Increasing uptake of performance-recording genetics through automated livestock management systems (Phase 2)

Summary

The uptake of performance-recorded genetic evaluation of beef cattle in extensive production systems of Northern Australia is currently low. Some seedstock producers do capture important information for genetic evaluation to generate estimated breeding values. However, the buying of bulls based on quantitative genetic traits has not been widely adopted by commercial producers. Reasons for the lack of industry participation in performance recording are multifaceted and industry anecdotes suggest there is a disconnect between seedstock and commercial producers. Commercial producers are not confident in the current methods used to derive estimates of genetic value. There are also challenges associated with collecting accurate performance data that is compliant with genetic evaluation systems. The future for genetic improvement in Northern Australian beef herds requires more cattle with more accurate, frequent and reliable performance measures. Future genetic improvement programs need to capture data with more autonomy, at lower costs and with less labour. This project explored new and emerging technologies that could fill this need.

Objectives

This project had several key objectives:
• validation of an Automated Livestock Management System (ALMS) and implementation of these systems into northern seedstock and commercial breeding operations, including ensuring effectiveness of the algorithms, automation, and authentication of the system in all seasons
• development of understanding around the impact of paddock conditions on accuracy of the ALMS and provide guidelines for industry
• quantification of the economic feasibility of using ALMS to record phenotypic traits for submission into BREEDPLAN.

The research undertaken in this project and the current commercial development of ALMS has now positioned the industry to enable the ultimate objective of expanding the number of producers that provide detailed, and accurate data for industry genetics evaluations.

Key findings

The project demonstrated that an ALMS could be developed and applied to collect performance recording data in extensive grazing systems of Northern Australia. However, this is not a simple process and producers intending to implement ALMS to collect phenotypic data need be aware that property paddock plans and water resources need to be carefully considered. Animals also require an initial training phase involving organisation and time commitment.

A key phenotype of interest to the Northern beef industry is date of birth. When the ALMS was operating under good conditions and being well maintained, a date of birth could be accurately predicted within seven days for more than 90% of cows.

One of the key challenges in using an ALMS to collect data for growth traits is the incomplete attendance of animals in the system. Under optimal conditions, where water was isolated, this project demonstrated that 400 Day Weights could be calculated for over 98% of the animals in a cohort using a simple in-paddock weighing approach. However, these optimal conditions are unlikely to be readily replicated in commercial and seedstock operations.

An extensive communication and industry engagement program provided significant insights across the project. Producer use case studies explored the challenges and benefits of the system and identified the various points of value and challenges to implementation. The development of commercially available systems with good support is likely to be well received by the industry.

Benefits to industry

This project has demonstrated that systems can be developed to automatically collect performance recording data for genetic evaluation programs. It has identified the challenges associated with implementing these systems across commercial and seedstock operations and provided guidance for producers. An economic analysis has demonstrated that a positive return on investment is likely at a farm level, primarily through labour savings.

The ability to automatically collect key phenotypic data such as date of birth and growth traits will ultimately enable more producers to commence performance recording and increase the utilisation of genetic evaluation programs in the northern beef industry.

Future research

There is an opportunity to explore the development of new traits that cannot currently be assessed using traditional means (e.g. compensatory gain, growth trajectories, calving ease & maternal investment). The further development of impossible/difficult to measure traits that are critical for the Northern beef industry could be the key to increasing uptake of genetic improvement technologies. A much broader economic analysis, although significantly more complicated, would provide guidance on the likely return on investment of research efforts in this domain.