problem and will build a cooperative partnership between farmers, claw trimmers, scientists, advisory services and veterinarians. The aims are: 1) Development of a standardized documentation and central electronic registration of findings from claw trimming as well as other selected animal welfare indicators in Austria, 2) development of a checklist addressing potential factors influencing animal welfare on farm, 3) deduction of recommendations for intervention measures, which will be provided to the farmers, 4) traing of claw trimmers and memebers of the milk recording scheme in assessing claw findings and animal welfare indicators as well as respective influencing factors.