Hostname: page-component-7dd5485656-7jgsp Total loading time: 0 Render date: 2025-10-30T18:01:02.455Z Has data issue: false hasContentIssue false
  • English
  • Français

Soybean row spacing and weed emergence time influence weed competitiveness and competitive indices

Published online by Cambridge University Press:  20 January 2017

Shawn M. Hock ,
Stevan Z. Knezevic ,
Alex R. Martin  and
John L. Lindquist
Shawn M. Hock
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583-0915
Alex R. Martin
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583-0915
John L. Lindquist
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583-0915
Get access Rights & Permissions [Opens in a new window]

Abstract

Weed competitiveness can be quantified with the concept of competitive index (CI), a relative scale of weed competitiveness. Field studies were conducted in 2002 and 2003 in northeastern and southeastern Nebraska to evaluate the influence of soybean row spacing and relative weed emergence time on the competitiveness of major weed species in soybean. Ten weed species were seeded in soybean spaced 19 and 76 cm apart at the planting, emergence, and first trifoliate leaf stages of soybean. Total weed dry matter (TDM), weed plant volume, and percent soybean yield loss were arbitrarily selected as a base for determining the CI for each weed species. Soybean yield loss was the least variable parameter used to quantify weed competitiveness and rank their CIs. In general, weeds grown with soybean planted in 19-cm rows produced less TDM, plant volume, and reduced soybean yield less than weed species grown in 76-cm rows. Later-emerging weeds produced less TDM, plant volume, and reduced soybean yield less than the early-emerging ones. In general, broadleaf species were more competitive than grass weed species. Common sunflower was the most competitive weed species in this study.

Keywords

Common sunflower, Helianthus annuus L. HELANsoybean, Glycine max (L.) Merr. ‘Agripro 2502’, ‘Agripro 2703’Competitive indexintegrated weed managementplant volumeweed competition

Information

Type
Weed Biology and Ecology
Information
Weed Science , Volume 54 , Issue 1 , February 2006 , pp. 38 - 46
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

Bauer, T. A., Mortensen, D. A., Wicks, G. A., Hayden, T. A., and Martin, A. R. 1991. Environmental variability associated with economic thresholds for soybeans. Weed Sci 39:564–569.Google Scholar
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci 43:375–380.CrossRefGoogle Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol 6:191–195.CrossRefGoogle Scholar
Cousens, R., Firbank, L. G., Mortimer, A. M., and Smith, R. G. R. 1988. Variability in the relationship between crop yield and weed density for winter wheat and Bromus sterilis . J. Appl. Ecol 25:1033–1044.Google Scholar
Evans, S. P., Knezevic, S. Z., Lindquist, J. L., Shapiro, C. A., and Blankenship, E. E. 2003. Nitrogen application influences the critical period for weed control in corn. Weed Sci 51:408–417.CrossRefGoogle Scholar
Forcella, F., King, R. P., Swinton, S. M., Buhler, D. D., and Gunsolus, J. L. 1996. Multi-year validation of a decision aid for integrated weed management in row crops. Weed Sci 44:650–661.Google Scholar
Gunther, P., Pestemer, W., James, T. K., and Rahman, A. 1993. Testing the German herbicide advisory system HERBASYS under different edaphic and climatic conditions in New Zealand. Pages 777–784 in 8th EWRS Symposium, Quantitative Approaches in Weed and Herbicide Research and Their Practical Applications. Braunschweig, Germany: European Weed Research Society.Google Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., van Acker, R. C., and Lindquist, J. L. 2002. Critical period for weed control: the concept and data analysis. Weed Sci 50:773–786.Google Scholar
Knezevic, S. Z., Evans, S. P., and Mainz, M. 2003. Row spacing influences the critical timing for weed removal in soybean (Glycine max). Weed Technol 17:666–673.CrossRefGoogle Scholar
Knezevic, S. Z., Horak, M. J., and Vanderlip, R. L. 1997. Relative time of redroot pigweed (Amaranthus retroflexus L.) emergence is critical in pigweed–sorghum [Sorghum bicolor (L.) Moench] competition. Weed Sci 45:502–508.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize. Weed Res 35:207–214.CrossRefGoogle 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
Martin, A. R., Haffield, J. L., Buhler, D. D., and Stewart, B. A. 1997. Models for weed management (in-field management tools). Pages 63–68 in Weed Biology, Soil Management and Weed Management. Chelsea, MI: Ann Arbor Press.Google Scholar
Medd, R. W. and Pandey, S. 1993. Compelling grounds for controlling seed production in Avena species (wild oats). Pages 769–776 in 8th EWRS Symposium, Quantitative Approaches in Weed and Herbicide Research and Their Practical Applications. Braunschweig, Germany: European Weed Research Society.Google Scholar
Neeser, C., Dille, J. A., Krishnan, G., Mortensen, D. A., Rawlinson, J. T., Martin, A. R., and Bills, L. B. 2004. WeedSOFT®: a weed management decision support system. Weed Sci 52:115–122.CrossRefGoogle Scholar
Regnier, E. E. and Harrison, S. K. 1993. Compensatory responses of common cocklebur (Xanthium strumarium) and velvetleaf (Abutilon theophrasti) to partial shading. Weed Sci 41:541–547.Google Scholar
Regnier, E. E. and Stoller, E. W. 1989. The effects of soybean (Glycine max) interference on the canopy architecture of common cocklebur (Xanthium strumarium), jimsonweed (Datura stramonium), and velvetleaf (Abutilon theophrasti). Weed Sci 37:187–195.CrossRefGoogle Scholar
Ritchie, S. W., Hanway, J. J., and Benson, G. O. 1993. How a Soybean Plant Develops. Special Rep. 53. Ames, IA: Iowa State University. 20 p.Google Scholar
[SAS] Statistical Analysis Systems. 1999. SAS OnLine Doc. Version 8. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Schweizer, E., Wiles, L., Lybecker, D., and Westra, P. 1994. Bioeconomic Modeling for Weed Management Decisions in Crops. Great Plains Agriculture Council Bull. 150, Pp. 135–141.Google Scholar
Stigliani, L. and Resina, C. 1993. SELOMA: expert system for weed management in herbicide-intensive crops. Weed Technol 7:550–559.CrossRefGoogle 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.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J 83:413–417.Google Scholar