Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-05-01T03:32:07.277Z Has data issue: false hasContentIssue false
  • English
  • Français

Influence of Weed Density and Distribution on Corn (Zea mays) Yield

Published online by Cambridge University Press:  12 June 2017

Mark J. Vangessel ,
Edward E. Schweizer ,
Karen A. Garrett  and
Philip Westra
Mark J. Vangessel
Affiliation:
Dep. Plant Pathol. and Weed Sci.
Edward E. Schweizer
Affiliation:
Water Manage. Res., Agric. Res. Serv., U.S. Dep. Agric, Colorado State Univ., Fort Collins, 80523
Karen A. Garrett
Affiliation:
Univ. Georgia, Savannah River Ecology Lab, Aiken, SC 29802
Philip Westra
Affiliation:
Dep. Plant Pathol. and Weed Sci., Colorado State Univ., Fort Collins, CO 80523
Get access Rights & Permissions [Opens in a new window]

Abstract

The impact of weed density and weed distribution on irrigated corn yield was investigated in Colorado. Weed densities examined were 0,33,50, or 100% of the indigenous weed population. A series of weed distribution treatments were achieved by varying the length of the weed-free and weedy zones within the corn row while maintaining a constant weed population of 33 or 50% of the indigenous weed level. Grain yield was affected by weed density, but not by weed distribution. Each additional weed reduced corn yield 8.5 and 2.3 kg ha−1 in 1991 and 1992, respectively. When corn yields were estimated with a computer weed/corn management model, weed densities 5 to 8 wk after planting provided a better yield reduction estimate than weed densities immediately before harvest.

Keywords

Corn, Zea mays L. ‘Pioneer 3779.’Bioeconomic modelinterspecific competitionintraspecific competitionspatial distributionWEEDCAMAmaranthus retroflexus L#3 AMARE
Type
Weed Biology and Ecology
Information
Weed Science , Volume 43 , Issue 2 , June 1995 , pp. 215 - 218
Copyright
Copyright © 1995 by the 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

1. Ball, D. A. and Shaffer, M. J. 1993. Simulating resource competition in multispecies agricultural plant communities. Weed Res. 33:299–310.CrossRefGoogle Scholar
2. Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.CrossRefGoogle Scholar
3. Byrd, J. D. and Coble, H. D. 1991. Interference of selected weeds in cotton (Gossypium hirsutum). Weed Technol. 5:263–269.CrossRefGoogle Scholar
4. Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6:191–195.CrossRefGoogle Scholar
5. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 39:124–129.CrossRefGoogle Scholar
6. Johnson, G. A., Mortensen, D. A., Martin, A. R., and Young, L. J. 1993. The spatial and numerical distribution of weed seedling populations in 12 Nebraska corn and soybean fields. Abstr. Weed Sci. Soc. Amer. 33:150.Google Scholar
7. King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34:972–979.CrossRefGoogle Scholar
8. Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.CrossRefGoogle Scholar
9. Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5:445–452.CrossRefGoogle Scholar
10. Patterson, D. T. 1985. Comparative ecophysiology of weeds and crops. Pages 101–129 in Duke, S. O., ed. Weed Physiology, Vol. I: Reproduction and Ecophysiology. CRC Press, Inc. Boca Raton, FL.Google Scholar
11. Radosevich, S. R. and Holt, J. S. 1984. Weed Ecology: Implications for Vegetation Management. John Wiley and Sons, NY. Pages 93–135.Google Scholar
12. Schweizer, E. E., Lybecker, D. W., Wiles, L. J., and Westra, P. 1993. Bioeconomic weed management models in crop production. Int. Crop Sci. 1:103–107.Google Scholar
13. Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337–342.CrossRefGoogle Scholar
14. van Heemst, H.D.J. 1985. The influence of weed competition on crop yield. Agric. Systems 18:81–93.CrossRefGoogle Scholar
15. Wiles, L. J., Wilkerson, G. G., and Gold, H. J. 1992. Value of information about weed distribution for improving postemergence control decisions. Crop. Prot. 11:547–554.CrossRefGoogle Scholar
16. Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546–553.CrossRefGoogle Scholar
17. Wilkerson, G. G., Modensa, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybean. Agron. J. 83:413–417.CrossRefGoogle Scholar