When genomic testing was first introduced to the dairy industry in 2009, there was considerable debate and pushback about the value and reliability of the information. At first, the industry was focused on determining if we could choose bulls without progeny testing – a sea change for the A.I. industry. Rapidly, A.I. companies completely altered their approach to bringing new sires to market – the timing, investment and reliability of information.
Looking back at the last 10 years, it’s exciting to reflect on the evolution and genetic progress from testing. Adoption was slow at first, but information gathered over the last decade has moved the dairy industry forward. It has created opportunities to make breeding and culling decisions for increased efficiency and profitability. Herds using genomic testing have an advantage.
A lifetime of information available at birth
The new genomic data from heifers provided insights on potential profitability, outside of physical appearance. A reliance on parent average (PA) shifted to genomic predicted transmitting ability (GPTA) and more reliable information to base breeding and early culling decisions.
Zoetis launched one of the first commercial genomic-testing labs for the dairy industry in August 2010. Any dairy producer could test their cattle, their youngstock and have new and better information to use for management decisions, like sire selection and reproduction strategies.
For most traits, it would take a cow’s entire lifetime, or longer, to have enough progeny and production records to match the reliability of the information we’re gaining from testing. For example, the reliability of GPTA from testing for productive life (PL) is comparable with the reliability of data from 50 actual daughter records. With genomic testing, we can get this information shortly after birth. We’ve seen dairies use these reliable insights to right-size their heifer inventory to reflect more profitable replacement herdmates.
Because of the predictability afforded by genomic testing, we continue to add and consider new traits. Genomics has given us the ability to look in places and at traits that weren’t possible before, including predictions for wellness traits to help predict disease risk in Holsteins and later on, Jerseys. Calf wellness traits were added in 2018. Last year, wellness traits for cow respiratory disease and milk fever (now for Holsteins) became available, along with fertility traits to provide insights on cow abortion, twinning and cystic ovaries, which can greatly impact lifetime profitability.
Access to cow and calf wellness trait insights, as well as new fertility traits, allows producers to consider the risk of many different disease states, such as mastitis, lameness, metritis, retained placenta, displaced abomasum, ketosis, milk fever, respiratory disease for calves and cows, calf livability and scours. Before, it was difficult to make progress with traits like these that have low heritability and no direct predictions.
Less intervention, more genetic variation in potential profit
All these wellness trait insights afford the opportunity for new multi-trait selection indexes, such as the Dairy Wellness Profit Index (DWP$), which incorporates a balance of fertility, health and production traits. Now, we can make a direct correlation from disease prediction and selection to an increase in productivity and, more importantly, potential lifetime profitability.
According to a field study ranking Holstein cows by DWP$, cows in the top 25% produced 21,460 pounds more energy corrected milk (ECM) through their lifetime to date than the lowest 25%.
The higher lifetime milk totals of the top 25% are due to both higher ECM per day (5 pounds more ECM per cow, per day) and longer herd life (205 additional days in milk per cow – that’s almost one full lactation more per cow). On average, cows in the top 25% of this ranking had $1,428 more income over feed cost (IOFC) when compared with the bottom 25%.
These tools help us identify the cows we often don’t think much about – the “invisible” cows that are the most profitable in the herd. Everyone knows who the least profitable cow is on their dairy, because she’s the lowest-producing cow or the cow that notoriously calves in with mastitis or spends a lot of time in the hospital pen.
But, not many farms can point out the ones that are driving the most profit, the ones that require the least intervention. Those cows do their job, and we don’t have to do anything special to realize their potential. I don’t know of any dairy producers who wouldn’t love to have more of those invisible cows.
A future beyond culling and breeding decisions
To create the cow that will be profitable in the future, we need to continue to examine our selection indexes and decide if the traits we’ve used in the past are still worth selecting against. We also need to evaluate the opportunities for each new trait.
With constant and rapid genetic progress, we have an opportunity to focus where our challenges remain and try to solve for them. Future testing insights might help us better target interventions or preventive support, like hoof trimming. The frontier is using genomics for management decisions and not just culling and breeding.
Genomics is providing information to shape the kind of cow we want for our operations. In the future, we may see information tailored to the needs of each herd – customized selection insights aligned to each herd’s strengths or weaknesses or to the unique needs of their region and climate.
Evaluating dairy cattle based on risk of disease has had impressive results. The insights from genomic-testing data are something everyone can put to work and profit from. This information is valuable regardless of herd size. It’s about making the next generation – the cow of the future – better. The genetic progress we’re seeing now is greater than just five years ago, and the rate of progress in the next five years could be even higher, with more opportunity to improve our future herd.
References omitted but are available upon request by sending an email to the editor.
