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The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn

Published online by Cambridge University Press:  20 January 2017

Maryse L. Leblanc ,
Daniel C. Cloutier ,
Katrine A. Stewart  and
Chantal Hamel
Daniel C. Cloutier
Affiliation:
Institut de malherbologie, Ste-Anne-de-Bellevue, QC, Canada H9X 3R9
Katrine A. Stewart
Affiliation:
Department of Plant Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada H9X 3V9
Chantal Hamel
Affiliation:
Agriculture and Agri-Food Canada, Swift Current, SK, Canada S9H 3X2
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Abstract

A mathematical model was developed to predict common lambsquarters seedling emergence in southwestern Quebec. The model was based on the thermal-time concept, using air temperatures in the double-sine calculation method. The model was built using data from five experiment-years for corn naturally infested with weed populations. Once developed, the model was calibrated using different crop seedbed preparation times. The base temperature was then adjusted for each time of seedbed preparation. A power regression function was used to relate adjusted base temperatures and the accumulated thermal units at seedbed preparation time. A modified Weibull function was then fitted to the field emergence data, expressed as the cumulative proportion of the total seedling emergence over the growing season as a function of cumulative thermal units. The simplicity and accuracy of this model would make it an excellent tool to predict common lambsquarters seedling emergence in field situations, facilitating the determination of the timing of scouting in integrated weed management systems.

Keywords

Common lambsquarters, Chenopodium album L. CHEALcorn, Zea mays L. ‘Pioneer 3921’Degree daysheat sumtemperaturethermal timethermal unitsweed emergenceweed emergence modeling
Type
Weed Biology
Information
Weed Science , Volume 51 , Issue 5 , October 2003 , pp. 718 - 724
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Allen, J. C. 1976. A modified sine wave method for calculating degree days. Environ. Entomol 5:388–396.CrossRefGoogle Scholar
Anonymous. 1997. Statewide IPM Project, Division of Agriculture and Natural Resources, University of California. Available at: http://www.ipm.ucdavis.edu/weather/ddconcepts.html. Accessed July 4, 2001.Google Scholar
Arnold, C. Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Am. Soc. Hortic. Sci 74:430–445.Google Scholar
Arnold, C. Y. 1960. Maximum-minimum temperatures as a basis for computing heat units. J. Am. Soc. Hortic. Sci 76:682–692.Google Scholar
Baskin, J. M. and Baskin, C. C. 1989. Role of temperature in regulating timing of germination in soil seed reserves of Thlaspi arvense L. Weed Res 29:317–326.CrossRefGoogle Scholar
Basset, 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
Bouwmeester, H. J. and Karssen, C. M. 1993. Seasonal periodicity in germination of seeds of Chenopodium album L. Ann. Bot 72:463–473.CrossRefGoogle Scholar
Buhler, D. D., Liebman, M., and Obrycki, J. J. 2000. Theoretical and practice challenges to an IPM approach to weed management. Weed Sci 48:274–280.Google Scholar
Colbach, N., Dürr, C., Richard, G., and Chauvel, B. 2000. Modelling black-grass (Alopecurus myosuroides Huds.) germination and emergence, in interaction with seed characteristics and movements and soil climate. XIe Colloque International sur la Biologie des Mauvaises Herbes, Dijon Septembre 2000. Ann. Assoc. Fr. Prot. Plantes 10:25–32.Google Scholar
[CPVQ] Conseil des Productions Végétales du Québec inc. 1994. Grilles de références en fertilisation. AGDEX 540. Publication 02-9401. 25 p.Google Scholar
Diener, G. H., Aldrich, R. A., and Schroeder, M. E. 1977. Heating soils and soil mixed with saturated air. Trans. ASAE 20:126–131.CrossRefGoogle 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.CrossRefGoogle Scholar
Fidanza, M. A., Dernoeden, P. H., and Zhang, M. 1996. Degree-days for predicting smooth crabgrass emergence in cool-season turfgrasses. Crop Sci 36:990–996.Google Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res 8:201–209. Available at: http://www.mrsars.usda.gov. Accessed July 4, 2001.Google Scholar
Grundy, A. C. and Mead, A. 2000. Modeling weed emergence as a function of meteorological records. Weed Sci 48:594–603.CrossRefGoogle Scholar
King, C. A. and Oliver, L. R. 1984. A model for predicting large crabgrass (Digitaria sanguinalis) emergence as influenced by temperature and water potential. Weed Sci 32:561–567.Google Scholar
Leblanc, M. 2001. Modeling Weed Emergence as Influenced by Environmental Conditions in Corn in Southwestern Québec. Ph.D. dissertation. McGill University, Montreal. Pp. 93–110, 145–166.Google Scholar
Leblanc, M. L. and Cloutier, D. C. 1996. Effet de la technique du faux-semis sur la levée des adventices annuelles. XeColloque International sur la Biologie des Mauvaises Herbes, Dijon Septembre 1996. Ann. Assoc. Nat. Prot. Plantes 10:29–34.Google Scholar
Leblanc, M. L., Cloutier, D. C., and Hamel, C. 2002a. Effect of water on common lambsquarters (Chenopodium album L.) and barnyardgrass [Echinochloa crus-galli (L.) Beauv.] seedling emergence in corn. Can. J. Plant Sci 82:855–859.CrossRefGoogle Scholar
Leblanc, M. L., Cloutier, D. C., Légère, A., Lemieux, C., Assémat, L., Benoit, D. L., and Hamel, C. 2002b. Effect of the presence or absence of corn on common lambsquarters (Chenopodium album L.) and barnyardgrass [Echinochloa crus-galli (L.) Beauv.] emergence. Weed Technol 16:638–644.Google Scholar
Leblanc, M. L., Cloutier, D. C., Leroux, G. D., and Hamel, C. 1998. Facteurs impliqués dans la levée des mauvaises herbes au champ. Phytoprotection 79:111–127.CrossRefGoogle Scholar
Mulugeta, D. and Stoltenberg, D. E. 1998. Influence of cohorts on Chenopodium album demography. Weed Sci 46:65–70.Google Scholar
Ogg, A. G. and Dawson, J. H. 1984. Time of emergence of eight weed species. Weed Sci 32:327–335.Google Scholar
Pruess, K. P. 1983. Day-degree methods for pest management. Environ. Entomol 12:613–619.CrossRefGoogle Scholar
Roman, E. S., Murphy, S. D., and Swanton, C. J. 2000. Simulation of Chenopodium album emergence. Weed Sci 48:217–224.Google Scholar
Roman, E. S., Thomas, A. G., Murphy, S. D., and Swanton, C. J. 1999. Modeling germination and seedling elongation of common lambsquarters (Chenopodium album). Weed Sci 47:149–155.CrossRefGoogle Scholar
Vleeshouwers, L. 1997a. Modelling Weed Emergence Patterns. Ph.D. dissertation. Wageningen Agricultural University, Wageningen, The Netherlands. 165 p.Google Scholar
Vleeshouwers, L. 1997b. Modelling the effect of temperature, soil penetration resistance, burial depth and seed weight on pre-emergence growth of weeds. Ann. Bot 79:553–563.CrossRefGoogle 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