Integration of data to drive data driven approaches for livestock improvement

Challenges

The livestock sector is continually under pressure to proactively embrace and incorporate sustainability drivers into their systems and forward improvement plans. These sectors are required to increase profitability by responding to changing market conditions while contributing to Scottish Government commitments on greenhouse gas emissions and biodiversity. An update to the Scottish climate change plan has accelerated the drive to net zero.

The application of genetics is a cost-effective way to improve the productivity and sustainability of livestock. Progress is permanent, sustainable, and cumulative. Genetic improvement is estimated to have resulted in between 50-90% of the overall animal improvements seen over the last 60 years. These improvements will be a major driver for the improvements in efficiency, productivity, and sustainability of Scotland’s livestock sector. However, there are several barriers to genetic improvement, one of which is the availability of sufficient phenotypes of any given quality.

We have previously been able to demonstrate the value of incorporating new data sources into these programmes. This involved developing:

• Novel methods of cleansing and merging structurally disparate and unstructured datasets
• New algorithms and data systems to extract new knowledge not previously possible

These innovations represented a step-change. Before this, genetic relationships between individual animals were only available for animals registered with a pedigree breed society. This data accounts for less than 40% of UK dairy cows and 10% of UK beef cattle. In addition, beef genetic evaluations were previously based on performance data from registered pedigree herds. This data represents less than 5% of the total beef population and, therefore, is not representative of the goal of improving production traits in commercial herds and of lower accuracy.

Integrating data from various sources has meant that information from 80% (5 million) more animals are now available for research and livestock improvement tools. Further data enables breeders to incorporate new traits of interest and analysis techniques into genetic improvement programmes. By working with these extended datasets, we have been able to research a vast array of traits that underpin efficiency and sustainability in dairy and beef systems.

This research demonstrated the benefits of incorporating government, abattoir, and private datasets in genetic improvement programmes. Moreover, using this data has shown that traits previously considered difficult to measure or low heritability (carcass weight, finishing age, carcass fat and protein, calf survival and lifetime survival and fertility of dairy cattle) are all heritable. This has led, for the first time, to the development of genetic and genomic tools to select for these traits.

Questions

Solutions

The primary aim of this project is to develop new systems to integrate data from disparate sources to help develop, test, and identify implementation routes for national-level livestock improvement tools and policies.

Enhancing the national livestock improvement database

We are gathering data from disparate sources into a single cohesive dataset which is being used to inform policy, demonstrate weaknesses in data and as input into both phenotypic and genetic analyses. We are working with public and private data owners and managers to demonstrate the utility of creating a shared and safe data environment for added value, for data owners, data users and the wider research environment.

Key data gaps in the current national livestock improvement datasets include more integration with ScotEID and poor representation of Scottish abattoir and condemnation data and these will be our early point of activity. We are aiming to integrate the improvement database with health scheme databases to help stakeholders – research and industry – begin to test new research hypotheses and improvement tool developments.

Data frameworks to support industry-led open data research and impact

Assimilating national datasets automatically creates linkages to other research requiring access to animal data. We are adding in new data from different sources (e.g., slaughter and condemnation data gaps) and integration with ScotEID systems and new data types and formats (e.g., health scheme data, image, and sensor data). This involves working with old and new data owners and users to build an understanding of the contents and capacity of this national coverage livestock improvement database.