Production And/ Or Profit?

Speaking at the recent Beef Improvement Federation Research Symposium and Annual Meeting, held in Columbia, Matt Spangler, from the University of Nebraska-Lincoln looks at focusing breeding objectives by selecting for profitable genetics, not high production genetics.
calendar icon 10 July 2010
clock icon 5 minute read

Steep increasing genetic trends for growth traits (weaning and yearling) and mature cow weight can be seen in many breeds but perhaps more alarming are those producers that have dramatically increased the genetic potential for milk production in their cow herds.

Conditional on the assumption that the Beef Cattle Industry is a For Profit organisation, then it would seem logical that profit (Revenue – Expenses) should drive our selection decisions. In order to actually do this, knowledge of environmental constraints, genetic antagonisms, and the selection tools that have the potential to measure profit are critical.

Environmental Constraints

The development of an obtainable breeding objective begins by clearly identifying environmental constraints and marketing goals.

If feed resources are limited in a stressful environment then selection for increased extreme output (high growth, milk, and red meat yield) could have negative impacts on the ability of cows to be successful breeders without the need for large quantities of harvested forage. The beginning of a profitable breeding objective is identifying what the environment will allow you to produce, at least until we have tools to apply direction selection to traits of adaptation.


Commercial producers who have not yet adopted crossbreeding are a burden to the beef industry.

We know that the two primary benefits of crossbreeding are complementing the strengths of two or more breeds and heterosis, neither of which create trait maximums. If we think about it simplistically, crossbreeding for a trait like weaning weight leaves us with a calf crop that is better than the average of the parental lines, not better than both parental lines.

Crossbreeding, if done correctly, seeks to optimise many traits through complementing breed strengths and produce animals that are better than the average of the parental lines that created them. The best tool that the commercial cattleman ever had is based on optimisation, not the production of extremes. So, it would stand to reason that within breed selection should have the same goal, optimums and not maximums.

Genetic Correlations

Unfortunately, all traits that might be included in a breeding objective are not independent of each other. Sometimes this is beneficial as we see a favourable correlated response, and other times these genetic correlations pit revenue against cost. A good example of this comes from the suite of weight traits. Depending on the targeted marketing endpoint either weaning weight (WW), yearling weight (YW) or carcase weight (CW) become a source of revenue and all are related to a major factor influencing the cost of production, mature cow weight (MW).

Although it is not intuitive, literature results show that of the immature traits, WW has the highest genetic correlation with mature cow weight.

Care should be given not to focus solely on the revenue portion, sale weight, but rather optimising input costs associated with mature weight and revenue sources from calf sale weight. The mature sale weight, CW, shows a strong and positive relationship with MW and again care should be taken to optimise selection between the two.

Selection for decreased input

In order to mitigate genetic antagonisms in an effort to select for profit, economic index values become the tool of choice. A bio-economic index (H) is simply a collection of EPDs that are relevant to a particular breeding objective, whereby each EPD is multiplied by an associated economic weight.

A high index value does not necessarily mean that an animal excels in all EPD categories given that superiority in trait can compensate for inferiority in other traits depending on how the EPDs are weighted in the index. A high index value should be thought of as excelling in the ability to meet a breeding objective.

The majority of economic index values are rigid (i.e. not catered to individual enterprises). A much more desirable method would use individualised index values where the bull with the highest index value may differ from one herd to the next, depending on how the animal fits the specific needs of each enterprise. While this would lead to more accurate identification of parents for the next generation, it becomes a challenging metric to use for advertisement purposes in the seedstock industry, which probably explains why this more desirable method of multiple-trait selection has not been exploited by the majority of breed associations.

New and Improved Tools

Genomic tools hold the potential to provide predictions for hard to measure traits that focus on input costs such as feed intake. Ideally, genomic predictions for feed intake would be incorporated into an economic index as a key component of input cost. However, accurate genomic predictions will require phenotypes.

The improvement of existing phenotypic databases for traits is also needed. It is critical that seedstock producers routinely turn in mature cow weights along with body condition scores to further aid in selecting for optimal weights and the development of tools such as the American Angus Associations Cow Energy value index ($EN) and the Red Angus Associations Maintenance Energy (ME) EPD. This will require participation in Whole (or Total) Herd reporting, a very necessary process for complete data collection and the development and delivery of genetic prediction tools.


Trends are rarely flat, as an industry we have measured ourselves by steep lines in one direction or the other. From a seedstock perspective this may have been perceived as necessary in order to differentiate themselves (either as breeders or as breeds) from others in the market place.

Clearly identifying your production environment and realistic production goals given that environment are critical. Profit lies in the optimisation of expense and revenue and optimisation is always more challenging than maximising outputs or minimising inputs. It will require more effort, detailed financial records, and a structured breeding objective that builds a cow herd based on optimum values and not extremes. One final thought, extremely low maintenance cows will push the lower threshold of what is biologically possible for weight and produce virtually no milk. High output cows will represent the other extreme, weigh more than most mature bulls and milk heavier than the best Holstein. Both excel in some measure of the profit equation (i.e. lowest cost or highest revenue) but neither promises to be profitable.

July 2010
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