In Search of the Right Metric

The Wisconsin DNR just kicked off its PR campaign for Operation Deer Watch this week. This program is designed to collect data about the state’s deer herd and it is used primarily to keep tabs on the overall deer population. This is serious business for folks who live in America’s Dairyland and the DNR has created detailed modeling techniques to estimate things like population growth and fawn production for the various DMUs (Deer Management Units). It all sounds very interesting but what really caught my attention were some of the metrics that the DNR used and what these metrics meant for the management of the herd. For example:

  • Fawn-to-doe (“fawn recruitment”) ratios – This measure helps track the productivity of female deer and can be used to estimate the population growth of the herd. The ratios depend on many factors (i.e. predation, disease, nutrition) and typically hover around 0.8 — a ratio that suggests that fawn reproduction was good and that fawn losses during spring/summer were low.
  • Buck-to-doe ratios – This measure looks at the proportion of males and females in the deer herd and is used to get an idea of the number of bucks available for harvest and an estimate of when females will reproduce. According to the Texas Parks and Wildlife, a typical 1:5 buck-to-doe ratio — probably the biological maximum — results in a young herd that must be culled fairly dramatically to maintain a stable population. A 1:1 buck-to-doe ratio leads to an older herd with better antler potential but with a reduced annual harvest. (Such a 1:1 ratio is apparently difficult to achieve … most well-managed herds will have a little less than two adult does per adult buck).
  • Buck age-structure – This measure is used to get a better understanding of the adult buck mortality rate. Age is one of the primary influences on the trophy status of bucks and the percent of yearling bucks in population should be high enough to produce a good population of mature males.

What strikes me about these metrics is how specific they are and how well they fit in with the DNR’s overall strategy. In fact, the more I read about herd management and the DNR’s data collection program, the more it reminded me of a business trying to optimize its performance through a better understanding of the current state of the market. I would say that there are still plenty of companies out there who operate with a lot less precision.

The difficulty that most businesses have with their metrics is that they don’t have the internal discipline to develop or maintain the right performance indicators they need to make decisions. Major stumbling blocks include: lack of common definitions and terms; lack of good data; and difficulties interpreting results. When faced with some or all of these issues, the tendency is to focus on things that are easier to measure but less relevant.

For the DNR, the metrics they use relate directly to policy decisions that can affect the quality of the deer herd — things like bag limits, habitat improvements, and the length and timing of the hunting season. Not everyone agrees with details of these policies, of course, but the metrics make it easier for everyone from local hunters to state politicians to determine the level of success or failure.

Further reading:

  • For more on deer metrics and herd management goals, visit here and here.
  • For some thoughts on Quality Deer Management (QDM) programs, try this site.
  • For details of Wisconsin’s Sex-Age-Kill (SAK) model (and more deer-related data then you ever imagined), check out this paper.


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