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More beef,
less labour,
bigger profit
​CQUniversity’s DataMuster is a fast, simple and accurate research tool for automatically monitoring and managing the performance of a herd, right down to the individual animal.
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The main reason I have gone down the path of implementing Walk Over Weighing into our Organic beef fattening operation is to be able to identify cattle that are ready to sell at a specific weight earlier.
By using the data I can forecast a date where numbers of cattle will be finished by and book them in accordingly. This also will help with not keeping bigger cattle that should be sold and replaced with a smaller animal, prompting better pasture utilisation.
This data will also be useful to analyse to identify when cattle have hit a plateau in certain paddocks to prompt the shifting to fresher grasses.
Hopefully, with the addition of the drafting module this can be set up in a situation where it can draft off prospective cattle for sale without having to muster and draft through the whole mob of 700.
This leaves the majority of the mob in the paddock doing what they do best.
Carl & Monica Anderson // Carisma Stn
Cutting down on labour costs
Monitoring cattle weight & suitability for market
Enhancing herd management decisions
Detecting when a cow has calved
Recording genetic data such as maternal parentage, reproductive efficiency & growth rates
Following extensive R&D at CQUniversity, the DataMuster research system has been deployed on working cattle stations since 2018. With a strong interest in the technology, we are looking to increase participation in the stud sector as we explore how ALMS data can become BREEDPLAN compliant. If you are an innovative seedstock producer interested in joining our agtech research trials - follow the links below on how you can get started.
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We hope to be able to use DataMuster across our whole herd to measure and monitor not just weight gain in growing animals but also identify fertility and efficiency traits in the breeders.
By being able to ‘mother-up’ calves to cows in our extensive rangeland we hope to identify those cow families which have the best inter-calving periods, calf weights, and calf weight gains.
This will allow us to track the fertility of the daughters, granddaughters, and further down the generations to identify the best genetic lines. Using this data if we identify a negative trait we will be able to remove the whole genetic line.
The value of DataMuster is that we will be measuring this data on our herd under our management in our environmental conditions.
Jay & Rebecca Mohr-Bell // Mathison Stn
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CQUniversity Australia,
Building 361, Ibis Avenue,
North Rockhampton Qld 4702