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Modeling seedling johnsongrass (Sorghum halepense) emergence as influenced by temperature and burial depth

Published online by Cambridge University Press:  12 June 2017

Eric P. Prostko ,
Hsini-I Wu  and
J. Michael Chandler
Hsini-I Wu
Affiliation:
Department of Biological/Industrial Engineering, Texas A&M University, College Station, TX 77843-3131
J. Michael Chandler
Affiliation:
Department of Soil & Crop Sciences, Texas A&M University, College Station, TX 77843-2474
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Abstract

Research was conducted to formulate a seedling johnsongrass emergence model as influenced by temperature and burial depth using the poikilotherm rate equation. A series of constant-temperature growth chamber experiments with johnsongrass seed buried at various depths in fritted clay was conducted to develop a temperature/burial emergence database. The poikilotherm rate equation was fit to the emergence data from burial depths of 0 to 2.5 cm at constant temperatures between 20 and 44 C. These data were then combined to formulate a single poikilotherm rate equation to model the emergence of seedling johnsongrass from 0 and 2.5 cm deep and 20 to 44 C. This combined model was validated against two independent emergence data sets with good results.

Keywords

Johnsongrass, Sorghum halepense (L.) Pers. SORHAMechanistic modelpoikilotherm rate equation
Type
Weed Biology and Ecology
Information
Weed Science , Volume 46 , Issue 5 , October 1998 , pp. 549 - 554
Copyright
Copyright © 1998 by the Weed Science Society of America 

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References

Literature Cited

Benech Arnold, R. L., Ghersa, C. M., Sanchez, R. A., and Insausti, P. 1990. A mathematical model to predict Sorghum halepense (L.) Pers. seedling emergence in relation to soil temperature. Weed Res. 30: 91–99.Google Scholar
Bhowmik, P. C. and Norris, R. F. 1996. Weed biology: survey and importance to weed management. Weed Sci. Soc. Am. Abstr. 36: 91.Google Scholar
Bridges, D. C. and Chandler, J. M. 1989. A population level temperature-dependent model of seedling johnsongrass (Sorghum halepense) flowering. Weed Sci. 37: 471–477.Google Scholar
Bridges, D. C., Wu, H., Sharpe, P.J.H., and Chandler, J. M. 1989. Modeling distributions of crop and weed seed germination time. Weed Sci. 37: 724–729.Google Scholar
Burnside, O. C. 1993. Weed science—the step child. Weed Technol. 7: 515–518.CrossRefGoogle Scholar
Castro-Martinez, E. 1979. Aspectos de la reproduccion del zacate johnson [Sorghum halepense (L.) Pers.] y su control quimico. M.S. thesis. ITESM, Mexico, 77 p.Google Scholar
de Candolle, A. P. 1855. Geographie botanique, raisonee. Paris: Masson. 606 p.Google Scholar
Dowler, C. C. 1992. Weed survey—southern states. Proc. South. Weed Sci. Soc. 45: 392–407.Google Scholar
Dowler, C. C. 1994. Weed survey—southern states. Proc. South. Weed Sci. Soc. 47: 270–299.Google Scholar
Durar, A. A., Steiner, J. L., Evett, S. R., and Skidmore, E. L. 1995. Measured and simulated surface soil drying. Agron. J. 87: 235–244.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agron. J. 85: 929–933.Google Scholar
Fritz, J. O., Vanderlip, R. L., Heiniger, R. W., and Abelhalim, A. Z. 1997. Simulating forage sorghum yields with SORKAM. Agton. J. 89: 64–68.Google Scholar
Gilmore, E. C. and Rogers, J. S. 1958. Heat units as a method of measuring maturity in corn. Agron. J. 50: 611–615.CrossRefGoogle Scholar
Holshouser, D. L. 1993. Modeling the phenological development of johnsongrass [Sorghum halepense (L.) Pers.] populations. . Texas A&M University, College Station, TX. 133 p.Google Scholar
Holshouser, D. L. and Chandler, J. M. 1996. Predicting flowering of rhizome johnsongrass (Sorghum halepense) populations using a temperature-dependent model. Weed Sci. 44: 266–272.CrossRefGoogle Scholar
Holshouser, D. L., Chandler, J. M., and Wu, H. 1996. Temperature-dependent model for non-dormant seed germination and rhizome bud break of johnsongrass (Sorghum halepense) . Weed Sci. 44: 257–265.Google Scholar
Katz, Y. H. 1952. The relationship between heat unit accumulation and the planting and harvesting of canning peas. Agron. J. 44: 74–78.CrossRefGoogle Scholar
Prostko, E. P., Wu, H., Chandler, J. M., and Senseman, S. A. 1997. Modeling weed emergence as influenced by burial depth using the Fermi-Dirac distribution function. Weed Sci. 45: 242–248.CrossRefGoogle Scholar
Rèamur, R.A.F. 1735. Thermometric observations made at Paris during the year 1735, compared to those made below the equator on the Isle of Mauritius, in Algiers, and on a few of our American islands. Paris Mem., Acad. Sci. 1735: 545. [In French]Google Scholar
[SAS] Statistical Analysis Systems. 1986. SAS System for Regression. Cary, NC: Statistical Analysis Systems Institute. 164 pp.Google Scholar
Schoolfield, R. M., Sharpe, P.J.H., and Magnuson, C. E. 1981. Non-linear regression of biological temperature dependent rate models based upon absolute reaction rate theory models. J. Theor. Biol. 88: 719–731.CrossRefGoogle Scholar
Sharpe, P.J.H. and DeMichele, D. W. 1977. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64: 649–670.Google Scholar
Shaw, W. C. 1983. The ARS national research program. in Hilton, J. I., ed. BARC Symposium 8. Totowa, NJ: Rowman and Allenheld.Google Scholar
Sinclair, T. R. and Seligman, N. G. 1996. Crop modeling: from infancy to maturity. Agron. J. 88: 698–704.CrossRefGoogle Scholar
Wagner, T. L., Wu, H., Sharpe, P.J.H., Schoolfield, R. M., and Coulson, R. N. 1984a. Modeling insect development rates: a literature review and application of a biophysical model. Ann. Entomol. Soc. Am. 77: 208–225.CrossRefGoogle Scholar
Wagner, T. L., Wu, H., Sharpe, P.J.H., and Coulson, R. N. 1984b. Modeling distributions of insect development time: a literature review and applications of the Weibull function. Ann. Entomol. Soc. Am. 77: 475–487.Google Scholar
Wagner, T. L., Wu, H., Feldman, R. M., Sharpe, P.J.H., and Coulson, R. N. 1985. Multiple-cohort approach for simulating development of insect populations under variable temperature. Ann. Entomol. Soc. Am. 78: 691–704.Google Scholar
Wang, J. Y. 1960. A critique of the heat unit approach to plant response studies. Ecology 41: 785–790.Google Scholar
Weaver, S. E., Kropff, M. J., and Groeneveld, R.M.W. 1992. Use of ecophysiological models for crop–weed interference: the critical period of weed interference. Weed Sci. 40: 302–307.Google Scholar
Wyse, D. L. 1991. Future of weed science research. Weed Sci. Soc. Am. Abstr. 31: 88.Google Scholar