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Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships

Published online by Cambridge University Press:  12 June 2017

John L. Lindquist,
David A. Mortensen,
Philip Westra,
W. J. Lambert,
Thomas T. Bauman,
Jason C. Fausey,
James J. Kells,
Steven J. Langton,
R. Gordon Harvey and
Brett H. Bussler
...Show all authors
David A. Mortensen
Affiliation:
University of Nebraska, Lincoln, NE 68583–0915
Philip Westra
Affiliation:
Colorado State University, Fort Collins, CO 80523
W. J. Lambert
Affiliation:
Purdue University, West Lafayette, IN 47907
Thomas T. Bauman
Affiliation:
Purdue University, West Lafayette, IN 47907
Jason C. Fausey
Affiliation:
Michigan State University, East Lansing, MI 48824
James J. Kells
Affiliation:
Michigan State University, East Lansing, MI 48824
Steven J. Langton
Affiliation:
University of Wisconsin, Madison, WI 53706
R. Gordon Harvey
Affiliation:
University of Wisconsin, Madison, WI 53706
Brett H. Bussler
Affiliation:
Monsanto Co., St. Louis, MO 63167
Kevin Banken
Affiliation:
Central, Morris, MN 56267
Sharon Clay
Affiliation:
South Dakota State University, Brookings, SD 57007
Frank Forcella
Affiliation:
USDA-ARS, Morris, MN 56267
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Extract

Variation in interference relationships have been shown for a number of crop-weed associations and may have an important effect on the implementation of decision support systems for weed management. Multiyear field experiments were conducted at eight locations to determine the stability of corn-foxtail interference relationships across years and locations. Two coefficients (I and A) of a rectangular hyperbola equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as foxtail density approaches zero and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations and varied across years at four locations. Maximum yield loss (A) varied between years at one location. Both coefficients varied among locations. Although 3 to 4 foxtail plants m−-1 row was a conservative estimate of the single-year economic threshold (Tc ) of foxtail density, variation in I and A resulted in a large variation in Tc . Therefore, the utility of using common coefficient estimates to predict future crop yield loss from foxtail interference between years or among locations within a region is limited.

Keywords

Foxtail, Setaria faberi Herrm.S. viridis (L.) Beauv.S. glauca (L.) Beauv.corn, Zea mays L.Competition, bioeconomic model, economic threshold, integrated weed management.

Information

Type
Weed Biology and Ecology
Information
Weed Science , Volume 47 , Issue 2 , April 1999, pp. 195 - 200
Copyright
Copyright © 1999 by the Weed Science Society of America 

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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.CrossRefGoogle Scholar
Bridges, D. C. 1992. Crop Losses Due to Weeds in the United States, 1992. Champaign, IL: Weed Science Society of America. 403 p.Google Scholar
Bussler, B. H., Maxwell, B. D., and Puettman, K. J. 1995. Using plant volume to quantify interference in corn (Zea mays) neighborhoods. Weed Sci. 43: 586–594.Google Scholar
Cardina, J., Regnier, E., 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
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 43: 375–380.Google 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. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107: 239–252.CrossRefGoogle Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2: 13–20.Google 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.CrossRefGoogle Scholar
Fausey, J. C., Kells, J. J., Swinton, S. M., and Renner, K. A. 1997. Giant foxtail (Setaria faberi) interference in nonirrigated corn (Zea mays). Weed Sci. 45: 256–260.Google Scholar
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.Google Scholar
Knake, E. L. and Slife, F. W. 1962. Competition of Setaria faberii with corn and soybeans. Weeds 10: 26–29.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus L.) in corn (Zea mays L.). Weed Res. 42: 568–573.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 (Zea mays L.). Weed Res. 35: 207–214.Google Scholar
Langton, S. J. and Harvey, R. G. 1994. Using alachlor impregnated on dry fertilizer to create varying giant foxtail populations for corn competition studies. Proc. North Cent. Weed Sci. Soc. 49: 18.Google Scholar
Lindquist, J. L., Maxwell, B. D., Buhler, D. D., and Gunsolus, J. L. 1995. Velvetleaf (Abutilon theophrasti) recruitment, survival, seed production and interference in soybean (Glycine max). Weed Sci. 43: 226–232.CrossRefGoogle Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, 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
Lotz, L.A.P., Christensen, S., Cloutier, D., Fernandez-Quintanilla, C., Legere, A., Lemieux, C., Lutman, P.J.W., Pardo Iglesias, A., Salonen, J., Sattin, M., Stigliani, L., and Tei, F. 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
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
Lybecker, D. W., Schweizer, E. E., and Westra, P. 1994. WEEDCAM Manual. Progress Report. EPA Contract DW12934950–01–1 Transfer Weed Management Expert System Technology for Reduced Corn Herbicide Use to Farmers, Extension Agents, and Crop Consultants. Ft. Collins, CO: Department of Agricultural Economics, Colorado State University. 49 p.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
Martin, A. R., Mortensen, D. A., and Lindquist, J. L. 1997. Decision support models for weed management: in-field management tools. Pages 363–369 in Hatfield, J. L., Buhler, D. D., and Stewart, B. A., eds. Integrated Weed and Soil Management. Chelsea, MI: Ann Arbor Press.Google Scholar
Ratkowsky, D. A. 1983. Nonlinear Regression Modeling: A Unified Practical Approach. New York: Marcel Dekker. 276 p.Google Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Sys. 44: 313–335.CrossRefGoogle 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