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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

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

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.

Type
Weed Biology and Ecology
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: 9199.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: 471477.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: 724729.Google Scholar
Burnside, O. C. 1993. Weed science—the step child. Weed Technol. 7: 515518.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: 392407.Google Scholar
Dowler, C. C. 1994. Weed survey—southern states. Proc. South. Weed Sci. Soc. 47: 270299.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: 235244.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agron. J. 85: 929933.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: 6468.Google Scholar
Gilmore, E. C. and Rogers, J. S. 1958. Heat units as a method of measuring maturity in corn. Agron. J. 50: 611615.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: 266272.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: 257265.Google Scholar
Katz, Y. H. 1952. The relationship between heat unit accumulation and the planting and harvesting of canning peas. Agron. J. 44: 7478.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: 242248.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: 719731.CrossRefGoogle Scholar
Sharpe, P.J.H. and DeMichele, D. W. 1977. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64: 649670.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: 698704.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: 208225.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: 475487.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: 691704.Google Scholar
Wang, J. Y. 1960. A critique of the heat unit approach to plant response studies. Ecology 41: 785790.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: 302307.Google Scholar
Wyse, D. L. 1991. Future of weed science research. Weed Sci. Soc. Am. Abstr. 31: 88.Google Scholar