Putting data to work

by John Stika, CAB Vice President-Business Development

The serious cattleman scrutinizes cost/benefit relationships for each management decision today. Whether you are expanding or trying to improve what you have, you must evaluate the potential return on investment (ROI).

Many of you have “answered the call” of the information age and committed to gathering feedlot and carcass data on your herds. You have an advantage, thanks to your investment in dollars and time—two years from breeding decisions to harvest—and your precious carcass data prize.

But are you using the advantage to improve ROI?

For years we have treated data itself as the prize, claiming victory upon its safe arrival. No wonder, when carcass data was difficult to gather and of questionable reliability. But today, accurate carcass data is easier to capture. Those who still treat ‘data’ as the prize are often those who let it gather dust on a trophy shelf.

Most of us have learned the real prize is the information that can be gleaned from the data.

Trying to make sense of carcass data can be intimidating and frustrating to new and veteran users alike. It may be unfamiliarity with numbers in general—or perhaps low tolerance for pain—that casts the data aside with other troublesome papers on the back desk corner before anything useful is ever gained from it (low ROI).

It doesn’t have to be that hard. You just want to identify variation within your herd and then begin to pinpoint the bright spots and address the problems areas.

The Value in Ranking

Using group, tag transfer, or detailed carcass data, these issues can be addressed by ranking the individual carcasses for carcass value, HCW or any one of the data points made available.

Using this ranking method one can quickly identify the light and heavyweight carcasses, YG 4s and 5s, and low quality-grade cattle. It’s easy to see the range for each parameter, so you can analyze the bottom 25% for value, looking at the reason these carcasses cost you money.

Utilizing a matrix

You might find it useful to look at the data in matrix form, especially when discerning the relationships between quality and YG (Table 2). Looking at the data in ranking, matrix or distribution format takes us a lot further down the road toward answering our questions as opposed to looking blankly at a bunch of randomly ordered rows and columns.

Table 2. Example of a Quality vs. Yield Grade Matrix.

	YG1	YG2	YG3	YG4	YG5	Total
Prime	10.0%	30.0%	21.0%	1.0%	0.0%	2.0%
Choice	15.8%	39.6%	23.3%	0.0%	0.0%	68.7%
Select	19.1%	13.0%	22.2%	3.5%	0.0%	27.8%
Standard	11.0%	30.5%	20.0%	0.0%	0.0%	1.5%
Total	15.9%	53.1%	26.5%	4.5%	0.0%	100.0%

Data’s advantage in herd decisions

The depth of knowledge you can learn takes on a new dimension when you elevate from group data to tag transfer. By simply adding the single data point of an ear-tag number, you can begin to tie particular carcass merit strengths and weaknesses back to particular cows and herd sires. By matching the carcass data with weaning weight, you can begin to identify those cows with pasture and carcass performance.

That will often point out cows whose calves should be sold at weaning to maximize profit and those that return more dollars by placing on feed. Another handy trick for evaluating your bull battery is to group the carcasses by sire group and rank the sires by total carcass value. You may find that bulls excel in different traits, and you can maximize their value by being more selective in which cows you breed them to.

Without question, detailed carcass data provides the greatest opportunity to accurately profile the carcass merit of you cowherd. However, in most cases a simple tag transfer is enough to answer the questions you have, and therefore provides a greater ROI.

Computer savvy data collection

Although a computer is not required to glean information from data, it’s easier when the data is in an “electronic format,” especially in the case of detailed data.

If you are visually oriented, the computer offers a number of possibilities to look for relationships and comparison. Putting data in graphic form lets you quickly get a visual impression of the carcass distribution for various traits and how they relate.

Comparision of Actual to Required Ribeye Areas Across HCW

For example this graph shows the carcass weight distribution for a set of cattle. We were also able to plot the average actual ribeye area for those carcasses with each weight break, against the ribeye area required for “average” muscling. At a quick glance you notice a few key points. First, the cattle fell within an acceptable carcass weight window, although a few fell outside. Secondly, the cattle were above average for muscling across almost every weight break.

This is just one example of the types of things that can be done to make “information-mining” less frustrating and more beneficial to your ROI.

Whether gathering data on your calves has become a ritual or a first-time event, consider a few key questions to maximize the ROI of our efforts:

1. Why collect the data?
2. What data do I need to collect?
3. How will I use the data?

If you cannot answer question No. 3 then you should return to the first question and again ask, “Why collect the data?”

If making sense of carcass data were a cakewalk everyone would do it. The fact is, everyone doesn’t. But for those who do, the opportunities to sustain a viable position within this industry are greater.