1. General Model Information

Name: GLYCIM

Acronym: GLYCIM


Main medium: air+terrestrial
Main subject: biogeochemistry
Organization level: organism
Type of model: partial differential equations, ordinary differential equations
Main application:
Keywords: crop growth, soybean, deterministic, dynamic simulation, rainfall, temperature, solar radiation, soil, management

Contact:

Basil Acock
USDA-ARS
Remote Sensing and Modeling Lab
Bldg. 007, Rm. 008, BARC-West
10300 Baltimore Avenue
Beltsville, MD 20705-2350
Phone: 301-504-5827
Fax : 301-504- 5823
email: bacock@asrr.ars.usda.gov

Author(s):

Basil Acock

Abstract:

GLYCIM is a dynamic simulation model with hourly time steps. It predicts growth and yield of a soybean crop in response to climate, soil and management practices by deterministic simulation of organ-level processes such as photosynthesis, transpiration, carbon partitioning, and organ growth and development. The model was developed by Acock with the help of colleagues at the USDA, ARS Crop Simulation Research Unit, MS, and was last documented by Acock and Trent (1991).

The model requires daily maximum and minimum temperature, precipitation and solar radiation data as input. Soils data are also required to execute the model (e.g. soil texture and hydraulic properties by horizons, organic matter and nitrogen content of the topsoil).

The model is designed for hourly time steps for a growing season. The model is executed for a single typical plant in a canopy.

Validation Procedures: Comparison of model predictions with field data for growth in height, weight, number of organs, stage of development, yield, etc.

Author of the abstract:

Basil Acock


II. Technical Information

II.1 Executables:

Operating System(s): DOS / Windows95 or Windows NT for GUICS (graphical user interface) Needs a 386 or better with a math coprocessor. Takesabout 20 minutes for a full season on the minimal machine. GLYCIM and GUICS ca be downloaded from ARS USDA ftp directory The complete set of instructions for downloading GUICS (GUI for cropSimulators) is as follows. Use a computer with Windows95 (as far as we know Windows NT can be used also).Make directories 'disk1' and 'disk2' somewhere on your hard disk.FTP to www. ncsr.arsusda.gov. Log-in as anonymous.Use your e-mail address as a password.Go to /pub/GUICS.Copy the content of the directory 'disk1' to your directory 'disk1'Copy the content of the directory 'disk2' to your directory 'disk2'Copy file 'manual.zip'.Disconnect FTP transfer.Unzip file 'manual.zip' It is a collection of MSWord7 files. Print file 'Chapter1.doc' which tells how to install GUICS.Go to your 'disk1' directory.Run file 'setup.exe'. You will be instructed. To look in Chapter1 may behelpful.Print file 'Chapter0.doc' to get a general idea of GUICS.

II.2 Source-code:

Programming Language(s): FORTRAN Source code directory

II.3 Manuals:

A User's Manual for GUICS is available from bacock@asrr.ars.usda.gov

II.4 Data:



III. Mathematical Information


III.1 Mathematics


III.2 Quantities

Daily min/ max temperature, rain, solar

III.2.1 Input

Daily min/ max temperature, rain, solarradiation. For each soil horizon: depth, texture, water release curve,saturated hydraulicconductivity, organic matter, water, and nitrogen content.
Model Input Data Source: Weather station and soils lab. Anything that can be measured on the plant and

III.2.2 Output

Anything that can be measured on the plant andin the soilcan be output for any time. The most common plant and soil descriptorsare outputweekly in a standard run.

Temporal Scale: Hourly steps for growing season.

Spatial Scale: A single typical plant in a canopy.


IV. References

Acock, B., and A. Trent. 1991. The soybean crop simulator, GLYCIM: documentationfor the modular version 91. Department of Plant, Soil andEntomological Sciences, University of Idaho, Moscow, Idaho, 242p.

Reddy, V. R., B. Acock, and F. D. Whisler. 1994. Crop management and input optimization using the soybean model GLYCIM. In V. M. Salokhe and Gajendra Singh (ed.) Proc. Intl. Agrl. Eng. Conf., Bangkok, Thailand, 6-9 December 1994, p. 453-460.

Haskett J., Pachepsky Ya.A., Acock, B. 1995. Estimation ofSoybean Yields atCounty and State Level Using GLYCIM: a Case Study for Iowa. AgronomyJournal,87:926-931.

Reddy, V. R., B. Acock, and F. D. Whisler. 1995. Crop management and input optimization with GLYCIM: differing cultivars. Computers and Electronics in Agriculture 13:37-50

Timlin, D.J, Ya. Pachepsky, B. Acock, and F. Whisler. 1996.IndirectEstimation of Soil Hydraulic Properties to Predict Soybean Yield using GLYCIM. Agricultural Systems, 52: 331-353.

Haskett, J., Ya. Pachepsky, and B. Acock. 1997. Increase ofCO2 and Climate Change Effects on Iowa Soybean Yield, Simulated Using GLYCIM. Agron. J. 89:165-174

V. Further information in the World-Wide-Web


VI. Additional remarks

Global change implications: This modeling system increases the relevance of crop growth models to global change research because interactions amongseveral crops may be examined. A link to a weather generator canallow a user to examine climate scenarios.

Source : CIESIN Report


Last review of this document by: B. Acock and T. Gabele: 16. 7. 1997 -
Status of the document:
last modified by Tobias Gabele Wed Aug 21 21:44:44 CEST 2002

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