Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Docs » Available Crop Models

Available Crop Models
headline bar

GUICS, 2DLEAF and 2DSOIL can be downloaded by clicking here.              

GLYCIM, MELONMAN, MAFES (a version of GOSSYM for cotton) are included in GUICS                   

Farm Decision Aids 

Graphical User Interface for Crop Simulators

Users of crop simulators often experience a need for working with several crop simulators to study effects of crop rotations, to compare simulators, or to obtain information for decision making within a farm operation. Existing crop simulators have different interfaces, and some of those are rudimentary. Despite all the differences between crop simulators, they have common features that have enabled us to develop GUICS, the first user interface which can support several simulators simultaneously. GUICS has a rich set of tools to help a user in assembling a simulation scenario, in viewing results, and in obtaining the weather data through phone lines. Developing GUICS involved research in the hierarchies of information use in simulators, and in system requirements from different groups of the users. GUICS has a fully object-oriented design and implementation. It is open to enhancements, e.g., using maps, using data bases to store datasets, and working with suites of models.


The on-farm application of the soybean simulation model GLYCIM was started in 1991 with a few selected farmers in Mississippi. This project was initiated in collaboration with scientists from Mississippi State University and the University of Idaho. A Graphical User Interface WINGLY was developed and interfaced with GLYCIM for its on-farm application. During the past year, a more comprehensive Graphical User Interface for Crop Simulators (GUICS) was developed and integrated with GLYCIM. GLYCIM is now being used by several farmers in Mississippi, Alabama, Lousiana, Arkansas, Missouri, Tennessee, Texas, Kansas and New Mexico. The model is also being used by State Extension specialists and in experimental station field plots in several soybean growing states. The farmers use GLYCIM for pre-plant planning decisions like the selection of cultivar/soil type combination, planting date, row spacing and post-plant decisions like irrigation scheduling, harvest timing and yield prediction. The use of the model for crop management decision making and input optimization by the growers is resulting in not only profits to growers but also improvements to environment and ground-water quality. In a recent survey by Mississippi State University, the soybean growers using GLYCIM attributed an increase in soybean yields of up to 29% and irrigation use efficiency of up to 400% to the use of GLYCIM.

Our on-farm experience with GLYCIM in farmers' fields helped us identify several weaknesses in the model during the past 6 years. These weaknesses were in the prediction of soybean phenology, especially floral initiation and anthesis and soybean response to short-term cold injury. A series of experiments were conducted in controlled-environment plant growth chambers and in the field and incorporated new algorithms in the model and improved the models' predictive capability under a range of conditions.

Cotton Production Model 

The Cotton Production Model (CPM) was developed with a modular structure using an object-oriented programming language, C++. The model draws upon the latest scientific knowledge available, and is intended to be used with a wide variety of cotton types across the entire US Cotton Belt.  CPM is written in C++ using a new modular structure that allows flexibility and adaptability.  This object-oriented structure should allow modules to be incorporated into process-based models of other crop species (see Acock, B. and V. R. Reddy. 1977.  Designing an object-oriented structure for crop models. Ecological Modeling 94: 33-44).  In addition to being modular and generic, CPM has other advantages over earlier models.  Compared to previous cotton models, CPM is more robust, more user-friendly, more easily maintained, and more easily updated with future advances in science.  The algorithms that simulate crop growth are derived in part from the best of each of the previous models, and they incorporate new physiological information as well.  A new feature of CPM is that it incorporates 2DSOIL, an excellent up-to-date soil and root process model (see Timlin, D. J., Y. Pachepsky, and B. Acock. 1996. A design for a modular, generic soil simulator to interface with plant models. Agronomy Journal 88:162-169 ).  2DSOIL tracks water movement through the soil-plant-atmosphere continuum with hourly time-steps. It also incorporates a new model of plant water relations that responds realistically to water stress.  CPM has updated treatments of carbon and nitrogen stresses compared to previous models, and it is designed for easy addition of responses to phosphorus and potassium. Because the growth of each leaf, inter-node and fruit is simulated separately, CPM should be easily linked to pest or disease models. 

 CPM has the potential to be useful as a decision aid for cotton farmers and crop production consultants.  If fully developed, it would be a valuable tool to optimize management inputs such as irrigation, fertilization, plant growth regulators, and defoliant application prior to harvest.  In its current version, however, CPM has not yet been fully validated to be useful as a decision aid.  The released version of CPM should be considered an advanced model suitable for research purposes.  ARS does not endorse its use for any other purpose at this time. Of particular importance to a decision aid model is the user interface.  The interface under which CPM has been developed and tested is one that was earlier developed for the soybean model, GLYCIM, and has been documented elsewhere (Acock, B., Pachepsky, Y. A., Mironenko, E. V., Whisler, F. D., and Reddy, V. R. 1999. GUICS: A Generic User Interface for On-Farm Crop Simulations. Agronomy Journal. 91:657-665).  CPM is part of the current release of GUICS and can be obtained from here.
Source code for CPM can be obtained from the USDA technology transfer site.

Potato Model

2DSOIL (described on p. 11) has been interfaced with a potato model (SIMPOTATO) that was developed by Tom Hodges of the ARS. This new model is called 2DSPUD. The process of interfacing the two models was relatively simple. The experience provides a validation of the modular design of 2DSOIL which was developed to interface with crop models. This model is an important contribution because there are no other 2D potato models available that can be used with trickle irrigation and simulate N dynamics in potato. Data were collected for two growing seasons on nitrogen movement, crop yield and soil water content in order to test the 2DSPUD model. The treatments include levels and timing of nitrogen application and irrigation including trickle irrigation. The model predicts the relative treatment effects on yield, water availability and nitrogen transport. The model has been released to farmers for input optimization and farm management after incorporating with GUICS.

A Simple Cantaloupe Phenology Model

South Texas cantaloupe producers have requested assistance in
developing methodologies for predicting crop developmental stages
and final harvest dates.  A simple, cultivar specific, cantaloupe
phenology model that uses standard weather data to predict leaf
appearance, crop developmental stages and final harvest date
is currently under development. The use of this model will allow
cantaloupe producers to accurately predict harvest date as well as
provide a tool for managing crop growth stage dependent applications
of fertilizer, pesticides and irrigation.

Two-dimensional Soil Process Models

Most crops are grown in rows and this introduces spatial variability in soil processes with respect to the row. However, this variability can be exploited to reduce chemical transport to groundwater or improve management of irrigation water. Unless a model can account for variability perpendicular to crop rows as well as vertically into the soil profile it will not be able to fully evaluate all possible management practices that can be used to make agriculture more efficient and less harmful to the environment. To address this concern we developed 2DSOIL, the first comprehensive, modular, two-dimensional soil simulator that can simulate the major physical, chemical and biological processes in soil. Fully implemented, principles of modular modeling facilitate the addition and replacement of modules, as well as the reuse of existing code. The modularity of 2DSOIL has been designed to make it easy to modify the model and to make it easy to incorporate into plant models. 2DSOIL was used to simulate the effect of several water and nitrogen management practices and was incorporated into ARS potato and cotton models, into the Root Zone Water Quality Model, and into the USGS Modular Modeling System.

New Model of Leaf Gas Exchange

Predicting crop production in current and future environmental conditions, and studying the mechanisms of photosynthesis demand a more comprehensive leaf gas exchange model than the empirical models that are currently in use. The 2DLEAF model includes (1) two-dimensional CO2, O2, and water vapor diffusion in the intercellular space schematized according to leaf anatomy, (2) CO2 assimilation by mesophyll cells, and (3) stomatal movements as a regulating factor. The model was used in (1) studies of the effect of leaf anatomy (stomatal aperture, stomatal density, internal leaf structure) on photosynthesis and transpiration of soybean, tomato, cotton, tobacco, and 37 wild plants, (2) estimation of the leaf gas exchange components (diffusion and assimilation) for wild-type and transgenic tobacco leaves, (3) analysis of the leaf gas exchange of eight genotypes of Pima cotton.