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