Cotton (Gossypium hirsutum and Gossypium barbadense) is an important commodity crop globally, providing sources of fiber, feed, food, and potentially fuel for diverse industries. The original DSSAT systems did not include a model for any fiber crops. Because of the importance of cotton in the southeastern United States, especially as part of common rotations with peanut (Arachis hypogaea), there was a need for the development of a comprehensive cotton model. Rather than developing a new set of code, the decision was made to use the CROPGRO module as a template. The emphasis was to obtain detailed physiological information to define the functions and parameters for the species file and experimental data for initial model calibration and evaluation. Because of the existing infrastructure of DSSAT, the cotton model could easily be added to DSSAT without creating different utilities for data input and application programs. A comprehensive review of CSM-CROPGRO-Cotton, in addition to other cotton simulation models, was recently published by Thorp et al. (2014a).
Evaluations of CSM-CROPGRO-Cotton have been conducted using field data from several of the primary cotton production regions in the U.S., including the southeast humid region (Garcia y Garcia et al., 2010; Paz et al., 2012), semi-arid west Texas (Modala et al., 2015), and central Arizona (Thorp et al., 2014b). The model has been applied to assess a variety of issues related to U.S. cotton production: impacts of climate variability and change (Adhikari et al., 2016; Garcia y Garcia et al., 2010; Paz et al., 2012), assessments of cotton water use and irrigation management strategies (Guerra et al., 2007; Modala et al., 2015; Thorp et al., 2015; 2017), and effects of elevation on dryland cotton yield (Mauget et al., 2017). Cammarano et al. (2012) used CSM-CROPGRO-Cotton to assess irrigation management strategies for Australian cotton production.
References
Adhikari, P., Ale, S., Bordovsky, J. P., Thorp, K. R., Modala, N. R., Rajan, N., Barnes, E. M., 2016. Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model. Agricultural Water Management 164, 317–330.
Cammarano, D., Payero, J., Basso, B., Wilkens, P., Grace, P., 2012. Agronomic and economic evaluation of irrigation strategies on cotton lint yield in Australia. Crop & Pasture Science 63, 647-655.
Garcia y Garcia, A., Persson, T., Paz, J. O., Fraisse, C., Hoogenboom, G., 2010. ENSO-based climate variability affects water use efficiency of rainfed cotton grown in the southeastern USA. Agriculture, Ecosystems and Environment 139, 629-635.
Guerra, L. C., Garcia y Garcia, A., Hook, J. E., Harrison, K. A., Thomas, D. L., Stooksbury, D. E., Hoogenboom, G., 2007. Irrigation water use estimates based on crop simulation models and kriging. Agricultural Water Management 89 (3), 199-207.
Mauget, S. A., Adhikari, P., Leiker, G., Baumhardt, R. L., Thorp, K. R. , Ale, S., 2017. Modeling the effects of management and elevation on West Texas dryland cotton production. Agricultural and Forest Meteorology 247, 385–398.
Modala, N. R., Ale, S., Rajan, N., Munster, C. L., DeLaune, P. B., Thorp, K. R. , Nair, S. S., Barnes, E. M., 2015. Evaluation of the CSM-CROPGRO-Cotton model for the Texas Rolling Plains region and simulation of deficit irrigation strategies for increasing water use efficiency. Transactions of the ASABE 58 (3), 685–696.
