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CPM - Cotton Production Model

Metadata Updated: February 4, 2022

A new process-based cotton model, CPM, has been developed to simulate the growth and development of upland cotton (Gossypium hirsutum L.) throughout the growing season with minimal data input. CPM predicts final cotton yield for any combination of soil, weather, cultivar and sequence of management actions. Over the last 30 years, the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) has conducted a wide range of research on cotton, including work to develop a series of "production models" designed to serve as decision aids to cotton producers. In 1996, ARS decided to develop a new "second generation" Cotton Production Model (CPM) that would retain the best features of the earlier versions in a new, more versatile, and more user friendly framework. The development process was completed to the stage of beta-testing, when the need to redirect limited resources to other priorities caused ARS to decide not to complete the validation process. ARS believes that CPM, while only partially validated, has the potential to make useful contributions to American cotton producers when completed. For these reasons, ARS decided to make the model available for further development and commercialization. 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.

Access & Use Information

Public: This dataset is intended for public access and use. License: Creative Commons CCZero

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Metadata Created Date November 10, 2020
Metadata Updated Date February 4, 2022

Metadata Source

Harvested from USDA JSON

Additional Metadata

Resource Type Dataset
Metadata Created Date November 10, 2020
Metadata Updated Date February 4, 2022
Publisher Agricultural Research Service
Identifier bea22828-7c82-452b-916d-8cae34a5df77
Data Last Modified 2022-01-11
Public Access Level public
Bureau Code 005:18
Metadata Context
Schema Version
Catalog Describedby
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Harvest Source Id d3fafa34-0cb9-48f1-ab1d-5b5fdc783806
Harvest Source Title USDA JSON
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Program Code 005:040
Source Datajson Identifier True
Source Hash 0dd1eb1297266ca41082f1e3ba4d8715a29a2972
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