AI and vegetation management in forestry
fore057 at csc.canterbury.ac.nz
fore057 at csc.canterbury.ac.nz
Wed Jul 17 04:42:18 EST 1991
Quite a number of people asked me to send copies of this when it was complete,
so I'm posting it on the board. Any comments would be welcome.
APPLICATION OF KNOWLEDGE-BASED PROGRAMMING TECHNIQUES TO
COST-EFFECTIVE SELECTION OF HERBICIDES IN FORESTRY
E.G. Mason, D.J. Geddes, B. Richardson & N.A. Davenhill School
of Forestry, University of Canterbury; Tasman Forestry Limited;
and New Zealand Forest Research Institute
This paper will be presented to the Joint Australian and New Zealand
Institutes of Foresters Conference on "The costs and benefits of change",
Christchurch, New Zealand, September 1991
Knowledge-based programming techniques were employed to build a
PC-based system for selecting herbicides, to improve the
cost-effectiveness of vegetation management regimes and increase
users' awareness of environmental hazards. The strategy used to
develop the tool is explained, and the structure of the system
described. It is written in PDC Prolog, and incorporates both
knowledge-based and traditional procedural programming structures.
The program is very user-friendly, with capabilities specifically
relevant to forest supervisors. It may be adapted to different
localities and herbicide/weed regimes without further programming.
The benefits of having this kind of decision-support tool in
managing plantations with the intensity and sensitivity needed
today are discussed.
A vegetation management adviser is one essential component of any
forest management decision-support system, and knowledge-based
programming techniques provide an excellent way to accomodate such
a tool. Jeffers (1989) and Mason (1989) outlined the form which
future computerised decision-support systems may take in forestry.
User-friendly, comprehensive and malleable environments are
possible, within which managers can select the types of analyses
they desire. These environments may comprise geographic
information systems, growth and yield models, other stand models,
forest-level models (simulators, linear programming, and dynamic
programming combinations), and other such useful tools. It is
knowledge-based programming, however, which enables full
integration of the tools, and which fills the gaps hitherto
occupied by handbooks, rules of thumb and/or experts.
Knowledge-based programming is a name given to a set of computer
programming techniques which enable machines to represent and
process qualitative, symbolic information in a logical way.
Saarenmaa (1989) comprehensively outlined these techniques within
a forestry context. Several varieties of knowledge-based
programming are available, but the two most commonly employed in
everyday applications are rule-based systems and object-oriented
programming. Expert systems are a subset of knowledge-based
The vegetation management components of forestry decision-support
systems are best implemented in a knowledge-based structure.
Design of vegetation management strategies or "regimes" involves
many non-numerical analyses. Experienced managers acquire a
qualitative understanding of the components of the problem: for
example, susceptibility of weeds to different herbicides; times of
year weeds are physiologically active; behaviour of weeds
following alternative treatments; effects of different weeds on
tree crops; and so on. This type of knowledge currently defies
WHY BUILD A VEGETATION MANAGEMENT ADVISER?
Criteria for expert system domain selection
Stock (1987) proposed the following seven criteria for a suitable
expert system domain, which term in this context means "knowledge
area represented". Designing a vegetation management regime meets
1) Expertise should be scarce and time consuming to learn, but
the task should take only a few hours or days.
Tasman Forestry Ltd., for example, employs an expert (D. J.
Geddes) in vegetation management, who acquired his knowledge
from many years of field experience. Field supervisors vary
in their abilities to design cost-effective vegetation
management strategies, and often rely on the recommendations
of a single expert within the organisation. During a test at
Tasman Forestry Ltd., supervisors prescribed treatments in
response to the same weed problems: their solutions varied in
cost by a factor of three (Geddes pers. comm.). In some cases
the treatments would have been unnecessarily expensive, whilst
in others they would have had a low level of control.
2) The problem domain should be narrow, but deep (highly
specialised), and there should be a large number of possible
Forest managers proficient in the design of vegetation
management regimes are specialists with an in-depth
understanding of the biology of local weed species and the
effects of many treatment alternatives. Different chemicals,
and/or different physical treatments are available, as set out
by, for example, Preest (1985), Davenhill (1985), and Preest &
Davenhill (1986) for the New Zealand scene. When these are
considered over a range of weed species, environments, and
seasons, the number of possibilities is large.
3) The problem solution should require heuristics (rules of
thumb), ie: a set of equations could not be used to arrive at
a satisfactory solution.
Given the range of qualitative rules required for effective
design of vegetation managment regimes, it is unlikely that a
set of equations would be adequate. In part this is because
models of weed behaviour are almost entirely qualitative, and
strategies for their control have often arisen from field
experience rather than from quantitative research.
4) Competent experts must be available and willing to help
In the system described here, one company expert (D. J.
Geddes) and one research expert (N. A. Davenhill) were much
involved in pooling their knowledge and interpreting knowledge
stored in data-bases.
5) The problem should be financially important enough to
warrant building the system.
Based on responses to a questionnaire which asked for areas to
be treated, it was estimated that New Zealand's forest
industry planned to spend approximately $7,000,000 annually on
vegetation management between 1987 and 1992 (Trewin & Mason
1989). The direct costs of effective vegetation management can
vary from just a few tens of dollars to several hundred
dollars per hectare, while the opportunity costs of
misapplication of techniques can be very high, in the form of
poor subsequent crop performance, or as unnecessary
6) Experts in the area should agree.
In New Zealand there is general agreement among experts about
the broad principles of vegetation management regime design.
Davenhill and Geddes occasionally differed in opinions but
only on points of detail.
7) Ample data, test cases, and potential users should be
available for testing the system.
Data, test cases and users were all available. Geddes (1987)
had compiled a very complete vegetation management manual for
Tasman Forestry Ltd., and the company's forest supervisors
were keen to help with the project.
Herbicides vary in their impact on the environment (Adams, 1988),
and managers should avoid using particular products in
circumstances where their use may pose a risk to adjacent crops,
wildlife, fisheries or people.
A computerised vegetation management adviser could accurately and
quickly alert supervisors when use of a herbicide may be risky.
In the system described here, warnings of potential hazards are
brought to the user's attention when a herbicide is selected, and
toxicity information is available at the touch of a key.
For inexperienced supervisors, a decision-support system could be
used to assist with training. It is difficult for them to cope
with the wide range of substances, application rates and methods,
non-chemical control methods, responses of weeds, and costs
involved in vegetation management. A computerised system can make
the problem more manageable, without removing them from the
Identification of gaps in knowledge
When knowledge is collated within a decision-support package, it
is common that important gaps in knowledge are highlighted. It
was therefore expected that the project would identify research
Component of a comprehensive decision support system
Future forestry decision-support systems are likely to comprise a
range of models, tools, and databases, and a vegetation management
adviser was deemed to be an important component of such a system.
As this was the first knowledge-based application implemented by
Tasman Forestry Ltd., it would serve as an indication of the
potential for such systems.
Construction of the system proceeded in four distinct stages: an
initial protoype; knowledge acquisition; coding; and a
A small prototype system was devised as a result of a brief
meeting with Tasman Forestry Ltd. staff, based on some information
contained in the firm's Weed Control manual. This was a crude
program, written in BASIC, which contained knowledge of three
weeds and ten herbicides. It served to illustrate the potential
for a knowledge-based system, and it elicited specific suggestions
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