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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: Jonghan.Ko@ars.usda.gov. 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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References

REFERENCES

ASCE-EWRI (2005). The ASCE Standardized Reference Evapotranspiration Equation. Environment and Water Resources Institute (EWRI) of ASCE, Standardization of Reference Evapotranspiration Task Committee Final Report. Available online at http://www.kimberly.uidaho.edu/water/asceewri/ascestzdetmain2005.pdf (verified 12 November 2008).Google Scholar
Bell, M. J., Wright, G. C. & Harch, G. R. (1993). Environmental and agronomic effects on the growth of four peanut cultivars in a subtropical environment I. Dry matter accumulation and radiation use efficiency. Experimental Agriculture 29, 473–490.CrossRefGoogle Scholar
Bouzaher, A., Shogren, J. F., Holtkamp, D., Gassman, P., Archer, D., Lakshminarayan, P., Carriquiry, A. L. & Reese, R. (1993). Agricultural Policies and Soil Degradation in Western Canada: An Agro-ecological Economic Assessment (Report 2: The environmental Modeling System). Technical Report 3/93. Ottawa, ON, Canada: Agricultural Canada, Policy Branch.Google Scholar
Cabelguenne, M., Jones, C. A., Marty, J. R., Dyke, P. T. & Williams, J. R. (1990). Calibration and validation of EPIC for crop rotation in southern France. Agricultural Systems 33, 153–171.CrossRefGoogle Scholar
Charles-Edwards, D. A., Doley, D. & Rimmington, G. M. (1986). Modelling Plant Growth and Development. Sydney: Academic Press.Google Scholar
Foale, M. A., Wilson, G. L., Coates, D. B. & Haydock, K. P. (1984). Growth and productivity of irrigated Sorghum bicolor (L. Moench) in Northern Australia. II. Low solar altitude as a possible seasonal constraint to productivity in the tropical dry season. Australian Journal of Agricultural and Resource Economics 35, 229–238.Google Scholar
Gallagher, J. N. & Biscoe, P. V. (1978). Radiation absorption, growth and yield of cereals. Journal of Agricultural Science, Cambridge 91, 47–60.CrossRefGoogle Scholar
Hargreaves, G. H. & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied Engineers in Agriculture 1, 96–99.CrossRefGoogle Scholar
Howell, T. A. & Musick, J. T. (1985). Relationships of dry matter production of field crops to water consumption. In Les Besoins en équ des Cultures. Conférence Internationale, Paris, 11–14 September 1984. pp. 247–269. Paris: INRA.Google Scholar
Howell, T. A., Meek, D. W. & Hatfield, J. L. (1983). Relationship of photosynthetically active radiation to shortwave radiation in the San Joaquin valley. Agricultural Meteorology 28, 157–175.CrossRefGoogle Scholar
Huang, M., Gallichand, J., Dang, T. & Shao, M. (2006). An evaluation of EPIC soil water and yield components in the gully region of Loess Plateau, China. Journal of Agricultural Science, Cambridge 144, 339–348.CrossRefGoogle Scholar
Huschke, R. E. (1959). Glossary of Meteorology. Boston, MA: American Meteorology Society.Google Scholar
Jackson, B. S. & Hearn, G. F. (1990). COTTAM: A Cotton Plant Simulation Model for an IBM PC Microcomputer. Texas Agricultural Experiment Station. Miscellaneous Publication MP-1685.Google Scholar
Kiniry, J. R., Jones, C. A., O'Toole, J. C., Blanchet, R., Cabelguenne, M. & Spanel, D. A. (1989). Radiation-use efficiency in biomass accumulation prior to grain filling for five grain-crop species. Field Crops Research 20, 51–64.CrossRefGoogle Scholar
Kiniry, J. R., Major, D. J., Izaurralde, R. C., Williams, J. R., Gassman, P. W., Morrison, M., Bergentine, R. & Zentner, R. P. (1995). EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Canadian Journal of Plant Science 75, 679–688.CrossRefGoogle Scholar
Kiniry, J. R., Tischler, C. R. & Van Esbroeck, G. A. (1999). Radiation use efficiency and leaf CO2 exchange for diverse C4 grasses. Biomass and Bioenergy 17, 95–112.CrossRefGoogle Scholar
Kiniry, J. R., Simpson, C. E., Schubert, A. M. & Reed, J. D. (2005). Peanut leaf area index, light interception, radiation use efficiency, and harvest index at three sites in Texas. Field Crops Research 91, 297–306.CrossRefGoogle Scholar
Ko, J., Maas, S. J., Lascano, R. J. & Wanjura, D. (2005). Modification of the GRAMI model for cotton. Agronomy Journal 97, 1374–1379.CrossRefGoogle Scholar
Ko, J., Piccinni, G., Steglich, E., Gerik, T. J., Marek, T., Howell, T. & Kemanian, A. (2007). Using EPIC simulation model to manage irrigated crops. ASA-CSSA-SSA 2007 International Annual Meetings, New Orleans, LA, November4–8. CD-ROM.Google Scholar
Maas, S. J. (1993). Parameterized model of gramineous crop growth: I. leaf area and dry mass simulation. Agronomy Journal 85, 348–353.CrossRefGoogle Scholar
Martin, S. M., Nearing, M. A. & Bruce, R. R. (1993). An evaluation of the EPIC model for soybeans grown in Southern Piedmont soils. Transactions of the American Society of Agricultural Engineers 36, 1327–1331.CrossRefGoogle Scholar
Meek, D. W., Hatfield, J. L., Howell, T. A., Idso, S. B. & Reginato, R. J. (1984). A generalized relationship between photosynthetically active radiation and solar radiation. Agronomy Journal 76, 939–945.CrossRefGoogle Scholar
Milroy, S. P. & Bange, M. P. (2003). Nitrogen and light responses of cotton photosynthesis and implications for crop growth. Crop Science 43, 904–913.CrossRefGoogle Scholar
Monsi, M. & Saeki, T. (1953). Über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion (On the factor light in plant communities and its importance for matter production). Japanese Journal of Botany 14, 22–52.Google Scholar
Monteith, J. L. (1965). Evaporation and the environment. In The State and Movement of Water in Living Organisms, XIX Symposium, Society for Experimental Biology, Swansea, pp. 205–234. Cambridge: Cambridge University Press.Google Scholar
Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London B 281, 277–294.Google Scholar
Morgan, C. L. S., Norman, J. M. & Lowery, B. (2003). Estimating plant-available water across a field with an inverse yield model. Soil Science Society of America Journal 67, 620–629.Google Scholar
Rosenberg, N. J., Blad, B. L. & Verma, S. B. (1983). Microclimate: The Biological Environment, 2nd edn.New York: John Wiley and Sons.Google Scholar
Rosenthal, W. D. & Gerik, T. J. (1991). Radiation use efficiency among cotton cultivars. Agronomy Journal 83, 655–658.CrossRefGoogle Scholar
Rosenthal, W. D., Gerik, T. J. & Wade, L. J. (1993). Radiation-use efficiency among grain sorghum cultivars and plant densities. Agronomy Journal 85, 703–705.CrossRefGoogle Scholar
Sinclair, T. R. & Horie, T. (1989). Leaf nitrogen, photosynthesis, and crop radiation use efficiency: a review. Crop Science 29, 90–98.CrossRefGoogle Scholar
Thornley, J. H. M. (1976). Mathematical Models in Plant Physiology. London: Academy Press.Google Scholar
Tollenaar, M. & Bruusema, T. W. (1988). Efficiency of maize dry matter production during periods of complete leaf area expansion. Agronomy Journal 80, 580–585.CrossRefGoogle Scholar
Wang, X., Williams, J. R., Izaurralde, R. C. & Atwood, J. D. (2004). Sensitivity and uncertainty analyses of crop yields and soil organic carbon simulated with EPIC. Transactions of the American Society of Agricultural Engineers 48, 1041–1054.CrossRefGoogle Scholar
Williams, J. R., Jones, C. A. & Dyke, P. T. (1984). A modelling approach to determining the relationship between erosion and soil productivity. Transactions of the American Society of Agricultural Engineers 27, 129–144.CrossRefGoogle Scholar
Williams, J. R., Jones, C. A., Kiniry, J. R. & Spanel, D. A. (1989). The EPIC crop growth model. Transactions of the American Society of Agricultural Engineers 32, 497–511.CrossRefGoogle Scholar