Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-23T00:03:55.257Z Has data issue: false hasContentIssue false
  • English
  • Français

Common lambsquarters (Chenopodium album) interference with corn across the northcentral United States

Published online by Cambridge University Press:  20 January 2017

David W. Fischer ,
R. Gordon Harvey ,
Thomas T. Bauman ,
Sam Phillips ,
Stephen E. Hart ,
Gregg A. Johnson ,
James J. Kells ,
Philip Westra  and
John Lindquist
R. Gordon Harvey
Affiliation:
Deceased. Department of Agronomy, University of Wisconsin, Madison, WI 53706
Thomas T. Bauman
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
Sam Phillips
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
Stephen E. Hart
Affiliation:
Department of Plant Biology and Pathology, Rutgers University, New Brunswick, NJ 08901
Gregg A. Johnson
Affiliation:
Department of Agronomy and Plant Genetics, University of Minnesota, Waseca, MN 56093-4521
James J. Kells
Affiliation:
Department of Crop and Soil Science, Michigan State University, East Lansing, MI 48824
Philip Westra
Affiliation:
Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523
John Lindquist
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583
Get access Rights & Permissions [Opens in a new window]

Abstract

Variation in crop–weed interference relationships has been shown for a number of crop–weed mixtures and may have an important influence on weed management decision-making. Field experiments were conducted at seven locations over 2 yr to evaluate variation in common lambsquarters interference in field corn and whether a single set of model parameters could be used to estimate corn grain yield loss throughout the northcentral United States. Two coefficients (I and A) of a rectangular hyperbola were estimated for each data set using nonlinear regression analysis. The I coefficient represents corn yield loss as weed density approaches zero, and A represents maximum percent yield loss. Estimates of both coefficients varied between years at Wisconsin, and I varied between years at Michigan. When locations with similar sample variances were combined, estimates of both I and A varied. Common lambsquarters interference caused the greatest corn yield reduction in Michigan (100%) and had the least effect in Minnesota, Nebraska, and Indiana (0% yield loss). Variation in I and A parameters resulted in variation in estimates of a single-year economic threshold (0.32 to 4.17 plants m−1 of row). Results of this study fail to support the use of a common yield loss–weed density function for all locations.

Keywords

Common lambsquarters, Chenopodium album L. CHEALcorn, Zea mays L. ‘DK404SR’, ‘DK493SR’, ‘DK592SR’, ‘Asgrow RX602SR’Bioeconomic modelcommon lambsquarterscorninterferenceyield loss
Type
Weed Management
Information
Weed Science , Volume 52 , Issue 6 , December 2004 , pp. 1034 - 1038
Copyright
Copyright © 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

Beckett, T. H., Stoller, E. W., and Wax, L. M. 1988. Interference of four annual weeds in corn (Zea mays). Weed Sci 36:764–769.Google Scholar
Cardina, J., Regnier, E., and Sparrow, D. 1995. Velvetleaf (Abutilon theophrasti) competition and economic thresholds in conventional and no-tillage corn (Zea mays). Weed Sci 43:81–87.CrossRefGoogle Scholar
Chu, C., Ludford, P. M., Ozbun, J. L., and Sweet, R. D. 1978. Effects of temperature and competition on the establishment and growth of redroot pigweed and common lambsquarters. Crop Sci 18:308–310.CrossRefGoogle Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol 6:191–195.Google Scholar
Conn, J. S. and Deck, R. E. 1995. Seed viability and dormancy of 17 weed species after 9.7 years of burial in Alaska. Weed Sci 43:583–585.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol 107:239–252.Google Scholar
Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1977. The World's Worst Weeds Distribution and Biology. Honolulu, HI: University Press of Hawaii. Pp. 84–91, 125–133, 153–168.Google Scholar
Kempenaar, C., Horsten, P. J. F. M., and Scheepens, P. C. 1996. Growth and competitiveness of common lambsquarters (Chenopodium album) after foliar application of Aschochyta caulina as a mycoherbicide. Weed Sci 44:609–614.CrossRefGoogle Scholar
Lindquist, J. L., Mortenson, D. A., Clay, S. A., Schment, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)–velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci 44:309–313.Google Scholar
Lindquist, J. L., Mortensen, D. A., and Westra, P. et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci 47:195–200.Google Scholar
Lotz, L. A. P., Christensen, S., and Cloutier, D. et al. 1996. Prediction of the competitive effects of weeds on crop yields based on the relative leaf area of weeds. Weed Res 36:93–101.Google Scholar
Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (Glycine max). Weed Sci 31:604–609.Google Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci 47:297–304.Google Scholar
Ratkowsky, D. A. 1983. Pages 135–153 in Owen, D. B. ed. Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker.Google Scholar
Sibuga, K. P. and Bandeen, J. 1980. Effects of green foxtail and lambs-quarters interference in field corn. Can. J. Plant Sci 60:1419–1426.CrossRefGoogle Scholar
Spitters, C. J. T., Kropff, M. J., and deGroot, W. 1989. Competition between maize and Echinochloa crus-galli analyzed by a hyperbolic regression model. Ann. Appl. Biol 115:541–551.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
Wiese, A. M. and Binning, L. K. 1987. Calculating the threshold temperature of development for weeds. Weed Sci 35:177–179.CrossRefGoogle Scholar