1. General Model Information

Name: Grazing Lands Application

Acronym: GLA


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

Contact:

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
Phone: 409-845-5548
Fax : 409-845-6430
email: jwstuth@rasc-sparc.tamu.edu

Author(s):

Jerry W. Stuth, J. Richard Conner, W. T. Hamilton

Abstract:

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 geographical regions.

Author of the abstract:

CIESIN (CONSORTIUM FOR INTERNATIONAL EARTH SCIENCE INFORMATION NETWORK):


II. Technical Information

II.1 Executables:

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 jwstuth@rasc-sparc.tamu.edu for more information. GLA Version 2.0.4 for DOS

II.2 Source-code:

Programming Language(s): C

II.3 Manuals:

GLA Version 2.0.4

II.4 Data:

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


III.1 Mathematics


III.2 Quantities


III.2.1 Input

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

III.2.2 Output

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
Temporal Scale:
Analysis can be performed at a daily, monthly, yearly, or across years timescale, depending on type of analysis desired.
Spatial Scale:
Ranch, pasture, plant community, patch (response unit)

IV. References

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

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