When we learned, a week or so ago, courtesy of the latest satellite mapping, that the world has three trillion trees, and that over the past 11,000 years, humans have reduced global tree stocks by roughly 50%, I immediately thought of BEETCOIN.
Here’s how I got there.
First, I thought of my two acres of meadow and woods and wondered what a backyard tree inventory would yield. Then I realized that about half of what may be close to two hundred trees here are aspens, but, that, since aspens are connected underground by a single root system, the whole enterprise of tree counting is thrown into considerable doubt.
Then I enjoyed the fact that while the satellite that does global tree mapping is traveling at 17,000 miles per hour and transmitting data at the speed of light, I was sitting on my deck, looking at my aspen grove (the word “my” in the most quotationable of quotation marks), wondering how old this grove is, how much an aspen tree grows each year, why elk are so taken with aspen bark, whether voles, which have arrived this year in considerable abundance, under and around the rodent-wire-protected raised beds of my garden, eat aspen roots, why chipmunks share my penchant for Italian parsley, what it means in the scheme of things when I add mushroom compost and kelp meal to what was previously mountain meadow soil but is now unrecognizable to any earthworms that previously called it home, and how in the world we can learn to call home a world that is being mapped and measured thus (as described in the Yale study that made the new estimates of the global tree inventory):
Following model averaging and bootstrapping, we applied the final negative binomial regression equations used in bootstrapping to pixel-level spatial data at the biome level. Regressions were run in a map algebra framework wherein equation intercepts and coefficients were applied independently to each pixel of our coregistered global covariates to produce a single map of forest tree density on a per-hectare scale. We then scaled our per-hectare forest density estimates to the 1-km scale based on the total area of forested land within each pixel, as estimated by the global 1-km consensus land cover data set for 2014 (ref. 6). This process was then validated using an older (2013) data set that used fine-scale (30 m) forest cover information, which revealed equivalent total tree counts. By multiplying our predicted forest density by the area of forest, we ensured that we did not overestimate tree densities in non-forested sites. From the resulting maps, summary statistics (mean tree density, total tree number) were derived for each polygonal area of interest. The variances of the global and biome-specific totals were calculated using a Taylor series approximation to account for the log-link negative binomial regression function and correlation among the regression-based predicted values.
Then I asked myself: What does all this have to do with the price of pork bellies in China or heirloom tomatoes at the Boulder farmers market?
And knowing that this is a question fit for a … for a … well, maybe for a fiduciary but definitely not for a guy sitting on his deck looking at a grove of aspen trees … I went on line to Slow Money’s impending BEETCOIN campaign, featuring eight Colorado small food enterprises, and considered bringing a little money back down to earth.
As I hope you will.