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Photothermal time describes common ragweed (Ambrosia artemisiifolia L.) phenological development and growth

Published online by Cambridge University Press:  12 June 2017

William Deen ,
L. Anthony Hunt  and
Clarence J. Swanton
William Deen
Affiliation:
University of Guelph, Guelph, ON, Canada N1G, 2W1
L. Anthony Hunt
Affiliation:
University of Guelph, Guelph, ON, Canada N1G, 2W1
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Abstract

The ability to predict weed phenological development under field conditions is fundamental to the development of mechanistic weed–crop competition models. We studied how phenological development of common ragweed grown under field conditions could be explained using temperature and photoperiod responses derived from growth room experiments. We also determined the relationship between phenological development and common ragweed leaf area, dry matter production, and partitioning. Phenological development of common ragweed emerging at different times in the field was described by photothermal time based on temperature and photoperiod responses derived from growth room experiments. Estimated dates of phenological events of common ragweed were within 4 d of recorded values. Common ragweed seedling density did not influence phenological development. Common ragweed leaf area development, biomass partitioning, and total biomass were related to photothermal time accumulation. The results of this study are consistent with our hypothesis that phenological development is a major factor influencing the outcome of weed–crop competition. Results obtained from this study can be incorporated into a mechanistic model of weed–crop competition.

Keywords

Common ragweed, Ambrosia artemisiifolia L. AMBELMechanistic modelsphotothermal timeweed competition
Type
Weed Biology and Ecology
Information
Weed Science , Volume 46 , Issue 5 , October 1998 , pp. 561 - 568
Copyright
Copyright © 1998 by the Weed Science Society of America 

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References

Literature Cited

Chapman, S. C., Hammer, G. L., and Meinke, H. 1993. A sunflower simulation model. I. Model development. Agron. J. 85: 725–735.Google Scholar
Bridges, D. C. and Chandler, J. M. 1989. A population level temperature-dependent model of seedling johnsongrass (Sorghum halapense) flowering. Weed Sci. 37: 471–477.CrossRefGoogle Scholar
Chikoye, D., Hunt, L. A., and Swanton, C. J. 1996. Simulation of competition for photosynthetically active radiation between common ragweed (Ambrosia artemisiifolia) and dry bean (Phaseolus vulgaris) . Weed Sci. 44: 545–554.Google Scholar
Deen, W., Hunt, T., and Swanton, C. J. 1998. Influence of temperature, photoperiod, and irradiance on the phenological development of common ragweed (Ambrosia artemisiifolia) . Weed Sci. 46: 555–560.Google Scholar
Flint, E. P., Patterson, D. T., Mortensen, D. A., Riechers, G. H., and Beyers, J. L. 1984. Temperature effects on growth and leaf production in three weed species. Weed Sci. 32: 655–663.Google Scholar
Ghersa, C. M. and Holt, J. S. 1995. Using phenology prediction in weed management: a review. Weed Res. 56: 461–470.Google Scholar
Goyne, P. J. and Schneirer, A. A. 1988. Temperature and photoperiod interactions with the phenological development of sunflower. Agron. J. 80: 777–784.Google Scholar
Grant, R. F. 1989. Simulation of maize phenology. Agron. J. 81: 451–457.CrossRefGoogle Scholar
Hodges, T. 1991. Temperature and water stress effects on phenology. Pages 7-13 in Hodges, T., ed. Predicting Crop Phenology. Boca Raton. FL: CRC Press.Google Scholar
Hunt, L. A. and Pararajasingham, S. 1995. CROPSIM-WHEAT: a model for describing the growth and development of wheat. Can. J. Plant Sci. 75: 619–632.CrossRefGoogle Scholar
Kropff, M. J. 1988. Modelling the effects of weeds on crop production. Weed Res. 28: 465–471.CrossRefGoogle Scholar
Major, D. R. and Kiniry, J. R. 1991. Predicting day length effects on phenological processes. Pages 15-28 in Hodges, T., ed. Predicting Crop Phenology. Boca Raton. FL: CRC Press.Google Scholar
McLachlan, S. M., Swanton, C. J., Weise, S., and Tollenaar, M. 1993. Effect of corn-induced shading and temperature on rate of leaf appearance in redroot pigweed (Amaranthus retroflexus L.). Weed Sci. 41: 590–593.Google Scholar
Miller, B. C., Foin, T. C., and Hill, J. E. 1993. Carice: a rice model for scheduling and evaluating management actions. Agron. J. 85: 938–947.Google Scholar
Patterson, D. T. 1993. Effects of temperature and photoperiod on growth and development of sicklepod (Cassia obtusifolia) Weed Sci. 41: 574–582.Google Scholar
Patterson, D. T. 1995. Effects of photoperiod on reproductive development in velvetleaf (Abutilon theophrasti). Weed Sci. 43: 627–633.CrossRefGoogle Scholar
Roman, E. S. 1998. Modelling seedling emergence of common lambsquarters in corn. . University of Guelph, Guelph, ON, Canada. 224 p.Google Scholar
[SAS] Statistical Analysis Systems. 1990. SAS User's Guide. Version 6.06. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Senseman, S. A. and Oliver, L. R. 1993. Flowering patterns, seed production, and somatic polymorphism of three weed species. Weed Sci. 41: 418–425.CrossRefGoogle Scholar
Tollenaar, M., Muldoon, J. F., and Daynard, T. B. 1984. Differences in rate of leaf appearance among maize hybrids and phases of development. Can. J. Plant Sci. 64: 759–767.Google Scholar
Tworkoski, T. 1992. Developmental and environmental effects on assimilate partitioning in Canada thistle (Cirsium arvense) . Weed Sci. 40: 79–85.Google Scholar
Wall, D. A. and Morrison, I. N. 1990. Phenological development and biomass allocation in Silene vulgaris . Weed Res. 30: 521–524.CrossRefGoogle Scholar
Weaver, S.E., Kropff, M. J., and Groeneveld, R.M.W. 1992. Use of ecophysiological models for crop-weed interference: the critical period or weed interference. Weed Sci. 40: 302–307.Google Scholar
Wilkerson, G. G., Jones, J. W., Coble, H. D., and Gunsolus, J. L. 1990. SOYWEED: a simulation model of soybean and common cocklebur growth and competition. Agron. J. 82: 1003–1010.CrossRefGoogle Scholar
Yegappan, T. M., Paton, D. M., Gates, C. T., and Muller, W. J. 1986. Water stress in sunflower (Helianthus annuus L.). Ann. Bot. 46: 61.CrossRefGoogle Scholar