Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-27T01:50:44.342Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling germination and seedling elongation of common lambsquarters (Chenopodium album)

Published online by Cambridge University Press:  12 June 2017

Erivelton S. Roman ,
A. Gordon Thomas ,
Stephen D. Murphy  and
Clarence J. Swanton
Erivelton S. Roman
Affiliation:
Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada N1G 2W1
A. Gordon Thomas
Affiliation:
Agriculture and Agri-Food Canada, Saskatoon Research Centre, Saskatoon, SK, Canada S7N 0X2
Stephen D. Murphy
Affiliation:
Department of Environmental and Resource Studies, University of Waterloo, Waterloo, ON, Canada N2L 3G1
Get access Rights & Permissions [Opens in a new window]

Extract

The ability to predict time of weed seedling emergence relative to the crop is an important component of a mechanistic model describing weed and crop competition. In this paper, we hypothesized that the process of germination could be described by the interaction of temperature and water potential and that the rate of seedling shoot and radicle elongation vary as a function of temperature. To test these hypotheses, incubator studies were conducted using seeds and seedlings of common lambsquarters. Probit analysis was used to account for variation in cardinal temperatures and base water potentials and to develop parameters for a new mathematical model that describes seed germination and shoot and radicle elongation in terms of hydrothermal time and temperature, respectively. This hydrothermal time model describes the phenology of seed germination using a single curve, generated from the relationship of temperature and water potential.

Keywords

Common lambsquarters, Chenopodium album L. CHEAL.Base temperature, base water potential, cardinal temperatures, Weibull function, hydrothermal time, ecophysiological models, integrated weed management, CHEAL.
Type
Weed Biology and Ecology
Information
Weed Science , Volume 47 , Issue 2 , April 1999 , pp. 149 - 155
Copyright
Copyright © 1999 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

Addae, P. C. and Pearson, C. J. 1992, Thermal requirements for germination and seedling growth of wheat. Aust. J. Agric. Res. 43: 585–594.Google Scholar
Alm, D. M., Stoller, E. W., and Wax, L. M. 1993. An index for predicting seed germination and emergence rates. Weed Technol. 7: 560–569.Google Scholar
Angus, J. F., Cunningham, R. B., Moncur, M. W., and Mackenzie, D. H. 1981. Phasic development in field crops. I. Thermal response in the seedling phase. Field Crops Res. 3: 365–378.Google Scholar
Bassett, I. J. and Crompton, C. W. 1978. The biology of Canadian weeds. 32. Chenopodium album L. Can. J. Plant Sci. 58: 1061–1072.Google Scholar
Bethea, R. M., Duran, S., and Boullion, T. L. 1995. Statistical Methods for Engineers and Scientists. New York: Marcel-Dekker, pp. 305–362.Google Scholar
Blacklow, W. M. 1972. Influence of temperature on germination and elongation of the radicle and shoot of corn. Crop Sci. 12: 647–650.Google Scholar
Bradford, K. J. 1990. A water relations analysis of seed germination rates. Plant Physiol. 94: 840–849.Google Scholar
Carberry, P. S. and Campbell, L. C. 1989. Temperature parameters useful for modeling the germination and emergence of pearl millet. Crop Sci. 29: 220–223.Google Scholar
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 43: 375–380.Google Scholar
Christensen, M., Meyer, S. F., and Allen, P. S. 1996. A hydrothermal time model of seed after-ripening in Bromus tectorum L. Seed Sci. Res. 6: 155–163.Google Scholar
Collet, D. 1994. Modelling Survival Data in Medical Research. New York: Chapman and Hall, pp. 71–78.Google Scholar
Dahal, P. and Bradford, K. J. 1994. Hydrothermal time analysis on tomato seed germination at suboptimal temperature and reduced water potential. Seed Sci. Res. 4: 71–80.Google Scholar
Dew, D. A. 1972. Index of competition for estimating crop loss due to weeds. Can. J. Plant Sci. 52: 921–927.Google Scholar
Dieleman, A., Hamil, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models for pigweed (Amaranthus spp.) interference on soybean (plycine mux). Weed Res. 43: 612–618.Google Scholar
Dracup, M., Davis, C., and Tapscott, H. 1993. Temperature and water requirements for germination and emergence of lupin. Aust. J. Exp. Agric. 33: 759–766.Google Scholar
Dumur, D., Pilbeam, C. J., and Craigon, J. 1990. Use of the Weibull function to calculate cardinal temperatures in faba bean. J. Exp. Bot. 41: 1423–1430.Google Scholar
Ellis, R. H. and Barret, S. 1994. Alternating temperatures and rate of seed germination in lentil. Ann. Bot. 74: 519–524.Google Scholar
Ellis, R. H., Covell, S., Roberts, E. H., and Summerfield, R. J. 1986. The influence of temperature on seed germination rate in grain legumes. II. Intraspecific variation in chickpea (Cicer arietinum L.) at constant temperatures. J. Exp. Bot. 37: 1503–1515.Google Scholar
Ellis, R. H., Hong, T. D., and Roberts, E. H. 1985. Handbook of Seed Technology for Genebanks. Volume I. Principles and Methodology. Rome: International Board for Plant Genetic Resources, pp. 42–53.Google Scholar
Ellis, R. H., Simon, G., and Covell, S. 1987. The influence of temperature on seed germination rate in grain legumes. III. A comparative of five faba bean genotypes at constant temperatures using a new screening method. J. Exp. Bot. 38: 1033–1043.Google Scholar
Fernandez-Quintanilla, C., Andujar, J. L., and Appleby, A. P. 1990. Characterization of the germination and emergence response to temperature and soil moisture of Avena fatua and A. sterilis . Weed Res. 30: 289–295.Google Scholar
Forcella, F. 1992. Prediction of weed seedlings densities from buried seed reserves. Weed Res. 32: 29–38.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agron. J. 85: 929–933.Google Scholar
Forcella, F., Eradat-Oskoui, K., and Wagner, S. W. 1993. Application of weed seedbank ecology to low-input management. Ecol. Appl. 3: 79–83.Google Scholar
Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Alm, D. A., Buhler, D. D., and Cardina, J. 1992. Weed seedbank of the U.S. Corn Belt: magnitude, variation, emergence and application. Weed Sci. 40: 636–644.Google Scholar
Frick, B. and Thomas, A. G. 1992. Weed surveys in different tillage systems in southwestern Ontario field crops. Can. J. Plant Sci. 72: 1337–1347.Google Scholar
Fyfield, T. P. and Gregory, P. J. 1989. Effects of temperature and water potential on germination, radicle elongation and emergence of mungbean. J. Exp. Bot. 40: 667–674.Google Scholar
Garcia-Huidobro, J., Monteith, J. L., and Squire, G. R. 1982. Time, temperature and germination of pearl millet (Pennisetum typhoides S. &H.). I. Constant temperature. J. Exp. Bot. 33: 288–296.Google Scholar
Ghersa, C. M. and Holt, J. S. 1996. Using phenology prediction in weed management: a review. Weed Res. 35: 461–470.Google Scholar
Gummerson, R. J. 1986. The effect of constant temperature and osmotic potentials on the germination of sugar beet. J. Exp. Bot. 37: 729–741.Google Scholar
Hagood, E. S. Jr., Bauman, T. T., Williams, J. L. Jr., and Schreiber, M. M. 1981. Growth analysis of soybeans (Glycine max) in competition with jimsonweed (Datura stramonium). Weed Sci. 29: 500–504.Google Scholar
Hardegree, S. P. and Emmerich, W. E. 1994. Seed germination response to polyethylene glycol solution depth. Seed Sci. Technol. 22: 1–7.Google Scholar
Harvey, S. J. and Forcella, F. 1993. Vernal seedling emergence model for common lambsquarters (Chenopodium album). Weed Sci. 41: 309–316.Google Scholar
Hatfield, J. L. and Egli, D. B. 1974. Effect of temperature on the rate of soybean hypocotyl elongation and field emergence. Crop Sci. 14: 423–426.Google Scholar
Hsu, F. H., Nelson, C. J., and Chow, W. S. 1984. A mathematical model to utilize the logistic function in germination and seedling growth. J. Exp. Bot. 35: 1629–1640.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42: 568–573.Google Scholar
Michel, B. E. 1983. Evaluation of water potentials of solutions of polyethylene glycol 8000 both in the absence and presence of other solutes. Plant Physiol. 72: 66–70.Google Scholar
Murdoch, A. J., Roberts, E. H., and Goedert, C. O. 1989. A model for germination responses to alternating temperatures. Ann. Bot. 63: 97–111.Google Scholar
Ni, B. R. and Bradford, K. J. 1992. Quantitative models characterizing seed germination responses to abcissic acid and osmoticum. Plant Physiol. 98: 1057–1068.Google Scholar
O'Donovan, J. T., de St. Remy, E. A., O'Sullivan, P. A., Dew, D. A., and Sharma, A. K. 1985. Influence of the relative time of emergence of wild oat (Avena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aestivum). Weed Sci. 33: 498–503.Google Scholar
Oryokot, J.O.E., Murphy, S. D., Thomas, A. G., and Swanton, C. J. 1997. Temperature- and moisture-dependent models of seed germination and shoot elongation in green and redroot pigweed (Amaranthus powellii, A. retroflexus). Weed Sci. 45: 488–496.Google Scholar
Ralston, M. L. and Jennrich, R. I. 1978. DUD, a derivative-free algorithm for non-linear least squares. Technometrics 20: 7–14.Google Scholar
Sokal, R. R. and Rohlf, F. J. 1981. Biometry. The Principles and Practices of Statistics in Biological Research. 2nd ed. San Francisco, CA: W. H. Freeman, pp. 496–499.Google Scholar
[SAS] Statistical Analysis Systems. 1990. SAS User's Guide. Version 6.06. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Swanton, C. J. and Murphy, S. D. 1996. Weed science beyond the weeds: the role of integrated weed management (IWM) in agroecosystem health. Weed Sci. 44: 437–445.Google Scholar
Thomas, A. G., Lefkovitch, L. P., Woo, S. L., Bowes, G. G., and Peschken, D. P. 1994. Effect of temperature on germination within and between diploid and tetraploid populations of Matricaria perforata Metal. Weed Res. 34: 187–198.Google Scholar
Tollenaar, M., Daynard, T. B., and Hunter, R. B. 1979. Effect of temperature on rate of leaf appearance and flowering date in maize. Crop Sci. 19: 363–366.Google Scholar
Vleeshouwers, L. M. 1997. Modelling the effect of temperature, soil penetration resistance, burial depth and seed weight on pre-emergence growth of weeds. Ann. Bot. 79: 553–563.Google Scholar
Warrington, I. J. and Kanemasu, E. T. 1983. Corn growth response to temperature and photoperiod. I. Seedling emergence, tassel initiation and anthesis. Agron. J. 75: 749–754.Google Scholar
Washitani, J. and Saeki, T. 1986. Germination response of Pinus densiflora seeds to temperature, light and interrupted imbibition. J. Exp. Bot. 37: 1376–1387.Google Scholar
Weaver, S. E., Tan, C. S., and Brain, P. 1988. Effect of temperature and soil moisture on time of emergence of tomatoes and four weed species. Can. J. Plant Sci. 68: 877–886.Google Scholar
Wheeler, T. R. and Ellis, R. H. 1991. Seed quality, cotyledon elongation at suboptimal temperatures, and the yield of onion. Seed Sci. Res. 1: 57–67.Google Scholar
Willmott, C. J. 1981. On the validation of models. Phys. Geogr. 2: 184–194.Google Scholar