The following post originally appeared in the June 2017 issue of the Forestry Source. It was written by SilviaTerra's lead biometrician, Dr. Nan Pond.
We're continuing the discussion in the Society of American Foresters's LinkedIn group - looking forward to hearing your questions and comments!
In the March edition of the Forestry Source, we discussed the importance of evaluating inventory decisions in the context of Cost + Loss. How much does a cruise cost to install, and how much do you lose by making imperfect decisions using the information from that cruise?
Taking this a step further, here’s a look at how this works for two different forest types - an upland hardwood forest and an unthinned loblolly pine plantation. In this example, we’ve simulated forest conditions. The upland hardwood forest simulation covers a 65 year old forest with about 150 ft2 of basal area per acre and 150 trees per acre. The loblolly pine plantation simulation covers a 13-year-old plantation, with a starting TPA of 400 and BA per acre of 160 ft2.
This analysis follows the process outlined in that previous article - identifying a forest condition of interest, finding a similar FIA plot, simulating a forest and then sampling from it, and then growing the forest and the samples forward and comparing management options. Each forest type was tested using 10 different FIA plots and 100 simulations derived from each plot - a total of 1000 simulations each.
The cruising methods compared were the same for both simulations - BAF 10, BAF 20, and 1/10th acre fixed radius plots, installed at 1 plot per 5 acres and 1 plot per 10 acres - a total of 6 different cruise methods. In both forest conditions, the fixed radius plots were the ‘winning’ methodology. Let’s note now that these results are very context-specific, and depend on the valuation and markets we chose, the discount rate, and the tested management options.
In the upland hardwood example, there was a tie - 4 of 10 FIA plots examined showed that 1/10th acre plots at 1 plot per 10 acres had the lowest cost+loss while another 4 showed the lowest cost+loss as 1/10th acre plots at 1 plot per 5 acres. For the loblolly plantation, the optimal cruise methodology was 1/10th acre fixed radius plots at 1 plot per 10 acres.
These results may be surprising - they were to us. My coworker even commented that he’d been “cruising plantations wrong for years!” The common line of thinking is that variable radius plots are faster, and easier. Because of this they’re far more likely to be chosen by cruisers and cruise managers. It’s cheaper to install a variable radius plot, and even more so to use a 20-factor prism instead of a 10 BAF.
In a cost+loss analysis, we’re able to see the real tradeoffs that are made. The trick here is that often, cruising decisions are made looking solely at the cost part of the equation - not the loss. In our simulations, the BAF plots were absolutely the cheapest approach - they take less time to install and involve measuring fewer trees. However, the loss side of the equation came into play and tipped the scales each time. In the loblolly plantation simulations, the mean loss from management decisions based on the BAF 20 cruises was on average twice as much as the loss from BAF 10 or the 1/10th acre plots. Similarly, the BAF 20 cruises had a mean loss of 3 times greater than cruises using 1/10th acre plots. The BAF 10 cruises showed twice the loss.
The takeaway from this shouldn’t be “always use 1/10th acre fixed radius plots” - the key is really to think about the tradeoffs being made when choosing a cruising method. The sampling method used is a meaningful and influential decision. A cruise that doesn’t fully represent the conditions in the stand can lead to costly management mistakes.
The same methodology can be used to evaluate other inventory approaches. Is it worth incorporating satellite imagery or LiDAR into your cruising process? Using the cost+loss approach, you can determine whether the increased precision of your cruise (and the resulting improvement in management) outweighs the cost of the additional data inputs. We’ll cover that in an upcoming article.
