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Economic Importance of Managing Spatially Heterogeneous Weed Populations

Published online by Cambridge University Press:  12 June 2017

John L. Lindquist ,
J. Anita Dieleman ,
David A. Mortensen ,
Gregg A. Johnson  and
Dawn Y. Wyse-Pester
John L. Lindquist
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915; jlindquist@unl.edu
J. Anita Dieleman
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915; jlindquist@unl.edu
David A. Mortensen
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915; jlindquist@unl.edu
Gregg A. Johnson
Affiliation:
University of Minnesota Southern Experiment Station, Waseca, MN 56093
Dawn Y. Wyse-Pester
Affiliation:
Colorado State University, Fort Collins, CO 80523
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Abstract

Three methods of predicting the impact of weed interference on crop yield and expected economic return were compared to evaluate the economic importance of weed spatial heterogeneity. Density of three weed species was obtained using a grid sampling scheme in 11 corn and 11 soybean fields. Crop yield loss was predicted assuming densities were homogeneous, aggregated following a negative binomial with known population mean and k, or aggregated with weed densities spatially mapped. Predicted crop loss was lowest and expected returns highest when spatial location of weed density was utilized to decide whether control was justified. Location-specific weed management resulted in economic gain as well as a reduction in the quantity of herbicide applied.

Keywords

Corn, Zea mays L.soybean, Glycine max (L.) MerrCompetitionnegative binomialpatchcornsamplingprecision agricultureintegrated pest managementAbutilon theophrastiABUTHSetaria spp.Amaranthus spp.ER, economic returnET, single year economic thresholdXeq, density equivalentYL, yield lossYLc, yield loss based on weed density within a cellYLk, yield loss based on mean weed density adjusted for the distribution of weed density within that fieldYLx, yield loss based on mean weed density for the entire field
Type
Research
Information
Weed Technology , Volume 12 , Issue 1 , March 1998 , pp. 7 - 13
Copyright
Copyright © 1997 by the Weed Science Society of America 

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References

Literature Cited

Auld, B. A., and Tisdell, C. A. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 3:81.Google Scholar
Bauer, T. A., and Mortensen, D. A. 1992. A comparison of economic and economic optimum thresholds for two annual weeds in soybeans. Weed Technol. 6:228–235.Google Scholar
Berti, A., and Zanin, G. 1994. Density equivalent: a method for forecasting yield loss caused by mixed weed populations. Weed Res. 34:327–332.CrossRefGoogle Scholar
Brain, P., and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.Google 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
Dieleman, J. A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (<i>Amaranthus</i> spp.) interference in soybean (<i>Glycine max</i> (L.) Merr.). Weed Sci. 43:612–618.Google Scholar
Johnson, G. A., Mortensen, D. A., and Martin, A. R. 1995a. A simulation of herbicide use based on weed spatial distributions. Weed Res. 35:197–205.Google Scholar
A, Johnson. G., Mortensen, D. A., Young, L. Y., and Martin, A. R. 1995b. The stability of weed seedling population models and parameters in eastern Nebraska corn (<i>Zea mays</i>) and soybean (<i>Glycine max</i>) fields. Weed Sci. 43:604–611.Google Scholar
Knake, E. L., and Slife, F. W. 1962. Competition of <i>Setaria faberii</i> 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 (<i>Amaranthus retroflexus</i>) in corn (<i>Zea mays</i>). Weed Sci. 42:568–573.CrossRefGoogle Scholar
Lindquist, J. L., Maxwell, B. D., Buhler, D. D., and Gunsolus, J. L. 1995. Modeling the population dynamics and economics of velvetleaf (<i>Abutilon theophrasti</i>) control in a corn (<i>Zea mays</i>)–soybean (<i>Glycine max</i>) rotation. Weed Sci. 43:269–275.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 (<i>Zea mays</i>)–velvetleaf (<i>Abutilon theophrasti</i>) interference relationships. Weed Sci. 44:309–313.Google Scholar
Marra, M. C., and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (<i>Glycine max</i>). Weed Sci. 31:604–609.Google Scholar
Maxwell, B. D., and Colliver, C. T. 1995. Expanding economic thresholds by including spatial and temporal weed dynamics. <i>In</i> Proceedings of the Brighton Crop Protection Conference–-Weeds. Vol. 13, pp. 1069–1076.Google Scholar
Mortensen, D. A., Dieleman, J. A., and Johnson, G. A. 1998. Reducing herbicide use through developments in methods and technologies for herbicide application. <i>In</i> Hatfield, J. L., Buhler, D. D., and Stewart, B. A., eds. Integrated Weed and Soil Management. Chelsea, MI: Ann Arbor Press. (in press)Google Scholar
Mortensen, D. A., Johnson, G. A., Wyse, D. Y., and Martin, A. R. 1995. Managing spatially variable weed populations. <i>In</i> Site-specific Management for Agricultural Systems. Madison, WI: pp. 397–415. ASA-CSSA-SSSA.Google Scholar
Navas, M. L., 1991. Using plant population biology in weed research: a strategy to improve weed management. Weed Res. 31:171–179.Google Scholar
Nordbo, E., and Christensen, S. 1995. Spatial variability of weeds. <i>In</i> Olesen, S. E., ed., Proceedings of the Seminar on Site Specific Farming. Danish Institute of Plant and Soil Science, SP-Report 26. pp. 67–89.Google Scholar
Sickman, T., 1995. Changing corn seeding rates on-the-go. Farmer/Dakota Farmer 113:27.Google Scholar
Staniforth, D. W., and Weber, C. R. 1956. Effects of annual weeds on the growth and yield of soybeans. Agron. J. 48:467–471.CrossRefGoogle 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.CrossRefGoogle Scholar
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.Google Scholar
van Groenendael, J. M., 1988. Patchy distribution of weeds and some implications for modelling population dynamics: a short literature review. Weed Res. 28:437–441.Google Scholar
Wallinga, J., 1995. A closer look at the spatial distribution of weeds—perspectives for patch spraying. Budapest: pp. 647–653. 9th European Weed Research Society Symposium.Google Scholar
Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (<i>Glycine max</i>) fields. Weed Sci. 40:554–557.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybeans. Agron. J. 83:413–417.Google Scholar
Wilson, B. J., and Brain, P. 1991. Long-term stability of distribution of <i>Alopecurus myosuroides</i> Huds. within cereal fields. Weed Res. 31:367–373.Google Scholar
Wyse-Pester, D. Y., 1996. Characterizing the stability of weed seedling populations. . University of Nebraska, Lincoln, NE. 117 p.Google Scholar