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Parameterization of EPIC crop model for simulation of cotton growth in South Texas

Published online by Cambridge University Press:  15 January 2009

J. KO ,
G. PICCINNI ,
W. GUO  and
E. STEGLICH
J. KO*
Affiliation:
United States Department of Agriculture–Agricultural Research Service, Agricultural Systems Research Unit, 2150 Centre Avenue, Building D, Suite 200, Fort Collins, CO 80526, USA
G. PICCINNI
Affiliation:
Monsanto Company, 700 Chesterfield Parkway West, Chesterfield, MO 63017, USA
W. GUO
Affiliation:
South Plains Precision Ag, 2810 N. Quincy, Plainview, TX 79072, USA
E. STEGLICH
Affiliation:
Blackland Research and Extension Center, Texas A&M University, 720 East Blackland Road, Temple, TX 760502, USA
*
*To whom all correspondence should be addressed. Email: [email protected]. Previously: Texas AgriLife Research and Extension Center, Texas A&M University, 1619 Garner Field Road, Uvalde, TX 78801, USA.
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Summary

Parameterization in crop simulation modelling is a general procedure to calibrate a crop model to explore the best fit for a certain regional environment of interest. The parameters of radiation use efficiency (RUE) and light interception coefficient (k) of cotton (Gossypium hirsutum) for different cultivars were estimated under various irrigation conditions in South Texas in 2006 and 2007. A calibration procedure was then performed for determination of RUE using the environmental policy impact calculator (EPIC) crop model (Williams et al.1984). This was carried out using data sets obtained separately from the data for parameter estimation. The estimates of k and RUE were 0·63 and 2·5 g/MJ, respectively, which were determined based on the field experiment and variation of simulated lint yield. When the parameters were used with EPIC to simulate the variability in lint yields, a correlation coefficient of 0·86 and root mean square error (RMSE) of 0·22 t/ha were obtained, presenting no significant differences (paired t-test: P=0·282) between simulation and measurement. The results demonstrate that an appropriate estimate of the model parameters including RUE is essential in order to make crop models reproduce field conditions properly in simulating crop growth, yield and other variables.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 147 , Issue 2 , April 2009 , pp. 169 - 178
Copyright
Copyright © 2009 Cambridge University Press

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