To build their FORPLAN models, planners would break their forests up into hundreds of different zones based on such factors as vegetation, the age of the timber, wildlife habitat, steepness of slope, whether the zone had roads, or any other criteria that seemed important. For each zone, planners had to identify management costs, timber values, timber yields, and the values and yields of other resources such as recreation, water, forage, and specific species of wildlife. Planners would set a goal such as maximizing timber or net economic value and could also set constraints, such as floors or limits on the amount of timber that could be cut. They could then give FORPLAN a goal, such as maximizing timber or profits, and it would allocate zones to timber, recreation, and other prescriptions and tell how much timber could be harvested from the forest for each of the next 15 decades.
As parodies of soviet planning, the forest plans were quite humorous. The data used in the models were often erroneous or fabricated. Many plans assumed, for example, that timber was worth 50 to 100 percent more than timber companies were actually paying for it. Others assumed that trees could grow far faster than is realistic. One actually projected that trees could grow 650 feet tall, nearly twice the height of the tallest trees in the world.
Many people described FORPLAN as a black box, that is, a machine whose inner workings were too complicated for most people to understand.2 Technically, FORPLAN used linear programming methods to find the optimal solution to any problem the planners gave it. But FORPLAN’s inner workings were much less important than the quality of the data planners entered into the computer. As an outsider, I suspected that the Forest Service would bias FORPLAN models toward timber, and I set a goal of reviewing at least a third of the plans to find out if this was true. Ultimately, I collected and read every draft and final plan and reviewed the actual FORPLAN computer runs and background data for well over half the 120 forest plans.
To write the forest plans required by the National Forest Management Act, the Forest service hired hundreds of recent graduates in economics, planning, operations research, and other technical fields. These people enthusiastically and often idealistically embraced the opportunity to prepare objective plans that would determine the future of nearly 10 percent of the nation’s land. Almost immediately, however, they ran into serious obstacles.
Data collection is one of the most important early steps in any planning process. Forest service rules required planners to use the “best available data”—but the emphasis was on available. Historically, the Forest service usually did a complete forest inventory before each 10-year timber management plan. An inventory would not measure every tree in a forest but would measure randomly or systematically selected plots scattered across the forest. In one common inventory procedure, one plot was measured for every 1,850 acres, so each plot was assumed to represent that many acres. If 10 plots were found to have 100-year-old Douglas fir trees, planners assumed the forest had 18,500 acres of 100-year-old Douglas firs.
Inventory specialists planned to measure the same plots every 10 years, providing information on how fast trees were growing and other changes in the forest. Reinventories also made it possible to identify and correct any errors in the previous inventory. In the 1970s, managers of one Oregon forest realized that one of its inventory crews had made a serious mistake in the 1960s: Contrary to directions, if a plot fell in a meadow or a lake, they moved the plot to the nearest forest. This led managers to underestimate the number of acres of meadows and lakes and overestimate the number of acres of productive forest.
Given planning deadlines and the fact that they were spending so much money on computer runs and newly hired experts, forest planners in the 1980s were rarely able to do new inventories. So they relied on data that were anywhere from 10 to 30 years old. These data were updated by subtracting the volume of timber cut in that time and adding the amount that planners thought trees would grow in that time. Obviously this meant they had no opportunity to correct errors in earlier inventories or their growth projections.
The few forests that did new inventories often took shortcuts to save time and money. Previous inventories collected a huge amount of data, including the height, age, diameter, and species of every tree in each plot, plus more general information such as the steepness of the slope, the direction the slope faced, and the species of shrubs growing under the trees. The computer age is supposed to enable people to consider and analyze ever-greater quantities of data. But FORPLAN could deal with only a limited number of variables, so planners decided not to collect any data FORPLAN couldn’t handle. This saved money in the short run, but reduced the reliability of the inventory and made it impossible to compare the inventory results with any future inventories that did collect more data.
Other forests completed their reinventories only after forest planning was well under way. A reinventory of Oregon’s Malheur National Forest found that trees measured in the previous inventory subsequently “shrank” in both diameter and height. This prompted speculation that the person in charge of the previous inventory had inflated the numbers to get answers more in keeping with the Forest Service’s timber goals.3 Since the reinventory was completed in the midst of forest planning, planners continued to use the older discredited data in FORPLAN.
Given information, however unreliable, about how much timber was standing in the forest, the next question planners had to answer was how fast trees could grow. Under the nondeclining flow policy, forests that had lots of old-growth timber couldn’t cut that timber any faster than the next generation of trees could grow. So second-growth yield tables that projected rapid growth allowed for more cutting of old-growth trees today.
The first plan I reviewed was for the Okanogan National Forest. Though located in arid eastern Washington, it based most of its growth projections on yield tables written for moist western Washington, which receives as much as four times the rainfall. Timber inventory data collected by the forest revealed that Okanogan growth rates were only about 60 percent of the rates projected by the western Washington yield tables.4
The Santa Fe National Forest itself discovered that actual timber volumes were only 80 percent of the numbers it had entered into FORPLAN. Rather than reenter all the yield tables, it decided to simply reduce the timber harvests proposed by FORPLAN by 20 percent. This seemed simple enough—except that planners asked FORPLAN to maximize the forest’s net economic value. Given the overestimated volumes, FORPLAN calculated that timber cutting was more lucrative than it really was. The higher volumes made it appear that only 11 percent of the forest would lose money on timber sales. I found that correcting the volumes increased this to 48 percent.5
Some forests had already cut much of their old growth, so—if you believed their second-growth yield tables—the main limiting factor to timber-cutting levels was the growth rate of the old growth. California national forests used yield tables that stunningly predicted old-growth forests would double in volume in as little as 20 years.6 Since old growth is normally considered to grow very slowly, these predictions were not credible and greatly distorted the plans.
Other national forest yield tables were even more absurd. University of Montana forestry professor Alan McQuillan found computer-generated yield tables used by Idaho’s Clearwater National Forest that predicted trees could grow 650 feet tall in 150 years.7 That’s nearly twice as tall as the tallest tree in the world and close to three times as tall as the tallest trees in Idaho.8 No one on the forest noticed the error, and planners didn’t correct it after McQuillan pointed it out.
When they weren’t overestimating timber growth rates, many planners overestimated timber prices. Timber prices had risen rapidly during the 1970s, partly due to speculation fueled by contracts that allowed purchasers to pay for timber up to five inflation-filled years after they bid.9 Many forests presumed that prices would continue to rise at similar rates for the next 50 years. Since FORPLAN did its calculations in decades, planners applied the prices predicted for the midpoint of each decade to that entire decade. Yet planning was taking place during the deepest recession in the second half of the 20th century, and actual prices had crashed well below the levels of the late 1970s. So planners found themselves in the odd position of using prices for the first decade that were higher than the forests had ever received at a time when actual prices were