Before a tree is planted, before the roots touch soil or the sunlight paints green across leaves, a map may already exist. Not a traditional one—but a map made of data, probabilities, and quiet assumptions. A forest drawn before it lives, imagined in yield classes and economic value.
This is the world explored in Chapter 6 of Applied Environmental Economics, where economists and geographers unite to model timber yield and its worth—not only to the landowner, but to society at large. It’s a chapter about foresight. About using today’s tools to glimpse tomorrow’s forests. And about how decisions made on a screen can shape landscapes for generations.
Yield Class: The DNA of Timber Economics
At the heart of timber modelling lies a simple but powerful concept: yield class. This is a measure of how much timber a particular tree species is expected to produce, on average, each year under specific conditions.
Think of it as the growth potential written into the landscape—shaped by soil, slope, rainfall, and sunlight. A Sitka spruce on nutrient-rich, sheltered land might yield double the timber of one planted on windswept moor. The same seedling, two different fates.
By knowing the yield class, economists can estimate how much wood a forest will produce over time—and how that translates into financial returns. But to do this at scale, across hundreds or thousands of square kilometers, they need more than intuition. They need models.
The Power of Prediction
In this chapter, the authors develop statistical models to predict yield classes for two tree species: Sitka spruce (a fast-growing conifer) and beech (a slower, native broadleaf). They draw upon two key datasets:
- The Forestry Commission’s Sub-Compartment Database (SCDB), which provides detailed information on actual timber yields at surveyed sites.
- The LandIS environmental database, which describes soil types, elevation, slope, climate, and more.
By linking these two sources, the researchers build a predictive model: a way to estimate what the yield class would be in areas that haven’t been surveyed yet, based on the environmental characteristics of the land.
The result? A map of potential. A tool that lets policymakers, farmers, and foresters see which lands are best suited to which species, and what kind of economic returns they might expect over time.
Location, Location, Location
But this is not a uniform picture. As the chapter shows, timber value is exquisitely spatial.
A tree that grows quickly and sells for a high price in one area might grow slowly and struggle to survive just a few kilometers away. Altitude, wind exposure, and soil pH all matter. So does access to markets and infrastructure.
By using Geographical Information Systems (GIS), the researchers overlay yield predictions with spatial data to produce maps of timber value, not just volume. These maps allow for nuanced cost-benefit analysis—revealing, for instance, that some areas are perfect for Sitka spruce under current subsidy regimes, while others would be better left to agriculture or rewilding.
The Role of Discounting and Time Horizons
Of course, trees don’t pay out immediately. Timber is a long game—sometimes 30 to 50 years before harvest. That’s why economic valuation must discount future revenues, translating them into Net Present Value (NPV).
The models explore different discount rates—3%, 5%, and beyond—reflecting society’s varying levels of patience and risk tolerance. A lower rate values the future more generously, making forestry investments more attractive. A higher rate favors short-term gain and tilts the balance toward agriculture or development.
These are not just numbers. They are choices about how we treat the future—and whether we see trees as slow-growing burdens or long-term assets.
A New Kind of Map
What emerges from this chapter is not just a spreadsheet or a graph—it is a new kind of map. One that merges ecology, economy, and ethics. One that lets us imagine a better forest before it exists.
Such maps can guide policy. They can show where subsidies would be most effective, where carbon storage could thrive, where habitat corridors might grow. They don’t eliminate uncertainty—but they reduce it enough to act wisely.
Closing the Distance Between Data and Forest
To some, modelling timber yield might seem abstract. Remote. A digital exercise far from the quiet of real forests. But look closer, and you’ll see something different.
You’ll see foresight. Care. The belief that decisions can be better—more grounded, more equitable, more sustainable—if made with both heart and data.
And so, before the spade hits soil, before the first seedling is tucked into earth, a different kind of work begins. The work of imagining, mapping, and aligning human hopes with the rhythm of land.
This is modelling and mapping timber yield. And it is, in its own way, a kind of planting too.