Genomics Database
Sylvania Station
Lead: Summer Willis
Region: Inland/East Pilbara
Hectares: 192,922
Environment: Mulga and acacia shrubland
Challenge Identified: Increasing whole herd performance, or, how to remove the underperformers and consistently breed from the high performers.
Status: Ongoing.
Rolling into a new project with university research in 2026.
Approach
Sylvania has been working on herd efficiency, productivity and optimisation for quite some time. PEN’s research project has boosted this management strategy. The goal is to create an easy-to-use reference database of every Sylvania animal. From that database, management decisions on keeping, breeding, or culling animals based on genetic and seasonal performance become much easier to make.
How do we achieve that? We aim to collect a DNA sample from every weaner as they come through at one of the two musters per year. That information, coupled with crush-side data collection on animals as they are processed for various on-station operations, is producing a large database.
DNA sample and RFID tag all weaners at every mustering round. This can be done using a tissue test (TSU) or a tail hair sample.
Record traits crush-side against every animal that is processed through the yards during various operations. Sylvania utilises Gallagher and Sapien KoolCollect technologies.
These entail:
- body condition score
- pregnancy test results
- foetal aging results
- wet/dry status
- body weight
- visual observations
Input all individual animal data from the station, feedlots, and DNA tissue test results into a database reference software. Sylvania is using Black Box to interpret and simplify how their data can be used.
Make management decisions from the Black Box software. This could include any of the following:
- de-stocking choices
- breeding choices
- culling choices
- bull to mob choices
Monitor decisions against the reference database, focusing on visual and economic observations of the results.
Produce a blueprint for other pastoral stations on how to implement a system like this without getting lost in the complexity and enormity.
Key Insights
1
Animals are beginning to be noted as ‘super cows’ when they are recorded as wet (calf at foot) and pregnant alongside the year that this occurred.
2
When selecting heifers in terms of ‘keep’ or ‘cull’, the database is providing insight into animals that may have been passed over, as their genetics are coming from ‘super cow’ dams.
3
Data on feedlot weights and time to processing weight will feed back into this system, allowing genetic choices to be made that improve cost efficiency.
4
The foundation being laid in this PEN project is just the beginning, the future of data + observations for genetic selection is looking very bright.
Impact & Results
The detailed results of this project regarding Return on Investment (ROI) are in the data collation and assessment stage. They will be uploaded as soon as they are finalised.