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In-Row and Between-Row Interference by Corn Modifies Annual Weed Control by Preemergence Residual Herbicide

Published online by Cambridge University Press:  20 January 2017

William W. Donald*
Affiliation:
USDA-ARS, 269 Agricultural Engineering Building, University of Missouri, Columbia, MO 65211
William G. Johnson
Affiliation:
USDA-ARS, 269 Agricultural Engineering Building, University of Missouri, Columbia, MO 65211
Kelly A. Nelson
Affiliation:
USDA-ARS, 269 Agricultural Engineering Building, University of Missouri, Columbia, MO 65211
*
Corresponding author's E-mail: [email protected]

Abstract

The presence of row crops, such as field corn, improves herbicidal control of weeds, but the impact of crop row position on herbicide dose–response relationships for weeds is unknown. At midseason at three site-years in Missouri, total weed cover (WC) was reduced by increasing soil residual herbicide rate in a dose-dependent response and was as much as 20% lower in-row (IR) than between-row (BR). Preemergence atrazine + S-metolachlor + clopyralid + flumetsulam at different rates (0×, 0.25×, 0.5×, 0.75×, and 1×, where 1× rate was 2,240 + 1,750 + 210 + 67 g ai/ha, respectively) were applied at planting in field corn to control giant foxtail, the chief weed present, and annual broadleaf weeds, largely common waterhemp. Lower herbicide rates were required to reduce IR WC to the same extent as BR WC, but these rates varied between site-years. At all three site-years, a least squares regression equation adequately described data variability relating corn yield to IR or BR WC (or both) (i.e., Y = a + bBR2, where Y is corn yield in kg/ha, BR is BR WC [%], and a and b are coefficients).

Type
Research
Copyright
Copyright © Weed Science Society of America 

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Footnotes

Current address: 1155 Lilly Hall, Purdue University, West Lafayette, IN 47907

References

Literature Cited

Anonymous. 2002. Missouri Crop and Weather Report. U.S. Department of Agriculture, National Agricultural Statistics Service. 37 p.Google Scholar
Bedmar, F., Manetti, P., and Monterubbianesi, G. 1999. Determination of the critical period of weed control in corn using a thermal basis. Pesqui. Agropecu. Bras. Brasilia 34:187193.Google 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. 17:755763.Google Scholar
Donald, W. W., Kitchen, N. R., and Sudduth, K. A. 2001. Between-row mowing + banded herbicide to control annual weeds and reduce herbicide use in no-till soybean (Glycine max) and corn (Zea mays). Weed Technol. 15:576584.Google Scholar
Fiebig, W. W., Shilling, D. G., and Knauft, D. A. 1991. Peanut genotype response to interference from common cocklebur. Crop Sci 31:12891292.Google Scholar
Gomez, K. A. and Gomez, A. A. 1984. Statistical Procedures for Agricultural Research. 2nd ed. New York: J. Wiley. Pp. 2030, 241–247, 316–356.Google Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40:441447.Google Scholar
Helsel, D. R. and Hirsch, R. M. 1992. Statistical Methods in Water Resources. New York: Elsevier. Pp. 157208, Appendix A.Google Scholar
Henry, W. T. and Bauman, T. T. 1989. Interference between soybeans (Glycine max) and common cocklebur (Xanthium strumarium) under Indiana field conditions. Weed Sci. 37:753760.Google Scholar
Hoshmand, A. R. 1994. Experimental Research Design and Analysis. A Practical Approach for Agricultural and Natural Sciences. Boca Raton, FL: CRC. Pp. 297345.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568573.Google Scholar
Knezevic, S. Z., Weiss, 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 35:207214.Google Scholar
Missouri Agricultural Statistics Service. 2001. 2001 Missouri Farm Facts. Jefferson City, MO: Missouri Department of Agriculture/Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service. P. 39.Google Scholar
Myers, R. H. and Montgomery, D. C. 2002. Response Surface Methodology. Process and Product Optimization Using Designed Experiments. 2nd ed. New York: J. Wiley. 798 p.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. 47:297304.Google 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 18:501508.Google 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 39:329343.Google Scholar
Pike, D. R., Stoller, E. W., and Wax, L. M. 1990. Modeling soybean growth and canopy apportionment in weed-soybean (Glycine max) competition. Weed Sci. 38:522527.Google Scholar
Rajcan, I. and Swanton, C. J. 2001. Understanding maize-weed competition: resource competition, light quality and the whole plant. Field Crops Res 71:139150.Google Scholar
Rikoon, J. S., Constance, D. H., and Galetta, S. 1996. Factors affecting farmer's use and rejection of banded pesticide applications. J. Soil Water Conserv 51:322329.Google Scholar
Ruiz, J. A., Sanchez, J. J., and Goodman, M. M. 1998. Base temperature and heat unit requirement of 49 Mexican maize races. Maydica 43:277282.Google Scholar
Schroder, D., Hatley, J. C., and Finley, R. M. 1984. The contribution of herbicides and other technologies to corn production in the corn belt region, 1964 to 1979. N. Cent. J. Agric. Econ 6:95104.Google Scholar
Stoller, E. W., Harrison, S. K., Wax, L. M., Regnier, E. E., and Nafziger, E. D. 1987. Weed interference in soybeans (Glycine max). Rev. Weed Sci 3:155182.Google Scholar
Taylor, K. L. and Hartzler, R. G. 2000. Effect of seed bank augmentation on herbicide efficacy. Weed Technol. 14:261267.Google Scholar
Zimdahl, R. L. 1980. Weed-Crop Competition, a Review. Corvallis, OR: Oregon State University. Pp. 46– 49:8485.Google Scholar