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PREDICTING FOREST GROWTH AND YIELD:
CURRENT ISSUES, FUTUREPROSPECTS
Papers presented at a seminar series and workshop
held at the University of Washington
January-March 1987
Edited by
H. N. Chappell
Douglas A. Maguire
College of Forest Resources
University of Washington
Seattle, Washington
Institute of Forest Resources
Contribution Number 58
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1987
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PREDICTING FOREST GROWTH AND YIELD:
CURRENT ISSUES, FUTURE PROSPECTS
Robert 0. Curtis
Successful modeling depends on a number of factors (in addition to the money
One might group these into three general categories: ( 1) formal
with which to do it).
technique, (2) art, and (3) data.
Formal Technique
In this category I include formal training in biometrics, simulation methods, and
computers.
Some of us can still remember analyses done by ocularly fitted curves and
alignment charts.
The rotary calculator put an end to these, and the computer led to
Along
procedures that allow us to handle large amounts of data and many variables.
with this has gone development of more efficient, more powerful, and more esoteric
methods of analysis.
In one of our seminars, George Furnival commented that development of new
methodology is a lot of fun and is certainly beneficial to researchers at performance
rating time, but doesn't necessarily have much effect on results.
In a way, I find it
comforting to read a paper that, after an involved analysis with autoregression
methods that I only half comprehend, concludes with the statement that the final
result differed very little from that obtained with ordinary least squares.
Maybe this is defensive
decry the value of progress,
allow us to do things that we
But it is still possible
data.
way of formal biometric theory.
rationalization on my part.
I certainly don't mean to
and the methods developed in recent years do indeed
could not do before, and to make more effective use of
to do useful work with fairly modest equipment in the
One thing the modeler definitely needs is a feeling for the way trees and stands
behave.
This is very important. It includes the ability to recognize nonsense in data
or predictions and to relate predictions to field observation and experience.
This
generally depends on a broad background of experience in silviculture and mensuration
quite as much as on any formal academic training.
Along with this goes the need for the quantitative way of thinking.
Formal
courses can help to develop this, but the aptitude must be there.
I can think of a
number of people who have had this to an outstanding degree and who have done
valuable work despite a modest formal academic background.
Over time, people with the appropriate aptitude and experience tend to develop
their own bag of tricks and ways of approaching problems, which are often effective
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,..._:,___.,......._.--.--- -...·---· -·-----· --. Predicting Forest Growth and Yield
81
even though you don't find them in the textbooks. This personal factor is one reason
The
why it is hard to demonstrate clearly that one model is superior to another.
people involved are often quite as important as the type of model.
We probably know enough about trees to
The third essential component is data.
But no manager would believe it.
construct a plausible growth model without data.
In any case, it would be too generalized to be of any practical use in a specific reallife situation.
With really good data covering the full range of conditions for which predictions
are wanted, it would be hard to go wrong.
In real life we're somewhere in between
these extremes.
We may have only a little data. More often, we have a lot of data·
but most of it has little relevance to the questions we have in mind.
There can be a degree of substitution among these three components; strength in
one can offset weakness in another.
But statistics cannot replace the art and data
components.
Over the last
number of people
gaining experience
shape with regard to
decade or so the schools have been turning out a considerable
well grounded in formal technique, and these people have been
and skills.
It seems to me that we are now probably in better
the first two components I've listed than with respect to data.
Our data problems are not usually a matter of quantity.
They are matters of
These are tied to modeling methodology and objec­
quality, compatibility, and kind.
And, second, what should we
tives.
First, what can we do with the data we have?
be doing to provide data for the things we will need to do in the future?
One often finds a considerable difference in viewpoint between people with an
The former are likely to
inventory/management background and those in research.
think in terms of averages of stands as they exist, and to distrust "research" results
as representing unrealistic idealized conditions.
The research types are likely to be
appalled at the degree of lumping that goes on and the crudity of some of the
applications and assumptions.
It is useful to bear in mind the differences in objec­
There is a basic difference between what some of us have
tives of these activities.
termed the "growth trend" data, produced by inventories, and the "treatment response"
data produced by properly designed field trials.
Inventories are primarily concerned with determining what's out there now.
The
growth information they produce is information about the recent growth of existing
It does not tell us much about growth
stands, which is determined by past practices.
very far in the future; it tells us little about response to stand treatments; and it
tells us nothing about what would happen if we did something radically different. My
own belief is that it is not possible to determine the quantitative effect of stand
treatments from inventory data. I know there are those who disagree.
Its objective is
In contrast to this, we have the typical research installation.
generally to determine the quantitative response to some treatment or treatments.
Given a series of such trials over an appropriate range of initial conditions, we try to
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Robert Curtis
develop general expressions that can be incorporated in simulators and then used to
answer management and growth prediction questions.
The inventory and research activities obviously have to mesh somehow.
We need
to make the best use of whatever information we can get, from both sources.
And,
research-derived relationships must ultimately be applied to existing conditions as a
starting point.
This gets us into the questions listed below.
There are maybe one or two of
these for which I think I know the answer; a good many I have opinions about but no
certainty that they are correct; and for a few of them I haven't the foggiest solution.
QUESTIONS
I. What are our objectives?
Do we have several?
of data and perhaps different types of models?
2. How do we mesh information from inventories and monitoring with experimental
information?
--in system development
--in system applications (inventory projections, planning applications,
silvicultural applications)
--what "monitoring" (definitions, objectives, uses)? --local calibration --validation --feedback 3. As far as design is concerned,
What should we do differently in the
--what have we done wrong in the past?
future?
--what planned experiments are needed versus sampling of operational areas?
--what about fixed area plots versus variable plots?
--what about replication? (do we need it? what kind?)
--what about permanent versus temporary plots?
--what treatments should be included? (where do we most need information?)
(mixed versus pure species, age
--what stand conditions should be included?
classes, etc.)
4. How does one get continuity, compatibility
problem), and adequate quality control?
Do they require different types
(the
"least
common
denominator"