Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-14T23:19:41.617Z Has data issue: false hasContentIssue false
  • English
  • Français

Functional relationships between giant ragweed (Ambrosia trifida) interference and sweet corn yield and ear traits

Published online by Cambridge University Press:  20 January 2017

Martin M. Williams II and
John B. Masiunas
John B. Masiunas
Affiliation:
Department of Natural Resources and Environmental Sciences, University of Illinois, 1201 West Gregory Drive, Urbana, IL 61801
Get accessRights & Permissions [Opens in a new window]

Abstract

Field experiments were conducted to quantify functional relationships between giant ragweed density and sweet corn yield and ear traits. A rectangular hyperbolic model was fit to yield loss measured in terms of marketable ear mass, appropriate for the processing industry, and boxes of 50 marketable ears, relevant to the fresh market industry. The initial slope of the hyperbolic yield loss function (I), which describes the linear portion of yield loss as weed density (weeds per square meter) approaches zero, was 119 for loss of ear mass and 97 for loss of boxes of ears. Furthermore, 10 of 12 ear traits including green ear mass, husked ear mass, ear length, filled ear length, ear width, number of kernels per row, number of rows, kernel depth, kernel mass, and kernel moisture content were significantly affected by giant ragweed interference.

Keywords

Giant ragweed, Ambrosia trifida L. AMBTRsweet corn, Zea mays L. ‘GH0937’Competitioncrop qualityeconomic thresholdyield loss

Information

Type
Research Article
Information
Weed Science , Volume 54 , Issue 5 , October 2006, pp. 948 - 953
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.)

Article purchase

Temporarily unavailable

References

Literature Cited

Anonymous. 1988. Agricultural Statistics. Washington, D.C.: U.S. Department of Agriculture, U.S. Government Printing Office.Google Scholar
Anonymous. 2003. Agricultural Statistics. Chapter IV. Washington, D.C.: United States Government Printing Office. Pp. 19–26.Google Scholar
Azanza, F., Bar-Zur, A., and Juvik, J. A. 1996. Variation in sweet corn kernel characteristics associated with stand establishment and eating quality. Euphytica. 87:7–18.CrossRefGoogle Scholar
Baysinger, J. A. and Sims, B. D. 1991. Giant ragweed (Ambrosia trifida) interferance in soybeans (Glycine max). Weed Sci. 39:358–362.Google Scholar
Bisikwa, J. 2001. Effects of Canopy Density on Growth and Development of Wild Proso Millet and Giant Foxtail. M.S. thesis. University of Minnesota, St. Paul, MN. 88 p.Google Scholar
Black, B. D., Griffin, J. L., Russin, J. S., and Snow, J. P. 1996. Soybean (Glycine max) response to weed interference and rhizoctonia foliar blight (Rhizoctonia solani). Weed Sci. 44:842–846.CrossRefGoogle 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
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Cox, W. J. 1996. Whole-plant physiological and yield responses of maize to plant density. Agron. J. 88:489–496.Google Scholar
Garwood, D. L., McArdle, F. J., Vanderslice, S. F., and Shannon, J. C. 1976. Postharvest carbohydrate transformations and processed quality of high sugar maize genotypes. J. Am. Soc. Hortic. Sci. 101:400–404.Google Scholar
Gilmore, E. C. and Rogers, J. S. 1958. Heat units for measuring maturity in corn. Agron. J. 50:611–615.Google Scholar
Harrison, S. K., Regnier, E. E., Schmoll, J. T., and Webb, J. E. 2001. Competition and fecundity of giant ragweed in corn. Weed Sci. 49:224–229.Google Scholar
Hassell, R. L., Dufault, R. J., and Phillips, T. L. 2003. Low-temperature germination response of su, se, sh2 sweet corn cultivars. HortTechnol. 13:136–141.Google Scholar
Johnson, W. G., Gibson, K., and Weller, S. 2005. Late season weed escapes in Indiana soybean fields. WSSA Abstracts. 45:57–58.Google Scholar
Jordan, N. 1989. A statistical analysis for area-of-influence experiments. Weed Technol. 3:114–121.Google Scholar
Kaukis, K. and Davis, D. W. 1986. Sweet corn breeding. Pages 477–512 in Basset, M. J. ed. Breeding Vegetable Crops. Westport, CN: AVI.Google Scholar
Lass, L. W., Callihan, R. H., and Everson, D. O. 1993. Forecasting the harvest date and yield of sweet corn by complex regression models. J. Am. Soc. Hort. Sci. 118:450–455.Google Scholar
Lindquist, J. L. 2001. Performance of INTERCOM for predicting corn-velvetleaf interference across north-central United States. Weed Sci. 49:195–201.Google Scholar
Lindquist, J. L. and Mortensen, D. A. 1998. Tolerance and velvetleaf (Abutilon theophrasti) suppressive ability of two old and two modern corn (Zea mays) hybrids. Weed Sci. 46:569–574.CrossRefGoogle Scholar
Magoon, C. A. and Culpepper, C. W. 1932. Response of sweet corn to varying temperatures from time of planting to canning maturity. USDA Tech. Bull. # 312. Washington, D.C.: U.S. Department of Agriculture, 39 p.Google Scholar
Neter, J., Wasserman, W., and Kutner, M. H. 1990. Pages 243–258 in Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs. 3rd ed. Homewood, IL: Richard D. Irwin.Google Scholar
Pataky, J. K. 1992. Relationships between yield of sweet corn and northern leaf blight caused by Exserohilum turcicum . Phytopathology. 82:370–375.CrossRefGoogle Scholar
Ratkowsky, D. A. 1983. Pages 135–153 in Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker.Google Scholar
Ritchie, S. W., Hanway, J. J., Benson, G. O., and Herman, J. C. 1993. How a Corn Plant Develops, Special Report No. 48. Ames, IA: Iowa State University Extension.Google Scholar
Simonne, E., Simonne, A., and Boozer, R. 1999. Yield, ear characteristics, and consumer acceptance of selected white sweet corn varieties in the Southeastern United States. HortTechnol. 9:289–293.Google Scholar
Stoller, E. W., Harrison, S. K., Regnier, E. E., Wax, L. M., and Nafziger, E. D. 1985. Weed interference in soybeans. Rev. Weed Sci. 3:155–181.Google Scholar
Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L., and King, R. P. 1994. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42:103–109.Google Scholar
Tetio-Kagho, F. and Gardner, F. P. 1988. Responses of maize to plant population density. II. Reproductive development, yield, and yield adjustments. Agron. J. 80:935–940.Google Scholar
Tollenaar, M., Aguilera, A., and Nissanka, S. P. 1997. Grain yield is reduced more by weed interference in an old than in a new maize hybrid. Agron. J. 89:239–246.CrossRefGoogle Scholar
Tracy, W. F. 2001. Sweet corn. Pages 155–197 in Hallauer, A. R., ed. Specialty Corns. 2nd ed. Boca Raton, FL: CRC Press.Google Scholar
Treat, C. L. and Tracy, W. F. 1994. Endosperm type effects on biomass production and on stalk and root quality in sweet corn. Crop Sci. 34:396–399.Google Scholar
Van Wychen, L. R., Harvey, R. G., and Wedberg, J. L. 2001. Interactions among weed, insect, and common rust treatments in sweet corn. Weed Sci. 49:206–216.Google Scholar
Webster, T. M., Loux, M. M., Regnier, E. E., and Harrison, S. K. 1994. Giant ragweed (Ambrosia trifida) canopy architecture and interference studies in soybean (Glycine max). Weed Technol. 8:559–564.CrossRefGoogle Scholar
Williams, M. M. II. 2006. Planting date influences critical period of weed control in sweet corn. Weed Sci. 54:927–932.Google Scholar