1. General Model Information

Name: GRAMI

Acronym: GRAMI


Main medium: terrestrial
Main subject: biogeochemistry
Organization level:
Type of model: not specified
Main application:
Keywords: crop, leaf area index, biomass growth, grain yield

Contact:

Stephen Maas
Cotton Research Station
17053 Shaffer Avenue
Shafter, CA 93263Telephone: 805-746-6391
Fax: 805-746-1619

S.J. Maas

Author(s):

S.J. Maas

Abstract:

GRAMI is a simulation model which estimates grain yield of sorghum, corn and spring wheat. The model requires inputs of air temperature, total daily solar radiation and planting date. The model simulates leaf area index, biomass growth and grain yield at the end of the growing season. The temporal scale of the model is a growing season. The spatial scale of the model is dependent on the scale of the input data. Simulations can be run at individual field or county level.

The model uses remotely sensed satellite observations as a component of data input.

Author of the abstract:

CIESIN


II. Technical Information

II.1 Executables:

Operating System(s): PC-486 environment

II.2 Source-code:

Programming Language(s): FORTRAN with a version in Turbo Basic

II.3 Manuals:



II.4 Data:



III. Mathematical Information


III.1 Mathematics


III.2 Quantities


III.2.1 Input

Model Input Data Requirements: Air temperature, total daily solar radiation, planting date,Remote sensing observation of Leaf Area Index.
Model Input Data Source: Weather data - NWS. Remote sensing - Satellite observation.

III.2.2 Output

Simulation of Leaf Area Index. Biomass growth and grain yield at season end.
Temporal Scale: Single growing season.Spatial Scale: Dependent on scale of input data. Individual fields-low resolution satellite on county level.

IV. References

Two part paper Part I: Agronomy Journal. 1993. Parametrical Modelof Granimeous Crop Growth; in Leaf Area al Dry Mass Simulation. Part II: Within SeasonSimulation Calibration. Volume 85, pp. 353-357.

V. Further information in the World-Wide-Web



VI. Additional remarks

Model linkage with remotely sensed data and the variable spatial scale can provide a useful tool to examine the effect of climate variability on crop production.


Last review of this document by: 22.May 1997
Status of the document:
last modified by Tobias Gabele Wed Aug 21 21:44:44 CEST 2002

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