Selecting cattle with genetics to make them more suitable in the face of dry periods and drought is a reality, according to genetic scientist Dr David Johnston.
David, a principle scientist at the Animal Genetics and Breeding Unit (AGBU), said producers can identify bulls whose progeny will be better suited for drought conditions.
Upgrades to BreedObject, an MLA funded software tool that assists with valuing multiple traits within a herd’s individual breeding objective and production system, better account for feed costs in different production systems, especially feed costs associated with the cow herd.
BreedObject Version 6.0, along with the availability of net feed intake (NFI) estimated breeding values (EBVs) allows producers to create selection indexes that improve feed use efficiency and other profit drivers, specifically for their herd. The software utilises BREEDPLAN EBVs to evaluate specific traits and link them with an economic value associated with improving the trait by one unit within the herd. Bulls can then be ranked on how profitable they will be to the herd through a $index, which is a combination of the multiple traits that impact profitability.
“There is no one trait that drives profitability – they all impact differently on costs and returns. When we’re setting breeding objectives, we’re valuing the genetics of a bull across an entire production system –not just maximizing profit to the processor, feedlot or on-farm,” David said.
Improving a herd’s resilience to drought by genetically decreasing feed costs can be achieved by evaluating the feed requirement for production, feed intake through residual or NFI EBVs and the changing price of feed. This is streamlined when using BreedObject as it combines these factors into a $EBV.
“In the Angus sire summary, there is a -1.33 to +1.67 kg spread in the NFI EBVs in some of the top bulls so their progeny will differ on average by 1.5kg feed intake a day for the same weight and gain,” David said.
Large differences between NFI EBVs on candidates for selection provides producers with the opportunity to improve feed costs in their herd. However, improving feed costs cannot be considered alone.
“Traits also differ in their degree of genetic control and some traits can cause an unfavourable change in another,” David said.
The NFI trait is slightly negatively correlated with weight but positive with fat and marbling. Although known correlations between traits are utilized within BreedObject the $index reported is driven by an animal’s EBV for each trait. It is important to review the essential traits in the breeding objective and make sure they are suitable before selecting a bull to use.
“The more feed efficient animals, on average, are leaner with less marbling but those correlations aren’t strong,’’ David said.
The upgrades to BreedObject 6.0 include enhanced feedlot phase modeling for pasture-feedlot systems, greater growth curve modeling, enhanced cow weight and cow condition score valuing, and methane (carbon) costing. The improvements will mean improved $indexes for producers to use when making selection decisions, which will result in industry breeding more profitable cattle, especially in systems with limited feed resources.
David recommended updating $indexes for beef breeds using BreedObject 6.0 to include improved modeling of feed costs and residual feed intake.
“We need to measure feed intake and generate NFI EBVs, with commercial producers using those indexes to buy bulls, and keep an eye on mature cow weight,” he said.
David said more research was needed on residual cow intake, development of cow body condition score EBVs and consider cow traits measured at the start of joining rather than weaning.
“BreedObject models your view of the future – the outcome of mating a bull to a cow today will be five to 10 years before you see it in the daughters within the herd,” he said.
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