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Influence of temperature, photoperiod, and irradiance on the phenological development of common ragweed (Ambrosia artemisiifolia)

Published online by Cambridge University Press:  12 June 2017

William Deen
Affiliation:
University of Guelph, Guelph, ON, Canada N1G 2W1
Tony Hunt
Affiliation:
University of Guelph, Guelph, ON, Canada N1G 2W1

Abstract

Implementation of an integrated weed management system requires prediction of the effect of weed competition on crop yield. Predicting outcomes of weed competition is complicated by genetic and environmental variation across years, locations, and management. Mechanistic models have the potential to account for this variability. Weed phenological development is an essential component of such models. Growth cabinet studies were conducted to characterize common ragweed's phenological response to temperature, photoperiod, and irradiance. Ragweed development occurred over a temperature range of 8.0 to 31.7 C, and this response to temperature was best characterized using a nonlinear function. A maximum leaf appearance rate of 1.02 leaves d−1 occurred at 31.7 C. Ragweed has a short juvenile phase, during which it was not sensitive to photoperiod. Following this juvenile phase, sensitivity to photoperiod was constant and continued until pistillate flowers were observed. Photoperiods of 14 h or less were optimal and resulted in maximum rates of development. Irradiance level affected ragweed phenological development only when combined with the additional stress of low temperatures. Data generated in this study can be used for the development of mechanistic weed competition models.

Type
Weed Biology and Ecology
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: 725735.Google Scholar
Dieleman, A., Hamill, A. S., Fox, G. C., and Swanton, C. J. 1996. Decision rules for postemergence control of pigweed (Amaranthus spp.) in soybean (Glycine max) . Weed Res. 44: 126132.Google Scholar
Frazee, R. W. and Stoller, E. W. 1974. Differential growth of corn, soybean and seven dicotyledonous weed seedlings. Weed Sci. 22: 336339.Google Scholar
Gebben, A. I. 1966. The Ecology of Common Ragweed, Ambrosia artemisiifolia L., in Southeastern Michigan. Ph.D. thesis. University of Michigan, Ann Arbor, MI. 247 p.Google Scholar
Ghersa, C. M. and Holt, J. S. 1995. Using phenology prediction in weed management: a review. Weed Res. 56: 461470.Google Scholar
Gmelig-Myelig, H. D. 1973. Effect of light intensity, temperature and day-length on the rate of leaf appearance of maize. Neth. J. Agric. Sci. 21: 6873.Google Scholar
Grant, R. F. 1989. Simulation of maize phenology. Agron. J. 81: 451457.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
Jackson, R. D. 1984. Effects of panicles on infrared thermometer measurements of canopy temperature in wheat. Agric. For. Meteorol. 32: 97105.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Res. 35: 207214.Google Scholar
Kropff, M. J. 1988. Modelling the effects of weeds on crop Production. Weed Res. 28: 465471.Google Scholar
Kropff, M. J. and van Laar, H. H. 1993. Modelling Crop–Weed Interactions. Wallingford, Great Britain: CAB International.Google Scholar
Kvalseth, T. O. 1985. Cautionary note about R2 . Am. Stat. 39: 279285.Google Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Keils, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)–velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44: 309313.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: 590593.Google Scholar
Medd, R. W. and Lovett, J. V. 1978. Biological studies of Carduus nutans (L.) spp. nutans II. Vernalization and phenological development. Weed Res. 18: 369372.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: 938947.Google Scholar
Papenfuss, H. D. and Salisbury, F. B. 1967. Aspects of clock resetting in flowering of Xanthium . Plant Physiol. 42: 15621568.Google Scholar
Pararajasingham, S. and Hunt, L. A. 1991. Wheat spike temperature in relation to base temperature for grain filling duration. Can. J. Plant Sci. 71: 6369.Google Scholar
Patterson, D. T. 1995a. Effects of environmental stress on weed/crop interactions. Weed Sci. 43: 483490.CrossRefGoogle Scholar
Patterson, D. T. 1995b. Effects of photoperiod on reproductive development in velvetleaf (Abutilon theophrasti). Weed Sci. 43: 627633.Google Scholar
Roché, C. T., Thill, D. C., and Shafii, B. 1997. Prediction of flowering in common crupina (Crupina vulgaris). Weed Sci. 45: 519528.Google Scholar
Salisbury, F. B. 1981. The twilight effect: initiating dark measurement in photoperiodism of Xantbium . Plant Physiol. 67: 12301238.Google Scholar
Shaykewich, C. F. 1995. An appraisal of cereal crop phenology modelling. Can. J. Plant Sci. 752: 329341.Google Scholar
Sinclair, T. R., Kitani, S., Hinson, K., Bruniard, J., and Horie, T. 1991. Soybean flowering date: linear and logistic models based on temperature and photoperiod. Crop Sci. 31: 786790.Google Scholar
Slafer, G. A. and Rawson, H. M. 1994. Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Aust. J. Plant Physiol. 21: 393426.Google Scholar
Slafer, G. A. and Rawson, H. M. 1996. Responses to photoperiod change with phenophase and temperature during wheat development. Field Crops Res. 46: 113.Google Scholar
Swan, J. B., Schneider, E. C., Moncrief, J. F., Paulson, W. H., and Peterson, A. E. 1987. Estimating corn growth, yield, and grain moisture from air growing degree days and residue cover. Agron. J. 79: 5360.Google Scholar
Swanton, C. J. and Murphy, S. 1996. Weed science beyond the weeds: the role of integrated weed management (IWM) in agroecosystem health. Weed Sci. 44: 437445.Google Scholar
Swanton, C. J. and Weise, S. 1991. Integrated weed management: the rationale and approach. Weed Technol. 5: 657663.Google Scholar
Tworkoski, T. 1992. Developmental and environmental effects on assimilate partitioning in Canada thistle (Cirsium arvense) . Weed Sci. 40: 7985.Google Scholar
Wall, D. A. and Morrison, I. N. 1990. Phenological development and biomass allocation in Silene vulgaris . Weed Res. 30: 521524.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 or weed interference. Weed Sci. 40: 302307.Google Scholar
Wilkerson, G. G., Jones, J. W., Boote, K. J., and Boul, G. S. 1989. Photoperiodically sensitive interval in time to flower of soybean. Crop Sci. 29: 721726.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: 10031010.Google Scholar