1. General Model Information
Name: Grazing Lands Application
Main medium: terrestrial
Main subject: biogeochemistry, agriculture
Organization level: landscape
Type of model: not specified, static-algebraic equations
Main application: decision support/expert system
Keywords: rangeland, management, stock levels, grazing schedules, nutrition, wildlife-livestock, optimisation tool, decision support system, dynamic programming, integer programming, linear programming, mixed-integer programming, multi-objective programming
Prof. Jerry W. Stuth
Center for Natural Resource Information Technology
Department of Rangeland Ecology and Management
Texas A&M University College
Station, TX 77843-2126
Fax : 409-845-6430
Jerry W. Stuth, J. Richard Conner, W. T. Hamilton
GLA is a decision support system (DSS) It assists researchers and policy
analysts to assess the economic and environmental impacts of various grazing
land management strategies. The model is also known as RSPM (Resource Systems
Planning Model). A variety of modeling and systems science components are
contained in the model, including: an expert system, dynamics programming,
integer programming, linear programming, mixed integer programming and
multi-objective programming. The model provides users with information on the
forage capacity, the optimum livestock-wildlife mix, grazing schedules, an
investment analysis and energy balance.
GLA allows users to characterize acreage, land use, soil and plant community,
growth profile and long-term management response of response units (i.e.
management units). The model examines a grazing land problem by addressing two
questions: what are the management goals and what is the forage and animal
inventories? The model analyzes the possible options and develops a long-term
strategy for management. The model calculates animal-plant productivity based
on land use practices on a farm.
The model uses daily weather as input to drive vegetation growth and examines
the effects of grazing by livestock and wildlife on the vegetation. Data on
the number and composition of the grazing area's plants and livestock are
required. Economic information, such as marker dollar profiles, animal
investment profiles and variable cost profiles are required as input.
Validation Procedures: Stocking rates predicted by the mixed population module are validated
against on-ranch historical data. Animal performance has been validated by using the NIRS fecal
profiling system in conjunction with monthly weighing of different classes of animals across
Author of the abstract:
CIESIN (CONSORTIUM FOR
INTERNATIONAL EARTH SCIENCE INFORMATION NETWORK):
II. Technical Information
Operating System(s): Runs on IBM PC or compatible with DOS 3.3 or greater or on UNIX386/486 AT&T platforms with UNIX 3.2. Requires 15MB of disk space and 640K ram. A mathco-processor is desirable. Paradise graphics cards are necessary for USDA machines. Newest versions of GLA-program and data are distributed via CD-ROM, please mail to email@example.com for more information. GLA Version 2.0.4 for DOS
Programming Language(s): C
GLA Version 2.0.4
Quality of Available: Plants, soils, range site, pasture fertility yield, and woodlandsuitability yield data are available through USDA-SCS and is good quality data.Quantity of Available: are available for all 32 western states in the US. All majorfeedstuff composition data are available through the NRC.Other Shortfalls: Preference of plant species must be compiled by field experts by majorregion. Will be available from state SCS range conservationists very soon.
III. Mathematical Information
Model Input Data Requirements:
Plant species names (common/scientific) and theirpreference by kind of animal, soil series/component names, growth curves, transect species yield,pastureland/cropland/hayland fertility yield relationships, grazable woodland canopy - foragestocking relationships, long-term stocking response to management (20 years), feedstuffnutritional composition, animal attribute information, market dollar profiles, animal performanceprofiles, investment profiles, variable costs profiles, and revenue profiles of enterprises.
Model Input Data Source:
USDA-Soil Conservation Service, National Research Councilfeedstuff values, expert opinion Model Output Data:
Forage capacity, optimum livestock-wildlife mix, forage balance, grazing
Model Output Data:
Forage capacity, optimum livestock-wildlife mix, forage balance, grazingschedules, investment analysis, nutritional crude protein and net energy balance, least-costrations for supplementing livestock
Analysis can be performed at a daily, monthly, yearly, or across years timescale, depending on type of analysis desired.
Ranch, pasture, plant community, patch (response unit)
Stuth, J. W., J. R. Conner, W. T. Hamilton, D. A. Riegel, B. Lyons, B.Myrick, and M. Couch 1990. RSPM - A resource system planning model for integrated resourcemanagement. J. Biogeography 17:531-540
V. Further information in the World-Wide-Web
VI. Additional remarks
This decision support system is useful for policy analysts interested inthe potential impacts of climate change and land use on livestock grazingpractices.
Last review of this document by: T. Gabele: 12. 6. 1997 -
Status of the document:
last modified by
Tobias Gabele Wed Aug 21 21:44:44 CEST 2002