In the heart of Australia, a groundbreaking study is challenging traditional methods in the cattle feeding industry. Researchers from the University of Queensland have explored the potential of automating feed allocation in feedlots, a move that could revolutionize the way we think about livestock management and precision agriculture. The study, led by S.V. Platts from the Animal Science Group, delves into the effects of algorithm-based feed allocation on the performance, health, and carcass outcomes of Brahman cross steers.
The research, published in the journal ‘Smart Agricultural Technology’ (translated from the original Chinese title), focuses on bunk management (BM), a critical aspect of feedlot operations. Traditionally, BM relies heavily on human evaluations, which, while effective, can be prone to errors. These errors can significantly impact the performance and health of the cattle throughout the feeding period. Platts and his team set out to evaluate two levels of BM automation to see if technology could match or even surpass human capabilities.
The study involved over 5,500 steers, divided into three treatment groups. The first group, SEMI, had feed remaining determined by a lidar bunk scanner, with humans making the final BM decisions. The second group, AUTO, relied entirely on an algorithm for BM decisions based on the scanner data. The third group, CON, followed conventional BM methods, with decisions made solely by humans.
The results were striking. There were no significant differences in average daily gain, feed-to-gain ratio, final live weight, morbidity, mortality, or hot standard carcass weight among the three groups. However, the CON treatment showed a tendency for improved dry matter intake (DMI) over the total feeding period. “This suggests that human bunk management decisions were more adaptive as days on feed increased,” Platts noted. “They better accounted for changes in cattle physiology and DMI as the cattle grew and matured.”
The study also highlighted some shortcomings in the automated system. The AUTO treatment had greater mean absolute deviation in DMI during certain periods, possibly due to larger and more frequent dry matter additions later in the feeding period. This indicates that while automation can achieve comparable results, there’s still room for improvement in the algorithms used.
So, what does this mean for the future of feedlot management? The study shows that automation can be a viable alternative to human bunk management, potentially reducing labor costs and increasing efficiency. However, it also underscores the need for more adaptive algorithms that can better account for the changing needs of the cattle as they grow.
For the energy sector, this research could have significant implications. As the demand for sustainable and efficient livestock management grows, so too will the need for technologies that can help meet these demands. Automation in feedlots could be a step towards more sustainable and efficient livestock management, reducing the environmental impact of the industry and improving the bottom line for farmers.
As Platts puts it, “This study is just the beginning. There’s still much to learn and improve upon, but the potential is there. We’re on the cusp of a new era in livestock management, and it’s an exciting time to be a part of it.” The future of feedlot management is looking increasingly automated, and with it, the potential for a more sustainable and efficient livestock industry.