This file was created by scanning the printed publication.
Errors identified by the software have been corrected;
however, some errors may remain.
DECISION SUPPORT SYSTEMS FOR
RESTORATION AND MANAGEMENT
OF ANNUAL RANGELANDS
J. Ross Wight
fires. Fire management and subsequent rehabilitation
have become major problems associated with the management of these rangelands.
Technical information on ecosystem components, their
interaction, and response to management and restoration
technology continue to increase. The information and
technology are scattered throughout the literature. Considerable knowledge has never been published and is
available only through interrogation of living experts.
Such fragmentation makes it difficult to organize the
information and develop management practices and
policies.
Assimilating, integrating, and interpreting information
from so many diverse sources is beyond the means of individual rangeland resource managers. Decision support
systems (DSS), which include components such as databases, expert systems, natural resource models, and geographical information systems (GIS), provide a means of
interpreting and integrating the available knowledge and
information into useable formats. Such systems appear
to be ideal tools for the management and restoration of
annual rangelands.
ABSTRACT
Natural resource managers are required to deal with an
increasing amount of information to maintain proficiency
in their management skills. The integration and interpretation of information is beyond the means and time constraints of most resource managers. Computerized decision support systems utilize tools like expert systems and
databases that include geographic information systems for
organizing, structuring, and delivering information from
basic and applied research as well as heuristics. This paper discusses the application of these systems to problems
of restoration and management of annual rangelands.
INTRODUCTION
Natural resource managers are required to deal with an
increasing amount of information to maintain proficiency
in their management skills. The integration and interpretation of information is beyond the means and time constraints of most resource managers. Computerized decision support systems utilize tools like expert systems and
databases that include geographical information systems
for organizing, structuring, and delivering information
from basic and applied research, as well as heuristics.
This paper discusses the application of these systems
to problems of restoration and management of annual
rangelands.
Exotic annuals are becoming the dominant vegetation
on vast areas of rangeland in the Western United States.
Environmental concerns associated with biodiversity and
soil stability, both of which are adversely affected by invasion of annual species, are at an all-time high. Restoration and management of these rangelands have become
a critical environmental issue and a challenging assignment. Rangeland ecosystems are complex systems of interacting components where management or manipulation of one component causes corresponding responses
throughout the system. Because of extreme spatial variability, changes due to management or treatment effects
are difficult to measure. Annual fires fueled by cured
stands of cheatgrass (Bromus tectorum} cause additional
problems on these annual rangelands. Such fires have
reduced vast areas to near monoculture stands of cheatgrass, which increases the incidence and extent of these
DECISION SUPPORT SYSTEMS
Expert systems, GIS, and natural resource models are
basic components of natural resource DSS. Expert systems can be described as computer programs that simulate the problem solving capabilities of several experts
and are able to draw and store inferences from information (Rajotte and Bowser 1991). Such information is supplied by experts in specific disciplines or domains and
model simulations. In addition to knowledge bases, expert systems include user interfaces, and inference engines, mechanisms that control the reasoning processes
(Stock 1987).
GIS are becoming the common method for storing information associated with specific unit areas of geography.
Such unit areas can be of almost any magnitude and include such information as soil slope, aspect, texture by
soil horizon, vegetation cover, herbage yields for the past
several years, and artificial structures.
Natural resource models such as SPUR (Simulation of
Production and Utilization of Rangelands) (Carlson and
Thurow 1992; Wight and Skiles 1987), WEPP (Water Erosion Prediction Project} (Lane and Nearing 1989}, and
SHAW (Simultaneous Heat and Water) (Flerchinger and
Saxton 1989a,b) have become increasingly important as
management tools. They can be used to predict the impact of proposed management strategies or the probability
Paper presented at the Symposium on Ecology, Management, and Restoration of Intermountain Annual Rangelands, Boise, ID, May 18-22, 1992.
J. Ross Wight, Range Scientist, USDA Agricultural Research Service,
Northwest Watershed Research Center, 800 Park Blvd., Plaza IV, Suite
105, Boise, ID 83712.
320
of occurrence of soil-water and temperature regimes that
are favorable for the germination and establishment of
specific plant species or the hatch and development of specific insects. In a DSS, models are used interactively with
the expert system to evaluate various management strategies and supply information to the knowledge base. The
GIS databases are major sources of input information for
running the models.
To be an effective management tool, DSS must be easy
to use. Input requirements must be within the expertise
of the DSS users. Once installed on the computer, the
DSS can be put in operation by typing a key word. The
user is then asked for information, usually one item at a
time. For example, one of the first requests might be for
the longitude and latitude. A map might then be displayed on the screen and the user is asked to use the cursor to outline the boundaries of the resource area under
consideration. This would allow appropriate information
to be transferred from the GIS database. Help screens
are available to explain each input request. If the DSS
requires the operation of complex models, the expert system component must be able to develop the parameters
for these models through user-supplied information such
as soil series name and plant species being grown. Much
of this information could be supplied by the GIS database.
No single DSS will provide all the information needed
by rangeland resource managers. At least initially, such
systems will be of limited scope and problem specific.
Within the Bureau of Land Management (BLM), rehabilitation of burned rangeland is a major concern and a good
example of a management problem that could be well addressed by a DSS. Fire rehabilitation is expensive. For
example, in 1992 fires burned on about 98,000 ha of Idaho
rangeland managed by BLM. Rehabilitation plans were
developed for about 37,000 of these hectares with an average projected rehabilitation cost of about $92/ha or approximately $3.4 million (BLM 1992). CUrrently, rehabilitation
is based on official guidelines and the expertise and past
experience of the responsible individual(s). The guidelines
are general in nature and lack sensitivity to the individuality of each burn and site condition. Different personnel using the same guidelines for the same fire could develop rehabilitation plans that are considerably different. The
development of a DSS to assist fire rehabilitation planners would significantly enhance the efficiency of the
planning process and effectiveness of the rehabilitation
treatments.
grass seed germination model for predicting the optimal
seeding plan (Hardegree 1993). The WEPP model may be
used to predict soil loss and deposition on the burned site,
which is part of the environmental analysis. The SPUR
model, which includes climate, hydrology, plant, animal
(both domestic and wildlife), and economic components,
may be included in the fire rehabilitation DSS as another
part of the environmental analysis. Different models
may be used to focus on the different perspectives in the
decision-making process. Knowledge acquisition through
model studies such as sensitivity analysis may be an
essential part in the development of the expert system.
In the prototype DSS described by Wight and He (1993),
a database management system would provide most of the
inputs for the simulation models. A knowledge-based
system would be used to simulate the reasoning process
of fire rehabilitation planners or resource managers in a
normal condition, which means the knowledge involved in
this process is based on past fire rehabilitation experiences, established routines of fire rehabilitation planning,
or both. Uncertainty knowledge could be used as part of
the rule base. However, since unexpected situations do
occur as a natural part of ecosystems dynamics, the resource managers would still play a key role in evaluating
and adjusting the recommendations made by the DSS.
The development of a DSS, as described by Wight and
He (1993), requires the coordination of an interdisciplinary effort and a vast array of site and other information.
Potential users of the DSS need to be involved in all
phases of system development. A team of domain experts
may include disciplines such as hydrology, soil science, geology, range science, forestry, wildlife, fire management,
engineering, and personnel management. Required site
information includes: soil and vegetation characteristics,
climate, erosion hazards, threatened or endangered species' habitats, fences and other structures, fire history,
past rehabilitation treatments, and wilderness and critical environmental concerns. This information is often
available in various formats and forms such as maps,
documents, and electronically stored data. An interdisciplinary team of experts and DSS users gather, process,
and analyze all available information for inclusion in the
DSS.
Development of DSS is an expensive, time-consuming
process. However, once completed, they can be disttibuted on computer diskettes and provide a common basis
among natural resource managers for making management decisions. DSS are so constructed that they can be
continually improved and updated based on user experience and availability of new information.
PROPOSED REHAB DSS
A proposed prototype DSS for making fire rehabilitation
plans is described by Wight and He (1993). In this system,
GIS techniques are applied for gathering, processing, storing, analyzing, and displaying information. Global Positioning Systems could be used to accurately locate the
burned sites and provide other details of the burned areas.
Simulation models are suggested for predicting the dynamic states of the hydrology, climate, and soil microenvironment in postbumed areas. For example, the SHAW
model may be employed to predict the temperature and
water content in the 1-cm soil surface layer. The temperature and water information may then be imported into a
SUMMARY
DSS will be a great asset to the management and restoration of annual rangelands. Storage and retrieval of information, especially expert knowledge, will be greatly
enhanced. The time and cost of the decision-making process will be reduced. Fire has become a critical issue on
western rangelands. Development of fire rehabilitation
plans for burned areas is an example of many applications
of DSS in the management of rangeland ecosystems. In
addition to organizing information and providing a format
321
or structure for making decisions, the use of DSS will provide a much needed consistency within and among agencies managing rangeland resources.
Lane, L. J.; Nearing, M. A., eds. 1989. USDA-water erosion prediction project: hillslope profile model documentation. NSERL Rep. 2. West LaFayette, IN: U.S.
Department of Agricullture, Agricultural Research Service, National Soil Erosion Research Laboratory. 263 p.
Rajotte, E. G.; Bowser, T. 1991. Expert systems: an aid to
the adoption of sustainable agriculture. In: Rice, B., ed.
Sustainable agriculture research and extension in
the field. Washington, DC: National Academy Press:
406-427.
Stock, M. 1987. AI and expert systems: an overview. AI
Applications. 1: 9-17.
Wight, J. R.; He, B. 1993. Decision support systems: new
tools for managing rangeland ecosystems. In: Proceedings of the application of advanced information conference; 1993 June 18-19; Spokane, WA. St. Joseph, MI:
American Society of Agricultural Engineers. [In press].
Wight, J. R.; Skiles, J. W., eds. 1987. SPUR-simulation
of production and utilization of rangelands: documentation and user guide. ARS-63. Washington, DC: U.S.
Department of Agriculture, Agricultural Research Service. 367 p.
REFERENCES
Bureau of Land Management. 1992. 1992 fire reviewIdaho. Boise, ID: Idaho State Office. 39 p.
Carlson, D. H.; Thurow, T. L. 1992. SPUR-91: workbook
and user guide. 1991 upgrade of the simulation of production and utilization of rangelands model. MP-1743.
College Station, TX: Texas A&M University, Department of Rangeland Ecology and Management. 259 p.
Flerchinger, G. N.; Saxton, K. E. 1989a. Simultaneous
heat and water model of a freezing snow-residue-soil
system: I. Theory and development. Transactions
American Society of Agricultural Engineers. 32:
573-578.
Flerchinger, G. N.; Saxton, K. E. 1989b. Simultaneous
heat and water model of a freezing snow-residue-soil
system: II. Field verification. Transactions American
Society of Agricultural Engineers. 32: 565-571.
Hardegree, Stuart P. 1993. [Personal communication].
USDA Agricultultural Research Service, 800 Park
Blvd., Plaza IV, Suite 105, Boise, ID 83712.
322