1. General Model Information

Name: Peanut Crop Growth Simulation Model (PNUTGRO)

Acronym: PNUTGRO


Main medium: terrestrial
Main subject: biogeochemistry
Organization level: population
Type of model: ordinary differential equations
Main application:
Keywords: Vegetative and reproductive development; photosynthesis,respiration, growth, partitioning, senescence, soil water flow, uptake

Contact:

Dr. Gerritt Hoogenboom
Department of Biological and Agricultural Engineering
University of Georgia-Georgia Experiment Station
Griffin, GA 30223-1797
Phone: 770-229-3438
Fax : 770-228-7218
email: gerrit@bae.griffin.peachnet.edu

Author(s):

Gerritt Hoogenboom

Abstract:

PNUTGRO is a deterministic and mechanistic simulation of physical, chemical, and biological processes in plant and related environment with the purpose to predict yield and related agronomic parameters. PNUTGRO simulates main plant processes as a function of weather, soil, and crop management conditions. The model input data consists of daily weather data (air temperature, precipitation, solar radiation), soil physical conditions of the profile by layer, soil chemical conditions of the profile by layer (nitrogen only), crop management conditions (planting date, spacing, irrigation management. Data is put in by the modeler and is mainly experimental data. The output data given by the model is predicted weight of leaves , stems, roots, pods, shells, seeds, LAI, SLA, root length density on a daily basis. PNUTGRO also predicts main phenological events such as flowering, maturity, ET, water uptake, transpiration. Inputs and outputs are on a daily basis, while some internal processes such as development are calculated on an hourly basis. Validation procedures include independent data sets collected at the University of Florida, and various locations in the U.S.A. and at international sites. A very large collection of data sets is available from India. There are currently about 30 data sets which have been used for model validation. The spacial scale of the model is field scale (actually point data because of the limitations of the input data). The model was developed by K.J. Boote, J.W. Jones, G. Hoogenboom. University of Florida and University of Georgia.

Author of the abstract: summarized from information given by Dr. Gerritt Hoogenboom


II. Technical Information

II.1 Executables:

Operating System(s): Model will operate on a personal computer with an 8088 processor, but simulation of one season can take as long as 15 minutes. On a 80486 computer a typical season will take about 10 seconds.

II.2 Source-code:

Programming Language(s): FORTRAN (Microsoft FORTRAN compiler)

II.3 Manuals:



II.4 Data:

Normally experimental data are used as inputs for the model. The quality of theses data depends on the quality of the experiment itself. For predictive purposes the model only requires soil profile and weather information as inputs. It is up to the user to define the quantity of data used in these simulations. shortfalls include the unavailability of solar radiation data, the unavailability of detailed soil profile information and the unavailability of detailed experimental data to determine cultivar specific parameters.

III. Mathematical Information


III.1 Mathematics


III.2 Quantities

Daily weather data (air temperature, precipitation, solar

III.2.1 Input

Daily weather data (air temperature, precipitation, solarradiation). Soil physical conditions of the profile by layer. Soil chemical conditions of the profile bylayer (nitrogen only). Crop management conditions (planting date, spacing, irrigationmanagement. Predict weight of leaves, stems, roots, pods, shells, seeds, LAI, SLA, root

III.2.2 Output

Predict weight of leaves, stems, roots, pods, shells, seeds, LAI, SLA, rootlength density on a daily basis. Predicts main phenological events such as flowering, maturity.Main water balance variables such as ET, water uptake, transpiration.
Temporal Scale: Inputs and outputs on a daily basis, while some internal processes such asdevelopment are calculated on an hourly basis.
Spatial Scale: Field scale (actually point data because of the limitations of the input data).

IV. References

Hoogenboom, G., J.W. Jones, and K.J. Boote. 1992. Modeling growth, development, and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO: A Review. Transactions of the ASAE 35. 2043-2056.
Boote, K.J., J.W. Jones, G. Hoogenboom, G.G. Wilkerson, and S.S. Jagtap. 1989. PNUTGRO v. 1.02. Peanut Crop Growth Simulation Model. User's Guide. Florida Agricultural Experiment Station Journal No. 8420. University of Florida, Gainesville, Florida, 76 pp.

V. Further information in the World-Wide-Web



VI. Additional remarks


Last review of this document by: Gerritt Hoogenboom and T. Gabele: 30. 07. 1997
Status of the document:
last modified by Tobias Gabele Wed Aug 21 21:44:47 CEST 2002

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