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Empirical corn yield loss estimation from common lambsquarters (Chenopodium album) and giant foxtail (Setaria faberi) in mixed communities

Published online by Cambridge University Press:  20 January 2017

David E. Stoltenberg
Affiliation:
Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI 53706
Chris M. Boerboom
Affiliation:
Department of Agronomy, University of Wisconsin, 1575 Linden Drive, Madison, WI 53706
Larry K. Binning
Affiliation:
Department of Horticulture, University of Wisconsin, 1575 Linden Drive, Madison, WI 53706

Abstract

Corn yield loss associated with common lambsquarters and giant foxtail in mixed-weed species communities was estimated from empirical equations based on early-season weed density, weed relative leaf area, or weed relative shoot volume in 1998 and 1999. The estimated maximum corn yield loss ranged up to 20% in 1998 but was 50% or more in 1999. Competition coefficients estimated from weed density (I values) or weed relative shoot volume (q V values) indicated that the weed species were equally competitive in 1998 but that common lambsquarters was more competitive than giant foxtail in 1999. In contrast, the relative leaf area–based competition coefficients (q L values) indicated that common lambsquarters and giant foxtail were equally competitive in both years. Weed species emerged at the same time as corn in 1998, whereas in 1999, common lambsquarters emerged 3 d earlier than corn and 1 d earlier than did giant foxtail. Earlier emergence of common lambsquarters was associated with greater cumulative intercepted photosynthetically active radiation (IPAR) per plant compared with that of giant foxtail. Competition coefficients estimated from weed relative leaf area were similar between years for common lambsquarters but differed for giant foxtail. Similarly, the relationship between cumulative estimated IPAR and early-season relative leaf area was stable between years for common lambsquarters but not for giant foxtail. Consequently, competition coefficients were more consistent for common lambsquarters than for giant foxtail in mixed communities. The results suggest that the competitive ability of common lambsquarters and giant foxtail may not differ greatly in corn, but variability in corn yield loss between years was not adequately explained by these empirical models.

Type
Research Article
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bussler, B. H., Maxwell, B. D., and Puettmann, K. 1995. Using plant volume to quantify interference in corn (Zea mays) neighborhoods. Weed Sci. 43:586594.Google Scholar
Campbell, G. S. 1981. Fundamentals of radiation and temperature relations. Pages 1140 In Lange, O. L., Nobel, P. S., Osmond, C. B., and Ziegler, H., eds. Physiological Plant Ecology I; Responses to the Physical Environment. New York: Springer-Verlag.CrossRefGoogle Scholar
Campbell, G. S. and Norman, J. M. 1998. An Introduction to Environmental Biophysics. New York: Springer-Verlag. pp. 247276.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. 43:375380.Google Scholar
Colquhoun, J., Stoltenberg, D. E., Binning, L. K., and Boerboom, C. M. 2001. Phenology of common lambsquarters growth parameters. Weed Sci. 49:177183.Google Scholar
Conley, S. P., Binning, L. K., Boerboom, C. M., and Stoltenberg, D. E. 2002. Estimating giant foxtail cohort productivity in soybean based on weed density, leaf area, or volume. Weed Sci. 50:7278.CrossRefGoogle Scholar
Connolly, J. and Wayne, P. 1996. Asymmetric competition between plant species. Oecologia. 108:311320.CrossRefGoogle ScholarPubMed
Cousens, R. 1986. A simple model related yield loss to weed density. Ann. Appl. Biol. 107:239252.CrossRefGoogle Scholar
Cousens, R., Brain, P., O’Donovan, J. T., and O'Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci. 35:720725.Google Scholar
Cowan, P., Weaver, S. E., and Swanton, C. J. 1998. Interference between pigweed (Amaranthus spp.), barnyardgrass (Echinochloa crus-galli), and soybean (Glycine max). Weed Sci. 46:533539.CrossRefGoogle Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Sci. 43:612618.CrossRefGoogle Scholar
Draper, N. R. and Smith, H. 1998. Applied Regression Analysis. 3rd ed. New York: J. Wiley. pp. 3376.Google 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. 45:256260.CrossRefGoogle Scholar
France, J. and Thornley, J.H.T. 1984. Mathematical Models in Agriculture. London: Butterworths. pp. 8082.Google Scholar
Graf, B., Gutierrez, A. P., Rakotobe, O., Zahner, P., and Delucchi, V. 1990. A simulation model for the dynamics of rice growth and development: Part II—The competition with weeds for nitrogen and light. Agric. Syst. 32:367392.Google Scholar
Gunst, R. F. and Mason, R. L. 1980. Regression Analysis and its Application: A Data-Oriented Approach. New York: Marcel Dekker. pp. 223229.Google Scholar
Harrison, S. K., Schmoll, J. T., and Webb, J. E. 2001. Competition and fecundity of giant ragweed in corn. Weed Sci. 49:224229.CrossRefGoogle Scholar
Hunt, R. 1982. Plant Growth Curves: The Functional Approach to Plant Growth Analysis. Baltimore, MD: University Park Press. pp. 128135.Google Scholar
Jasieniuk, M., Maxwell, B. D., Anderson, R. L., et al. 1999. Site-to-site and year-to-year variation in Triticum aestivum-Aegilops cylindrica interference relationships. Weed Sci. 47:529537.Google 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. 45:502508.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. 35:207214.CrossRefGoogle Scholar
Kropff, M. J. 1993. Mechanisms of competition for light. Pages 3360 In Kropff, M. J. and van Laar, H. H., eds. Modelling Crop-Weed Interactions. Wallingford, U.K.: CAB International.Google Scholar
Kropff, M. J., Lotz, L.A.P., Weaver, S. E., Bos, H. J., Wallinga, J., and Migo, T. 1995. A two parameter model for prediction of crop loss by weed competition from early observations of relative leaf area of the weeds. Ann. Appl. Biol. 126:329346.Google Scholar
Kropff, M. J. and Spitters, C.J.T. 1991. A simple model of crop loss by weed competition from early observations on relative leaf area of the weeds. Weed Res. 31:97105.Google Scholar
Lindquist, J. L. 2001. Mechanisms of crop loss due to weed competition. Pages 233253 In Peterson, R.K.D. and Higley, L. G., eds. Biotic Stress and Yield Loss. Boca Raton, FL: CRC.Google Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Kells, J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)-velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44:309313.Google Scholar
Lindquist, J. L., Mortensen, D. A., Westra, P., et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci. 47:195200.Google Scholar
Lotz, L.A.P., Christensen, S., 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. 36:93101.CrossRefGoogle Scholar
Mashingaidze, A. B. 1990. Comparison of Leaf Area Expansion Rates in Four Crops and Seven Weeds Under Two Temperature Regimes. . Iowa Sate University, Ames, IA. pp. 3234.Google Scholar
Massinga, R. A., Currie, R. S., Horak, M. J., and Boyer, J. Jr. 2001. Interference of Palmer amaranth in corn. Weed Sci. 49:202208.CrossRefGoogle Scholar
Moechnig, M. J. 2001. Characterization of Common Lambsquarters and Giant Foxtail Communities and Their Influence on Corn-Yield Loss. . University of Wisconsin-Madison, Madison, WI. 69 p.Google Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999. 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
Radosevich, S., Holt, J., and Ghersa, C. 1997. Weed Ecology: Implications for Management. New York: J. Wiley. pp. 163301.Google Scholar
Seibert, A. C. and Pearce, R. B. 1993. Growth analysis of weed and crop species with reference to seed weight. Weed Sci. 41:5256.Google Scholar
Snedecor, G. W. and Cochran, W. G. 1989. Statistical Methods. Ames, IA: Iowa State University Press. pp. 252253.Google Scholar
Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. F., and King, R. P. 1994. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42:103109.Google Scholar
Van Acker, R. C., Lutman, P. J., and Froud-Williams, R. J. 1997. Predicting yield loss due to interference from two weed species using early observations of relative weed leaf area. Weed Res. 37:287299.Google Scholar
Weaver, S. E. 1991. Size-dependant economic thresholds for three broadleaf weed species in soybeans. Weed Technol. 5:674679.CrossRefGoogle Scholar
Wiederholt, R. J. and Stoltenberg, D. E. 1996. Absence of differential fitness between giant foxtail (Setaria faberi) accessions resistant and susceptible to acetyl-coenzyme a carboxylase inhibitors. Weed Sci. 44:1824.Google Scholar