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Estimated corn yields using either weed cover or rated control after pre-emergence herbicides

Published online by Cambridge University Press:  20 January 2017

William W. Donald
William W. Donald*
Affiliation:
USDA-ARS, Cropping Systems and Water Quality Research Unit, 269 Agricultural Engineering Building, University of Missouri-Columbia, Columbia, MO 65211; [email protected]
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Abstract

Because soil-residual PRE herbicides reduce and delay annual weed emergence and decrease later weed growth, susceptible weeds surviving or recovering from herbicide treatment reduce crop yields less than do untreated weeds. Recently, corn yields were shown to be reduced differently by untreated weeds emerging in and between crop rows. However, equations have not been reported before that relate corn yield to in-row and between-row weed cover of mixed weed populations recovering from PRE soil-residual herbicides. Published data from PRE herbicide screening research for 3 site-yr in Missouri were reanalyzed to characterize this relation. In-row and between-row weed cover of mixed weed populations, chiefly giant foxtail and common waterhemp, were measured from photographs at midsummer. In 2 of 3 site-yr and with the 3 site-yr average, corn yields were a nonlinear function of both in-row and between-row weed cover recovering from various PRE soil-residual herbicide treatments. In 1 of 3 site-yr, corn yields were a nonlinear function of only between-row total weed cover. Subdividing weed cover into in-row and between-row subpopulations in equations accounted for more data variability in yield estimates than including either subpopulation alone. For all 3 site-yr after PRE herbicide treatment, corn yields were a nonlinear function of only between-row visually rated total weed control. Visual evaluation was less sensitive than photographic weed cover for measuring the contribution of in-row weeds to corn yield loss and characterizing the functional form of the equations.

Keywords

Common waterhemp, Amaranthus rudis Sauer AMATAgiant foxtail, Setaria faberii Herrm. SETFAcorn, Zea mays L. ZEAMX ‘Pioneer 33G28’Competitioninterferenceweedyield
Type
Weed Management
Information
Weed Science , Volume 54 , Issue 2 , April 2006 , pp. 373 - 379
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Adcock, T. E. and Banks, P. A. 1991. Effects of preemergence herbicides on competitiveness of selected weeds. Weed Sci 39:54–56.CrossRefGoogle Scholar
Black, I. D. and Dyson, C. B. 1993. An economic threshold model for spraying herbicides in cereals. Weed Res 33:279–290.CrossRefGoogle Scholar
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci 45:276–282.CrossRefGoogle Scholar
Bussler, B. H., Maxwell, B. D., and Puettmann, K. J. 1995. Using plant volume to quantify interference in corn (Zea mays) neighborhoods. Weed Sci 43:586–594.CrossRefGoogle Scholar
Donald, W. W. 2006. Between observer differences limit rated weed control. Weed Technol. In press.Google Scholar
Donald, W. W., Archer, D., Johnson, W. G., and Nelson, K. 2004b. Zone herbicide application controls annual weeds and reduces residual herbicide use in corn. Weed Sci 52:821–833.CrossRefGoogle Scholar
Donald, W. W. and Johnson, W. G. 2003. Interference effects of weed-infested bands in or between crop rows on field corn (Zea mays) yield. Weed Technol 17:755–763.CrossRefGoogle Scholar
Donald, W. W., Johnson, W. G., and Nelson, K. A. 2004a. In-row and between-row interference by corn (Zea mays) modifies annual weed control by preemergence residual herbicide. Weed Technol 18:487–504.CrossRefGoogle Scholar
Harvey, R. G. and Wagner, C. R. 1994. Using estimates of weed pressure to establish crop yield loss equations. Weed Technol 8:114–118.CrossRefGoogle Scholar
Hoshmand, A. R. 1994. Experimental Research Design and Analysis. A Practical Approach for Agricultural and Natural Sciences. Boca Raton, FL: CRC Press. Pp. 59–170.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.CrossRefGoogle Scholar
Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop-weed interference: relations amongst weed density, relative time of weed emergence, relative leaf area, and yield loss. Weed Sci 40:296–301.CrossRefGoogle Scholar
Lindquist, J. L. and Knezevic, S. Z. 2001. Quantifying crop yield response to weed populations: applications and limitations. Pages 205–232 in Peterson, R.K.D. and Higley, L. G. eds. Biotic Stress and Yield Loss. Boca Raton, FL: CRC Press.Google Scholar
Lindquist, J. L., Mortensen, D. A., and Johnson, B. E. 1998. Mechanisms of corn tolerance and velvetleaf suppressive ability. Agron. J 990:787–792.CrossRefGoogle Scholar
McLachlan, S. M., Tollenaar, M., Swanton, C. J., and Weiss, S. F. 1993. Effect of corn-induced shading on dry matter accumulation, distribution, and architecture of redroot pigweed (Amaranthus retroflexus). Weed Sci 41:568–573.CrossRefGoogle Scholar
Motulsky, H. and Christopoulos, A. 2003. GraphPad PRISM Version 4.0. Fitting Models to Biological Data Using Linear and Nonlinear Regression. A Practical Guide to Curve Fitting. San Diego, CA: GraphPad Software. Pp. 143–159.Google Scholar
Murphy, C., Lemerle, D., Jones, R., and Harden, S. 2002. Use of density to predict crop yield loss between variable seasons. Weed Res 42:377–384.CrossRefGoogle Scholar
Myers, R. H. and Montgomery, D. C. 2002. Response Surface Methodology. Process and Product Optimization Using Designed Experiments. 2nd ed. New York: John Wiley & Sons. Pp. 17–154.Google Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999a. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci 47:297–304.CrossRefGoogle Scholar
Ngouajio, M., Leroux, G. D., and Lemieux, C. 1999b. Influence of images recording height and crop growth stage on leaf cover estimates and their performance in yield prediction models. Crop Prot 18:501–508.CrossRefGoogle Scholar
Ngouajio, M., Leroux, G. D., and Lemieux, C. 1999c. A flexible sigmoidal model relating crop yield to weed relative leaf cover and its comparison with nested models. Weed Res 39:329–343.CrossRefGoogle Scholar
SPSS. 2001. SPSS User's Guide. Volume 11. Chicago: SPSS.Google Scholar
Streibig, J. C. and Kudsk, P. 1992. Herbicide Bioassays. Boca Raton, FL: CRC Press. 270 p.Google Scholar
Weaver, S. E. 1991. Size-dependent economic thresholds for three broadleaf weed species in soybeans. Weed Technol 5:674–679.CrossRefGoogle Scholar