Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-19T01:20:22.992Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling weed emergence as influenced by burial depth using the Fermi-Dirac distribution function

Published online by Cambridge University Press:  12 June 2017

Eric P. Prostko ,
Hsin-I Wu ,
James M. Chandler  and
Scott A. Senseman
Hsin-I Wu
Affiliation:
Center for Biosystems Modeling, Industrial Engineering Department, Texas A&M University, College Station, TX 77843-2474
James M. Chandler
Affiliation:
Department of Soil and Crop Sciences, Texas Agricultural Experiment Station, Texas A&M University, College Station, TX 77843-2474
Scott A. Senseman
Affiliation:
Department of Soil and Crop Sciences, Texas Agricultural Experiment Station, Texas A&M University, College Station, TX 77843-2474
Get access Rights & Permissions [Opens in a new window]

Abstract

Research was conducted to determine the suitability of the Fermi-Dirac distribution function for modeling the seedling emergence of downy brome, johnsongrass, and round-leaved mallow, as influenced by burial depth. Six sets of previously published emergence data were used to formulate the model and test its adequacy. Two independent johnsongrass emergence data sets were used to validate the model. Constant temperature growth chamber studies were conducted to evaluate the effects of temperature and moisture on the model parameters. The Fermi-Dirac distribution function was found to adequately describe the seedling emergence of downy brome, johnsongrass, and round-leaved mallow as indicated by a good visual data fit, narrow confidence intervals for the model parameters, and regression analysis of observed vs. modeled data. Although this function is a model used in physical science, its parameters can be related to abiotic factors such as soil texture, temperature, and moisture.

Keywords

Downy brome, Bromus tectorum L. BROTEjohnsongrass, Sorghum halepense (L.) Pers. SORHAround-leaved mallow, Malva pusilla SmEmpirical modelmechanistic modelburial depthsoil textureBROTESORHA
Type
Weed Biology and Ecology
Information
Weed Science , Volume 45 , Issue 2 , April 1997 , pp. 242 - 248
Copyright
Copyright © 1997 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Alm, D. M., Stoller, E. W., and Wax, L. M. 1993. An index model for predicting seed germination and emergence rates. Weed Technol. 7: 560–569.Google Scholar
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. Amer. Abst. 36: 91.Google Scholar
Blackshaw, R. E. 1990. Influence of soil temperature, soil moisture, and seed burial depth on the emergence of round-leaved mallow (Malva pusilla). Weed Sci. 38: 518–521.Google Scholar
Burnside, O. C. 1993. Weed science-the step child. Weed Technol. 7: 515–518.Google Scholar
Chandler, J. M. 1977. Competition of spurred anoda, velvetleaf, prickly sida, and venice mallow in cotton. Weed Sci. 25: 151–158.Google Scholar
Duke, S. O. 1985. The physiology of weed seed dormancy and germination. In Duke, S. O. Weed Physiology: Reproduction and Ecophysiology, Volume I. Boca Raton, FL: CRC Press, p. 44.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
Eaton, B. J., Russ, O. G., and Feltner, K. C. 1976. Competition of velvetleaf, prickly sida, and venice mallow in soybeans. Weed Sci. 24: 224–228.Google Scholar
Egley, G. H. 1983. Weed seed and seedling reductions by soil solarization with transparent polyethylene sheets. Weed Sci. 31: 404–409.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agron. J. 85: 929–933.Google Scholar
Holshouser, D. L. 1993. Modeling the phenological development of johnsongrass [Sorghum halepense (L.) Pers.] populations. Ph.D. dissertation. Texas A&M University, College Station, Tx. 133 p.Google Scholar
King, C. A. and Oliver, L. R. 1994. A model for predicting large crabgrass (Digitaria sanquinalis) emergence as influenced by temperature and water potential. Weed Sci. 42: 561–567.Google Scholar
Kropff, M. J. 1988. Modelling the effects of weeds on crop production. Weed Res. 28: 465–471.Google Scholar
McWhorter, C. G. 1972. Factors affecting johnsongrass thizome production and germination. Weed Sci. 20: 41–45.Google Scholar
Oliver, L. R. 1979. Influence of soybean (Glycine max) planting date on velvetleaf (Abutilon theophrasti) competition. Weed Sci. 27: 183–188.Google Scholar
Olson, R. L., Sharpe, P.J.H., and Wu, H. 1985. Whole-plant modelling: a continuous-time Markov (CTM) approach. Ecol. Modelling 29: 171–187.Google Scholar
[SAS] Statistical Analysis Systems. 1986. SAS System for Regression. Cary, NC: Statistical Analysis Systems Institute. 164 pp.Google Scholar
Sears, F. W. 1950. The Fermi-Dirac statistics. In Sears, F. W. An Introduction to Thermodynamics: The Kinetic Theory of Gases, and Statistical Mechanics. Reading, MA: Addison-Wesley, pp. 325–332.Google Scholar
Sharpe, P.J.H. and DeMichele, D. W. 1977. Reaction kinetics of poikilotherm development. J. Theo. Biol. 64: 649–670.CrossRefGoogle ScholarPubMed
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
Wicks, G. A., Burnside, O. C., and Fenster, C. R. 1971. Influence of soil type and planting depth on downy brome seed. Weed Sci. 19: 82–86.Google Scholar
Wilen, C. A. and Holt, J. S. 1995. Prediction of yellow nutsedge (Cyperus esculentus L.) emergence using a degree day model. Weed Sci. Soc. Amer. Abst. 35: 52.Google Scholar