Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-23T07:50:30.985Z Has data issue: false hasContentIssue false

Estimating giant foxtail cohort productivity in soybean based on weed density, leaf area, or volume

Published online by Cambridge University Press:  20 January 2017

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

Abstract

Understanding weed–crop interactions is critical in predicting crop yield loss, but it is also important to understand how these interactions affect weed productivity. Therefore, research was conducted to characterize the weed relative leaf area and weed relative volume of several giant foxtail cohorts in soybean, and to assess weed density and cohort emergence time, weed relative leaf area, and weed relative volume as predictors of giant foxtail shoot biomass and fecundity. Giant foxtail cohorts emerged at VE (emergence), VC (cotyledon), V1 (first node), and V3 (third node) soybean growth stages and were thinned to densities of 0, 4, 16, 36, and 64 plants m−2. Based on weed density and cohort emergence time, the maximum shoot biomass per square meter or the maximum fecundity per square meter differed between years. In contrast, shoot biomass or fecundity per plant, as weed density approached zero, and the rate at which shoot biomass or fecundity decreased exponentially, as time increased, were similar between years. Based on the weed relative leaf area, the cohort effect on giant foxtail shoot biomass differed between years, whereas the cohort effect on giant foxtail fecundity was similar between years. Maximum giant foxtail shoot biomass per square meter or fecundity per square meter differed between years when estimated from weed relative leaf area. Based on the weed relative volume, the cohort effect on giant foxtail shoot biomass per square meter or fecundity per square meter was similar between years, as was the maximum giant foxtail shoot biomass per square meter or fecundity per square meter. The temporal stability of weed relative volume, used to describe giant foxtail shoot biomass or fecundity, may aid in improving bioeconomic weed management models.

Type
Research Article
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Bauer, T. A. and Mortensen, D. A. 1992. A comparison of economic and economic optimum thresholds for two annual weeds in soybean. Weed Technol. 6:228235.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. 45:276282.CrossRefGoogle Scholar
Buhler, D. D. and Oplinger, E. S. 1990. Influence of tillage systems on annual weed densities and control in solid-seeded soybean (Glycine max). Weed Sci. 38:158165.Google Scholar
Bussan, A. J., Boerboom, C. M., and Stoltenberg, D. E. 2000. Response of Setaria faberi demographic processes to herbicide rates. Weed Sci. 48:445453.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. 43:586594.CrossRefGoogle Scholar
Chikoye, D. and Swanton, C. J. 1995. Evaluation of three empirical models depicting Ambrosia artemisiifolia competition on white bean. Weed Res. 35:421428.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.CrossRefGoogle Scholar
Colquhoun, J. B., Stoltenberg, D. E., Binning, L. K., and Boerboom, C. M. 2001. Phenology of common lambsquarters (Chenopodium album) growth parameters. Weed Sci. 49:177183.Google Scholar
Conley, S. P. 2001. Interference among giant foxtail (Setaria faberi), common lambsquarters (Chenopodium album), and soybean (Glycine max). Ph.D. dissertation. University of Wisconsin, Madison, Madison, WI. pp. 115.Google 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.Google Scholar
Draper, N. R. and Smith, H. 1998. Applied Regression Analysis. 3rd ed. New York: J. Wiley. pp. 1576 (see also pp. 135169 and pp. 505553).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. 45:256260.Google Scholar
Forcella, F. and Banken, K. R. 1996. Relationships among green foxtail (Setaria viridis) seedling development, growing degree days, and time of nicosulfuron application. Weed Technol. 10:6067.Google Scholar
Harrison, S. K. 1990. Interference and seed production by common lambsquarters (Chenopodium album) in soybean (Glycine max). Weed Sci. 38:113118.CrossRefGoogle Scholar
Harrison, S. K., Stevens, C. S., and Wax, L. M. 1985. Interference and control of giant foxtail (Setaria faberi) in soybean (Glycine max). Weed Sci. 33:203208.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 cylindrical interference relationships. Weed Sci. 47:529537.CrossRefGoogle Scholar
Knake, E. L. 1977. Giant foxtail, the most serious annual grass weed in the Midwest. Weeds Today 9:1920.Google Scholar
Knake, E. L. and Slife, F. W. 1962. Competition of Setaria faberi with corn and soybean. Weeds 10:2629.Google Scholar
Knake, E. L. and Slife, F. W. 1969. Effect of time of giant foxtail removal from corn and soybean. Weed Sci. 17:281283.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.Google Scholar
Kropff, M. J. and Lotz, L. A. P. 1992. Optimization of weed management systems: the role of ecological models of interplant competition. Weed Technol. 6:462470.Google Scholar
Kropff, M. J., Lotz, L. A. P., Weaver, S. E., Bos, H. J., Wallinga, J., and Migo, T. 1995. A two coefficient model for prediction of crop yield 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 yield loss by weed competition from early observations on relative leaf area of weeds. Weed Res. 31:97105.CrossRefGoogle Scholar
Kropff, M. J., Weaver, S. E., and Smitts, M. A. 1992. Use of ecophysiological models for crop-weed interference: relations among weed density, relative time of weed emergence, relative leaf area, and yield loss. Weed Sci. 40:296301.Google Scholar
Lindquist, J. L., Mortenson, D. A., Westra, P. et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci. 47:195200.Google Scholar
Mashingaidzee, A. B. 1990. Comparison of leaf expansion rates in four crops and seven weeds under two temperature regimes. . Iowa State University, Ames, IA. 85 p.Google Scholar
Mickelson, J. A. and Harvey, R. G. 1999. Effects of Eriochloa villosa density and time of emergence on growth and seed production in Zea mays . Weed Sci. 47:687692.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1997. Weed and seedbank management with integrated methods as influenced by tillage. Weed Sci. 45:706715.Google Scholar
Radosevich, S., Holt, J., and Ghersa, C. 1997. Weed Ecology: Implications for Management. New York: J. Wiley. 62 p.Google Scholar
Remison, S. U. and Snaydon, R. W. 1980. A comparison of root and shoot competition between Dactylis glomerata and Holcus lanatus . Grass Forage Sci. 35:183187.CrossRefGoogle Scholar
Ritchie, S. W., Hanway, J. J., Thompson, H. E., and Benson, G. O. 1997. How a Soybean Plant Develops. Iowa State University of Science and Technology Cooperative Extension Service, Special Rep. 53. pp. 316.Google Scholar
Satorre, E. H. and Snaydon, R. W. 1992. A comparison of root and shoot competition between spring cereals and Avena fatua L. Weed Res. 32:4555.Google Scholar
Schweizer, E. E., Lybecker, D. W., and Wiles, L. J. 1998. Important biological information needed for bioeconomic weed management models. Pages 124 In Hatfield, J. L., Buhler, D. D., and Stewart, B. A., eds. Integrated Weed and Soil Management. Chelsea, MI: Ann Arbor Press.Google Scholar
Swanton, C. J. and Murphy, S. D. 1996. Weed science beyond the weeds: the role of integrated weed management (IWM) in agroecosystem health. Weed Sci. 44:437445.Google Scholar
Teughels, H., Nuis, I., Van Hecke, P., and Impens, I. 1995. Competition in a global change environment: the importance of different plant traits for competitive success. J. Biogeography 22:297305.Google Scholar
Van Acker, R. C., Lutman, P. J. W., 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
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.CrossRefGoogle Scholar